Second Order Normalization in the Generalized Photogravitational Restricted Three Body Prob

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GeneOntologyAnalysis：基因本体论分析

Annotation sources: publications (TAS), bioinformatics (IEA), genetics (IGI), assays (IDA), phenotypes (IMP), etc.
17
GO tree example
GO tree: A child can have more than one parent
⎯ Standard assignment of genes into functional categories ⎯ Controlled vocabulary for describing biological meanings
u Gene Ontology or GO project at NCBI
2) Define controlled terms (ontologies) for description of gene products from 3 aspects:
u Biological process (DNA repair, mitosis) u Molecular function (protein serine/threonine kinase activity, transcription factor
Gene Ontology -Cellular Component
/GO_nature_genetics_2000.pdf
Any one gene can be a member of more than one GO classification
21
Temporal snapshots of Go terms and mappings are available in BioC (~700, April 2014)

N ENG DA-EPOCH-R

Dose-Adjusted EPOCH-Rituximab Therapy in Primary Mediastinal B-Cell LymphomaKieron Dunleavy, M.D., Stefania Pittaluga, M.D., Ph.D., Lauren S. Maeda, M.D., Ranjana Advani, M.D., Clara C. Chen, M.D., Julie Hessler, R.N., Seth M. Steinberg, Ph.D., Cliona Grant, M.D., George Wright, Ph.D., Gaurav Varma,M.S.P.H., Louis M. Staudt, M.D., Ph.D., Elaine S. Jaffe, M.D., and Wyndham H. Wilson, M.D., Ph.D.N Engl J Med 2013; 368:1408-1416April 11, 2013DOI: 10.1056/NEJMoa1214561Share:AbstractArticleReferencesCiting Articles (26)LettersKaplan–Meier Estimates of Event-free and Overall Survival of Patients with Primary Mediastinal B-CellLymphoma Receiving DA-EPOCH-R, According to Study Group.Cardiac Ejection Fraction after Treatment with DA-EPOCH-R in 42 Patients in the Prospective NCI Cohort.Primary mediastinal B-cell lymphoma is adistinct pathogenetic subtype of diffuse large-B-cell lymphoma that arises in the thymus.1,2Although it comprises only 10% of cases of diffuse large-B-cell lymphoma, primary mediastinal B-cell lymphoma, which predominantly affects young women,3 is aggressive and typically ismanifested by a localized, bulky mediastinal mass, often with pleural and pericardial effusions.Less commonly, the disease involves extranodal sites, including the lung, kidneys, gastrointestinal organs, or brain.4,5 This disease is clinically and biologically related to nodular sclerosingHodgkin's lymphoma; the putative cell of origin for both conditions is a thymic B cell.1,2The molecular features of primary mediastinal B-cell lymphoma, and its relationship to Hodgkin'slymphoma and other types of diffuse large-B-cell lymphoma, have been studied.1,2,6-8 Mostpatients with primary mediastinal B-cell lymphoma have mutations in the B-cell lymphoma 6 gene (BCL6), usually along with somatic mutations in the immunoglobulin heavy-chain gene, suggesting late-stage germinal-center differentiation.6,7 Unlike other types of diffuse large-B-cell lymphoma,primary mediastinal B-cell lymphoma involves defective immunoglobulin production despite theexpression of the B-cell transcription factors OCT-2, BOB.1, and PU.1. More than half of patients with the disease also have amplification of the REL proto-oncogene and the JAK2 tyrosine kinase gene, which frequently are found in patients with Hodgkin's lymphoma, suggesting that thesediseases are related.9,10 Furthermore, genes that are more highly expressed in primarymediastinal B-cell lymphoma than in other types of diffuse large-B-cell lymphoma arecharacteristically overexpressed in Hodgkin's lymphoma.2Prospective studies in primary mediastinal B-cell lymphoma are few, which has led to conflictingfindings and a lack of treatment standards.11-14 Nonetheless, several observations have emerged from the literature. First, in most patients, adequate tumor control is not achieved with standardimmunochemotherapy, necessitating routine mediastinal radiotherapy.13-15 Second, even withradiotherapy, which is associated with serious late side effects, 20% of patients have diseaseprogression.11,13 Third, more aggressive chemotherapy is associated with an improvedoutcome.12,13 Consistent with this observation, we found that the dose-intense chemotherapy regimen consisting of dose-adjusted etoposide, doxorubicin, and cyclophosphamide with vincristine and prednisone (DA-EPOCH) had a favorable overall survival rate (79%) without consolidation radiotherapy in patients with primary mediastinal B-cell lymphoma.16 On the basis of the hypothesis that rituximab may improve treatment, we undertook a phase 2, prospective study of DA-EPOCH plus rituximab (DA-EPOCH-R) to determine whether it would improve outcomes and obviate the need for radiotherapy.METHODSStudy ConductThe study was designed and the manuscript was written by the last author. All authors reviewed and approved the draft of the manuscript submitted for publication. All the authors vouch for the adherence of the study to the protocol (available with the full text of this article at ) and for the completeness and accuracy of the data and analysis. The prospective study was approved by the institutional review board of the National Cancer Institute (NCI). All patients provided written informed consent. The retrospective analysis was approved by the institutional review board at Stanford University.Filgrastim was provided to the NCI through an agreement with Amgen, which played no role in the study design, analysis, or data collection. No other commercial support was provided for the prospective study.Prospective NCI StudyPatientsFrom November 1999 through August 2012, we prospectively enrolled 51 patients with untreated primary mediastinal B-cell lymphoma in an uncontrolled phase 2 study of DA-EPOCH-R. The primary study objectives were the rate of complete response, the rate of progression-free survival, and the toxicity of DA-EPOCH-R.All eligible patients had not received any previous systemic chemotherapy, had adequate organ function, and had negative results on testing for the human immunodeficiency virus; among women with childbearing potential, a negative test for pregnancy was required. Any localized mediastinal masses (stage I) had to measure at least 5 cm in the greatest dimension. Evaluations included standard blood tests, whole-body computed tomography (CT), and bone marrow biopsy. Assessment of cardiac function, by means of echocardiography, and of central nervous system disease, with the use of CT or magnetic resonance imaging (MRI) and flow cytometry or cytologic analysis of cerebral spinal fluid, were performed if clinically indicated.Study TherapyPatients received chemotherapy consisting of DA-EPOCH-R with filgrastim for 6 to 8 cycles.17,18 Disease sites were evaluated after cycles 4 and 6. Patients with a reduction of more than 20% inthe greatest diameter of their tumor masses between cycles 4 and 6 received 8 cycles of treatment. Patients with a reduction of 20% or less between cycles 4 and 6 discontinued therapy after 6 cycles. The method of administering the DA-EPOCH-R is summarized in the Supplementary Appendix (available at ).We used standard criteria for tumor response to assess the study end points.19,20 We used 18F-fluorodeoxyglucose–positron-emission tomography–CT (FDG-PET-CT) after therapy to evaluate residual masses. Patients who had a maximum standardized uptake value greater than that of the mediastinal blood pool in the residual mediastinal mass underwent repeat scans at approximately 6-week intervals until normalization or stabilization. Mediastinal blood pool activity was defined as the maximum standardized uptake value over the great vessels and ranged from 1.5 to 2.5 in the study population. Tumor biopsy was performed as clinically indicated. Patients with evidence of thymic rebound underwent repeat CT at 6-week intervals until stabilization. All FDG-PET-CT scans were reviewed and scored by the same nuclear-medicine physician. No patients received radiation treatment during this prospective study.Independent, Retrospective Stanford StudyTo provide an independent assessment of DA-EPOCH-R, we collaborated with investigators at Stanford University Medical Center who had begun to use DA-EPOCH-R in 2007 to treat primary mediastinal B-cell lymphoma.21 They reviewed all charts from 2007 through 2012 and found 16 previously untreated patients who had been consecutively treated with DA-EPOCH-R; none required radiotherapy. NCI investigators confirmed the presence of primary mediastinal B-cell lymphoma in all 16 patients, according to the WHO [World Health Organization] Classification of Tumours of Haematopoietic and Lymphoid Tissues, 4th edition.3 Standard immunohistochemical studies were performed as indicated.3,18Other Comparative DataTo provide a long-term assessment of the DA-EPOCH platform, we reviewed the pathological data for all patients from our phase 2 study of DA-EPOCH in patients with diffuse large-B-cell lymphoma, which also did not permit radiotherapy, and identified 18 patients with primary mediastinal B-cell lymphoma.16Statistical AnalysisWe calculated the duration of overall survival from the date of enrollment until the time of death or last follow-up. The duration of event-free survival was calculated from the date of enrollment until the date of progression, radiotherapy, discovery of a second mass, or time of last follow-up. We used the Kaplan–Meier method to determine the probability of overall or event-free survival.22 Patients' characteristics were compared by means of Fisher's exact test for dichotomous variables and by means of the Wilcoxon rank-sum test for continuous variables. All P values are two-tailed.The median follow-up was calculated from the date of enrollment through November 2012, the date of the most recent update.RESULTSBaseline Characteristics and Clinical OutcomesThe 51 patients enrolled in the NCI phase 2 prospective study had a median age of 30 years (range, 19 to 52) and a median tumor diameter of 11 cm; 59% were women (Table 1TABLE 1Baseline Characteristics of the Study Patients.). Indicators of advanced disease included bulky tumor with a greatest diameter of 10 cm or more (in 65% of patients), an elevated lactate dehydrogenase level (in 78%), and stage IV disease (in 29%).The 16 patients identified in the retrospective Stanford study had baseline characteristics similar to those of our 51 patients (Table 1) except for a significantly lower frequency of extranodal disease and significantly older age; 56% of patients had bulky disease, and 44% of patients had stage IV disease.At a median follow-up of 63 months (range, 3 to 156), the event-free survival rate in the prospective NCI study was 93% (95% confidence interval [CI], 81 to 98), and the overall survivalrate was 97% (95% CI, 81 to 99) (Figure 1A and 1B FIGURE 1Kaplan–Meier Estimates of Event-free and Overall Survival of Patients with Primary Mediastinal B-Cell Lymphoma Receiving DA-EPOCH-R, According to Study Group.). Three patients had evidence of disease after DA-EPOCH-R treatment; two had persistent focal disease, as detected on FDG-PET-CT, and one had disease progression. Two of these patients underwent mediastinal radiotherapy, and one was observed after excisional biopsy. All three patients became disease-free. One later died from acute myeloid leukemia, while still in remission from his primary mediastinal B-cell lymphoma.In the retrospective Stanford cohort, over a median follow-up of 37 months (range, 5 to 53), 100% of patients (95% CI, 79 to 100) were alive and event-free (Figure 1C and 1D).Finally, we assessed the outcome for 18 patients with primary mediastinal B-cell lymphoma who were enrolled in our phase 2 study of DA-EPOCH.16 These patients had baseline characteristics similar to those in the prospective DA-EPOCH-R study (data not shown). Over a median follow-up of 16 years, the event-free and overall survival rates were 67% (95% CI, 44 to 84) and 78% (95% CI, 55 to 91), respectively. No cardiac failure or second tumors were observed.The event-free and overall survival rates were greater with the addition of rituximab in the NCI prospective cohort than in the cohort of 18 patients who received DA-EPOCH alone (P=0.007 andP=0.01, respectively). This finding suggests that the addition of rituximab may account for the improvement and is consistent with other reports.11FDG-PET-CT FindingsTo identify DA-EPOCH-R treatment failures early, the 36 patients who were found to have residual mediastinal masses in the prospective study underwent FDG-PET-CT in order to optimize curative radiotherapy. Half the patients had a maximum standardized uptake value that was no more than the value in the mediastinal blood pool, which represents the upper limit of the normal range ofuptake (Table 2TABLE 2FDG-PET-CT Findings after DA-EPOCH-R Therapy in the Prospective NCI Cohort.). The other half had a maximum standardized uptake value that was more than the value in the mediastinal blood pool. Although diffuse or focal uptake within the residual tumor mass that is higher than that in the mediastinal blood pool has been considered indicative of lymphoma,20 among these 18 patients, only 3 (with maximum standardized uptake values of 5.9, 10.2, and 14.5) were found to have residual lymphoma. Thus, FDG-PET-CT had a positive predictive value of 17% and a negative predictive value of 100%.Among the 15 patients with a maximum standardized uptake value greater than that in the mediastinal blood pool who did not have disease, 10 underwent repeat FDG-PET-CT; the other 5 did not undergo additional screening, because their initial FDG-PET-CT scans were interpreted as unlikely to represent disease. The 10 patients underwent 1 to 6 additional FDG-PET-CT scans (total, 26); all the findings were interpreted as false positive results on the basis of stabilization or improvement of the maximum standardized uptake value. None of the 10 patients had a recurrence of lymphoma during follow-up.Three patients underwent post-treatment biopsy. One, with a maximum standardized uptake value of 5.9, had a viable tumor of less than 1 cm in area. Owing to the uncertain importance of this finding, the patient was followed for 7 years without treatment, and the tumor did not recur during follow-up. Two patients, with maximum standardized uptake values of 4.6 and 6.4, had negative biopsy results and no tumor recurrence during 6 years of follow-up.In two patients, treatment failed but repeat biopsy was not performed. One patient had disease progression on CT during treatment, and the other had a post-treatment maximum standardized uptake value that increased from 10.2 to 19, consistent with disease progression.Dose and Toxicity of DA-EPOCH-R in the NCI StudyIn the NCI study, 90% of patients received six cycles, and 10% received eight cycles, of DA-EPOCH-R. More than half the 51 patients had an escalation to at least dose level 4, representing a 73% increase over dose level 1; 6% of patients did not have a dose escalation. More than half thepatients received 69 mg of doxorubicin per square meter of body-surface area for at least one cycle and cumulative doses of 345 to 507 mg per square meter. To assess cardiac toxic effects, ejection fractions were measured in 42 patients. All had normal ejection fractions up to 10 yearsafter treatment (Figure 2FIGURE 2Cardiac Ejection Fraction after Treatment with DA-EPOCH-R in 42 Patients in the Prospective NCI Cohort.). There was no significant relationship between the ejection fraction and the length of time since treatment (P=0.30) or between the ejection fraction and the cumulative doxorubicin dose (P=0.20), and no significant interaction between the dose and time interval (P=0.40).Toxicity was assessed during the administration of all 294 cycles of DA-EPOCH-R. The targeted absolute neutrophil count of less than 500 cells per cubic milliliter occurred during 50% of cycles. Thrombocytopenia (<25,000 platelets per cubic millimeter) occurred during 6% of cycles, and hospitalization for fever and neutropenia occurred during 13% of cycles. Nonhematopoietic toxic effects were similar to those that have been reported previously.17,18 One patient died from acute myeloid leukemia while in remission from his primary mediastinal B-cell lymphoma, 49 months after treatment. Owing to the unexpected severe neutropenia during treatment in this patient, we looked for a germline telomerase mutation, which is associated with chemotherapy intolerance and myeloid leukemia.23 Telomere shortening (length, 2.5 SD below the mean) and a heterozygous mutation for the telomerase reverse transcriptase gene (TERT) codon Ala1062Thr were identified.DISCUSSIONThe use of DA-EPOCH-R obviated the need for radiotherapy in all but 2 of 51 patients (4%) with primary mediastinal B-cell lymphoma in a prospective cohort, and no patients had recurring disease over a median follow-up of more than 5 years (maximum, >13). Furthermore, in an independent retrospective cohort, treatment with DA-EPOCH-R in patients with primary mediastinal B-cell lymphoma resulted in an event-free survival rate of 100%. Despite the limitations of the phase 2 study and the retrospective study, these findings suggest that DA-EPOCH-R is a therapeutic advance for this type of lymphoma. Our results suggest that rituximab significantly improves the outcome of chemotherapy in patients with primary mediastinal B-cell lymphoma.The toxicity of DA-EPOCH-R was similar to that reported previously.16 The use of neutrophil-based dose adjustment maximized the delivered dose and limited the incidence of fever and neutropenia to 13% of the cycles. The infusional schedule of doxorubicin allowed for the delivery of high maximal and cumulative doses of doxorubicin without clinically significant cardiac toxic effects.24,25We used post-treatment FDG-PET-CT to identify patients who had persistent disease and a possible need for radiotherapy. Unlike the high clinical accuracy of FDG-PET-CT in other aggressive lymphomas,20 we found the technique to have a poor positive predictive value in primary mediastinal B-cell lymphoma. We frequently observed residual mediastinal masses that continued to shrink for 6 months, suggesting that inflammatory cells might account for the FDG uptake. These findings indicate that FDG-PET-CT uptake alone is not accurate for determining the presence of disease in these patients.There is no established standard treatment for primary mediastinal B-cell lymphoma. Although R-CHOP chemotherapy (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) has become a de facto standard, it is not universally accepted.11,12 Most strategies also incorporate consolidation radiotherapy to overcome the inadequacy of immunochemotherapy, although some observers have questioned its routine use.12,26 The most accurate assessment of R-CHOP and radiotherapy is a subgroup analysis of patients with primary mediastinal B-cell lymphoma in the Mabthera International Trial Group study of R-CHOP–based treatment.11 Among 44 patients, 73% received radiotherapy, with an event-free survival rate of 78% at 34 months.11 These results indicate that patients who receive R-CHOP–based treatment, most of whom are young women, may have serious long-term consequences of radiotherapy, including second tumors and the acceleration of atherosclerosis and anthracycline-mediated cardiac damage.27Current standard therapy is also inadequate for children with primary mediastinal B-cell lymphoma. In a recent subgroup analysis in the FAB/LMB96 international study, the event-free and overall survival rates were 66% and 73%, respectively, among children receiving a multiagent pediatric regimen.28Retrospective studies have long suggested that patients with primary mediastinal B-cell lymphoma have improved outcomes with the receipt of regimens of increased dose intensity.13 Dose intensity appears to be important in treating Hodgkin's lymphoma, a closely related disease.29 Indeed, outcomes associated with the use of DA-EPOCH-R may well be related to dose intensity as well as the continuous infusion schedule.30 DA-EPOCH therapy involves the administration of pharmacodynamic doses to normalize drug exposure among patients and maximizes the rate of administration. DA-EPOCH may also more effectively modulate the expression of BCL6,7 which encodes a key germinal-center B-cell transcription factor that suppresses genes involved in lymphocyte activation, differentiation, cell-cycle arrest (p21 and p27Kip1), and response to DNA damage (p53 and ATR) and that is expressed by most primary mediastinal B-cell lymphomas (Table 1).31 The inhibition of topoisomerase II also leads to down-regulation of BCL6 expression, suggesting that regimens directed against topoisomerase II may have increased efficacy in treating primary mediastinal B-cell lymphoma. In this regard, DA-EPOCH-R was designed to inhibit topoisomerase II by including two topoisomerase II inhibitors, etoposide and doxorubicin, and maximizing topoisomerase II inhibition by way of extended drug exposure.16In conclusion, our results indicate that DA-EPOCH-R had a high cure rate and obviated the need for radiotherapy in patients with primary mediastinal B-cell lymphoma. To provide confirmatory evidence, an international trial of DA-EPOCH-R in children with primary mediastinal B-cell lymphoma has been initiated ( number, NCT01516567).。

VAM包：变异调整马哈拉诺比距方法版本1.1.0说明书

Package‘V AM’November5,2023Type PackageTitle Variance-Adjusted MahalanobisVersion1.1.0Author H.Robert FrostMaintainer H.Robert Frost<***********************>Description Contains logic for cell-speciﬁc gene set scoring of single cell RNA sequencing data. Depends R(>=3.6.0),MASS,MatrixImports methods(>=3.6.0)Suggests Seurat(>=4.0.0),SeuratObject(>=4.0.0),sctransform(>=0.3.2)License GPL(>=2)Copyright Dartmouth CollegeEncoding UTF-8NeedsCompilation noRepository CRANDate/Publication2023-11-0516:30:02UTCR topics documented:V AM-package (2)createGeneSetCollection (2)vam (3)vamForCollection (5)vamForSeurat (6)Index812createGeneSetCollection VAM-package Variance-Adjusted MahalanobisDescriptionImplementation of Variance-adjusted Mahalanobis(V AM),a method for cell-speciﬁc gene set scor-ing of scRNA-seq data.DetailsPackage:V AMType:PackageVersion: 1.0.0Date:2021License:GPL-2NoteThis work was supported by the National Institutes of Health grants K01LM012426,R21CA253408, P20GM130454and P30CA023108.Author(s)H.Robert FrostReferences•Frost,H.R.(2020).Variance-adjusted Mahalanobis(V AM):a fast and accurate method forcell-speciﬁc gene set scoring.biorXiv e-prints.doi:https:///10.1101/2020.02.18.954321createGeneSetCollectionUtility function to help create gene set collection list objectDescriptionUtility function that creates a gene set collection list in the format required by vamForCollection() given the gene IDs measured in the expression matrix and a list of gene sets as deﬁned by the IDs of the member genes.vam3UsagecreateGeneSetCollection(gene.ids,gene.set.collection,min.size=1,max.size) Argumentsgene.ids Vector of gene IDs.This should correspond to the genes measured in the gene expression data.gene.set.collectionList of gene sets where each element in the list corresponds to a gene set andthe list element is a vector of gene IDs.List names are gene set names.Mustcontain at least one gene set.min.size Minimum gene set size afterﬁltering out genes not in the gene.ids vector.Gene sets whose post-ﬁltering size is below this are removed from theﬁnal collectionlist.Default is1and cannot be set to less than1.max.size Maximum gene set size afterﬁltering out genes not in the gene.ids vector.Gene sets whose post-ﬁltering size is above this are removed from theﬁnal collectionlist.If not speciﬁed,noﬁltering is performed.ValueVersion of the input gene.set.collection list where gene IDs have been replaced by position indices, genes not present in the gene.ids vector have been removed and gene sets failing the min/max size constraints have been removed.See AlsovamExamples#Create a collection with two sets defined over3genescreateGeneSetCollection(gene.ids=c("A","B","C"),gene.set.collection=list(set1=c("A","B"),set2=c("B","C")),min.size=2,max.size=3)vam Variance-adjusted Mahalanobis(VAM)algorithmDescriptionImplementation of the Variance-adjusted Mahalanobis(V AM)method,which computes distance statistics and one-sided p-values for all cells in the speciﬁed single cell gene expression matrix.This matrix should reﬂect the subset of the full expression proﬁle that corresponds to a single gene set.The p-values will be computed using either a chi-square distribution,a non-central chi-square distribution or gamma distribution as controlled by the center and gamma arguments for the one-sided alternative hypothesis that the expression values in the cell are further from the mean (center=T)or origin(center=F)than expected under the null of uncorrelated technical noise,i.e., gene expression variance is purely technical and all genes are uncorrelated.4vamUsagevam(gene.expr,tech.var.prop,gene.weights,center=FALSE,gamma=TRUE)Argumentsgene.expr An n x p matrix of gene expression values for n cells and p genes.tech.var.prop Vector of technical variance proportions for each of the p genes.If speciﬁed, the Mahalanobis distance will be computed using a diagonal covariance matrixgenerated using these proportions.If not speciﬁed,the Mahalanobis distanceswill be computed using a diagonal covariance matrix generated from the samplevariances.gene.weights Optional vector of gene weights.If speciﬁed,weights must be>0.The weights are used to adjust the gene variance values included in the computation of themodiﬁed Mahalanobis distances.Speciﬁcally,the gene variance is divided bythe gene weight.This adjustment means that large weights will increase theinﬂuence of a given gene in the computation of the modiﬁed Mahalanobis dis-tance.center If true,will mean center the values in the computation of the Mahalanobis statis-tic.If false,will compute the Mahalanobis distance from the origin.Default isF.gamma If true,willﬁt a gamma distribution to the non-zero squared Mahalanobis dis-tances computed from a row-permuted version of gene.expr.The estimatedgamma distribution will be used to compute a one-sided p-value for each cell.Iffalse,will compute the p-value using the standard chi-square approximation forthe squared Mahalanobis distance(or non-central if center=F).Default is T. ValueA data.frame with the following elements(row names will match row names from gene.expr):•"cdf.value":1minus the one-sided p-values computed from the squared adjusted Mahalanobis distances.•"distance.sq":The squared adjusted Mahalanobis distances for the n cells.See AlsovamForCollection,vamForSeuratExamples#Simulate Poisson expression data for10genes and10cellsgene.expr=matrix(rpois(100,lambda=2),nrow=10)#Simulate technical variance proportionstech.var.prop=runif(10)#Execute VAM to compute scores for the10genes on each cellvam(gene.expr=gene.expr,tech.var.prop=tech.var.prop)#Create weights that prioritize the first5genesgene.weights=c(rep(2,5),rep(1,5))vamForCollection5 #Execute VAM using the weightsvam(gene.expr=gene.expr,tech.var.prop=tech.var.prop,gene.weights=gene.weights)vamForCollection VAM method for multiple gene setsDescriptionExecutes the Variance-adjusted Mahalanobis(V AM)method(vam)on multiple gene sets,i.e.,a gene set collection.UsagevamForCollection(gene.expr,gene.set.collection,tech.var.prop,gene.weights,center=FALSE,gamma=TRUE)Argumentsgene.expr An n x p matrix of gene expression values for n cells and p genes.gene.set.collectionList of m gene sets for which scores are computed.Each element in the list cor-responds to a gene set and the list element is a vector of indices for the genes inthe set.The index value is deﬁned relative to the order of genes in the gene.exprmatrix.Gene set names should be speciﬁed as list names.tech.var.prop See description in vamgene.weights See description in vam.If speciﬁed as a single vector of weights,weights must be speciﬁed for all p genes and the same weights are used for all gene sets.Touse different weights for each set,specify as a list of the same length as thegene.set.collection list.In this case,each list element should be a vector ofgene weights of the same length as the size of the corresponding gene set.center See description in vamgamma See description in vamValueA list containing two elements:•"cdf.value":n x m matrix of1minus the one-sided p-values for the m gene sets and n cells.•"distance.sq":n x m matrix of squared adjusted Mahalanobis distances for the m gene sets and n cells.See Alsovam,vamForSeuratExamples#Simulate Poisson expression data for10genes and10cellsgene.expr=matrix(rpois(100,lambda=2),nrow=10)#Simulate technical variance proportionstech.var.prop=runif(10)#Define a collection with two disjoint sets that span the10genescollection=list(set1=1:5,set2=6:10)#Execute VAM on both sets using default values for center and gammavamForCollection(gene.expr=gene.expr,gene.set.collection=collection,tech.var.prop=tech.var.prop)#Create weights that prioritize the first2genes for the first set#and the last2genes for the second setgene.weights=list(c(2,2,1,1,1),c(1,1,1,2,2))#Execute VAM using the weightsvamForCollection(gene.expr=gene.expr,gene.set.collection=collection,tech.var.prop=tech.var.prop,gene.weights=gene.weights)vamForSeurat VAM wrapper for scRNA-seq data processed using the Seurat frame-workDescriptionExecutes the Variance-adjusted Mahalanobis(V AM)method(vamForCollection)on normalized scRNA-seq data stored in a Seurat object.If the Seurat NormalizeData method was used for normalization,the technical variance of each gene is computed as the proportion of technical variance(from FindVariableFeatures)multiplied by the variance of the normalized counts.If SCTransform was used for normalization,the technical variance for each gene is set to1(the nor-malized counts output by SCTransform should have variance1if there is only technical variation). UsagevamForSeurat(seurat.data,gene.weights,gene.set.collection,center=FALSE,gamma=TRUE,sample.cov=FALSE,return.dist=FALSE)Argumentsseurat.data The Seurat object that holds the scRNA-seq data.Assumes normalization has already been performed.gene.weights See description in vamForCollectiongene.set.collectionList of m gene sets for which scores are computed.Each element in the listcorresponds to a gene set and the list element is a vector of indices for the genesin the set.The index value is deﬁned relative to the order of genes in the relevantseurat.data Assay object.Gene set names should be speciﬁed as list names.center See description in vamgamma See description in vamsample.cov If true,will use the a diagonal covariance matrix generated from the sample vari-ances to compute the squared adjusted Mahalanobis distances(this is equivalentto not specifying tech.var for the vam method).If false(default),will use thetechnical variances as determined based on the type of Seurat normalization.return.dist If true,will return the squared adjusted Mahalanobis distances in a new Assayobject called"V AM.dist".Default is F.ValueUpdated Seurat object that hold the V AM results in one or two new Assay objects:•If return.dist is true,the matrix of squared adjusted Mahalanobis distances will be storedin new Assay object called"V AM.dist".•The matrix of CDF values(1minus the one-sided p-values)will be stored in new Assay objectcalled"V AM.cdf".See Alsovam,vamForCollectionExamples#Only run example code if Seurat package is availableif(requireNamespace("Seurat",quietly=TRUE)&requireNamespace("SeuratObject",quietly=TRUE)){ #Define a collection with one gene set for the first10genescollection=list(set1=1:10)#Execute on the pbmc_small scRNA-seq data set included with SeuratObject#See vignettes for more detailed Seurat examplesvamForSeurat(seurat.data=SeuratObject::pbmc_small,gene.set.collection=collection)}Index∗ﬁlecreateGeneSetCollection,2vam,3vamForCollection,5vamForSeurat,6∗packageVAM-package,2 createGeneSetCollection,2vam,3,3,5–7VAM-package,2vamForCollection,4,5,6,7vamForSeurat,4,5,68。

植物学专有名词

前沿·热点Research Hot/Frontiers种系发生Phylogeny生命起源Origins of Life群落多样性Community diversity生物多样性Biodiversity外来物种Alien species物种共存Species coexistence生态系统健康Ecosystem HealthDNA条形码DNA barcoding信号转导Signal transduction细胞周期调控Cell cycle regulation细胞增殖cell proliferation细胞凋亡Cell Apoptosis生物钟Biological clock生物适应性Biochemical adaptation网络生物学Network biology生物控制论Biological Cybernetics染色体重排Chromosomal rearrangements 进化论The theory of evolution自然选择natural selection人工选择artificial selection细胞分裂cell division选择育种selective breeding遗传漂变Genetic drift种系发生phylogeny脱氧核糖核酸DNA生命起源Origin of life古生物学paleontology种系遗传学phylogenetics表型学phenetics共同祖先common descent物种形成Speciation古生菌archaea灵长类primates板块构造论plate tectonics无树大草原savannah寄生parasitism共生symbiosis生命之树Tree of life遗传分类学cladistics开放系统open system动态平衡dynamic equilibrium查尔斯·达尔文Charles Darwin动物Animals植物plants真菌fungi原生生物protists细菌bacteria原核生物prokaryote昆虫类insects病毒viruses多细胞生物multicellular organism热血动物warm-blood生态系统ecosystems系统发生树Phylogenetic tree新陈代谢metabolism遗传学Genetics遗传heredity变异variation核苷酸nucleotides氨基酸amino acids遗传密码genetic code泛生论pangenesis染色体chromosomes遗传连锁genetic linkage螺旋结构helical structure信使RNAmessenger RNA氨基酸序列amino acid sequence遗传的特征 Features of inheritance孟德尔Gregor Mendel等位基因alleles纯合子homozygote杂合子heterozygote基因型genotype表现型phenotype显性dominance隐性recessiveness不完全显性incomplete dominance共显性codominance有性繁殖sexual reproduction谱系图pedigree charts异位显性epistasis遗传力heritability遗传的分子基础 Molecular basis for inheritance 腺嘌呤adenine胞嘧啶cytosine鸟嘌呤guanine胸腺嘧啶thymine双螺旋double helix碱基对base pairs染色质chromatin核小体nucleosomes组蛋白histone单倍体haploid二倍体diploid性连锁遗传病sex-linked disorder无性生殖asexual reproduction有丝分裂mitosis配子gametes生殖细胞germ cells连锁图linkage map分子生物学中心法则central dogma of molecular biology 镰状细胞性贫血sickle-cell anemia核糖体RNArRNA转移核糖核酸tRNA微RNAmicroRNA苯（丙）酮尿症phenylketonuria转录因子Transcription factors大肠杆菌Escherichia coli色氨酸tryptophan负反馈negative feedback阻遏物repressor胞间信号intercellular signals副突变paramutation遗传改变Genetic change诱变mutagenesis紫外辐射UV radiationDNA修复DNA repair减数分裂meiosis适合度fitness果蝇Drosophila群体遗传学Population genetics适应adaptation分子钟molecular clock进化树evolutionary trees模式生物model organisms医学遗传学Medical genetics孟德尔随机化Mendelian randomization同源基因orthologues药物遗传学pharmacogenetics连接酶ligase分子克隆molecular cloning重组DNArecombinant DNA遗传多样性genetic diversity生物量biomass分子生物学Molecular biology分子筛molecular sieve核糖核酸RNA蛋白质的生物合成protein biosynthesis 生物大分子biomolecules复制replication翻译translation转录transcription克隆clone聚合酶链式反应PCR质粒plasmid载体vector启动子元件promoter elements抗生素耐性antibiotic resistance结合conjugation转导transduction转染transfection电穿孔electroporation显微注射microinjection三级结构tertiary structure定量多聚酶链反应QPCR电泳electrophoresis毛细管作用capillary action胚胎干细胞株embryonic stem cell lines SDS-PAGEnorthern blot放射自显影autoradiography增强子Enhancer抗体antibody蔗糖梯度sucrose gradient粘度测定法viscometry保护生物学Conservation biology生物多样性biodiversity种群动态population dynamics稀有种rare species自然保护运动conservation movement 灭绝extinction脊椎动物vertebrates无脊椎动物invertebrates人口过剩overpopulation砍伐森林deforestation放牧过度overgrazing刀耕火种法slash and burn应用生态学Applied ecology濒危物种Endangered species环境保护论Environmentalism迁地保护Ex-situ conservation基因污染Genetic pollution就地保护In situ conservation发育生物学Developmental Biology细胞生长cell growth形态发生morphogenesis分化differentiation染色体畸变chromosomal aberrations个体发生ontogeny细胞凋亡apoptosis先天性疾病congenital disorders内稳态homeostasis神经胚形成neurulation胚胎发生embryogenesis性别决定sex determination原肠胚形成gastrulation适应能力adaptive capacity衰老senescence细胞信号传导Cell signaling信号转导Signal transduction器官发生organogenesis胚胎发生embryogenesis形态发生morphogenesis生态学Ecology次级生产力secondary productivity捕食predationecozones食物链food chains大气圈atmosphere生物群落biocoenosis分解者Decomposers互利共生mutualism光合作用photosynthesis初级生产者primary producers初级消费者primary consumers三级消费者tertiary consumers食肉动物carnivores杂食动物omnivores生境biotope生物地球化学循环biogeochemical cycle氮循环nitrogen cycle陆地生态系统terrestrial ecosystems森林生态系统forest ecosystems水圈hydrosphere生物圈biosphere岩石圈lithosphere生态位ecological niche海洋生态系统marine ecosystems生态危机Ecological crisis血亲consanguinity植物学Botany/Plant Biology(Plant Science)真菌类fungi被子植物angiosperms藻类algae双子叶植物dicotyledon单子叶植物monocot裸子植物gymnosperm分类学taxonomy形态学morphology藓类Mosses苔类liverworts园艺学Horticulture花粉Pollen植物病理学Phytopathology植物生理学Plant physiology草本植物Herbs温室气体greenhouse gas地衣类lichens水循环water cycle古植物学家Paleobotanists果菜类fruit vegetable食性层次trophic level食品安全food security作物育种plant breeding人类植物学Ethnobotany遗传学定律genetic laws四氢大麻酚tetrahydrocannabinol跳跃基因jumping genes细胞生物学Cell biology显微镜microscope核糖体ribosomes细胞质cytoplasm膜蛋白membrane proteins内质网endoplasmic reticulum高尔基体Golgi apparatus细胞骨架cytoskeletal线粒体mitochondria细胞核nucleus叶绿体chloroplasts溶酶体lysosomes主动运输Active transportmRNA剪接mRNA splicing自吞噬Autophagy纤毛Cilia微管microtubule鞭毛Flagella脂质双层Lipid bilayerImmunostaining原位杂交In situ hybridization免疫沉淀反应Immunoprecipitation免疫组化immunohistochemistry生命周期life cycles神经生物学Neurobiology计算神经科学computational neuroscience 认知神经科学cognitive neuroscience行为神经学behavioral neuroscience生物精神病学biological psychiatry神经病学neurology神经心理学neuropsychology神经心理学neuropsychology树状突dendrites光受体photoreceptors动作电位action potentialsquid giant axon突触synapses神经系统nervous system多巴胺Dopamine去甲肾上腺素Norepinephrine肾上腺素Epinephrine黑色素Melanin神经递质neurotransmitters褪黑激素MelatoninP物质Substance P兴奋性突触后电位EPSP抑制性突触后电位IPSP神经元neuron听力系统auditory system嗅觉系统olfactory system视觉系统visual system胶质细胞glial cell神经板neural plate系统生物学Systems BiologyDNA微阵列DNA microarrays蛋白质组学Proteomics质谱测定法mass spectrometry高性能液体色谱HPLC phosphoproteomics糖蛋白质组学glycoproteomics代谢物组学Metabolomics Biomicsprocess calculi信息提取information extraction转录组学transcriptomics代谢组学metabolomics酶动力学enzyme kinetics糖组学Glycomics生物学的其他学科Other fields太空生物学Astrobiology微生物学Microbiology古生物学Paleontology寄生虫学Parasitology生理学Physiology动物学Zoology。

SODA：主要效应和交互效应选择的R包说明书

Package‘sodavis’October14,2022Type PackageTitle SODA:Main and Interaction Effects Selection for LogisticRegression,Quadratic Discriminant and General Index ModelsVersion1.2Depends R(>=3.0.0),nnet,MASS,mvtnormDate2018-05-12Author Yang Li,Jun S.LiuMaintainer Yang Li<*********************>Description Variable and interaction selection are essential to classiﬁcation in high-dimensional set-ting.In this package,we provide the implementation of SODA procedure,which is a forward-backward algorithm that selects both main and interaction effects under logistic regres-sion and quadratic discriminant analysis.We also provide an extension,S-SODA,for deal-ing with the variable selection problem for semi-parametric models with continuous responses. License GPL-2NeedsCompilation noRepository CRANDate/Publication2018-05-1321:24:03UTCR topics documented:mich_lung (2)pumadyn (2)soda (3)soda_trace_CV (4)s_soda (5)s_soda_model (6)s_soda_pred (7)s_soda_pred_grid (8)Index912pumadyn mich_lung Gene expression data for Michigan lung cancer study in Beer et al.(2002)DescriptionGene expression data of5217genes for n=86subjects,with62subjects in"good outcomes"(class1)and24subjects in"poor outcomes"(class2),from the microarray study of Beer et al.(2002).Usagedata(mich_lung)FormatResponse variable vector and design matrix on86observations for expression of5217genes.ReferencesBeer et al.(1999)Gene-expression proﬁles predict survival of patients with lung adenocarcinoma.Nature medicine,286(8):816-824.pumadyn Pumadyn datasetDescriptionThis is a dataset synthetically generated from a realistic simulation of the dynamics of a Unimation Puma560robot arm.Usagedata(pumadyn)FormatResponse variable vector and design matrix on4499in-sample and3693out-sample observations for32predictor variables.ReferencesCorke,P.I.(1996).A Robotics Toolbox for MATLAB.IEEE Robotics and Automation Magazine, 3(1):24-32.soda3 soda SODA algorithm for variable and interaction selectionDescriptionSODA is a forward-backward variable and interaction selection algorithm under logistic regression model with second-order terms.In the forward stage,a stepwise procedure is conducted to screen for important predictors with both main and interaction effects,and in the backward stage SODA remove insigniﬁcant terms so as to optimize the extended BIC(EBIC)criterion.SODA is appli-cable for variable selection for logistic regression,linear/quadratic discriminant analysis and other discriminant analysis with generative model being in exponential family.Usagesoda(xx,yy,norm=F,debug=F,gam=0,minF=3)Argumentsxx The design matrix,of dimensions n*p,without an intercept.Each row is an observation vector.yy The response vector of dimension n*1.norm Logicalﬂag for xx variable quantile normalization to standard normal,prior to performing SODA algorithm.Default is norm=FALSE.Quantile-normalizationis suggested if the data contains obvious outliers.debug Logicalﬂag for printing debug information.gam Tuning paramter gamma in extended BIC criterion.EBIC for selected set S:EBIC=-2*log-likelihood+|S|*log(n)+2*|S|*gamma*log(p) minF Minimum number of steps in forward interaction screening.Default is minF=3.ValueEBIC Trace of extended Bayesian information criterion(EBIC)score.Type Trace of step type("Forward(Main)","Forward(Int)","Backward").Var Trace of selected variables.Term Trace of selected main and interaction terms.final_EBIC Final selected term set EBIC score.final_Var Final selected variables.final_Term Final selected main and interaction terms.Author(s)Yang Li,Jun S.Liu4soda_trace_CVReferencesLi Y,Liu JS.(2017).Robust Variable and Interaction Selection for Logistic Regression and Multiple Index Models.Technical Report.Examples##(uncomment the code to run)##simulation study with1main effect and2interactions#N=250;#p=1000;#r=0.5;#s=1;#H=abs(outer(1:p,1:p,"-"))#S=s*r^H;#S[cbind(1:p,1:p)]=S[cbind(1:p,1:p)]*s#xx=as.matrix(data.frame(mvrnorm(N,rep(0,p),S)));#zz=1+xx[,1]-xx[,10]^2+xx[,10]*xx[,20];#yy=as.numeric(runif(N)<exp(zz)/(1+exp(zz)))#res_SODA=soda(xx,yy,gam=0.5);#cv_SODA=soda_trace_CV(xx,yy,res_SODA)#cv_SODA##Michigan lung cancer dataset#data(mich_lung);#res_SODA=soda(mich_lung_xx,mich_lung_yy,gam=0.5);#cv_SODA=soda_trace_CV(mich_lung_xx,mich_lung_yy,res_SODA)#cv_SODAsoda_trace_CV Calculate a trace of cross-validation error rate for SODA forward-backward procedureDescriptionThis function takes a SODA result variable as input,and calculates the cross-validation error for each step of the SODA procedure.Usagesoda_trace_CV(xx,yy,res_SODA)Argumentsxx The design matrix,of dimensions n*p,without an intercept.Each row is an observation vector.yy The response vector of dimension n*1.res_SODA SODA result varaible.See example below.s_soda5Author(s)Yang Li,Jun S.LiuExamples#Michigan lung cancer dataset(uncomment the code to run)#data(mich_lung);#res_SODA=soda(mich_lung_xx,mich_lung_yy,gam=0.5);#cv_SODA=soda_trace_CV(mich_lung_xx,mich_lung_yy,res_SODA)#cv_SODAs_soda S-SODA algorithm for general index model variable selectionDescriptionS-SODA is an extension of SODA to conduct variable selection for general index models with continuous response.S-SODAﬁrst evenly discretizes the continuous response into H slices,and then apply SODA on the discretized pared with existing variable selection methods based on the Sliced Inverse Regression(SIR),SODA requires neither the linearity nor the constant variance condition and is much more robust.Usages_soda(x,y,H=5,gam=0,minF=3,norm=F,debug=F)Argumentsx The design matrix,of dimensions n*p,without an intercept.Each row is an observation vector.y The response vector of dimension n*1.H The number of slices.gam EBIC penalization coefﬁcient parameter for SODA.minF Minimum number of steps in forward interaction screening.Default is minF=3.norm If set as True,S-SODAﬁrst marginally quantile-normalize each predictor to the standard normal distribution.debug If print debug information.ValueBIC Trace of extended Bayesian information criterion(EBIC)score.Var Trace of selected variables.Term Trace of selected main and interaction terms.best_BIC Final selected term set EBIC score.best_Var Final selected variables.best_Term Final selected main and interaction terms.6s_soda_modelExamples##(uncomment the code to run)##Simulation:x1/(1+x2^2)example#N=500#x1=runif(N,-3,+3)#x2=runif(N,-3,+3)#x3=x1/exp(x2^2)+rnorm(N,0,0.2)#ss=s_soda_model(cbind(x1,x2),x3,H=25)###true surface in grid#MM=50#xx1=seq(-3,+3,length.out=MM)#xx2=seq(-3,+3,length.out=MM)#yyy=matrix(0,MM,MM)#for(i in1:MM)#for(j in1:MM)#yyy[i,j]=xx1[i]/exp(xx2[j]^2)###predicted surface#ppp=s_soda_pred_grid(xx1,xx2,ss,po=1)##par(mfrow=c(1,2),mar=c(1.75,3,1.25,1.5))#persp(xx1,xx2,yyy,theta=-45,xlab="X1",ylab="X2",zlab="Y")#persp(xx1,xx2,ppp,theta=-45,xlab="X1",ylab="X2",zlab="Pred")###Pumadyn dataset##data(pumadyn);##s_soda(pumadyn_isample_x,pumadyn_isample_y,H=25,gam=0)s_soda_model S-SODA model estimation.DescriptionS-SODA assumes within each slice the X vector follow multivariate normal distribution.This function estimates the mean vector and covariance matrix of X for each slice.Usages_soda_model(x,y,H=10)Argumentsx The design matrix,of dimensions n*p,without an intercept.Each row is an observation vector.y The response vector of dimension n*1.H The number of slices.Valueint_h Slice index.int_p Proportion of samples in each slice.int_l Length of each slice(max-min response).int_m Mean vector of covariates in each slice.int_v Covariance matrix of covariates in each slice.s_soda_pred Predict the response y using S-SODA model.DescriptionS-SODA assumes within each slice the X vector follow multivariate normal distribution.This function predicts the response y by reverting the P(X|slice(y))to P(slice(y)|X),and estimates the E(y|X)as sum_h E(y|slice(y)=h,X)P(slice(y)=h|X)Usages_soda_pred(x,model,po=1)Argumentsx The design matrix,of dimensions n*p,without an intercept.Each row is an observation vector.model S-SODA model estimated from s_soda_model function.po Order of terms in X to approximate E(y|slice(y)=h,X).If po=0,E(y|slice(y)=h, X)is the mean of y in slice h.If po=1,E(y|slice(y)=h,X)is the linear regressionof X to predict y in slice h.If po=2,the linear regression also include2nd orderterms of X.ValuePredicted response.s_soda_pred_grid Predict the response y using S-SODA model in a2-dimensional grid.DescriptionCalls function s_soda_pred in a2-dimensional grid deﬁned by x1and x2.Usages_soda_pred_grid(xx1,xx2,model,po=1)Argumentsxx1Grid breakpoints for predictor1.xx2Grid breakpoints for predictor2.model S-SODA model estimated from s_soda_model.po Order of terms in X to approximate E(y|slice(y)=h,X).ValuePredicted response.Index∗Predictions_soda_pred,7s_soda_pred_grid,8∗S-SODAs_soda,5s_soda_model,6s_soda_pred,7s_soda_pred_grid,8∗SODAsoda,3soda_trace_CV,4∗cross-validationsoda_trace_CV,4∗datasetsmich_lung,2pumadyn,2∗general index models_soda,5∗interaction_selections_soda,5soda,3∗logistic_regressionsoda,3∗quadratic_discriminant_analysis soda,3mich_lung,2mich_lung_xx(mich_lung),2 mich_lung_yy(mich_lung),2 pumadyn,2pumadyn_isample_x(pumadyn),2 pumadyn_isample_y(pumadyn),2 pumadyn_osample_x(pumadyn),2 pumadyn_osample_y(pumadyn),2 s_soda,5s_soda_model,6s_soda_pred,7s_soda_pred_grid,8soda,3soda_trace_CV,4 9。

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义席义博１，２＋，张皓１，２＋，杨　波２，陈熙勐１，２，贺培凤１△，卢学春２，１△（１．山西医科大学管理学院，太原０３０００１；２．解放军总医院南楼血液科，国家老年疾病临床医学研究中心北京１００８５３）【摘要】　目的：筛选肝细胞癌（ＨＣＣ）预后不良相关基因，并探讨其临床意义。

方法：在基因表达综合数据库（ＧＥＯ）中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因（ＤＥＧｓ），再运用生物学信息注释及可视化数据库（ＤＡＶＩＤ）和蛋白相互作用数据库（Ｓｔｒｉｎｇ）分别进行功能富集分析和蛋白质互作用网络的构建。

利用癌症基因组图谱数据库（ＴＣＧＡ）和Ｃｏｘ比例风险回归模型对相关差异基因进行预后分析。

结果：找到一个符合条件的人类ＨＣＣ数据库（ＧＳＥ８４４０２），共筛选出１１４１个差异表达基因（ＤＥＧｓ），其中上调基因７２０个，下调基因４２１个。

基因功能富集分析和蛋白质互作用分析结果显示ＣＤＫ１、ＣＤＣ６、ＣＣＮＡ２、ＣＨＥＫ１、ＣＥＮＰＥ、ＰＩＫ３Ｒ１、ＲＡＣＧＡＰ１、ＢＩＲＣ５、ＫＩＦ１１和ＣＹＰ２Ｂ６为ＨＣＣ预后的关键基因。

ＴＣＧＡ数据库和Ｃｏｘ回归模型分析显示ＣＤＣ６、ＰＩＫ３Ｒ１、ＲＡＣＧＡＰ１和ＫＩＦ１１的表达升高，ＣＥＮＰＥ的表达降低与ＨＣＣ预后不良密切相关。

结论：ＣＤＣ６、ＣＥＮＰＥ、ＰＩＫ３Ｒ１、ＲＡＣＧＡＰ１和ＫＩＦ１１可能和ＨＣＣ的预后不良相关，可作为未来ＨＣＣ预后研究的参考标志物。

【关键词】　肝细胞癌；预后不良基因；生物信息学；Ｃｏｘ比例风险回归模型【中图分类号】Ｒ７３．３【文献标识码】Ａ【文章编号】１０００６８３４（２０１９）０１０９０００９【ＤＯＩ】１０．１２０４７／ｊ．ｃｊａｐ．５７６４．２０１９．０２１ＢｉｏｉｎｆｏｒｍａｔｉｃｓａｎａｌｙｓｉｓｏｆｇｅｎｅｓｒｅｌａｔｅｄｔｏｐｏｏｒｐｒｏｇｎｏｓｉｓｏｆｈｕｍａｎｈｅｐａｔｏｃｅｌｌｕｌａｒｃａｒｃｉｎｏｍａａｎｄｉｔｓｃｌｉｎｉｃａｌｓｉｇｎｉｆｉｃａｎｃｅＸＩＹｉｂｏ１，２＋，ＺＨＡＮＧＨａｏｍｉｎ１，２＋，ＹＡＮＧＢｏ２，ＣＨＥＮＸｉｍｅｎｇ１，２，ＨＥＰｅｉｆｅｎｇ１△，ＬＵＸｕｅｃｈｕｎ２，１△（１．ＳｃｈｏｏｌｏｆＭａｎａｇｅｍｅｎｔ，ＳｈａｎｘｉＭｅｄｉｃａｌＵｎｉｖｅｒｓｉｔｙ，Ｔａｉｙｕａｎ０３０００１；２．ＤｅｐａｒｔｍｅｎｔｏｆＨｅｍａｔｏｌｏｇｙ，ＳｏｕｔｈＢｕｉｌｄｉｎｇ，ＧｅｎｅｒａｌＨｏｓｐｉｔａｌｏｆｔｈｅＰｅｏｐｌｅ＇ｓＬｉｂｅｒａｔｉｏｎＡｒｍｙ，ＮａｔｉｏｎａｌＣｅｎｔｅｒｆｏｒＣｌｉｎｉｃａｌＲｅｓｅａｒｃｈｏｆＧｅｒｉａｔｒｉｃＤｉｓｅａｓｅｓ，Ｂｅｉｊｉｎｇ１００８５３，Ｃｈｉｎａ）【ＡＢＳＴＲＡＣＴ】Ｏｂｊｅｃｔｉｖｅ：Ｔｏｓｃｒｅｅｎｇｅｎｅｓａｓｓｏｃｉａｔｅｄｗｉｔｈｐｏｏｒｐｒｏｇｎｏｓｉｓｏｆｈｅｐａｔｏｃｅｌｌｕｌａｒｃａｒｃｉｎｏｍａ（ＨＣＣ）ａｎｄｔｏｅｘｐｌｏｒｅｔｈｅｃｌｉｎｉｃａｌｓｉｇｎｉｆｉｃａｎｃｅｏｆｔｈｅｓｅｇｅｎｅｓ．Ｍｅｔｈｏｄｓ：ＴｈｅｐｒｏｐｅｒｅｘｐｒｅｓｓｉｏｎｐｒｏｆｉｌｅｄａｔａｏｆＨＣＣｗａｓｏｂｔａｉｎｅｄｆｒｏｍｔｈｅＧｅｎｅＥｘｐｒｅｓｓｉｏｎＯｍｎｉｂｕｓ（ＧＥＯ）ｄａｔａｂａｓｅ，ａｎｄｔｈｅｄｉｆｆｅｒｅｎｔｉａｌｌｙｅｘｐｒｅｓｓｅｄｇｅｎｅｓ（ＤＥＧｓ）ｗｅｒｅｉｄｅｎｔｉｆｉｅｄｂｙｄｉｆｆｅｒｅｎｔｉａｌｅｘｐｒｅｓｓｉｏｎａｎａｌｙｓｉｓ．ＴｈｅＤＡＶＩＤａｎｄＳｔｒｉｎｇｄａｔａｂａｓｅｗｅｒｅｕｓｅｄｆｏｒｆｕｎｃｔｉｏｎｅｎｒｉｃｈｍｅｎｔａｎａｌｙｓｉｓａｎｄｔｏｃｏｎｓｔｒｕｃｔｔｈｅｐｒｏｔｅｉｎｐｒｏｔｅｉｎｉｎｔｅｒａｃｔｉｏｎ（ＰＰＩ）ｎｅｔｗｏｒｋｒｅｓｐｅｃｔｉｖｅｌｙ．ＴｈｅＣａｎｃｅｒＧｅｎｏｍｅＡｔｌａｓ（ＴＣＧＡ）ｄａｔａｂａｓｅａｎｄｔｈｅＣｏｘＰｒｏｐｏｒｔｉｏｎａｌＨａｚａｒｄＭｏｄｅｌｗｅｒｅｕｓｅｄｆｏｒｐｒｏｇｎｏｓｉｓａｎａｌｙｓｉｓｏｆｔｈｅＤＥＧｓ．Ｒｅｓｕｌｔｓ：ＡｅｌｉｇｉｂｌｅｈｕｍａｎＨＣＣｄａｔａｓｅｔ（ＧＳＥ８４４０２）ｍｅｔｔｈｅｒｅｑｕｉｒｅｍｅｎｔｓ．Ａｔｏｔａｌｏｆ１１４１ｄｉｆｆｅｒｅｎｔｉａｌｌｙｅｘｐｒｅｓｓｅｄｇｅｎｅｓｗｅｒｅｉｄｅｎｔｉｆｉｅｄ，ｉｎｃｌｕｄｉｎｇ７２０ｕｐｒｅｇｕｌａｔｅｄａｎｄ４２１ｄｏｗｎｒｅｇｕｌａｔｅｄｇｅｎｅｓ．ＴｈｅｒｅｓｕｌｔｓｏｆｆｕｎｃｔｉｏｎｅｎｒｉｃｈｍｅｎｔａｎａｌｙｓｉｓａｎｄＰＰＩｎｅｔｗｏｒｋｐｅｒｆｏｒｍｅｄｔｈａｔＣＤＫ１、ＣＤＣ６、ＣＣＮＡ２、ＣＨＥＫ１、ＣＥＮＰＥ、ＰＩＫ３Ｒ１、ＲＡＣＧＡＰ１、ＢＩＲＣ５、ＫＩＦ１１ａｎｄＣＹＰ２Ｂ６ｗｅｒｅｐｒｏｇｎｏｓｉｓｋｅｙｇｅｎｅｓ．ＡｎｄｔｈｅｐｒｏｇｎｏｓｉｓａｎａｌｙｓｉｓｓｈｏｗｅｄｔｈａｔｔｈｅｅｘｐｒｅｓｓｉｏｎｓｏｆＣＤＣ６、ＰＩＫ３Ｒ１、ＫＩＦ１１ａｎｄＲＡＣＧＡＰ１ｗｅｒｅｉｎｃｒｅａｓｅｄ，ａｎｄｔｈｅｅｘｐｒｅｓｓｉｏｎｏｆＣＥＮＰＥｗａｓｄｅｃｒｅａｓｅｄ，ｗｈｉｃｈｗａｓｃｌｏｓｅｌｙｒｅｌａｔｅｄｔｏｐｒｏｇｎｏｓｉｓｏｆＨＣＣ．Ｃｏｎｃｌｕｓｉｏｎ：ＣＤＣ６、ＣＥＮＰＥ、ＰＩＫ３Ｒ１、ＫＩＦ１１ａｎｄＲＡＣＧＡＰ１ｍａｙｂｅｃｌｏｓｅｌｙｒｅｌａｔｅｄｔｏｐｏｏｒｐｒｏｇｎｏｓｉｓｏｆＨＣＣ，ａｎｄｃａｎｂｅｕｓｅｄａｓｍｏｌｅｃｕｌａｒｂｉｏｍａｒｋｅｒｓｆｏｒｆｕｔｕｒｅｒｅｓｅａｒｃｈｏｆＨＣＣｐｒｏｇｎｏｓｉｓ．【ＫＥＹＷＯＲＤＳ】　ｈｅｐａｔｏｃｅｌｌｕｌａｒｃａｒｃｉｎｏｍａ；　ｐｏｏｒｐｒｏｇｎｏｓｉｓｇｅｎｅｓ；　ｂｉｏｉｎｆｏｒｍａｔｉｃｓ；　ＣｏｘＰｒｏｐｏｒｔｉｏｎａｌＨａｚａｒｄＭｏｄｅｌ【基金项目】２０１７年度国家老年疾病临床医学研究中心招标课题（ＮＣＲＣＧＰＬＡＧＨ２０１７０１１）；解放军总医院转化医学项目（２０１７ＴＭ０２０）；山西省重点研发计划项目（２０１８０３Ｄ３１０６７）【收稿日期】２０１８１００９【修回日期】２０１８１１０５　△【通讯作者】Ｔｅｌ：１３２４１８９２８６３，１３９３４５６９９２８；Ｅｍａｉｌ：ｌｕｘｕｅｃｈｕｎ＠１２６．ｃｏｍ，ｈｅｐｅｉｆｅｎｇ２００６＠１２６．ｃｏｍ．＋：共同第一作者肝细胞癌（ｈｅｐａｔｏｃｅｌｌｕｌａｒｃａｒｃｉｎｏｍａ，ＨＣＣ）是原发性肝癌中最常见的类型，占原发性肝癌的８３％，也是全球癌症死亡的第二大主要原因［１］。

荧光定量pcr基因相对表达量计算方法

荧光定量pcr基因相对表达量计算方法Quantitative Polymerase Chain Reaction (qPCR) is a widely used technique in molecular biology to measure gene expression levels. It is often used to analyze the relative expression of genes in different samples. For researchers studying gene expression, calculating the relative gene expression levels is crucial for understanding the biological processes underlying various cellular activities.荧光定量PCR（qPCR）是分子生物学中广泛使用的一种技术，用于测量基因表达水平。

它通常用于分析不同样本中基因的相对表达量。

对于研究基因表达的研究人员来说，计算基因的相对表达水平对于理解各种细胞活动背后的生物过程至关重要。

One common method for calculating gene expression is the 2^-ΔΔCT method. This method involves first determining the threshold cycle (CT) values for both the gene of interest and the reference gene in each sample. The ΔCT value is then calculated by subtract ing the reference gene CT value from the gene of interest CT value. The ΔΔCT value is obtained by subtracting the ΔCT value of the control sample from the ΔCT value of the experimental sample.一种常见的计算基因表达的方法是2^-ΔΔCT方法。

毕赤酵母表达手册

Pichia Expression KitVersion M01110225-0043Pichia Expression KitA Manual of Methods for Expression of Recombinant Proteins in Pichia pastorisCatalog no. K1710-01tech_service@iiINDIVIDUAL PICHIA EXPRESSION KIT LICENSE AGREEMENTThe Pichia Expression Kit is based on the yeast Pichia pastoris. Pichia pastoris was developed into an expression system by scientists at Salk Institute Biotechnology/Industry Associates (SIBIA) for high-level expression of recombinant proteins. All patents for Pichia pastoris and licenses for its use as an expression system are owned by Research Corporation Technologies, Inc. Tucson, Arizona. Invitrogen has an exclusive license to sell the Pichia Expression Kit to scientists for research purposes only, under the terms described below. Use of Pichia pastoris by commercial corporations requires the user to obtain a commercial license as detailed below. Before using the Pichia Expression Kit, please read the following license a greement. If you do not agree to be bound by its terms, contact Invitrogen within 10 days for authorization to return the unused Pichia Expression Kit and to receive a full credit. If you do agree to the terms of this Agreement, please complete the User Registration Card and return it to Invitrogen before using the kit.INDIVIDUAL PICHIA EXPRESSION KIT LICENSE AGREEMENTInvitrogen Corporation (INVITROGEN) grants you a non-exclusive license to use the enclosed Pichia Expression Kit (EXPRESSION KIT) for academic research or for evaluation purposes only. The EXPRESSION KIT is being transferred to you in furtherance of, and reliance on, such license. You may not use the EXPRESSION KIT, or the materials contained therein, for any commercial purpose without a license for such purpose from RESEARCH CORPORATION TECHNOLOGIES, INC., Tucson, Arizona. Commercial purposes include the use in or sale of expressed proteins as a commercial product, or use to facilitate or advance research or development of a commercial product. Commercial entities may conduct their evaluation for one year at which time this license automatically terminates. Commercial entities will be contacted by Research Corporation Technologies during the evaluation period regarding the purchase of a commercial license.Access to the EXPRESSION KIT must be limited solely to those officers, employees and students of your institution who need access thereto in order to perform the above-described research or evaluation. You must inform each of such officer, employee and student of the provisions of this Agreement and require them to agree, in writing, to be bound by the provisions of this Agreement. You may not distribute the EXPRESSION KIT to others, even those within your own institution. You may transfer modified, altered or original material from the EXPRESSION KIT to a third party following notification of INVITROGEN such that the recipient can be licensed. You may not assign, sub-license, rent lease or otherwise transfer this License or any of the rights or obligation hereunder, except as expressly permitted.This License is effective until terminated. You may terminate it at any time by destroying all Pichia expression products in your control. It will also terminate automatically if you fail to comply with the terms and conditions of the Agreement. You shall, upon termination of the License, destroy all Pichia Expression Kits in your control, and so notify INVITROGEN in writing.This License Shall be governed in its interpretation and enforcement by the laws of the State of California.Product User Registration CardPlease complete and return the enclosed Product User Registration Card for each Pichia Expression Kit that you purchase. This will serve as a record of your purchase and registration and will allow Invitrogen to provide you with technical support and manual updates. It will also allow Invitrogen to update you on future developments of and improvements to the Pichia Expression Kit. The agreement outlined above becomes effective upon our receipt of your User Registration Card or 10 days following the sale of the Pichia Expression Kit to you. Use of the kit at any time results in immediate obligation to the terms and conditions stated in this Agreement.Technical ServicesInvitrogen provides Technical Services to all of our registered Pichia Expression Kit users. Please contact us if you need assistance with the Pichia Expression Kit.United States Headquarters:Japanese Headquarters European Headquarters:Invitrogen Corporation1600 Faraday AvenueCarlsbad, CA 92008 USATel: 1 760 603 7200Tel (Toll Free): 1 800 955 6288 Fax: 1 760 602 6500E-mail:tech_service@ Invitrogen Japan K.K.Nihonbashi Hama-Cho Park Bldg. 4F2-35-4, Hama-Cho, NihonbashiTel: 81 3 3663 7972Fax: 81 3 3663 8242E-mail: jpinfo@Invitrogen Ltd3 Fountain DriveInchinnan Business ParkPaisley PA4 9RF, UKTel (Free Phone Orders): 0800 269 210Tel (General Enquiries): 0800 5345 5345Fax: +44 (0) 141 814 6287E-mail: eurotech@iiiivTable of ContentsMaterials (vii)Purchaser Notification (x)Product Qualification (xii)Introduction (1)Overview (1)Experimental Outline (3)Recombination and Integration in Pichia (7)Methods (11)Pichia Strains (11)E. coli Strains (13)Selecting a Pichia Expression Vector (14)pHIL-D2 (16)pPIC3.5 (17)pHIL-S1 (18)pPIC9 (19)Signal Sequence Processing (20)Cloning into the Pichia Expression Vectors (21)Transformation into E. coli (26)Preparation of Transforming DNA (27)Growth of Pichia for Spheroplasting (30)Preparation of Spheroplasts (32)Transformation of Pichia (34)Screening for Mut+ and Mut S Transformants (36)PCR Analysis of Pichia Integrants (40)Expression of Recombinant Pichia Strains (42)Analysis by SDS-Polyacrylamide Gel Electrophoresis (45)Optimization of Pichia Protein Expression (47)Scale-up of Expression (49)Protein Purification and Glycosylation (51)Recipes (53)E. coli Media Recipes (53)Pichia Media Recipes (54)Appendix (59)Electroporation of Pichia (59)PEG 1000 Transformation Method for Pichia (60)Lithium Chloride Transformation Method (61)Total DNA Isolation from Pichia (62)Detection of Multiple Integration Events (63)Procedure for Total RNA Isolation from Pichia (64)β-Galactosidase Assay (65)Technical Service (67)References (69)vviMaterialsKit Contents Box 1: Spheroplast Module. Store at room temperature.Reagent Amount ComponentsSOS media 20 ml 1 M Sorbitol0.3X YPD10 mM CaCl2Sterile Water 2 x 125 ml Autoclaved, deionized waterSE 2 x 125 ml 1 M Sorbitol25 mM EDTA, pH 8.0SCE 2 x 125 ml 1 M Sorbitol10 mM Sodium citrate buffer, pH 5.81 mM EDTA1 M Sorbitol2 x 125 ml --CaS 2 x 60 ml 1 M Sorbitol10 mM Tris-HCl, pH 7.5;10 mM CaCl240% PEG 25 ml 40% (w/v) PEG 3350 (Reagent grade) in waterCaT 25 ml 20 mM Tris-HCl, pH 7.520 mM CaCl2Stab Vials: Pichia and E. coli stabs. Store at +4°C.Phenotype(Pichia only)GenotypeStrain Amountstab his4Mut+GS115 1stab arg4 his4 aox1::ARG4 Mut S, Arg+KM71 1GS115 Albumin 1 stab HIS4Mut SGS115 β-Gal 1 stab HIS4Mut+stab F´ {pro AB, lac I q, lac Z∆M15, Tn10 (Tet R)} mcr A,TOP10F´ 1∆(mrr-hsd RMS-mcr BC), φ80lac Z∆M15, ∆lac X74,deo R, rec A1, ara D139, ∆(ara-leu)7697, gal U,gal K, rps L (Str R), end A1, nup G λ-.Box 2: Spheroplast Module. Store at -20°C.ComponentsReagent AmountZymolyase 10 x 20 µl 3 mg/ml Zymolyase in water(100,000 units/g lytic activity)1 M DTT 10 x 1 ml 1 M dithiothreitol in watercontinued on next pageviiKit Contents,continuedVector Box. Store at -20°C.Reagent DescriptionpHIL-D210 µg, lyophilized in TE, pH 8.0Vector for intracellular expression in PichiapPIC3.510 µg, lyophilized in TE, pH 8.0Vector for intracellular expression in PichiapHIL-S110 µg, lyophilized in TE, pH 8.0 Vector for secreted expression in Pichia. Uses the PHO1 signal sequencepPIC910 µg, lyophilized in TE, pH 8.0 Vector for secreted expression in Pichia. Uses the α-factor signal sequencePrimer Box. Store at -20°C.5´ AOX1 sequencing primer2 µg (312 pmoles), lyophilized5´-GACTGGTTCCAATTGACAAGC-3´3´ AOX1 sequencing primer2 µg (314 pmoles), lyophilized5´-GCAAATGGCATTCTGACATCC-3´α-Factor sequencing primer2 µg (315 pmoles), lyophilized5´-TACTATTGCCAGCATTGCTGC-3´Media The following prepackaged media is included for your convenience. Instructions for use are provided on the package.Media Amount Yield YP Base Medium 2 pouches 2 liters of YP mediumYP Base Agar Medium 2 pouches 2 liters of YP mediumYeast Nitrogen Base 1 pouch 500 ml of 10X YNBFor transformation of Pichia by spheroplasting, the Pichia Spheroplast Module isavailable separately from Invitrogen (see below for ordering information).Product Reactions or Amount Catalog no.Pichia Spheroplast Module 10 spheroplast preparations(50 transformations)K1720-01continued on next pageviiiRequired Equip-ment and Supplies (not provided) • 30°C rotary shaking incubator• Water baths capable of 37°C, 45°C, and 100°C• Centrifuge suitable for 50 ml conical tubes (floor or table-top)• Baffled culture flasks with metal covers (50 ml, 250 ml, 500 ml, 1000 ml, and 3 L)• 50 ml sterile, conical tubes• 6 ml and 15 ml sterile snap-top tubes (Falcon 2059 or similar)• UVSpectrophotometer• Mini agarose gel apparatus and buffers• Polyacrylamide Gel Electrophoresis apparatus and buffers• Media for transformation, growth, screening, and expression (see Recipes, pages 53-58) • 5% SDS solution (10 ml per transformation)• Sterile cheesecloth or gauze• Breaking Buffer (see Recipes, page 58)• Acid-washed glass beads (available from Sigma)• Replica-plating equipment (optional)• BeadBreaker™ (optional)ixPurchaser NotificationIntroduction The Pichia Expression Kit is based on the yeast Pichia pastoris. Pichia pastoris wasdeveloped into an expression system by scientists at Salk Institute Biotechnology/ IndustryAssociates (SIBIA) and Phillips Petroleum for high-level expression of recombinantproteins. All patents for Pichia pastoris and licenses for its use as an expression system areowned by Research Corporation Technologies (RCT), Inc., Tucson, Arizona. Forinformation on commercial licenses, please see page x.The Nature of the Invitrogen License Invitrogen has an exclusive license to sell the Pichia Expression Kit to scientists for research purposes only, under the terms described below. Use of Pichia pastoris by commercial entities for any commercial purpose requires the user to obtain a commercial license as detailed below. Before using the Pichia Expression Kit, please read the following license agreement. If you do not agree to be bound by its terms, contact Invitrogen within 10 days for authorization to return the unused Pichia Expression Kit and to receive a full credit. If you do agree to the terms of this license agreement, please complete the User Registration Card and return it to Invitrogen before using the kit.Pichia pastoris Patents Pichia pastoris is covered by one or more of the following U.S. patents and corresponding foreign patents owned and licensed by Research Corporation Technologies:4,683,293 4,808,537 4,812,405 4,818,700 4,837,148 4,855,231 4,857,467 4,879,231 4,882,279 4,885,242 4,895,800 4,929,555 5,002,876 5,004,688 5,032,516 5,122,465 5,135,868 5,166,329Individual Pichia Expression Kit License Agreement Invitrogen Corporation ("Invitrogen") grants you a non-exclusive license to use the enclosed Pichia Expression Kit ("Expression Kit") for academic research or for evaluation purposes only. The Expression Kit is being transferred to you in furtherance of, and reliance on, such license. You may not use the Expression Kit, or the materials contained therein, for any commercial purpose without a license for such purpose from Research Corporation Technologies, Inc., Tucson, Arizona.Definition of Commercial Purpose Commercial purposes include:(a) any use of Expression Products in a Commercial Product(b) any use of Expression Products in the manufacture of a Commercial Product(c) any sale of Expression Products(d) any use of Expression Products or the Expression Kit to facilitate or advanceresearch or development of a Commercial Product(e) any use of Expression Products or the Expression Kit to facilitate or advance anyresearch or development program the results of which will be applied to thedevelopment of Commercial Products"Expression Products" means products expressed with the Expression Kit, or with the use of any vectors or host strains in the Expression Kit. "Commercial Product" means any product intended for sale or commercial use.Commercial entities may conduct their evaluation for one year at which time this license automatically terminates. Research Corporation Technologies will contact commercial entities during the evaluation period regarding their desire for a commercial license.continued on next pagexPurchaser Notification, continuedIndividual Responsibilities Access to the Expression Kit must be limited solely to those officers, employees and students of your institution who need access to perform the above-described research or evaluation. You must inform each such officer, employee and student of the provisions of this license agreement and require them to agree, in writing, to be bound by the provisions of this license agreement. You may not distribute neither the Expression Kit nor the vectors or host strains contained in it to others, even to those within your own institution. You may only transfer modified, altered, or original material from the Expression Kit to a third party following written notification of, and written approval from, Invitrogen so that the recipient can be licensed. You may not assign, sub-license, rent, lease or otherwise transfer this license agreement or any of the rights or obligation thereunder, except as expressly permitted by Invitrogen and RCT.Termination of License This license agreement is effective until terminated. You may terminate it at any time by destroying all Pichia expression products in your control. It will also terminate auto-matically if you fail to comply with the terms and conditions of the license agreement. You shall, upon termination of the license agreement, destroy all Pichia Expression Kits in your control, and so notify Invitrogen in writing.This License shall be governed in its interpretation and enforcement by the laws of the State of California.Contact for Commercial Licensing Bennett Cohen, Ph.D.Research Corporation Technologies 101 North Wilmot Road, Suite 600 Tucson, Arizona 85711-3335 Phone: (520) 748-4400Fax: (520)748-0025User Registration Card Please complete and return the enclosed User Registration Card for each PichiaExpression Kit that you purchase. This will serve as a record of your purchase and regis-tration and will allow Invitrogen to provide you with technical support and manualupdates. It will also allow Invitrogen to update you on future developments and improve-ments to the Pichia Expression Kit. The agreement outlined above becomes effectiveupon our receipt of your User Registration Card or 10 days following the sale of thePichia Expression Kit to you. Use of the kit at any time results in immediate obligation tothe terms and conditions stated in this license agreement.xiProduct QualificationIntroduction This section describes the criteria used to qualify the components in the PichiaExpression Kit.Vectors All expression vectors are qualified by restriction enzyme digestion. Restriction digests must demonstrate the correct banding pattern when electrophoresed on an agarose gel.Spheroplast Reagents The spheroplast reagents are qualified by spheroplast preparation of GS115 following the protocol provided in the Pichia Expression Kit manual. At least 70% of the Pichia pastoris cells must form spheroplasts in 30 minutes or less.Pichia Strains The Pichia strains are by demonstrating viability of the culture. Single colonies should arise within 48 hours after streaking on YPD medium from the stabPrimers Sequencing primers are lot tested by automated DNA sequencing experiments.Buffers andSolutionsAll buffers and solutions are extensively tested for sterility.Media All Pichia growth and expression media are qualified by growing the GS115 Pichiastrain.xiiIntroductionOverviewReview Articles The information presented here is designed to give you a concise overview of the Pichia pastoris expression system. It is by no means exhaustive. For further information, pleaseread the articles cited in the text along with recent review articles (Buckholz and Gleeson,1991; Cregg et al., 1993; Sreekrishna et al., 1988; Wegner, 1990). A general review offoreign gene expression in yeast is also available (Romanos et al., 1992).General Characteristics of Pichia pastoris As a eukaryote, Pichia pastoris has many of the advantages of higher eukaryotic expression systems such as protein processing, protein folding, and posttranslational modification, while being as easy to manipulate as E. coli or Saccharomyces cerevisiae. It is faster, easier, and less expensive to use than other eukaryotic expression systems such as baculovirus or mammalian tissue culture, and generally gives higher expression levels. As a yeast, it shares the advantages of molecular and genetic manipulations with Saccharomyces, and has the added advantage of 10- to 100-fold higher heterologous protein expression levels. These features make Pichia very useful as a protein expression system.Similarity to Saccharomyces Many of the techniques developed for Saccharomyces may be applied to Pichia including: • transformation by complementation• genedisruption• genereplacementIn addition, the genetic nomenclature used for Saccharomyces has been applied to Pichia. For example, the HIS4 gene in both Saccharomyces and Pichia encodes histidinol dehydrogenase. There is also cross-complementation between gene products in both Saccharomyces and Pichia. Several wild-type genes from Saccharomyces complement comparable mutant genes in Pichia. Genes such as HIS4, LEU2, ARG4, TRP1, and URA3 all complement their respective mutant genes in Pichia.Pichia pastoris as a Methylotrophic Yeast Pichia pastoris is a methylotrophic yeast, capable of metabolizing methanol as its sole carbon source. The first step in the metabolism of methanol is the oxidation of methanol to formaldehyde using molecular oxygen by the enzyme alcohol oxidase. This reaction generates both formaldehyde and hydrogen peroxide. To avoid hydrogen peroxide toxicity, methanol metabolism takes place within a specialized cell organelle called the peroxisome, which sequesters toxic by-products from the rest of the cell. Alcohol oxidase has a poor affinity for O2, and Pichia pastoris compensates by generating large amounts of the enzyme. The promoter regulating the production of alcohol oxidase drives heterologous protein expression in Pichia.Two Alcohol Oxidase Proteins The AOX1 and AOX2 genes code for alcohol oxidase in Pichia pastoris. The AOX1 gene product accounts for the majority of alcohol oxidase activity in the cell. Expression of the AOX1 gene is tightly regulated and induced by methanol to high levels, typically > 30% ofthe total soluble protein in cells grown with methanol as the carbon source. The AOX1 gene has been isolated and the AOX1 promoter is used to drive expression of the gene of interest (Ellis et al., 1985; Koutz et al., 1989; Tschopp et al., 1987a). While AOX2 is about 97% homologous to AOX1, growth on methanol is much slower than with AOX1. This slowgrowth allows isolation of Mut S strains (aox1) (Cregg et al., 1989; Koutz et al., 1989).continued on next page1Overview, continuedExpression Expression of the AOX1 gene is controlled at the level of transcription. In methanol-grown cells approximately 5% of the polyA+ RNA is from the AOX1 gene. The regulation of theAOX1 gene is a two step process: a repression/derepression mechanism plus an inductionmechanism (e.g. GAL1 gene in Saccharomyces (Johnston, 1987)). Briefly, growth onglucose represses transcription, even in the presence of the inducer methanol. For thisreason, growth on glycerol is recommended for optimal induction with methanol. Pleasenote that growth on glycerol (derepression) is not sufficient to generate even minute levelsof expression from the AOX1 gene. The inducer, methanol, is necessary for detectablelevels of AOX1 expression (Ellis et al., 1985; Koutz et al., 1989; Tschopp et al., 1987a).Phenotype of aox1 mutants Loss of the AOX1 gene, and thus a loss of most of the cell's alcohol oxidase activity, results in a strain that is phenotypically Mut S (Methanol utilization slow). This has in the past been referred to as Mut. The Mut S designation has been chosen to accurately describe the phenotype of these mutants. This results in a reduction in the cells' ability to metabolize methanol. The cells, therefore, exhibit poor growth on methanol medium. Mut+ (Methanol utilization plus) refers to the wild type ability of strains to metabolize methanol as the sole carbon source. These two phenotypes are used when evaluating Pichia transformants for integration of your gene (Experimental Outline, page 3).Intracellular and Secretory Protein Expression Heterologous expression in Pichia can be either intracellular or secreted. Secretion requires the presence of a signal sequence on the expressed protein to target it to the secretory pathway. While several different secretion signal sequences have been used successfully, including the native secretion signal present on some heterologous proteins, success has been variable. The secretion signal sequence from the Saccharomyces cerevisiaeα factor prepro peptide has been used most successfully (Cregg et al., 1993; Scorer et al., 1993).The major advantage of expressing heterologous proteins as secreted proteins is that Pichia pastoris secretes very low levels of native proteins. That, combined with the very low amount of protein in the minimal Pichia growth medium, means that the secreted heterologous protein comprises the vast majority of the total protein in the medium and serves as the first step in purification of the protein (Barr et al., 1992). Note: If there are recognized glycosylation sites (Asn-X-Ser/Thr) in your protein's primary sequence, glycosylation may occur at these sites.Posttranslational Modifications In comparison to Saccharomyces cerevisiae, Pichia may have an advantage in the glyco-sylation of secreted proteins because it may not hyperglycosylate. Both Saccharomyces cerevisiae and Pichia pastoris have a majority of N-linked glycosylation of the high-mannose type; however, the length of the oligosaccharide chains added posttranslationally to proteins in Pichia (average 8-14 mannose residues per side chain) is much shorter than those in S. cerevisiae (50-150 mannose residues) (Grinna and Tschopp, 1989; Tschopp et al., 1987b). Very little O-linked glycosylation has been observed in Pichia.In addition, Saccharomyces cerevisiae core oligosaccharides have terminal α1,3 glycan linkages whereas Pichia pastoris does not. It is believed that the α1,3 glycan linkages in glycosylated proteins produced from Saccharomyces cerevisiae are primarily responsible for the hyper-antigenic nature of these proteins making them particularly unsuitable for therapeutic use. Although not proven, this is predicted to be less of a problem for glycoproteins generated in Pichia pastoris, because it may resemble the glycoprotein structure of higher eukaryotes (Cregg et al., 1993).2Experimental OutlineSelection of Vector and Cloning To utilize the strong, highly inducible P AOX1 promoter for expression of your protein, four expression vectors are included in this kit. pHIL-D2 and pPIC3.5 are used for intracellular expression while pHIL-S1 and pPIC9 are used for secreted expression (see pages 14-19 for more information). Before cloning your insert, you must...• decide whether you want intracellular or secreted expression.• analyze your insert for the following restriction sites: Sac I, Stu I, Sal I, Not I, and Bgl II. These sites are recommended for linearizing your construct prior to Pichiatransformation. If your insert has all of these sites, see pages 28-29 for alternate sites.Transformation and IntegrationTwo different phenotypic classes of His+ recombinant strains can be generated: Mut+ and Mut S. Mut S refers to the "Methanol utilization slow" phenotype caused by the loss of alcohol oxidase activity encoded by the AOX1 gene. A strain with a Mut S phenotype has a mutant aox1 locus, but is wild type for AOX2. This results in a slow growth phenotype on methanol medium. Transformation of strain GS115 can yield both classes of transformants, His+ Mut+ and His+Mut S, while KM71 yields only His+ Mut S since the strain itself is Mut S. Both Mut+ and Mut S recombinants are useful to have as one phenotype may favor better expression of your protein than the other. Due to clonal variation, you should test 6-10 recombinants per phenotype. There is no way to predict beforehand which construct or isolate will better express your protein. We strongly recommend that you analyze Pichia recombinants by PCR to confirm integration of your construct (see page 40).Once you have successfully cloned your gene, you will then linearize your plasmid to stimulate recombination when the plasmid is transformed into Pichia. The table below describes the types of recombinants you will get by selective digestion of your plasmid. RestrictionEnzymeIntegration Event GS115 Phenotype KM71 PhenotypeSal I or Stu I Insertion at his4His+ Mut+ His+ Mut SSac I Insertion at 5´AOX1 regionHis+ Mut+ His+ Mut SNot I or Bgl II Replacement atAOX1 locusHis+ Mut SHis+ Mut+His+ Mut S (notrecommended, see page 11)Expression and Scale-up After confirming your Pichia recombinants by PCR, you will test expression of both His+Mut+ and His+ Mut S recombinants. This will involve growing a small culture of each recombinant, inducing with methanol, and taking time points. If looking for intracellular expression, analyze the cell pellet from each time point by SDS polyacrylamide gel electrophoresis (SDS-PAGE). If looking for secreted expression, analyze both the cellpellet and supernatant from each time point. We recommend that you analyze your SDS-PAGE gels by both Coomassie staining and Western blot, if you have an antibody to your protein. We also suggest checking for protein activity by assay, if one is available. Not all proteins express to the level of grams per liter, so it is advisable to check by Western blotor activity assay, and not just by Coomassie staining of SDS-PAGE gels for production of your protein.Choose the Pichia recombinant strain that best expresses your protein and optimizeinduction based on the suggestions on pages 47-48. Once expression is optimized, scale-up your expression protocol to produce more protein.continued on next page3。

异常值和子组识别统计（OASIS）参考手册说明书

Reference Manual for Outlier and Subgroup IdentiﬁcationStatistics(OASIS)Stan Pounds and Iwona PawlikowskaFebruary6,20141IntroductionModern high-throughput technologies allow researchers to idencorrelate with biologically or clinically important traits.It is also possible to discover new biological processes by identifying transcriptomic features that have outliers or multiple modes in their expression distributions.Outliers or subgroups in expression mayﬂag features that aﬀect disease biology or indicate a genomic abnormality such as translocation or deletion.The statistics and bioinformatics literature proposes several OASIS methods.Here,we adapted some of them,i.e.leave-one-out,least median squares,the dip test and maximum spacing test,and introduced a new method i.e.most informative spacing test for OASIS.Leave-one-out(LOO)procedure leaves out one data value,compute the mean and standard deviation of the remaining data values,and then compare the left-out data value to those summary statistics.Rousseeuw[1]notes that LOO is an eﬀective method for detection of single outliers but also shows that LOO is not an eﬀective method for detection of multiple outliers.Thus,Rousseeuw proposes least median squares(LMS)as a robust method to detect multiple outliers.LMSﬁrst identiﬁes the narrowest interval that includes at least50%of the data values and then uses the center and width of this interval that captures the’bulk’of the data to determine whether other data values are outliers.Rousseeuw shows that LMS eﬀectively identiﬁes outliers even when up to50%of the observations are outliers.The dip test developed by Hartigan and Hartigan[2]is another potentially robust OASIS method that is not widely used in the bioinformatics and genomics literature.The dip test evaluates the null hypothesis that a set of data values is unimodal.The dip statistic is the largest diﬀerence between the empirical distribution function(EDF)and the unimodal distribution function that minimizes the maximum diﬀerence from the EDF.Thus,a signiﬁcant dip statistic indicates compelling evidence that a particular set of data values has multiple modes.Intuitively,the diﬀerences between consecutive ordered data values are very informative regarding the existence of outliers or multiple modes.Pyke[3]called these diﬀerences spacings and derived their theoretical properties under many statistical models.Pounds[4]successfully used Pyke’s work to accurately estimate the fraction of clonable DNA.Therefore,we use Pyke’s theory to develop two novel OASIS methods for analysis of transcriptomic expression data.Here,we borrow ideas from the OASIS methods in the bioinformatics and statistics literatures and [5]to develop the most informative spacing test(MIST)[6].For each individual expression variable, MIST computes the diﬀerences between consecutive order statistics(spacings)and multiplies each spacing by the geometric mean of the sizes of the two groups it deﬁnes.The spacing with the largest value of this statistic is considered to be the most informative spacing and its signiﬁcance is determined by simulation.In this document,we describe how to use OASIS package.12InstallationThe latest version of OASIS package can be found in the link /site/depts/biostats/software and R CRAN version will be available soon.>#Install packages>#Load the package>library(OASIS)3M7exampleExample data set M7example[7]contains mRNA-seq exon read counts for14patients with Acute Megakaryoblastic Leukemia(AMKL)treated at St Jude Children’s Research Hospital.It is a dataframe with504rows and18columns.Theﬁrst four columns gene,chrom,loc.start and loc.endcontain annotation information such as gene symbol,chromosomal location and also start and endlocation of exon.The remaining14columns are raw mRNA-seq exon count data for children withAMKL.>#Load the example data set>data(M7example)>#show>head(M7example)gene chrom loc.start loc.end SJAMLM7001.D SJAMLM7003.D SJAMLM7004.D1stoyru16423339442341212712SRL164239370424296561254403SRL164239375424296561254404SRL164239377424296561254405TMEM8A164240054241512132967046TMEM8A16424005424174213296706SJAMLM7005.D SJAMLM7006.D SJAMLM7007.D SJAMLM7008.D SJAMLM7009.D SJAMLM7010.D1132020239131742190339131742190439131742190 5256237196184429289 6256237198184429289SJAMLM7011.D SJAMLM7012.D SJAMLM7013.D SJAMLM7014.D SJAMLM7015.D110021281851899381851899481851899528129822923874262813002302387434Data preparationThe main function of the package,row.oasis,operates on a data frame with rows containing ex-pression features and columns as subjects.Before we proceed with OASIS analysis,we need tonormalize data unless each subject has expression in the interval(0,1).We propose positive quantiletransformation(PQT)using pq.transform.2>#specify columns of data to transform>data.columns=5:18>#perform positive quantile transformation of specified columns of data>pqt.data=pq.transform(M7example,data.columns,add.row.id=TRUE)>head(pqt.data)row.id gene chrom loc.start loc.end SJAMLM7001.D SJAMLM7003.D SJAMLM7004.D11stoyru16423339442341210.050420170.16804980.0434782622SRL16423937042429650.407563030.73443980.3409611033SRL16423937542429650.407563030.73443980.3409611044SRL16423937742429650.407563030.73443980.3409611055TMEM8A164240054241510.716386550.79045640.8764302166TMEM8A164240054241740.716386550.79045640.87871854 SJAMLM7005.D SJAMLM7006.D SJAMLM7007.D SJAMLM7008.D SJAMLM7009.D SJAMLM7010.D 10.035343040.12842110.060041410.00000000.041928720.0000000 20.320166320.25052630.238095240.39484980.257861640.0000000 30.320166320.25052630.238095240.39484980.257861640.0000000 40.320166320.25052630.238095240.39484980.257861640.0000000 50.760914760.77473680.697722570.68240340.844863730.8402626 60.760914760.77473680.699792960.68240340.844863730.8402626 SJAMLM7011.D SJAMLM7012.D SJAMLM7013.D SJAMLM7014.D SJAMLM7015.D10.034042550.00000000.00000000.052083330.0315126120.157446810.25420170.11205070.247916670.5063025230.157446810.25420170.11205070.247916670.5063025240.157446810.25420170.11205070.247916670.5063025250.789361700.73949580.59619450.733333330.9264705960.789361700.74159660.59830870.733333330.92857143The input data data.set can be a data frame or name of a tab-delimited dataﬁle.If data.columns is NULL,then all columns of data set will be transformed.If data.columns is a character vector, then all columns of data set with a name found in data.columns will be transformed.If data.columns is a numeric vector,then the columns with those numeric indices will be transformed.By default data.columns are all column’s names of input data set.The parameter add.row.id indicates whether to add a column with row identiﬁers and by default is set to TRUE.For each subject,PQT normalizes the raw expression values by determining their quantile against the positive raw expression values.Now we can perform OASIS analysis on normalized data set.>data.columns=data.columns+1#since there is an additional column row.id>#define s0>s0=1/(2*(nrow(M7example)))>#perform OASIS and calculate values of simulations statistics>res=row.oasis(pqt.data,s0=s0,data.columns,sim.stats=NULL,nsim=1000) Parameters data.set and data.columns are deﬁned in the same way as in the function pq.transform. s0is a constant added to the scale estimate prior to computing the t-statistic in LOO and LMS. Default value for s0is1/(2*(nrow(data.set))).Parameter unitize,by default set to FALSE,indicates whether to unitize rows of data.set.By default we assume that input data set is transformed using PQT or some other normalization is used,so that values of each row lie in(0,1).In the other case, we transform input data into values that lies along a line crossing y-axis in the point1/(n+1),where n denotes sample size.The transformation is inspired by the fact that for n iid random variables X1,...,X n from the uniform(0,b),the scale b can be estimated as(n+1)/nX(n),where X(n)is the largest order statistic.User can choose which OASIS method to perform using the parameter method:3•mast-maximum spacing,•mist-most informative spacing,•dip-dip test,•lms.mop-least median squares with minimum outlier p-value,•lms.sst-least median squares with sum of squared t-statistics,•loo.mop-leave-one-out with minimum outlier p-value,•loo.sst-leave-one-out with sum of squared t-statistics.By default,“all”,function will compute all OASIS methods.P-value for each test is based on simulation from the normal distribution and simulation from the uniform distribution for each test.For larger data sets simulations are computationally intensive and we recommend to perform them once and store the ing parameter sim.stats=NULL we can calculate a data frame with simulated OASIS statistics.Otherwise one can provide a data frame with previously calculated simulated statistics.The number of simulations is set to10000by default.>names(res)[1]"oasis.res""sim.stats">names(res$oasis.res)[1]"row.id""gene""chrom""loc.start"[5]"loc.end""mast.stat""p.mast""mast.index" [9]"mast.hi""mist.stat""mist.index""mist.hi"[13]"lms.bulk1""lms.bulk2""lms.bulk.index1""lms.bulk.index2" [17]"lms.bulk.size""lms.center""lms.scale""lms.maxt" [21]"lms.mop""lms.sst""dip.stat""loo.maxt" [25]"loo.maxt.index""loo.mop""loo.sst">names(res$sim.stats)[1]"im.pvalue""im.pvalue""im.pvalue" [4]"im.pvalue""im.pvalue""im.pvalue" [7]"im.pvalue""mast.stat.zsim.pvalue""mist.stat.zsim.pvalue" [10]"lms.mop.zsim.pvalue""lms.sst.zsim.pvalue""dip.stat.zsim.pvalue" [13]"loo.mop.zsim.pvalue""loo.sst.zsim.pvalue"The result of function row.oasis depends on the parameter sim.stats.If sim.stats=NULL the result is given in the form of a list with names”oasis.res”and”sim.stats”.”oasis.res”is a data frame with the following columns:•mast.stat maximum spacing statistic(MAST)•p.mast p-value for mast statistic•mast.index index of maximum spacing•mast.hi number of data points located to the right of the pair of points deﬁning the MAST •mist.stat most informative spacing test(MIST)statistic4•mist.index index of most informative spacing•mist.hi number of data points located to the right of the pair of points deﬁning the MIST •lms.bulk narrowest interval covering half the data•lms.bulk.index indices of sorted data values that deﬁne the endpoints of the narrowest interval covering half the data•lms.bulk.size width of narrowests interval covering half the data•lms.center least median square(LMS)estimate of center=mean of observations in the nar-rowest interval covering half the data•lms.scale LMS scale estimate obtained by matching narrowest interval covering half the data to theﬁrst and third quartiles of the normal distribution•lms.maxt maximum outlier t-statistic by LMS•lms.mop minimum outlier p-value by LMS•lms.sst sum of squared outlier t-statistics by LMS•dip.stat dip statistic•loo.maxt maximum outlier t-statistics by leave-one-out(LOO)•loo.maxt.index index of maximum absolute outlier t-statistic•loo.mop minimum outlier p-value by LOO•loo.sst sum of squared outlier t-stats by LOO>names(res$sim.stats)[1]"im.pvalue""im.pvalue""im.pvalue" [4]"im.pvalue""im.pvalue""im.pvalue" [7]"im.pvalue""mast.stat.zsim.pvalue""mist.stat.zsim.pvalue" [10]"lms.mop.zsim.pvalue""lms.sst.zsim.pvalue""dip.stat.zsim.pvalue" [13]"loo.mop.zsim.pvalue""loo.sst.zsim.pvalue"“sim.stats”is a data frame with the same number of rows as the data set and14columns that represent p-values for each of the oasis statistic for uniform distribution(“usim”)and p-values for each of the oasis statistic for normal distribution(“zsim”).4.1MIST and MASTThese tests sort the data values and then compute the diﬀerences(spacings)between consecutive ordered data values.MAST:A very large spacing may indicate an extreme outlier or correspond to the diﬀerence between two very distinct subgroups.The maximum spacing is computed and its value is compared to the distribution of the maximum spacing of a set of independent uniform(0,1) observations to obtain a p-value.MIST:Each spacing is multiplied by a factor that is a function of its position relative the data values(number of data values to the left and number of data values to the right)to give spacing information.The factor is deﬁned such that it has the largest value for spacing dividing two balanced subgroups of data and it has the smallest value if the spacing separates largest of smallest observation from the rest of the data.The MIST statistic is calculated as the maximum of values of spacing across all observations.The observed MIST statistic is compared to its distribution under the null model that all observations are independent identically distributed uniform(0,1)and normal(0,1).54.2LMSEach observation is tested for possible outlier using least median squares(LMS).LMS identiﬁes the narrowest interval that covers at least50%of the data and assumes that interval deﬁnes the“bulk”of the observations.Next,t-tests are used to evaluate whether each individual observation is an outlier relative to a normal distribution withﬁrst and third quartiles corresponding to the endpoints of the interval.The sum of squared t-statistics(SST)and minimum outlier p-value(MOP)are computed to summarize the results.s0is a small positive number that the user may add to the LMS estimate of variance to avoid a large number ties for biologically not meaningful data(such as many zeros in RNA-seq data).Our implementationﬁrst determines the empirical null distribution for LMS-MOP and LMS-SST by computing the smallest p-value across samples and sum of squared t-statistics for each of a large number(default=10,000)of data sets with equal sample size generated from the normal(0,1)and uniform(0,1)distributions.Then,the observed MOP of each variable is compared to this empirical null distribution.The p-value is the proportion of empirical null MOPs that are greater than or equal to the observed MOP.The p-value for SST is calculated analougously.By default,the empirical null distributions are calculated(sim.stats=NULL).4.3DIPThis test evaluates whether the distribution of the data values appears multimodal.The dip test compares the observed empirical distribution function(EDF)of the data to the unimodal distri-bution function(UDF)that minimizes the maximum diﬀerence between the EDF and the UDF. Hartigan and Hartigan[2]call this minimax diﬀerence the dip statistic.Hartigan and Hartigan prove that the uniform distribution is asymptotically the”least favorable”unimodal distribution and thus recommend that the test be performed by comparing the observed value of the dip statis-tic to its empirical null distributions obtained by generating many data sets of equal size from the uniform(0,1)and normal(0,1)distributions.Then,the observed dip statistic of each variable is compared to this empirical null distribution.The p-value is the proportion of empirical null dip statistics that are greater than or equal to the observed dip er can insert empirical null distributions of the dip statistic(default sim.stats=NULL).4.4LOOFor each subject i,leave-one-out(LOO)computes the mean and standard deviation of the remaining observations(with subject i left out).Then,for each subject i,an outlier t-statistic and outlier p-value is calculated.Similarly to LMS,two statistics are calculated to summarize the collection of outlier p-values,minimum outlier p-value(MOP)and sum of squared t-statistics.There can be some cases that have large number of ties in the LOO procedure(such as many zeros in RNA-seq count data)so that leave-one-out variance estimate s i=0for some subject i and|t i|=∞andp LOO i =0.Then,these features will be extremely signiﬁcant but usually not of biological interest.To prevent these technical problems,the user may add a small positive constant s0to s i.The statistical signiﬁcance of LOO-MOP and LOO-SST is calculated in the similar way to LMS-MOP and LMS-SST.>#or we can insert values of simulations statistics>res.new=row.oasis(pqt.data,s0=s0,data.columns,sim.stats=res$sim.stats)>names(res.new)[1]"row.id""gene""chrom""loc.start"[5]"loc.end""mast.stat""p.mast""mast.index" [9]"mast.hi""mist.stat""mist.index""mist.hi"[13]"lms.bulk1""lms.bulk2""lms.bulk.index1""lms.bulk.index2"6Figure 1:OASIS plot of 50random variables from uniform(0,1)distribution (left)and 50random variables from normal(0,1)distribution (right).MISTMASTLMS LOO100.20.40.60.81MIST MAST LMS LOO0100.20.40.60.81[17]"lms.bulk.size""lms.center""lms.scale""lms.maxt"[21]"lms.mop""lms.sst""dip.stat""loo.maxt"[25]"loo.maxt.index""loo.mop""loo.sst"5Generate plot for OASIS methodsThe package OASIS provides one.oasis.plot to visualize results of OASIS methods.This function plots data and conﬁdence intervals for all OASIS methods for a vector of data values.The usage of one.oasis.plot is shown in the following examples:>par(mfrow=c(1,2))>u=runif(50)>one.oasis.plot(u)>z=rnorm(50)>one.oasis.plot(z,unitize=TRUE)User can specify signiﬁcance level for conﬁdence intervals in the parameter alpha ,by default set to 0.01.We can make a plot of an exon of GLIS2gene that was found to have bimodal distribution [7]due to fusion with highly expressed gene CBFA2T3.7Figure2:OASIS plot of an exon of GLIS2from M7example.DIP 00.20.40.60.818References[1]P.J.Rousseeuw,“Least median of squares regression,”Journal of the American statistical asso-ciation,vol.79,no.388,pp.871–880,1984.[2]J.A.Hartigan and P.M.Hartigan,“The dip test of unimodality,”The Annals of Statistics,vol.13,pp.70–84,1985.[3]R.Pyke,“Spacings,”Journal of the Royal Statistical Society.Series B(Methodological),vol.27,no.3,pp.395–449,1965.[4]S.Pounds,“Estimating the fraction of clonable genomic DNA,”Bulletin of Mathematical Biology,vol.63,no.5,pp.995–1002,2001.[5]P.Tong,Y.Chen,X.Su,and K.R.Coombes,“SIBER:systematic identiﬁcation of bimodallyexpressed genes using RNAseq data,”Bioinformatics,vol.29,no.5,pp.605–613,2013.[6]I.Pawlikowska,G.Wu,M.Edmonson,Z.Liu,T.Gruber,J.Zhang,and S.Pounds,“The mostinformative spacing test eﬀectively discovers biologically relevant outliers or multiple modes in expression,”Bioinformatics,2014.[7]T.A.Gruber,rson,J.Zhang,C.S.Koss,S.Marada,H.Q.Ta,S.-C.Chen,X.Su,S.K.Ogden,J.Dang,G.Wu,V.Gupta,A.K.Andersson,S.Pounds,L.Shi,J.Easton,M.I.Barbato,H.L.Mulder,J.Manne,J.Wang,M.Rusch,S.Ranade,R.Ganti,M.Parker,J.Ma,I.Radtke,L.Ding,G.Cazzaniga,A.Biondi,S.M.Kornblau,F.Ravandi,H.Kantarjian,S.D.Nimer, K.D¨o hner,H.D¨o hner,T.L.Ley,P.Ballerini,S.Shurtleﬀ,D.Tomizawa,S.Adachi,Y.Hayashi,A.Tawa,L.-Y.Shih,D.-C.Liang,J.E.Rubnitz,C.-H.Pui,E.R.Mardis,and J.Wilson,R.K.Downing,“An Inv(16)(p13.3q24.3)-Encoded CBFA2T3-GLIS2Fusion Protein Deﬁnes an Aggressive Subtype of Pediatric Acute Megakaryoblastic Leukemia,”Cancer Cell,vol.22,no.5, pp.683–697,2012.9。

核盘菌通过类似整联蛋白SSITL...

核盘菌通过类似整联蛋白（SSITL）抑制寄主的抗病反应目录摘要 (I)ABSTRACT (IV)缩略词表 (VIII)1. 前言综述 (1)1.1 核盘菌的危害及其防治 (1)1.1.1 核盘菌的危害及其生物学特性 (1)1.1.2 作物菌核病的防治研究 (1)1.2 植物病原菌与寄主植物的互作 (5)1.2.1 植物天然的的物理及生理生化防卫屏障 (5)1.2.2 植物的先天免疫系统 (6)1.2.3 植物的后天免疫系统 (10)1.2.4 植物的非寄主抗性 (13)1.2.5 不同类型植物病原菌的侵染策略以及互作方式 (14)1.2.6 核盘菌的侵染策略 (16)1.3基因功能研究的策略 (19)1.3.1丝状真菌的遗传转化的研究进展 (19)1.3.2基因的超标达、敲除和沉默 (20)1.3.3 蛋白质的定位 (24)1.4 Integrin以及Integrin–like基因的研究进展 (26)1.4.1 整联蛋白的结构 (27)1.4.2 整联蛋白的信号传导 (29)1.4.3整联蛋白在微生物中的生物学功能 (30)1.5 本项研究的目的和意义 (32)2. 材料与方法 (33)2.1 菌株及植物材料 (33)2.2 基因的生物信息学分析 (33)2.3 核酸的实验操作 (34)2.3.1 DNA的提取 (34)2.3.2 质粒的提取 (34)2.3.3 总RNA的提取 (35)2.3.4 RT和Real–Time PCR (35)2.3.5 Northern blot (36)2.4 蛋白质的实验操作 (37)2.4.1 SSITL的原核表达 (37)2.4.2 抗体血清的制备、效价（ELISA）以及特异性（Western blot）的检测 (37)2.4.3 SSITL的免疫胶体金亚细胞定位 (39)2.4.4 核盘菌侵染洋葱表皮过程中SSITL的免疫荧光定位 (40)2.5 相关载体的构建 (40)2.6 ATMT介导的真菌和植物转化 (41)2.7 生物学特性的实验研究 (43)2.7.1 生长速度、致病力、菌丝顶端分支以及菌落形态的观察 (43)华中农业大学2012届博士研究生学位论文2.7.2 菌核的培养、大小及重量的测定和菌核萌发的研究 (43)2.7.3 核盘菌产草酸能力的测定 (44)2.7.4 核盘菌侵染拟南芥叶片过程的观察 (45)2.8 SSITL与植物诱导抗性的关系 (45)2.8.1 核盘菌侵染拟南芥过程中SSITL基因的表达情况 (45)2.8.2 核盘菌侵侵染拟南芥过程中拟南芥局部抗性的动态变化 (45)2.8.3 核盘菌侵侵染拟南芥过程中拟南芥系统抗性的动态变化 (46)2.8.4 SSITL在植物中表达对植物的抗病性的影响 (46)3. 结果与分析 (47)3.1 SSITL的生物信息学分析 (47)3.1.1 SSITL的序列分析 (47)3.1.2 SSITL蛋白的同源比对分析及高级结构预测 (49)3.2 SSITL对核盘菌生物学特性的影响 (53)3.2.1 SSITL基因在核盘菌不同生长时期的表达 (53)3.2.2 SSITL基因沉默对核盘菌生物学特性的影响 (53)3.3 SSITL抗体的制备以及免疫胶体金亚细胞定位 (61)3.3.1 SSITL的原核诱导表达 (61)3.3.2 抗血清效价以及特异性测定 (63)3.3.3 SSITL蛋白的亚细胞定位 (63)3.4 SSITL基因在核盘菌与植物互作过程中的作用 (67)3.4.1 核盘菌侵染拟南芥时，SSITL基因的表达情况 (67)3.4.2 核盘菌SSITL对拟南芥局部防卫反应的影响 (68)3.4.3 核盘菌SSITL对拟南芥系统防卫反应的影响 (70)3.4.4 SSITL在寄主植物中瞬时表达对植物抗病性的影响 (74)3.4.5 SSITL在寄主植物中组成型表达对植物抗病性的影响 (79)3.4.6 SSITL的表达对烟草的影响 (81)4. 讨论 (83)4.1 SSITL基因生物学功能的深入探讨 (83)4.1.1 SSITL基因的序列分析 (83)4.1.2 SSITL基因的功能分析 (85)4.2 SSITL参与抑制植物诱导抗性 (87)4.2.1 SSITL基因在核盘菌侵染过程中被诱导表达 (88)4.2.2 SSITL参与抑制植物的局部抗性 (88)4.2.3 SSITL参与抑制植物的系统抗性 (89)4.2.4 SSITL基因在植物中表达后，植物的抗性受到抑制 (90)4.3 研究SSITL的互作蛋白以及作用机理 (90)4.4 结论与展望 (92)5. 参考文献 (94)附录： (116)博士期间发表的论文 (121)致谢 (122)核盘菌通过类似整联蛋白（SSITL）抑制寄主的抗病反应摘要核盘菌（Sclerotinia sclerotiorum）属于子囊菌门，是一种世界性分布的典型的死体营养型病原真菌。

Agilent RMA Probe Summarization 说明书

RMA Probe SummarizationGeneSpring GX 7.3.1AndGeneSpring GX 9.01ContentsProbe Summarization Algorithms (3)Definition and Applications (3)RMA (4)A nalyzing Affymetrix Expression Data (5)GeneSpring GX 7.3.1 (5)GeneSpring GX 9.0 (12)2Probe Summarization AlgorithmsDefinition and ApplicationsProbe summarization algorithms perform the following 3 key tasks:Background CorrectionNormalizationProbe Summarization (i.e. conversion of probe level values to probeset expression values in a robust, i.e., outlier resistant manner)The order of the last two steps could differ for different probe summarization algorithms.For probe intesnsity measurements from Affymetrix Gene expression chips, one of the algorithms used in both GeneSpring GX 7.3.1 and GeneSpring GX 9.0 is RMA3RMARMA (Robust Multi-array Analysis) is a method for normalizing and summarizing probe-level intensity measurements from Affymetrix Gene Chips®. Starting with the probe-level data from a set of Gene Chips, the perfect-match (PM) values are background-corrected, normalized and finally summarized resulting in a set of expression measures. The three steps of the process are outlined below.Background CorrectionIt has been argued that background correction is the most crucial step for probe level processing.The background correction used in RMA is a non-linear correction, done on a per-chip basis. It is isbased on the distribution of PM values amongst probes on an Affymetrix array. PM values are a mixture of a background signal, caused by optical noise and non-specific binding, plus a signal, whichis what we are trying to detect. The background is estimated as expectation of the signal (S) conditioned on observed PM values (O), using a kernel density estimation in both GeneSpring GX7.3.1 and GeneSpring GX 9.0. However,, however GeneSpring GX 7.3.1 uses direct convolution while GeneSpring GX 9.0 uses Fast Fourier Transformation.NormalizationNormalization is necessary so that multiple chips can be compared to each other, and analyzed together. The normalization procedure is aimed at making the distributions identical across arrays.The normalization used in RMA is quantile normalization. This usually gives very sharp normalizations.Both GeneSpring GX 7.3.1 and GeneSpring GX 9.0 use quantile normalization. Note that, in this procedure, all the arrays are used and no chip is discarded based on extreme value considerations.SummarizationOnce the probe-level PM values have been background-corrected and normalized, they need to be summarized into expression measures, so that the result is a single expression measure per probe-set, per chip. The summarization used is motivated by the assumption that observed log-transformed PM values follow a linear additive model containing a probe affinity effect, a gene specific effect (the expression level) and an error term. For RMA, the probe affinity effects are assumed to sum to zero, and the gene effect (expression level) is estimated using median polishing. Median polishing is a robust model fitting technique, that protects against outlier probes.Both GeneSpring GX 7.3.1 and GeneSpring GX 9.0 use same methodology for summarization.4Analyzing Affymetrix Expression DataGeneSpring GX 7.3.1The following steps need to be performed in GeneSpring GX 7.3.1 to analyze Affymetrix gene expression chips :Step 1 : Import DataSelect the data file you want to import in GeneSpring GX 7.3.1 using File > Import Data5Step 2 : Choose File format and select the appropriate genomeGeneSpring GX 7.3.1 automatically recognizes the file format and displays it for standard Affymetrix expression; Agilent one color and two color; Illumina; and Codelink chips.Step 3 : Choose the PreprocessorSelect the appropriate preprocessing algorithm –‘RMA’ or ‘GC RMA’. You might be asked to define the location of the CDF file or Array Definition file.6Step 4 : Choose more data filesThis window allows you to add more files of the same type to add to your experiment during the import process.7Step 5 : Sample Attributes windowThis window allows you to add sample attributes, which are required for MIAME compliance. This is an optional step and can be performed at a later stage as well.8Step 6 : Experiment CreationAfter the data files have been successfully imported and samples have been created, GeneSpring GX 7.3.1 prompts you to create an experiment from these samples.Provide an appropriate name for the New Experiment910Step 7 : New Experiment ChecklistAfter the Experiment is created, you get the option to define Experiment Normalizations, Parameters, interpretation and Cross Gene Error Model.Step 8 : Experiment NormalizationsThis window allows you to define what normalization(s) need to be performed on your data.For affymetrix data preprocessed using RMA or GC RMA preprocessor, ‘data transformation’ and ‘per chip’ normalization needs to be deleted at this step, as these normalization steps have already been performed during preprocessing.11GeneSpring GX 9.0The following steps need to be performed in GeneSpring GX 9.0 to analyze Affymetrix gene expression chips :Step 1 : Create New ExperimentCreate a new experiment using Project > New Experiment12Step 2 : Experiment DescriptionProvide an appropriate Name and Experiment type (or, chip type) for the new experimentYou can also define the Workflow type –‘Guided Workflow’ or ‘Advanced Analysis’Guided workflow is designed to assist the user throughout the creation and analysis of an experiment with a set of default parameters, while in the Advanced Analysis, the parameters can be changed to suit individual requirements.1314Step 3 : Load DataAn experiment can be created using either the data files or else using samples. Upon loading data files, GeneSpring GX associates the files with the technology (see below) and creates samples. These samples are stored in the system and can be used to create another experiment via the Choose Samples option. For selecting data files and creating an experiment, click on the Choose File(s) button.Navigate to the appropriate folderSelect the files of interest and select Open to proceed.There are two things to be noted here. Upon creating an experiment of a specific chip type for the first time, the tool asks to download the technology from the GeneSpring GX update server. If an experiment has been created previously with the same technology, GeneSpring GX then directly proceeds with experiment creation.15Step 4 : Select ARR filesARR files are Affymetrix files that hold annotation information for each sample CEL and CHP file and are associated with the sample based on the sample name. These are imported as annotations to the sample.16Step 5 : Select Probe Summarization and Normalization optionsSelect RMA as the Probe Summarization algorithm from the drop down list.Subsequent to probe set summarization, baseline Transformation of the data can be performed. The baseline options include:Do not perform baselineBaseline to median of all samplesBaseline to median of control samplesNote : ‘Baseline Transformation’ in GeneSpring GX 9.0 is equivalent to ‘per gene normalization’ in GeneSpring GX 7.3.1Clicking Finish creates an experiment, which is displayed as a Box Whisker plot in the active view. Alternative views can be chosen for display by navigating to View in Toolbar.17。

copy number analysis

Copy Number AnalysisIntroductionCopy number analysis is a widely used method in genetics and genomics research to study variations in the number of copies of a particular segment of DNA. This analysis helps in understanding the genetic basis of various diseases, including cancer, and can provide valuable insights into disease development, progression, and treatment.Significance of Copy Number AnalysisCopy number variations (CNVs) are structural changes in the DNA that involve duplications or deletions of large segments of the genome. These variations can have profound effects on gene expression and function, as they can alter the dosage of genes. Therefore, copy number analysis is crucial for identifying genetic factors underlying diseases and understanding their molecular mechanisms.Techniques for Copy Number AnalysisSeveral techniques are available for copy number analysis, each with its own advantages and limitations. Some of the commonly used techniques include:1. Comparative Genomic Hybridization (CGH)CGH is a microarray-based method that compares the fluorescenceintensity of test and reference genomic DNA. It can identify DNA copy number alterations by measuring the ratio of fluorescence signals between the two samples. CGH provides a genome-wide analysis of copy number changes, but it is limited by its relatively low resolution.2. Single Nucleotide Polymorphism (SNP) ArraysSNP arrays simultaneously genotype thousands of SNPs and can also detect copy number changes. This technique is based on the measurement ofallele-specific signal intensities. SNP arrays offer higher resolution compared to CGH and can detect smaller CNVs. However, they are limited to the detection of copy number changes at SNP loci.3. Next-Generation Sequencing (NGS)NGS technologies, such as whole-genome sequencing and targeted sequencing, have revolutionized copy number analysis. By sequencing the DNA fragments and mapping them back to the reference genome, NGS can identify copy number alterations. NGS provides high-resolution and accurate copy number analysis but requires extensive bioinformatics analysis.Steps in Copy Number AnalysisCopy number analysis typically involves the following steps:1. Data PreprocessingRaw data obtained from copy number analysis techniques require preprocessing to remove noise and artifacts. This involves background correction, normalization, and quality control.2. SegmentationSegmentation is the process of dividing the genome into regions of similar copy number. Various algorithms, such as Circular Binary Segmentation (CBS) and Hidden Markov Models (HMM), are used for this purpose.3. Copy Number CallingCopy number calling assigns a discrete copy number state to each segmented region. Techniques like the GISTIC algorithm and the CopyNumber Variation Analysis of Cancer (CONVAC) method are commonly usedfor copy number calling.4. Visualization and InterpretationThe final step involves visualizing and interpreting the copy number alterations. This can be done using various software tools and visualization techniques, such as heatmaps and Circos plots. Interpretation of the copy number alterations requires integrative analysis with other genomic data, such as gene expression and functional annotation.Applications of Copy Number AnalysisCopy number analysis has diverse applications in genetics and genomics research:1. Cancer ResearchCopy number alterations play a crucial role in cancer development and progression. Copy number analysis can identify oncogenes and tumor suppressor genes, characterize cancer subtypes, and guide personalized treatment strategies.2. Genetic DiseasesCNVs are associated with numerous genetic disorders and birth defects. Copy number analysis helps in identifying disease-causing genes, understanding disease mechanisms, and facilitating genetic counseling.3. PharmacogenomicsCopy number analysis can pred ict an individual’s response to certain drugs. It can help in identifying genetic markers that influence drug metabolism and efficacy, enabling personalized medicine approaches.4. Evolutionary GeneticsCopy number variations contribute to genome evolution and adaptation. Studying copy number changes in different species can provide insights into evolutionary processes and the genetic basis of species-specific traits.Challenges in Copy Number AnalysisWhile copy number analysis is a powerful tool, it faces certain challenges:1. Data Quality and NoiseCopy number analysis relies on high-quality data, and noise in the data can affect the accuracy of results. Proper data preprocessing andquality control are essential to minimize noise effects.2. Resolution and SensitivityDifferent analysis techniques have varying resolutions and sensitivities for detecting copy number alterations. It is important to choose the appropriate technique based on the research question and desired level of resolution.3. Computational AnalysisCopy number analysis generates large amounts of genomic data, requiring extensive computational analysis and resources. Advanced bioinformatics tools and expertise are necessary for accurate analysis and interpretation of results.4. Biological ComplexityInterpretation of copy number alterations is complex due to the multi-dimensional nature of genomic data. Combining copy number analysis with other data types, such as gene expression and functional annotation, is crucial for a comprehensive understanding of the biological implications.ConclusionCopy number analysis is a vital technique in genetics and genomics research, enabling the identification of disease-causing genes, understanding disease mechanisms, and guiding treatment approaches. With advancements in technology and data analysis, copy number analysis continues to contribute valuable insights into the genetic basis of various diseases and evolutionary processes.。

植物实时荧光定量PCR内参基因的选择_胡瑞波

32
中国农业科技导报
11卷
达最为稳定。 Jian等 [ 19] 分析了 10个大豆内参基因 (ACT2 /
7 , ACT11 , TUA, TUB, ELF1 α, UBC2 , ELF1 b, CYP2 , G6PD, UBQ10 )在 21 个不同发育时期、组织器官等样本中的表达稳定性 , 结果表明 10个内参基因的表达稳定性存在显著差异 , 在所有供试样品中 ELF1 b和 CYP2 稳定性最好 , G6 PD和 UBQ10 的稳定性最差。在不同的组织器官或发育时期样品中 , 其稳定性也不同 , ELF1 b和 CYP2 适合于组织器官样品的表达分析 , 而 ACT2 /7 和 TUA在不同发育时期的样品中表达稳定性最好。
摘要 :实时荧光定量 RT-PCR(real-timequantitativereversetranscriptionPCR, qRT-PCR)具有定量准确、灵敏度高和高通量等特点 , 已被广泛应用于基因的表达分析。常规 qRT-PCR采用相对定量进行分析 , 其关键步骤是选择合适的稳定内参基因进行校正和标准化。持家基因被广泛用作内参基因 , 但在所有生理条件下均稳定表达的理想内参基因并不存在。大多数传统内参基因已不能满足 qRT-PCR准确定量的要求。基于基因芯片表达数据和 EST数据库并结合 qRT-PCR, 可以筛选稳定性好的新的内参基因。简要综述了植物 qRT-PCR 内参基因的研究进展 , 并就内参基因的选择中应注意的问题进行了探讨。关键词 :实时荧光定量 RT-PCR;内参基因 ;geNorm;基因表达中图分类号 :Q789 文献标识码 :A 文章编号 :1008-0864(2009)06-0030-07

晚期前列腺癌的雄激素阻断医治

晚期前列腺癌的雄激素阻断医治【摘要】探讨间歇性雄激素阻断医治与持续性雄激素阻断医治晚期前列腺癌的疗效和不良反映。

方式65例晚期前列腺癌患者分为两组，A组34例行间歇性雄激素阻断（IAB）医治，B组31例行持续性雄激素阻断（CAB）医治，比较两组在疾病进展时间和不良反映方面的不同。

结果A组中位随访时间为个月，B组中位随访时间为个月。

A、B组疾病进展率别离为%和%，两组比较不同有统计学意义（P=）。

A、B组疾病中位进展时间别离为个月、个月，两组比较不同有统计学意义（P=）。

在有骨转移患者中，A、B组疾病中位进展时间别离为个月、个月，两组比较不同有统计学意义（P=）。

在无骨转移患者中，A、B组疾病中位进展时间别离为个月、个月，两组比较不同有统计学意义（P=）。

不良反映发生率别离为A组发生潮热症状%、乳腺肿痛%、骨质疏松%。

B组发生潮热症状%、乳腺肿痛%、骨质疏松%。

两组比较不同有统计学意义：潮热症状P=，乳腺肿痛P=，骨质疏松P=。

结论对晚期前列腺癌患者IAB医治可以延缓病变的进展，减少雄激素阻断致使的不良反映，提高患者的生活质量，应作为晚期前列腺癌患者的首选医治。

【关键词】前列腺肿瘤雄激素阻断不良反映［Abstract］Objective To compare the efficacy of intermittent androgen blockade （IAB ）versus total continuous androgen blockade（CAB ）in the treatment of late prostate The study included 65 patients with late prostate patients （group A ）received IAB，and 31 patients （group B ）received time to disease progression and side effect rates was compared between the 2 The median follow-up was months in group A and months in group disease progression rate was % in group A and % in group B，with significant difference between the 2 groups （P=）.The median time to disease progression was months in group A and months in group B，there was significant difference between the 2 groups （P= ）.In patients with skeletal metastasis，the median time to disease progression was months in group A and months in group B，with significant difference between the 2 groups （P= ）.In patients without skeletal metastasis，the median time to disease progression was months in group A and months in group B，with significant difference between the 2 groups （P= ）.Side effect rates were found in more patients of group B than in group A，including hot flash （% vs %，P=），gynecomastia （% vs %，P=），osteoporosis （% vs %，P= ）.Conclusion IAB is the first choice of endocrine treatment for late prostate cancer.［Key words］prostatic neoplasm; androgen blockade;side effect前列腺癌是老年男性最多见的肿瘤之一，其发病率呈逐年上升趋势［1］，雄激素阻断是已失去根治机缘的晚期前列腺癌患者的首选医治方式。

转录组测序到底在做什么（二）

转录组测序到底在做什么（⼆）经过《转录组测序到底在做什么（⼀）》的介绍，我们已经完成前期分析，拿到了clean data，下⾯请跟随⼩微，进⼊到转录组测序真正的核⼼分析内容。

核⼼分析在获得clean data后，我们需要将得到的clean data回贴（mapping）到参考基因组或参考转录组上（有参转录组）。

在这个步骤中，回贴的⽐例（mapping rate）就显得⾄关重要。

如对于⼈的转录组，⼀般期望回贴到参考基因组上的⽐例能达到70%-90%。

⽽回帖到参考转录组时，这个⽐例会略低，因为数据中未注释的转录本⽆法进⾏回贴。

另外，覆盖度也⼗分重要。

例如在回贴中发现转录本的5’端覆盖度较低⽽3’端覆盖度较⾼，则表⽰样本质量较差，发⽣了⼀定的降解。

⽽对于没有参考基因组、参考转录组（⽆参转录组）或参考基因组及转录组不完整的样本，则需要对得到的数据进⾏从头拼接，组装出转录组序列。

（图2）对于有参转录组在进⾏回贴分析后，还可对参考转录组中未注释的新的转录本进⾏鉴定与分析。

也可以对转录本的变异进⾏分析，如对SNP位点的分析、InDel分析、不同类型的可变剪切的分析等。

还可基于mapping的结果进⾏融合基因的分析。

图⼆完成回贴、组装、新转录本预测、变异分析、融合基因等的分析后，就进⼊到转录组最核⼼的部分，即转录本的表达定量和差异表达分析。

转录本的表达定量，即对各转录本测到的reads数进⾏转录本长度、测序深度等因素的均⼀化后进⾏的表达量评估。

在双端测序中常使⽤FPKM（fragments per kilobase of exon model per million mapped reads）这⼀指标来衡量，即每1百万个fragments中map到外显⼦的每1K个碱基上的reads个数，其中的fragment指在双端测序中由插⼊⽚段两端的⼀组reads所确定的⼀个⽚段（fragment）。

⽽差异表达分析则是在转录本定量的基础上，为找出不同样本组中表达量发⽣显著差异的转录本，同时确定其表达量的变化的趋势及倍数所进⾏的分析。

基因VIII名词解释

附录1．名词解释A腺嘌呤(adenine)abortive transduction 流产转导：转导的DNA片段未掺入到受体的染色体中，此DNA片段不能复制，只能传给两个子细胞中的一个，沿着单个细胞线传递。

acentric chromosome无着丝粒染色体：指缺乏着丝粒的染色体或染色单体。

achondroplasia 软骨发育不全：人类的一种常染色体显性遗传病，表型为四肢粗短，鞍鼻，腰椎前凸。

acroce n tric chromosome近端着丝粒染色体：着丝粒位于染色体末端附近。

active site 活性位点：蛋白质结构中具有生物活性的结构域。

adap t ation适应：在进化中一些生物的可遗传性状发生改变，使其在一定的环境能更好地生存和繁殖。

adenine 腺嘌呤：在DNA中和胸腺嘧啶配对的碱基。

albino 白化体：一种常染色体隐性遗传突变。

动物或人的皮肤及毛发呈白色，主要因为在黑色素合成过程中，控制合成酪氨酸酶的基因发生突变所致。

allele等位基因：一个座位上的基因所具有的几种不同形式之一。

allelic frequencies等位基因频率：在群体中存在于所有个体中某一个座位上等位基因的频率。

allelic exclusion等位排斥：杂合状态的免疫球蛋白基因座中，只有一个基因因重排而得以表达，其等位基因不再重排而无活性。

allopolyploi d y异源多倍体：多倍体的生物中有一套或多套染色体来源于不同物种。

Ames test埃姆斯测验法： Bruce Ames 于1970年人用鼠伤寒沙门氏菌（大鼠）肝微粒体法来检测某些物质是否有诱变作用。

amino acids氨基酸：是构成蛋白质的基本单位，自然界中存在20种不同的氨基酸。

aminoacyl-tRNA氨基酰- tRNA：tRNA的氨基臂上结合有相应的氨基酸，并将氨基酸运转到核糖体上合成蛋白质。

aminoacyl-tRNA synthetase氨基酰- tRNA合成酶：催化一个特定的tRNA结合到相应的tRNA分子上。

温度与条锈病菌双胁迫下小麦内参基因的选择

( Ｂ０７０４９) 通讯作者: 胡小平ꎬ教授ꎬ主要从事植物病理学研究ꎻ Ｅ￣ｍａｉｌ:ｘｐｈｕ＠ｎｗｓｕａｆ.ｅｄｕ.ｃｎ第一作者: 王军娟ꎬ女ꎬ陕西太白人ꎬ博士研究生ꎬ主要从事植物病理学研究ꎻ Ｅ￣ｍａｉｌ:ｚｉｘｕａｎ１１３９＠１２６.ｃｏｍꎮ
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植物病理学报
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结果产生偏差[１] ꎮ 为了得到更准确的数据ꎬ需用内参基因对实时定量ＰＣＲ数据进行均一化处理ꎮ 常用的内参基因为持家基因 ( ｈｏｕｓｅ￣ｋｅｅｐｉｎｇｇｅｎｅｓ) ꎬ如１８Ｓ核糖体ＲＮＡ(１８ＳｒＲＮＡ) 、肌动蛋白基因( ＡＣＴＩＮ) 、微管蛋白基因( ＴＵＢＬＩＮ) 、３￣磷酸甘油醛脱氢酶基因( ＧＡＰＤＨ) 等ꎮ 但一些持家基因在不同的试验条件下表达不稳定ꎬ 不是通用内参基因[２] ꎮ 因此ꎬ筛选特定实验体系下的内参基因对于准确分析目的基因的表达非常重要ꎮ 筛选内参基因的研究已有诸多报道ꎬ 如马铃薯[３] 、拟南芥[４] 、水稻[５] 、大豆[６] 、杨树[７] 、番茄[８] 、鳉鱼[９] 和人[１０] 等ꎮ 小麦在不同发育阶段、不同组织[１１] 以及受不同生物[１２ꎬ１３] 和非生物胁迫[１１] 过程中ꎬ表达最稳定的内参基因各不相同ꎮ 关于小麦在条锈病菌 ( 生物) 和温度( 非生物) 双胁迫下内参基因的筛选尚未见报道ꎮ 本研究以ＣＤＣ( Ｃｅｌｌｄｉｖｉｓｉｏｎｃｏｎｔｒｏｌｐｒｏｔｅｉｎ ) 、ＲＬＩ ( ＲＮａｓｅＬｉｎｈｉｂｉｔｏｒ￣ｌｉｋｅｐｒｏｔｅｉｎ ) 、ＡＣＴＩＮ和２６Ｓ ( ＡＴＰ￣ｄｅｐｅｎｄｅｎｔ２６Ｓｐｒｏｔｅａｓｏｍｅｒｅｇｕｌａｔｏｒｙｓｕｂｕｎｉｔ) 基因作为小麦候选内参基因ꎬ通过实时荧光定量ＰＣＲ技术ꎬ对小麦受高温和条锈病菌双胁迫不同阶段候选内参基因的转录水平进行检测ꎬ 采用ｇｅＮｏｒｍ [１] 和ＮｏｒｍＦｉｎｄｅｒ[１４] 软件对检测结果进行分析ꎬ以期筛选出小麦在温度及条锈病菌双重胁迫下的最佳内参基因ꎮ 小麦品种常因条锈病菌生理小种毒性变异而丧失抗病性ꎬ沦为感病品种ꎬ给小麦生产造成重大损失[１５] ꎮ 因此ꎬ选育非小种专化的持久抗病性品种已成为解决小种专化抗性丧失问题的重要途径[１６ꎬ１７] ꎮ 科学研究和生产实践证明ꎬ 小偃６号是一种典型的高温诱导且无小种专化性的持久性抗条锈病品种[１６] ꎬ研究其高温诱导抗条锈病的分子机制对于抗病品种培育具有重要的意义ꎮ 本研究确定的温度及条锈病菌双重胁迫下基因表达量研究用的内参基因ꎬ将为高温抗病性分子机制研究奠定基础ꎮ

遗传学名词解释(英文)

细菌遗传合成代谢功能的突变型(anabotic function mutants)合成代谢功能(anabolic functions)：野生型(wild type)在基本培养基上具有合成和生长所必需的有机物的功能营养缺陷型(auxotroph)：野生型品系的任何一个基因突变，都不能进行一个特定的生化反应，从而阻碍整个合成代谢功能的实现分解代谢功能的突变型(catabolic functional mutation分解代谢功能(catabolic function):指野生型E coli能利用比葡萄糖复杂的不同碳源，转化成葡萄糖或其他简单的糖类，也能把复杂的氨基酸或脂肪分子降解成乙酸或三羧酸循环的中间产物的功能抗性突变型细菌由于某基因的突变而对某些噬菌体或抗菌素产生抗性(resistant)，从而使其不能吸附或吸附在这种突变细菌上的能力降低conjugation (接合生殖)F因子又称性因子或致育因子(sex or fertility factor)，它是能独立增殖的环状DNA分子F＋细菌丢失F因子，成为F－细菌(acriflavine处理)F-受体细胞只接受部分的供体染色体，这样的细胞称为部分二倍体(partial diploid)或半合子(merozygote)内基因子(endogenote)和外基因子(exogenote)重组作图(recombination mapping)是根据基因间重组率进行基因定位末端（outside marker），受体部位(recept site)：外源DNA片段进入受体细菌形成临时性通道的特定区域感受态细胞(receptor site)：能接受外源DNA分子并被转化的细菌细胞感受态因子(competence factor)：促进转化作用的酶或蛋白质分子噬菌体所携带供体(细菌)染色体片段是完全随机的，即供体基因组中所有基因具有同等机会被转导形成部分二倍体，经交换和重组后，形成转导频率大致相等的不同转导子，这种转导称为普遍性转导(general transduction)共转导或并发转导(cotransduction)：指两个基因同时转导的现象，如果两个基因共转导的频率愈高，表明两个基因连锁愈紧密，相反共转导频率愈低，则表明两个基因距离愈远双因子转导(two-factor transduction)实验：就是每次观察两个基因的转导，通过每两个基因的共转导频率确定这些基因在染色体上的顺序溶菌酶（lysozyme)原噬菌体(prophage)或原病毒(provirus)：是指整合到宿主染色体中的噬菌体基因组溶源性(lysogeny)：有些细菌带有某种噬菌体，但并不立即导致溶菌，这种现象称为溶源性；这种细菌称为溶源性细菌或溶源菌(lysogenic bacterium)，此过程称为溶源周期裂解途径：裂解周期(lytic cycle)溶源途径：溶源周期(lysogenic cycle)条件致死突变型。