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GENE-counter:一个用于分析 RNA-Seq 数据以检测基因表达差异的计算流程。

GENE-counter: a computational pipeline for the analysis of RNA-Seq data for gene expression differences.

机构信息

Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America.

出版信息

PLoS One. 2011;6(10):e25279. doi: 10.1371/journal.pone.0025279. Epub 2011 Oct 6.

DOI:10.1371/journal.pone.0025279
PMID:21998647
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3188579/
Abstract

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts.

摘要

GENE-counter 是一个完整的基于 Perl 的计算流程,用于分析 RNA-Seq(RNA 测序)数据以进行差异基因表达分析。除了在真核模式生物的转录组研究中使用外,GENE-counter 还适用于没有可用基因组参考序列的原核生物和非模式生物。对于比对,GENE-counter 配置为 CASHX、Bowtie 和 BWA,但用户可以使用任何首选的符合序列比对/映射 (SAM) 的程序。为了分析差异基因表达数据,GENE-counter 可以与基于负二项式分布变体的三个统计软件包中的任何一个一起运行。默认方法是我们基于负二项式分布的过参数化版本开发的新的简单统计测试。GENE-counter 还包括三种用于评估富含基因本体论 (GO) 术语的差异表达特征的方法。结果是透明的,数据系统地存储在 MySQL 关系数据库中,以方便进行额外的分析和质量评估。我们使用下一代测序生成了一个源自拟南芥大量研究的防御反应的小规模 RNA-Seq 数据集,并使用 GENE-counter 处理数据。总的来说,微阵列分析的支持以及来自三个统计软件包中的每一个的观察到的和实质性的重叠结果表明,GENE-counter 非常适合处理小样本量和基因计数高度变化的独特特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/98f5e2434a37/pone.0025279.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/8672f7d4a2b0/pone.0025279.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/072aaafeb0c7/pone.0025279.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/141f29c655c4/pone.0025279.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/98f5e2434a37/pone.0025279.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/8672f7d4a2b0/pone.0025279.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/072aaafeb0c7/pone.0025279.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/141f29c655c4/pone.0025279.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d3e/3188579/98f5e2434a37/pone.0025279.g004.jpg

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1
RNA-seq: technical variability and sampling.RNA-seq:技术变异性和采样。
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2
Computational methods for transcriptome annotation and quantification using RNA-seq.基于 RNA-seq 的转录组注释和定量的计算方法。
Nat Methods. 2011 Jun;8(6):469-77. doi: 10.1038/nmeth.1613. Epub 2011 May 27.
3
Full-length transcriptome assembly from RNA-Seq data without a reference genome.无参考基因组的 RNA-Seq 数据的全长转录组组装。
BMC Bioinformatics. 2021 Jun 3;22(1):298. doi: 10.1186/s12859-021-04211-7.
4
Integrative Differential Expression Analysis for Multiple EXperiments (IDEAMEX): A Web Server Tool for Integrated RNA-Seq Data Analysis.多实验综合差异表达分析(IDEAMEX):用于综合RNA测序数据分析的网络服务器工具
Front Genet. 2019 Mar 29;10:279. doi: 10.3389/fgene.2019.00279. eCollection 2019.
5
Plasmodium vivax readiness to transmit: implication for malaria eradication.间日疟原虫的传播易感性:对疟疾根除的影响
BMC Syst Biol. 2019 Jan 11;13(1):5. doi: 10.1186/s12918-018-0669-4.
6
Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.基于生物学重复次数和文库大小的RNA测序差异基因表达分析的优化
Front Plant Sci. 2018 Feb 14;9:108. doi: 10.3389/fpls.2018.00108. eCollection 2018.
7
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J Nutr Biochem. 2017 Apr;42:72-83. doi: 10.1016/j.jnutbio.2017.01.001. Epub 2017 Jan 12.
8
Characterisation of Early-Life Fecal Microbiota in Susceptible and Healthy Pigs to Post-Weaning Diarrhoea.易患和健康仔猪早期粪便微生物群对断奶后腹泻的特征分析
PLoS One. 2017 Jan 10;12(1):e0169851. doi: 10.1371/journal.pone.0169851. eCollection 2017.
9
miARma-Seq: a comprehensive tool for miRNA, mRNA and circRNA analysis.miARma-Seq:一种用于miRNA、mRNA和环状RNA分析的综合工具。
Sci Rep. 2016 May 11;6:25749. doi: 10.1038/srep25749.
10
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J Comput Biol. 2016 Apr;23(4):239-47. doi: 10.1089/cmb.2015.0205. Epub 2016 Mar 7.
Nat Biotechnol. 2011 May 15;29(7):644-52. doi: 10.1038/nbt.1883.
4
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.通过高度多重化 RNA 测序进行单细胞转录组特征分析。
Genome Res. 2011 Jul;21(7):1160-7. doi: 10.1101/gr.110882.110. Epub 2011 May 4.
5
RNA-sequence analysis of human B-cells.人类 B 细胞的 RNA 测序分析。
Genome Res. 2011 Jun;21(6):991-8. doi: 10.1101/gr.116335.110. Epub 2011 May 2.
6
RNA-seq reveals cooperative metabolic interactions between two termite-gut spirochete species in co-culture.RNA-seq 揭示了两种共生在白蚁肠道内的螺旋体之间的合作代谢相互作用。
ISME J. 2011 Jul;5(7):1133-42. doi: 10.1038/ismej.2011.3. Epub 2011 Feb 17.
7
Haplotype and isoform specific expression estimation using multi-mapping RNA-seq reads.利用多映射 RNA-seq reads 进行单倍型和异构体特异性表达估计。
Genome Biol. 2011;12(2):R13. doi: 10.1186/gb-2011-12-2-r13. Epub 2011 Feb 10.
8
Transcriptome-wide sequencing reveals numerous APOBEC1 mRNA-editing targets in transcript 3' UTRs.转录组测序揭示了大量 APOBEC1 mRNA 编辑靶标在转录本 3'UTR 中。
Nat Struct Mol Biol. 2011 Feb;18(2):230-6. doi: 10.1038/nsmb.1975. Epub 2011 Jan 23.
9
A pipeline for RNA-seq data processing and quality assessment.RNA-seq 数据处理和质量评估的流水线。
Bioinformatics. 2011 Mar 15;27(6):867-9. doi: 10.1093/bioinformatics/btr012. Epub 2011 Jan 13.
10
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Nature. 2011 Mar 24;471(7339):473-9. doi: 10.1038/nature09715. Epub 2010 Dec 22.