Replicates, read numbers, and other important experimental design considerations for microbial RNA-seq identified using Bacillus thuringiensis datasets

Lu, Tse -Yuan; Mehlhorn, Tonia L; Pelletier, Dale A.; Hauser, Loren John; Brown, Steven D.; Manga, Punita; Klingeman, Dawn Marie; Wilson, Charlotte M.

doi:10.3389/fmicb.2016.00794

Title: Replicates, read numbers, and other important experimental design considerations for microbial RNA-seq identified using Bacillus thuringiensis datasets

Journal Article · 31 May 2016 · Frontiers in Microbiology

DOI: https://doi.org/10.3389/fmicb.2016.00794 · OSTI ID:1255680

Lu, Tse -Yuan ^[1]; Mehlhorn, Tonia L ^[1]; Pelletier, Dale A. ^[1]; Hauser, Loren John ^[2]; Brown, Steven D. ^[2]; Manga, Punita ^[3]; Klingeman, Dawn Marie ^[1]; Wilson, Charlotte M. ^[1]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

RNA-seq is being used increasingly for gene expression studies and it is revolutionizing the fields of genomics and transcriptomics. However, the field of RNA-seq analysis is still evolving. Therefore, we specifically designed this study to contain large numbers of reads and four biological replicates per condition so we could alter these parameters and assess their impact on differential expression results. Bacillus thuringiensis strains ATCC10792 and CT43 were grown in two Luria broth medium lots on four dates and transcriptomics data were generated using one lane of sequence output from an Illumina HiSeq2000 instrument for each of the 32 samples, which were then analyzed using DESeq2. Genome coverages across samples ranged from 87 to 465X with medium lots and culture dates identified as major variation sources. Significantly differentially expressed genes (5% FDR, two-fold change) were detected for cultures grown using different medium lots and between different dates. The highly differentially expressed iron acquisition and metabolism genes, were a likely consequence of differing amounts of iron in the two media lots. Indeed, in this study RNA-seq was a tool for predictive biology since we hypothesized and confirmed the two LB medium lots had different iron contents (~two-fold difference). Furthermore, this study shows that the noise in data can be controlled and minimized with appropriate experimental design and by having the appropriate number of replicates and reads for the system being studied. We outline parameters for an efficient and cost effective microbial transcriptomics study.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1255680

Journal Information:: Frontiers in Microbiology, Vol. 7; ISSN 1664-302X

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 9 works

Citation information provided by
Web of Science

References (91)

Studying bacterial transcriptomes using RNA-seq Croucher, Nicholas J.; Thomson, Nicholas R. Current Opinion in Microbiology, Vol. 13, Issue 5 https://doi.org/10.1016/j.mib.2010.09.009	journal	October 2010
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data Li, Jun; Tibshirani, Robert Statistical Methods in Medical Research, Vol. 22, Issue 5 https://doi.org/10.1177/0962280211428386	journal	November 2011
RNA sequencing: advances, challenges and opportunities Ozsolak, Fatih; Milos, Patrice M. Nature Reviews Genetics, Vol. 12, Issue 2 https://doi.org/10.1038/nrg2934	journal	December 2010
How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? Haas, Brian J.; Chin, Melissa; Nusbaum, Chad BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-734	journal	January 2012
Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment Gierliński, Marek; Cole, Christian; Schofield, Pietà Bioinformatics, Vol. 31, Issue 22 https://doi.org/10.1093/bioinformatics/btv425	journal	July 2015
RNA-seq differential expression studies: more sequence or more replication? Liu, Yuwen; Zhou, Jie; White, Kevin P. Bioinformatics, Vol. 30, Issue 3 https://doi.org/10.1093/bioinformatics/btt688	journal	December 2013
Clostridium thermocellum ATCC27405 transcriptomic, metabolomic and proteomic profiles after ethanol stress Yang, Shihui; Giannone, Richard J.; Dice, Lezlee BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-336	journal	January 2012
Design issues for cDNA microarray experiments Yang, Yee Hwa; Speed, Terry Nature Reviews Genetics, Vol. 3, Issue 8 https://doi.org/10.1038/nrg863	journal	August 2002
A comparison of methods for differential expression analysis of RNA-seq data Soneson, Charlotte; Delorenzi, Mauro BMC Bioinformatics, Vol. 14, Issue 1 https://doi.org/10.1186/1471-2105-14-91	journal	March 2013
Comparison of software packages for detecting differential expression in RNA-seq studies Seyednasrollah, F.; Laiho, A.; Elo, L. L. Briefings in Bioinformatics, Vol. 16, Issue 1 https://doi.org/10.1093/bib/bbt086	journal	December 2013
Differential expression analysis for sequence count data Anders, Simon; Huber, Wolfgang Genome Biology, Vol. 11, Issue 10 https://doi.org/10.1186/gb-2010-11-10-r106	journal	October 2010
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays Marioni, J. C.; Mason, C. E.; Mane, S. M. Genome Research, Vol. 18, Issue 9 https://doi.org/10.1101/gr.079558.108	journal	July 2008
Review Application of RNA-seq to reveal the transcript profile in bacteria Pinto, A. C.; Melo-Barbosa, H. P.; Miyoshi, A. Genetics and Molecular Research, Vol. 10, Issue 3 https://doi.org/10.4238/vol10-3gmr1554	journal	January 2011
ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads Miller, Christopher A.; Hampton, Oliver; Coarfa, Cristian PLoS ONE, Vol. 6, Issue 1 https://doi.org/10.1371/journal.pone.0016327	journal	January 2011
Choice of Bacterial Growth Medium Alters the Transcriptome and Phenotype of Salmonella enterica Serovar Typhimurium Blair, Jessica M. A.; Richmond, Grace E.; Bailey, Andrew M. PLoS ONE, Vol. 8, Issue 5 https://doi.org/10.1371/journal.pone.0063912	journal	May 2013
Parallel Evolution of Bacillus thuringiensis Toxin Resistance in Lepidoptera Baxter, Simon W.; Badenes-Pérez, Francisco R.; Morrison, Anna Genetics, Vol. 189, Issue 2 https://doi.org/10.1534/genetics.111.130971	journal	August 2011
Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns Moulos, Panagiotis; Hatzis, Pantelis Nucleic Acids Research, Vol. 43, Issue 4 https://doi.org/10.1093/nar/gku1273	journal	December 2014
Kinetic analysis and modeling of oleate and ethanol stimulated uranium (VI) bio-reduction in contaminated sediments under sulfate reduction conditions Zhang, Fan; Wu, Wei-Min; Parker, Jack C. Journal of Hazardous Materials, Vol. 183, Issue 1-3 https://doi.org/10.1016/j.jhazmat.2010.07.049	journal	November 2010
Direct RNA sequencing Ozsolak, Fatih; Platt, Adam R.; Jones, Dan R. Nature, Vol. 461, Issue 7265 https://doi.org/10.1038/nature08390	journal	September 2009
Sequencing depth and coverage: key considerations in genomic analyses Sims, David; Sudbery, Ian; Ilott, Nicholas E. Nature Reviews Genetics, Vol. 15, Issue 2 https://doi.org/10.1038/nrg3642	journal	January 2014
How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets Peixoto, Lucia; Risso, Davide; Poplawski, Shane G. Nucleic Acids Research, Vol. 43, Issue 16 https://doi.org/10.1093/nar/gkv736	journal	July 2015
Field-evolved resistance by western corn rootworm to multiple Bacillus thuringiensis toxins in transgenic maize Gassmann, A. J.; Petzold-Maxwell, J. L.; Clifton, E. H. Proceedings of the National Academy of Sciences, Vol. 111, Issue 14 https://doi.org/10.1073/pnas.1317179111	journal	March 2014
A scaling normalization method for differential expression analysis of RNA-seq data Robinson, Mark D.; Oshlack, Alicia Genome Biology, Vol. 11, Issue 3, Article No. R25 https://doi.org/10.1186/gb-2010-11-3-r25	journal	March 2010
Complete Genome Sequences for 35 Biothreat Assay-Relevant Bacillus Species: TABLE 1 Johnson, Shannon L.; Daligault, Hajnalka E.; Davenport, Karen W. Genome Announcements, Vol. 3, Issue 2 https://doi.org/10.1128/genomeA.00151-15	journal	April 2015
Transcriptomics in the RNA-seq era McGettigan, Paul A. Current Opinion in Chemical Biology, Vol. 17, Issue 1 https://doi.org/10.1016/j.cbpa.2012.12.008	journal	February 2013
RNA-seq: from technology to biology Marguerat, Samuel; Bähler, Jürg Cellular and Molecular Life Sciences, Vol. 67, Issue 4 https://doi.org/10.1007/s00018-009-0180-6	journal	October 2009
Count-based differential expression analysis of RNA sequencing data using R and Bioconductor Anders, Simon; McCarthy, Davis J.; Chen, Yunshun Nature Protocols, Vol. 8, Issue 9 https://doi.org/10.1038/nprot.2013.099	journal	August 2013
Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data Mi, Gu; Di, Yanming; Schafer, Daniel W. PLOS ONE, Vol. 10, Issue 3 https://doi.org/10.1371/journal.pone.0119254	journal	March 2015
Highly sensitive and ultrafast read mapping for RNA-seq analysis Medina, I.; Tárraga, J.; Martínez, H. DNA Research, Vol. 23, Issue 2 https://doi.org/10.1093/dnares/dsv039	journal	January 2016
Characterization of an Environmental Strain of Bacillus thuringiensis from a Hot Spring in Western Himalayas Alam, Syed Imteyaz; Bansod, Sunita; Goel, Ajay Kumar Current Microbiology, Vol. 62, Issue 2 https://doi.org/10.1007/s00284-010-9743-x	journal	August 2010
Design and validation issues in RNA-seq experiments Fang, Z.; Cui, X. Briefings in Bioinformatics, Vol. 12, Issue 3 https://doi.org/10.1093/bib/bbr004	journal	April 2011
Tools for mapping high-throughput sequencing data Fonseca, Nuno A.; Rung, Johan; Brazma, Alvis Bioinformatics, Vol. 28, Issue 24 https://doi.org/10.1093/bioinformatics/bts605	journal	October 2012
RNA-Seq: a revolutionary tool for transcriptomics Wang, Zhong; Gerstein, Mark; Snyder, Michael Nature Reviews Genetics, Vol. 10, Issue 1 https://doi.org/10.1038/nrg2484	journal	January 2009
The role of replicates for error mitigation in next-generation sequencing Robasky, Kimberly; Lewis, Nathan E.; Church, George M. Nature Reviews Genetics, Vol. 15, Issue 1 https://doi.org/10.1038/nrg3655	journal	December 2013
The power and promise of RNA-seq in ecology and evolution Todd, Erica V.; Black, Michael A.; Gemmell, Neil J. Molecular Ecology, Vol. 25, Issue 6 https://doi.org/10.1111/mec.13526	journal	March 2016
Fundamentals of experimental design for cDNA microarrays Churchill, Gary A. Nature Genetics, Vol. 32, Issue S4 https://doi.org/10.1038/ng1031	journal	December 2002
Bacillus thuringiensis: A story of a successful bioinsecticide Bravo, Alejandra; Likitvivatanavong, Supaporn; Gill, Sarjeet S. Insect Biochemistry and Molecular Biology, Vol. 41, Issue 7 https://doi.org/10.1016/j.ibmb.2011.02.006	journal	July 2011
Bacillus thuringiensis HD-1 Cry ⁻ : development of a safe, non-insecticidal simulant for Bacillus anthracis Bishop, A. H.; Robinson, C. V. Journal of Applied Microbiology, Vol. 117, Issue 3 https://doi.org/10.1111/jam.12560	journal	June 2014
Statistical Design and Analysis of RNA Sequencing Data Auer, Paul L.; Doerge, R. W. Genetics, Vol. 185, Issue 2 https://doi.org/10.1534/genetics.110.114983	journal	May 2010
Transcriptome analysis using next-generation sequencing Mutz, Kai-Oliver; Heilkenbrinker, Alexandra; Lönne, Maren Current Opinion in Biotechnology, Vol. 24, Issue 1 https://doi.org/10.1016/j.copbio.2012.09.004	journal	February 2013
Global transcriptome analysis of Clostridium thermocellum ATCC 27405 during growth on dilute acid pretreated Populus and switchgrass Wilson, Charlotte M.; Rodriguez, Miguel; Johnson, Courtney M. Biotechnology for Biofuels, Vol. 6, Issue 1 https://doi.org/10.1186/1754-6834-6-179	journal	January 2013
RNA-seq Data: Challenges in and Recommendations for Experimental Design and Analysis: RNA-seq Data: Experimental Design and Analysis Williams, Alexander G.; Thomas, Sean; Wyman, Stacia K. Current Protocols in Human Genetics, Vol. 83, Issue 1 https://doi.org/10.1002/0471142905.hg1113s83	journal	October 2014
Phylogenetic analysis of Bacillus thuringiensis serovars based on 16S rRNA gene restriction fragment length polymorphisms Joung, K. -B.; Côté, J. -C. Journal of Applied Microbiology, Vol. 90, Issue 1 https://doi.org/10.1046/j.1365-2672.2001.01227.x	journal	January 2001
Next-generation transcriptome assembly Martin, Jeffrey A.; Wang, Zhong Nature Reviews Genetics, Vol. 12, Issue 10 https://doi.org/10.1038/nrg3068	journal	September 2011
From RNA-seq reads to differential expression results Oshlack, Alicia; Robinson, Mark D.; Young, Matthew D. Genome Biology, Vol. 11, Issue 12 https://doi.org/10.1186/gb-2010-11-12-220	journal	January 2010
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis Dillies, M. -A.; Rau, A.; Aubert, J. Briefings in Bioinformatics, Vol. 14, Issue 6 https://doi.org/10.1093/bib/bbs046	journal	September 2012
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Love, Michael I.; Huber, Wolfgang; Anders, Simon Genome Biology, Vol. 15, Issue 12 https://doi.org/10.1186/s13059-014-0550-8	journal	December 2014
Bacillus thuringiensis and related insect pathogens. Aronson, A. I.; Beckman, W.; Dunn, P. Microbiological Reviews, Vol. 50, Issue 1 https://doi.org/10.1128/MMBR.50.1.1-24.1986	journal	January 1986
RNA-seq: from technology to biology Marguerat, Samuel; Bähler, Jürg Cellular and Molecular Life Sciences, Vol. 67, Issue 4 https://doi.org/10.1007/s00018-009-0180-6	journal	October 2009
Characterization of an Environmental Strain of Bacillus thuringiensis from a Hot Spring in Western Himalayas Alam, Syed Imteyaz; Bansod, Sunita; Goel, Ajay Kumar Current Microbiology, Vol. 62, Issue 2 https://doi.org/10.1007/s00284-010-9743-x	journal	August 2010
Transcriptomics in the RNA-seq era McGettigan, Paul A. Current Opinion in Chemical Biology, Vol. 17, Issue 1 https://doi.org/10.1016/j.cbpa.2012.12.008	journal	February 2013
Bacillus thuringiensis: A story of a successful bioinsecticide Bravo, Alejandra; Likitvivatanavong, Supaporn; Gill, Sarjeet S. Insect Biochemistry and Molecular Biology, Vol. 41, Issue 7 https://doi.org/10.1016/j.ibmb.2011.02.006	journal	July 2011
Kinetic analysis and modeling of oleate and ethanol stimulated uranium (VI) bio-reduction in contaminated sediments under sulfate reduction conditions Zhang, Fan; Wu, Wei-Min; Parker, Jack C. Journal of Hazardous Materials, Vol. 183, Issue 1-3 https://doi.org/10.1016/j.jhazmat.2010.07.049	journal	November 2010
Studying bacterial transcriptomes using RNA-seq Croucher, Nicholas J.; Thomson, Nicholas R. Current Opinion in Microbiology, Vol. 13, Issue 5 https://doi.org/10.1016/j.mib.2010.09.009	journal	October 2010
Direct RNA sequencing Ozsolak, Fatih; Platt, Adam R.; Jones, Dan R. Nature, Vol. 461, Issue 7265 https://doi.org/10.1038/nature08390	journal	September 2009
Fundamentals of experimental design for cDNA microarrays Churchill, Gary A. Nature Genetics, Vol. 32, Issue S4 https://doi.org/10.1038/ng1031	journal	December 2002
RNA-Seq: a revolutionary tool for transcriptomics Wang, Zhong; Gerstein, Mark; Snyder, Michael Nature Reviews Genetics, Vol. 10, Issue 1 https://doi.org/10.1038/nrg2484	journal	January 2009
RNA sequencing: advances, challenges and opportunities Ozsolak, Fatih; Milos, Patrice M. Nature Reviews Genetics, Vol. 12, Issue 2 https://doi.org/10.1038/nrg2934	journal	December 2010
Next-generation transcriptome assembly Martin, Jeffrey A.; Wang, Zhong Nature Reviews Genetics, Vol. 12, Issue 10 https://doi.org/10.1038/nrg3068	journal	September 2011
Sequencing depth and coverage: key considerations in genomic analyses Sims, David; Sudbery, Ian; Ilott, Nicholas E. Nature Reviews Genetics, Vol. 15, Issue 2 https://doi.org/10.1038/nrg3642	journal	January 2014
The role of replicates for error mitigation in next-generation sequencing Robasky, Kimberly; Lewis, Nathan E.; Church, George M. Nature Reviews Genetics, Vol. 15, Issue 1 https://doi.org/10.1038/nrg3655	journal	December 2013
Design issues for cDNA microarray experiments Yang, Yee Hwa; Speed, Terry Nature Reviews Genetics, Vol. 3, Issue 8 https://doi.org/10.1038/nrg863	journal	August 2002
A systematic exploration reveals the potential of spermidine for hypopigmentation treatment through the stabilization of melanogenesis-associated proteins Brito, Sofia; Heo, Hyojin; Cha, Byungsun Scientific Reports, Vol. 12, Issue 1 https://doi.org/10.1038/s41598-022-18629-3	journal	August 2022
Salinity tolerance mechanisms of an Arctic Pelagophyte using comparative transcriptomic and gene expression analysis Freyria, Nastasia J.; Kuo, Alan; Chovatia, Mansi Communications Biology, Vol. 5, Issue 1 https://doi.org/10.1038/s42003-022-03461-2	journal	May 2022
Field-evolved resistance by western corn rootworm to multiple Bacillus thuringiensis toxins in transgenic maize Gassmann, A. J.; Petzold-Maxwell, J. L.; Clifton, E. H. Proceedings of the National Academy of Sciences, Vol. 111, Issue 14 https://doi.org/10.1073/pnas.1317179111	journal	March 2014
Design and validation issues in RNA-seq experiments Fang, Z.; Cui, X. Briefings in Bioinformatics, Vol. 12, Issue 3 https://doi.org/10.1093/bib/bbr004	journal	April 2011
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis Dillies, M. -A.; Rau, A.; Aubert, J. Briefings in Bioinformatics, Vol. 14, Issue 6 https://doi.org/10.1093/bib/bbs046	journal	September 2012
Comparison of software packages for detecting differential expression in RNA-seq studies Seyednasrollah, F.; Laiho, A.; Elo, L. L. Briefings in Bioinformatics, Vol. 16, Issue 1 https://doi.org/10.1093/bib/bbt086	journal	December 2013
Tools for mapping high-throughput sequencing data Fonseca, Nuno A.; Rung, Johan; Brazma, Alvis Bioinformatics, Vol. 28, Issue 24 https://doi.org/10.1093/bioinformatics/bts605	journal	October 2012
RNA-seq differential expression studies: more sequence or more replication? Liu, Yuwen; Zhou, Jie; White, Kevin P. Bioinformatics, Vol. 30, Issue 3 https://doi.org/10.1093/bioinformatics/btt688	journal	December 2013
Highly sensitive and ultrafast read mapping for RNA-seq analysis Medina, I.; Tárraga, J.; Martínez, H. DNA Research, Vol. 23, Issue 2 https://doi.org/10.1093/dnares/dsv039	journal	January 2016
Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns Moulos, Panagiotis; Hatzis, Pantelis Nucleic Acids Research, Vol. 43, Issue 4 https://doi.org/10.1093/nar/gku1273	journal	December 2014
How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets Peixoto, Lucia; Risso, Davide; Poplawski, Shane G. Nucleic Acids Research, Vol. 43, Issue 16 https://doi.org/10.1093/nar/gkv736	journal	July 2015
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays Marioni, J. C.; Mason, C. E.; Mane, S. M. Genome Research, Vol. 18, Issue 9 https://doi.org/10.1101/gr.079558.108	journal	July 2008
Bacillus thuringiensis HD-1 Cry ⁻ : development of a safe, non-insecticidal simulant for Bacillus anthracis Bishop, A. H.; Robinson, C. V. Journal of Applied Microbiology, Vol. 117, Issue 3 https://doi.org/10.1111/jam.12560	journal	June 2014
The power and promise of RNA-seq in ecology and evolution Todd, Erica V.; Black, Michael A.; Gemmell, Neil J. Molecular Ecology, Vol. 25, Issue 6 https://doi.org/10.1111/mec.13526	journal	March 2016
Correction for Johnson et al., “Complete Genome Sequences for 35 Biothreat Assay-Relevant Bacillus Species” Johnson, Shannon L.; Daligault, Hajnalka E.; Davenport, Karen W. Genome Announcements, Vol. 6, Issue 3 https://doi.org/10.1128/genomea.01144-17	journal	January 2018
Complete Genome Sequence of Bacillus thuringiensis subsp. chinensis Strain CT-43 He, J.; Wang, J.; Yin, W. Journal of Bacteriology, Vol. 193, Issue 13 https://doi.org/10.1128/jb.05085-11	journal	May 2011
Bacillus thuringiensis and related insect pathogens Aronson, A. I.; Beckman, W.; Dunn, P. Microbiological Reviews, Vol. 50, Issue 1 https://doi.org/10.1128/mr.50.1.1-24.1986	journal	March 1986
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data Li, Jun; Tibshirani, Robert Statistical Methods in Medical Research, Vol. 22, Issue 5 https://doi.org/10.1177/0962280211428386	journal	November 2011
A comparison of methods for differential expression analysis of RNA-seq data Soneson, Charlotte; Delorenzi, Mauro BMC Bioinformatics, Vol. 14, Issue 1 https://doi.org/10.1186/1471-2105-14-91	journal	March 2013
Clostridium thermocellum ATCC27405 transcriptomic, metabolomic and proteomic profiles after ethanol stress Yang, Shihui; Giannone, Richard J.; Dice, Lezlee BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-336	journal	January 2012
How deep is deep enough for RNA-Seq profiling of bacterial transcriptomes? Haas, Brian J.; Chin, Melissa; Nusbaum, Chad BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-734	journal	January 2012
From RNA-seq reads to differential expression results Oshlack, Alicia; Robinson, Mark D.; Young, Matthew D. Genome Biology, Vol. 11, Issue 12 https://doi.org/10.1186/gb-2010-11-12-220	journal	January 2010
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Love, Michael I.; Huber, Wolfgang; Anders, Simon Genome Biology, Vol. 15, Issue 12 https://doi.org/10.1186/s13059-014-0550-8	journal	December 2014
ReadDepth: A Parallel R Package for Detecting Copy Number Alterations from Short Sequencing Reads Miller, Christopher A.; Hampton, Oliver; Coarfa, Cristian PLoS ONE, Vol. 6, Issue 1 https://doi.org/10.1371/journal.pone.0016327	journal	January 2011
Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data Mi, Gu; Di, Yanming; Schafer, Daniel W. PLOS ONE, Vol. 10, Issue 3 https://doi.org/10.1371/journal.pone.0119254	journal	March 2015
Statistical Design and Analysis of RNA Sequencing Data Auer, Paul L.; Doerge, R. W. Genetics, Vol. 185, Issue 2 https://doi.org/10.1534/genetics.110.114983	journal	May 2010
Parallel Evolution of Bacillus thuringiensis Toxin Resistance in Lepidoptera Baxter, Simon W.; Badenes-Pérez, Francisco R.; Morrison, Anna Genetics, Vol. 189, Issue 2 https://doi.org/10.1534/genetics.111.130971	journal	August 2011
Review Application of RNA-seq to reveal the transcript profile in bacteria Pinto, A. C.; Melo-Barbosa, H. P.; Miyoshi, A. Genetics and Molecular Research, Vol. 10, Issue 3 https://doi.org/10.4238/vol10-3gmr1554	journal	January 2011
Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment Gierliński, Marek; Cole, Christian; Schofield, Pietà arXiv https://doi.org/10.48550/arxiv.1505.00588	text	January 2015

Cited By (1)

Robust principal component analysis for accurate outlier sample detection in RNA-Seq data Chen, Xiaoying; Zhang, Bo; Wang, Ting BMC Bioinformatics, Vol. 21, Issue 1 https://doi.org/10.1186/s12859-020-03608-0	journal	June 2020