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74. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative
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75. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome
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81. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript
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82. Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing
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83. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data.
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84. Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis of differential gene expression
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85. Lamarre S, Frasse P, Zouine M, Labourdette D, Sainderichin E, Hu G, et al. Optimization of an
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86. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey
of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
87. Gonzalez E, Joly S. Impact of RNA-seq attributes on false positive rates in differential
expression analysis of de novo assembled transcriptomes. BMC Res Notes. 2013;6:503.
88. Mandelboum S, Manber Z, Elroy-Stein O, Elkon R. Recurrent functional misinterpretation of
RNA-seq data caused by sample-specific gene length bias. PLoS Biol. 2019;17(11):e3000481.
89. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev
Genet. 2009;10(1):57–63.
90. Li X, Cooper NGF, O’Toole TE, Rouchka EC. Choice of library size normalization and
statistical methods for differential gene expression analysis in balanced two-group comparisons
for RNA-seq studies. BMC Genomics. 2020;21(1):75.
91. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 2010;11:94.
174
R. Bharti and D. G. Grimm
alignment formats. Bioinformatics. 2015;31(12):2032–4.
71. Tischler G, Leonard S. biobambam: tools for read pair collation based algorithms on BAM files.
Source Code Biol Med. 2014;9(1):13.
72. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics.
2012;28(16):2184–5.
73. Buels R, Yao E, Diesh CM, Hayes RD, Munoz-Torres M, Helt G, et al. JBrowse: a dynamic web
platform for genome visualization and analysis. Genome Biol. 2016;17:66.
74. Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative
genomics viewer. Nat Biotechnol. 2011;29(1):24–6.
75. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome
browser at UCSC. Genome Res. 2002;12(6):996–1006.
76. Kallio MA, Tuimala JT, Hupponen T, Klemela P, Gentile M, Scheinin I, et al. Chipster: userfriendly analysis software for microarray and other high-throughput data. BMC Genomics.
2011;12:507.
77. Jin H, Wan YW, Liu Z. Comprehensive evaluation of RNA-seq quantification methods for
linearity. BMC Bioinform. 2017;18(Suppl 4):117.
78. Teng M, Love MI, Davis CA, Djebali S, Dobin A, Graveley BR, et al. A benchmark for RNAseq quantification pipelines. Genome Biol. 2016;17:74.
79. Anders S, Pyl PT, Huber W. HTSeq–a Python framework to work with high-throughput
sequencing data. Bioinformatics. 2015;31(2):166–9.
80. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features.
Bioinformatics. 2010;26(6):841–2.
81. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript
assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching
during cell differentiation. Nat Biotechnol. 2010;28(5):511–5.
82. Roberts A, Pachter L. Streaming fragment assignment for real-time analysis of sequencing
experiments. Nat Methods. 2013;10(1):71–3.
83. Anders S, Reyes A, Huber W. Detecting differential usage of exons from RNA-seq data.
Genome Res. 2012;22(10):2008–17.
84. Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis of differential gene expression
analysis tools for single-cell RNA sequencing data. BMC Bioinform. 2019;20(1):40.
85. Lamarre S, Frasse P, Zouine M, Labourdette D, Sainderichin E, Hu G, et al. Optimization of an
RNA-Seq differential gene expression analysis depending on biological replicate number and
library size. Front Plant Sci. 2018;9:108.
86. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, et al. A survey
of best practices for RNA-seq data analysis. Genome Biol. 2016;17:13.
87. Gonzalez E, Joly S. Impact of RNA-seq attributes on false positive rates in differential
expression analysis of de novo assembled transcriptomes. BMC Res Notes. 2013;6:503.
88. Mandelboum S, Manber Z, Elroy-Stein O, Elkon R. Recurrent functional misinterpretation of
RNA-seq data caused by sample-specific gene length bias. PLoS Biol. 2019;17(11):e3000481.
89. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev
Genet. 2009;10(1):57–63.
90. Li X, Cooper NGF, O’Toole TE, Rouchka EC. Choice of library size normalization and
statistical methods for differential gene expression analysis in balanced two-group comparisons
for RNA-seq studies. BMC Genomics. 2020;21(1):75.
91. Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 2010;11:94.
174
R. Bharti and D. G. Grimm