212        Bioinformatics

17. DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, Reich M,

Winckler W, Getz G: RNA-SeQC: RNA-seq metrics for quality control and process optimiza-

tion. Bioinformatics 2012, 28(11):1530–1532.

18. Wang L, Wang S, Li W: RSeQC: quality control of RNA-seq experiments. Bioinformatics

2012, 28(16):2184–2185.

19. Anders S, Pyl PT, Huber W: HTSeq--a Python framework to work with high-throughput

sequencing data. Bioinformatics 2015, 31(2):166–169.

20. Liao Y, Smyth GK, Shi W: featureCounts: an efficient general purpose program for assigning

sequence reads to genomic features. Bioinformatics 2013, 30(7):923–930.

21. Robinson MD, Oshlack A: A scaling normalization method for differential expression analy-

sis of RNA-seq data. Genome Biol 2010, 11(3):R25.

22. Benjamini Y, Speed TP: Summarizing and correcting the GC content bias in high-­throughput

sequencing. Nucleic Acids Res 2012, 40(10):e72–e72.

23. Maza E: In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization

Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design.

Front Genet 2016, 7:164.

24. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mam-

malian transcriptomes by RNA-Seq. Nat Methods 2008, 5(7):621–628.

25. Wagner GP, Kin K, Lynch VJ: Measurement of mRNA abundance using RNA-seq data:

RPKM measure is inconsistent among samples. Theory Biosci 2012, 131(4):281–285.

26. Bushel PR, Ferguson SS, Ramaiahgari SC, Paules RS, Auerbach SS: Comparison of

Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data. Front

Genet 2020, 11.

27. Robinson MD, Oshlack A: A scaling normalization method for differential expression analy-

sis of RNA-seq data. Genome Biol 2010, 11(3):R25.

28. Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol

2010, 11(10):R106.

29. Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normal-

ization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010,

11(1):94.

30. Finotello F, Di Camillo B: Measuring differential gene expression with RNA-seq: challenges

and strategies for data analysis. Brief Funct Genomics 2015, 14(2):130–142.

31. Ver Hoef JM, Boveng PL: Quasi-Poisson vs. negative binomial regression: how should we

model overdispersed count data? Ecology 2007, 88(11):2766–2772.

32. Law CW, Zeglinski K, Dong X, Alhamdoosh M, Smyth GK, Ritchie ME: A guide to creating

design matrices for gene expression experiments. F1000Res 2020, 9: 1444.

33. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential

expression analysis of digital gene expression data. Bioinformatics 2010, 26(1):139–140.

34. Carlson M: org.Hs.eg.db: Genome wide annotation for human. R package version 3100 2019.

35. Mead A: Review of the development of multidimensional scaling methods. J R Stat Soc D

1992, 41(1):27–39.

36. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful

approach to multiple testing. J R Stat Soc B 1995, 57(1):289–300.

37. Consortium GO: The Gene Ontology (GO) database and informatics resource. Nucleic Acids

Res 2004, 32(suppl_1):D258–D261.

38. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S,

Okuda S, Tokimatsu T et al: KEGG for linking genomes to life and the environment. Nucleic

Acids Res 2007, 36(suppl_1):D480–D484.

39. vidger: Create rapid visualizations of RNAseq data in R [https://bioconductor.org/packages/

release/bioc/html/vidger.html]