Systems Biology (Methods in Molecular Biology)

(Tina Sui) #1

  1. Fowles JS, Brown KC, Hess AM et al (2016)
    Intra- and interspecies gene expression models
    for predicting drug response in canine osteo-
    sarcoma. BMC Bioinformatics 17:93.https://
    doi.org/10.1186/s12859-016-0942-8

  2. Dhawan D, Paoloni M, Shukradas S et al
    (2015) Comparative gene expression analyses
    identify luminal and basal subtypes of canine
    invasive urothelial carcinoma that mimic pat-
    terns in human invasive bladder cancer. PLoS
    One 10:e0136688. https://doi.org/10.
    1371/journal.pone.0136688

  3. Seok J, Warren HS, Cuenca AG et al (2013)
    Genomic responses in mouse models poorly
    mimic human inflammatory diseases. Proc
    Natl Acad Sci 110:3507–3512.https://doi.
    org/10.1073/pnas.1222878110

  4. Shay T, Jojic V, Zuk O et al (2013) Conserva-
    tion and divergence in the transcriptional pro-
    grams of the human and mouse immune
    systems. Proc Natl Acad Sci 110:2946–2951.
    https://doi.org/10.1073/pnas.1222738110

  5. Chan ET, Quon GT, Chua G et al (2009)
    Conservation of core gene expression in verte-
    brate tissues. J Biol 8:33.https://doi.org/10.
    1186/jbiol130

  6. Brawand D, Soumillon M, Necsulea A et al
    (2011) The evolution of gene expression levels
    in mammalian organs. Nature 478:343–348.
    https://doi.org/10.1038/nature10532

  7. Lin S, Lin Y, Nery JR et al (2014) Comparison
    of the transcriptional landscapes between
    human and mouse tissues. Proc Natl Acad Sci
    111:17224–17229. https://doi.org/10.
    1073/pnas.1413624111

  8. Gilad Y, Mizrahi-Man O (2015) A reanalysis of
    mouse ENCODE comparative gene expression
    data. F1000Research 4:121. 10.12688/
    f1000research.6536.1

  9. Sudmant PH, Alexis MS, Burge CB (2015)
    Meta-analysis of RNA-seq expression data
    across species, tissues and studies. Genome
    Biol 16:287. https://doi.org/10.1186/
    s13059-015-0853-4

  10. H€anzelmann S, Castelo R, Guinney J (2013)
    GSVA: gene set variation analysis for microarray
    and RNA-Seq data. BMC Bioinformatics 14:7.
    https://doi.org/10.1186/1471-2105-14-7

  11. NIH Genomic Data Commons Data Portal
    (2016) v. 4.0.https://gdc-portal.nci.nih.gov

  12. Ripley BD (2001) The R project in statistical
    computing (2001). MSOR Connections.
    Newsl LTSN Maths Stat OR Network 1:23–25

  13. Ihaka R, Gentleman R (1995) R: a language for
    data analysis and graphics. J Comp Graph Stat
    5:299–314

  14. Hornik K (2012) The comprehensive R archive
    network. Comput Stat 4:394–398.https://
    doi.org/10.1002/wics.1212

  15. Wickham H (2007) Reshaping data with the
    {reshape} package. J Stat Software 21:1–20

  16. Wickham H (2009) ggplot2: elegant graphics
    for dataanalysis. Springer, New York, NY

  17. Love MI, Huber W, Anders S (2013) Moder-
    ated estimation of fold change and dispersion
    for RNA-seq data with DESeq2. Genome Biol
    15:550. https://doi.org/10.1186/PRE
    ACCEPT-8897612761307401

  18. Smedley D, Haider S, Ballester B et al (2009)
    BioMart—biological queries made easy. BMC
    Genomics 10:22.https://doi.org/10.1186/
    1471-2164-10-22

  19. Cunningham F, Amode MR, Barrell D et al
    (2015) Ensembl 2015. Nucleic Acids Res 43:
    D662–D669.https://doi.org/10.1093/nar/
    gku1010

  20. Ashburner M, Ball CA, Blake JA et al (2000)
    Gene ontology: tool for the unification of biol-
    ogy. The gene ontology consortium. Nat Genet
    25:25–29.https://doi.org/10.1038/75556

  21. Subramanian A, Tamayo P, Mootha VK et al
    (2005) Gene set enrichment analysis: a
    knowledge-based approach for interpreting
    genome-wide expression profiles. Proc Natl
    Acad Sci 102:15545–15550.https://doi.org/
    10.1073/pnas.0506580102

  22. Liberzon A (2014) A description of the Molec-
    ular Signatures Database (MSigDB) Web site.
    Methods Mol Biol 1150:153–160. https://
    doi.org/10.1007/978-1-4939-0512-6_9

  23. Molecular Signatures Database (MSigDB)
    (2016) v. 5.2.http://software.broadinstitute.
    org/gsea/msigdb

  24. RobinsonMD,McCarthyDJ,Smyth GK (2010)
    edgeR: a bioconductor package for differential
    expression analysis of digital gene expression
    data. Bioinformatics 26:139–140.https://doi.
    org/10.1093/bioinformatics/btp616

  25. Wickham H (2014) Tidy data. J Stat Software
    59:10.10.18637/jss.v059.i10

  26. Lin Y, Golovnina K, Chen Z-X et al (2016)
    Comparison of normalization and differential
    expression analyses using RNA-Seq data from
    726 individual Drosophila melanogaster. BMC
    Genomics 17:28.https://doi.org/10.1186/
    s12864-015-2353-z

  27. George NI, Chang C-W (2014) DAFS: a data-
    adaptive flag method for RNA-sequencing data
    to differentiate genes with low and high expres-
    sion. BMC Bioinformatics 15:92.https://doi.
    org/10.1186/1471-2105-15-92

  28. Cox MAA, Cox TF (2001) Multidimensional
    scaling, 2nd edn. Chapman and Hall, Boca
    Raton, FL

  29. Benjamini Y, Hochberg Y (1995) Controlling
    the false discovery rate: a practical and powerful
    approach to multiple testing. J R Stat Soc B
    57:289–300


Cross-Species RNA-Seq Analysis 305
Free download pdf