BgeeDB, an R package for retrieval of curated expression datasets and for gene list expression localization enrichment tests

F1000Research

Volumn 5, Issue , 2018, Pages

  Komljenovic, Andrea ^a,b   Roux, Julien ^b,c   Wollbrett, Julien ^a,b   Robinson Rechavi, Marc ^a,b   Bastian, Frederic B ^a,b  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

^c UNIVERSITY OF BASEL   (Switzerland)

Author keywords

Bioconductor; Collective data access; Gene enrichment analysis; Gene expression; R package

Indexed keywords

ANATOMICAL CONCEPTS; ARTICLE; EXPRESSED SEQUENCE TAG; GENE EXPRESSION; GENE ONTOLOGY; HUMAN TISSUE; IN SITU HYBRIDIZATION; INFORMATION RETRIEVAL; WILD TYPE;

EID: 85043984531     PISSN: 20461402     EISSN: 1759796X     Source Type: Journal    
DOI: 10.12688/f1000research.9973.2     Document Type: Article

References (67)

1. Rung J, Brazma A: Reuse of public genome-wide gene expression data. Nat Rev Genet. 2013; 14(2): 89–99.
2. Ioannidis JP, Allison DB, Ball CA, et al.: Repeatability of published microarray gene expression analyses. Nat Genet. 2009; 41(2): 149–55.
3. Wan X, Pavlidis P: Sharing and reusing gene expression profiling data in neuroscience. Neuroinformatics. 2007; 5(3): 161–75.
4. R Development Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2007.
5. Huber W, Carey VJ, Gentleman R, et al.: Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 2015; 12(2): 115–21.
6. Gentleman RC, Carey VJ, Bates DM, et al.: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004; 5(10): R80.
7. Kauffmann A, Rayner TF, Parkinson H, et al.: Importing ArrayExpress datasets into R/Bioconductor. Bioinformatics. 2009; 25(16): 2092–4.
8. Davis S, Meltzer PS: GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007; 23(14): 1846–7.
9. Zhu Y, Stephens RM, Meltzer PS, et al.: SRAdb: query and use public next-generation sequencing data from within R. BMC Bioinformatics. 2013; 14(1): 19.
10. Kolesnikov N, Hastings E, Keays M, et al.: ArrayExpress update--simplifying data submissions. Nucleic Acids Res. 2015; 43(Database issue): D1113–6.
11. Barrett T, Wilhite SE, Ledoux P, et al.: NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013; 41(Database issue): D991–5.
12. Kodama Y, Shumway M, Leinonen R, et al.: The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res. 2012; 40(Database issue): D54–D6.
13. BrainStars Bioconductor package.
14. Kasukawa T, Masumoto KH, Nikaido I, et al.: Quantitative expression profile of distinct functional regions in the adult mouse brain. PLoS One. 2011; 6(8): e23228.
15. ImmuneSpaceR Bioconductor package.
16. Bioconductor Package Maintainer: ExperimentHub: Client to access ExperimentHub resources. R package version 1.6.0. 2018.
17. ExpressionAtlas Bioconductor package.
18. Petryszak R, Keays M, Tang YA, et al.: Expression Atlas update--an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 2016; 44(D1): D746–52.
19. Collado-Torres L, Nellore A, Kammers K, et al.: recount: A large-scale resource of analysis-ready RNA-seq expression data. bioRxiv. 2016.
20. Frazee AC, Langmead B, Leek JT: ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics. 2011; 12(1): 449.
21. recount Bioconductor package.
22. Bastian F, Parmentier G, Roux J, et al.: Bgee: Integrating and Comparing Heterogeneous Transcriptome Data Among Species. Data Integr Life Sci. 2008; 124–31.
23. GTEx Consortium: Human genomics. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science. 2015; 348(6235): 648–60.
24. Melé M, Ferreira PG, Reverter F, et al.: Human genomics. The human transcriptome across tissues and individuals. Science. 2015; 348(6235): 660–5.
25. Alexa A, Rahnenführer J, Lengauer T: Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006; 22(13): 1600–7.
26. topGO Bioconductor package.
27. Rhee SY, Wood V, Dolinski K, et al.: Use and misuse of the gene ontology annotations. Nat Rev Genet. 2008; 9(7): 509–15.
28. Ashburner M, Ball CA, Blake JA, et al.: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1): 25–9.
29. The Gene Ontology Handbook. Dessimoz C, Škunca N, editors: Humana Press; 2016; XII, 305.
30. Haendel MA, Balhoff JP, Bastian FB, et al.: Unification of multi-species vertebrate anatomy ontologies for comparative biology in Uberon. J Biomed Semantics. 2014; 5(1): 21.
31. Mungall CJ, Torniai C, Gkoutos GV, et al.: Uberon, an integrative multi-species anatomy ontology. Genome Biol. 2012; 13(1): R5.
32. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Series B Stat Methodol. 1995; 57(1): 289–300.
33. Tissue Specific Expression Analysis (TSEA) version 1. [cited 2018 June 14].
34. Dougherty JD, Schmidt EF, Nakajima M, et al.: Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res. 2010; 38(13): 4218–30.
35. Xu X, Wells AB, O'Brien DR, et al.: Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J Neurosci. 2014; 34(4): 1420–31.
36. Angeles-Albores D, N Lee RY, Chan J, et al.: Tissue enrichment analysis for C. elegans genomics. BMC Bioinformatics. 2016; 17(1): 366.
37. Marbach D, Lamparter D, Quon G, et al.: Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat Methods. 2016; 13(4): 366–70.
38. Lee RYN, Howe KL, Harris TW, et al.: WormBase 2017: molting into a new stage. Nucleic Acids Res. 2018; 46(D1): D869–D874.
39. Zerbino DR, Achuthan P, Akanni W, et al.: Ensembl 2018. Nucleic Acids Res. 2018; 46(D1): D754–D761.
40. Kersey PJ, Allen JE, Allot A, et al.: Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species. Nucleic Acids Res. 2018; 46(D1): D802–D808.
41. Bray NL, Pimentel H, Melsted P, et al.: Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016; 34(5): 525–7.
42. Robinson MD, Oshlack A: A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010; 11(3): R25.
43. Rosikiewicz M, Comte A, Niknejad A, et al.: Uncovering hidden duplicated content in public transcriptomics data. Database (Oxford). 2013; 2013: bat010.
44. Rosikiewicz M, Robinson-Rechavi M: IQRray, a new method for Affymetrix microarray quality control, and the homologous organ conservation score, a new benchmark method for quality control metrics. Bioinformatics. 2014; 30(10): 1392–9.
45. Wu Z, Irizarry RA, Gentleman R, et al.: A Model-Based Background Adjustment for Oligonucleotide Expression Arrays. J Am Stat Assoc. 2004; 99(468): 909–17.
46. Hubbell E, Liu WM, Mei R: Robust estimators for expression analysis. Bioinformatics. 2002; 18(12): 1585–92.
47. Schuster EF, Blanc E, Partridge L, et al.: Correcting for sequence biases in present/absent calls. Genome Biol. 2007; 8(6): R125.
48. Wang QT, Piotrowska K, Ciemerych MA, et al.: A genome-wide study of gene activity reveals developmental signaling pathways in the preimplantation mouse embryo. Dev Cell. 2004; 6(1): 133–44.
49. Wu Z, Irizarry RA, Gentleman R, et al.: A Model-Based Background Adjustment for Oligonucleotide Expression Arrays. J Am Stat Assoc. 2004; 99(468): 909–17.
50. Merkin J, Russell C, Chen P, et al.: Evolutionary dynamics of gene and isoform regulation in Mammalian tissues. Science. 2012; 338(6114): 1593–9.
51. Brawand D, Soumillon M, Necsulea A, et al.: The evolution of gene expression levels in mammalian organs. Nature. 2011; 478(7369): 343–8.
52. 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): 281–5.
53. Li B, Dewey CN: RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011; 12: 323.
54. Roux J, Rosikiewicz M, Robinson-Rechavi M: What to compare and how: Comparative transcriptomics for Evo-Devo. J Exp Zool B Mol Dev Evol. 2015; 324(4): 372–82.
55. Gilad Y, Mizrahi-Man O: A reanalysis of mouse ENCODE comparative gene expression data [version 1; referees: 3 approved, 1 approved with reservations]. F1000Res. 2015; 4: 121.
56. Leek JT, Scharpf RB, Bravo HC, et al.: Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010; 11(10): 733–9.
57. Akey JM, Biswas S, Leek JT, et al.: On the design and analysis of gene expression studies in human populations. Nat Genet. 2007; 39(7): 807–8.
58. Deane CM, Salwiński Ł, Xenarios I, et al.: Protein Interactions: Two Methods for Assessment of the Reliability of High Throughput Observations. Mol Cell Proteomics. 2002; 1(5): 349–56.
59. Kotlyar M, Pastrello C, Sheahan N, et al.: Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 2016; 44(D1): D536–D41.
60. Futschik ME, Carlisle B: Noise-robust soft clustering of gene expression time-course data. J Bioinform Comput Biol. 2005; 3(4): 965–88.
61. Mfuzz Bioconductor package.
62. Robinson MD, McCarthy DJ, Smyth GK: edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1): 139–40.
63. edgeR Bioconductor package.
64. Howe DG, Bradford YM, Conlin T, et al.: ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics. Nucleic Acids Res. 2013; 41(Database issue): D854–60.
65. biomaRt Bioconductor package.
66. Timmons JA, Szkop KJ, Gallagher IJ: Multiple sources of bias confound functional enrichment analysis of global-omics data. Genome Biol. 2015; 16(1): 186.
67. Komljenovic A, Roux J, Robinson-Rechavi M, et al.: BgeeDB/ BgeeDB_R: Bgee R package release 2.6.2. Zenodo. 2018. http://www.doi.org/10.5281/zenodo.1293418