The current status and challenges in computational analysis of genomic big data

Big Data Research

Volumn 2, Issue 1, 2015, Pages 12-18

  Qin, Yiming ^a   Yalamanchili, Hari Krishna ^a   Qin, Jing ^a,b   Yan, Bin ^a,c,d   Wang, Junwen ^a,b  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF HONG KONG   (China)

^c UNIVERSITY OF HONG KONG   (Hong Kong)

^d SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

Author keywords

Gene regulatory networks; Genomic big data; Integrative data analysis; Next generation sequencing; OMICS

Indexed keywords

BIG DATA; DATA HANDLING; GENES; THROUGHPUT;

'OMICS'; BASIC-ELEMENTS; BIOLOGICAL MACROMOLECULE; COMPUTATIONAL ANALYSIS; CURRENT STATUS; GENE REGULATORY NETWORKS; GENOMIC BIG DATA; GENOMICS; INTEGRATIVE DATA ANALYSE; NEXT-GENERATION SEQUENCING;

GENOME;

EID: 84925687964     PISSN: None     EISSN: 22145796     Source Type: Journal    
DOI: 10.1016/j.bdr.2015.02.005     Document Type: Review

References (75)

1. Hershey A.D., Chase M. Independent functions of viral protein and nucleic acid in growth of bacteriophage. J. Gen. Physiol. 1952, 36:39-56.
2. Kapranov P., et al. RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 2007, 316:1484-1488.
3. Mercer T.R., Dinger M.E., Mattick J.S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 2009, 10:155-159.
4. Ranganathan K., Sivasankar V. MicroRNAs - biology and clinical applications. J. Oral Maxillofac. Pathol. 2014, 18:229-234.
5. Skolnick J., Fetrow J.S. From genes to protein structure and function: novel applications of computational approaches in the genomic era. Trends Biotechnol. 2000, 18:34-39.
6. Boeckmann B., et al. Protein variety and functional diversity: Swiss-Prot annotation in its biological context. C. R. Biol. 2005, 328:882-899.
7. Crick F. On protein synthesis. Symp. Soc. Exp. Biol. 1958, 12:138-163.
8. Crick F. Central dogma of molecular biology. Nature 1970, 227:561-563.
9. Noble I. Human genome finally complete 2003 [cited 2014]; Available from:. http://news.bbc.co.uk/2/hi/science/nature/2940601.stm.
10. Wetterstrand K. DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP) 2014 [cited August 4, 2014]; Available from:. http://www.genome.gov/sequencingcosts.
11. Wright A.F. Nature Encyclopedia of the Human Genome 2003, 959-968. Nature Publishing Group, London.
12. MacDonald J.R., et al. The database of genomic variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 2014, 42:D986-D992.
13. Feuk L., Carson A.R., Scherer S.W. Structural variation in the human genome. Nat. Rev. Genet. 2006, 7:85-97.
14. Mah J.T.L., Low E.S.H., Lee E. Insilico SNP analysis and bioinformatics tools: a review of the state of the art to aid drug discovery. Drug Discov. Today 2011, 16:800-809.
15. Genomes Project Consortium, et al. A map of human genome variation from population-scale sequencing. Nature 2010, 467:1061-1073.
16. A haplotype map of the human genome. Nature 2005, 437:1299-1320. International HapMap Consortium.
17. Genomes Project Consortium, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 2007, 449:851-861.
18. International HapMap Consortium, et al. Integrating common and rare genetic variation in diverse human populations. Nature 2010, 467:52-58.
19. Kingsmore S.F., et al. Genome-wide association studies: progress and potential for drug discovery and development. Nat. Rev. Drug Discov. 2008, 7:221-230.
20. N.a.t. TCGA Program Overview NHGRI, 2014 [cited August 2014]; Available from:. http://cancergenome.nih.gov/abouttcga/overview.
21. Chin L., Andersen J.N., Futreal P.A. Cancer genomics: from discovery science to personalized medicine. Nat. Med. 2011, 17:297-303.
22. Chin L., et al. Making sense of cancer genomic data. Genes Dev. 2011, 25:534-555.
23. Li H., Ruan J., Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008, 18:1851-1858.
24. DePristo M.A., et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 2011, 43:491-498.
25. Xu F., et al. A fast and accurate SNP detection algorithm for next-generation sequencing data. Nat. Commun. 2012, 3:1258.
26. Wang W., et al. FaSD-somatic: a fast and accurate somatic SNV detection algorithm for cancer genome sequencing data. Bioinformatics 2014, 30:2498-2500.
27. Wang Z., Gerstein M., Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10:57-63.
28. Barrett T., et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013, 41:D991-D995.
29. Nagalakshmi U., et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008, 320:1344-1349.
30. HiSeq System Performance Parameters 2014 [cited September 2014]; Available from:. http://systems.illumina.com/systems/hiseq_2500_1500/performance:specifications.ilmn.
31. Trapnell C., Pachter L., Salzberg S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25:1105-1111.
32. Trapnell C., et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 2010, 28:511-515.
33. Guttman M., et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 2010, 28:503-510.
34. Yalamanchili H.K., et al. SpliceNet: recovering splicing isoform-specific differential gene networks from RNA-Seq data of normal and diseased samples. Nucleic Acids Res. 2014.
35. Amaral P.P., et al. lncRNAdb: a reference database for long noncoding RNAs. Nucleic Acids Res. 2011, 39:D146-D151.
36. Li H.D., et al. The emerging era of genomic data integration for analyzing splice isoform function. Trends Genet. 2014, 30:340-347.
37. Tang F., et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 2010, 5:516-535.
38. Trapnell C., et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 2014, 32:381-386.
39. Marinov G.K., et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 2014, 24:496-510.
40. Yalamanchili H.K., Xiao Q.W., Wang J. A novel neural response algorithm for protein function prediction. BMC Syst. Biol. 2012, 6(Suppl. 1):S19.
41. Dhingra V., et al. New frontiers in proteomics research: a perspective. Int. J. Pharm. 2005, 299:1-18.
42. Altelaar A.F., Munoz J., Heck A.J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 2013, 14:35-48.
43. Baker P.R., Chalkley R.J. MS-viewer: a web-based spectral viewer for proteomics results. Mol. Cell. Proteomics 2014, 13:1392-1396.
44. Mann M. Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 2006, 7:952-958.
45. Wiese S., et al. Protein labeling by iTRAQ: a new tool for quantitative mass spectrometry in proteome research. Proteomics 2007, 7:340-350.
46. Syka J.E., et al. Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proc. Natl. Acad. Sci. USA 2004, 101:9528-9533.
47. Pinkse M.W., et al. Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. Anal. Chem. 2004, 76:3935-3943.
48. Tissot B., et al. Glycoproteomics: past, present and future. FEBS Lett. 2009, 583:1728-1735.
49. Wilkins M.R., et al. High-throughput mass spectrometric discovery of protein post-translational modifications. J. Mol. Biol. 1999, 289:645-657.
50. Ren J., et al. CSS-Palm 2.0: an updated software for palmitoylation sites prediction. Protein Eng. Des. Sel. 2008, 21:639-644.
51. Kim M.S., et al. A draft map of the human proteome. Nature 2014, 509:575-581.
52. Vizcaino J.A., et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 2014, 32:223-226.
53. Bird A. Perceptions of epigenetics. Nature 2007, 447:396-398.
54. Yang S., et al. Correlated evolution of transcription factors and their binding sites. Bioinformatics 2011, 27:2972-2978.
55. Landt S.G., et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 2012, 22:1813-1831.
56. Konig J., et al. Protein-RNA interactions: new genomic technologies and perspectives. Nat. Rev. Genet. 2011, 13:77-83.
57. Zhao Z., et al. Circular chromosome conformation capture (4C) uncovers extensive networks of epigenetically regulated intra- and interchromosomal interactions. Nat. Genet. 2006, 38:1341-1347.
58. Dostie J., et al. Chromosome Conformation Capture Carbon Copy (5C): a massively parallel solution for mapping interactions between genomic elements. Genome Res. 2006, 16:1299-1309.
59. Gavrilov A., et al. Chromosome conformation capture (from 3C to 5C) and its ChIP-based modification. Methods Mol. Biol. 2009, 567:171-188.
60. van Berkum N.L., et al. Hi-C: a method to study the three-dimensional architecture of genomes. J. Vis. Exp. 2010.
61. Dekker J., Marti-Renom M.A., Mirny L.A. Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat. Rev. Genet. 2013, 14:390-403.
62. Rao S.S., et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014, 159:1665-1680.
63. Krueger F., Andrews S.R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 2011, 27:1571-1572.
64. Xi Y., Li W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinform. 2009, 10:232.
65. Smith A.D., et al. Updates to the RMAP short-read mapping software. Bioinformatics 2009, 25:2841-2842.
66. Zheng H., et al. CpGIMethPred: computational model for predicting methylation status of CpG islands in human genome. BMC Med. Genomics 2013, 6(Suppl. 1):S13.
67. Esteller M. Epigenetics in cancer. N. Engl. J. Med. 2008, 358:1148-1159.
68. Callinan P.A., Feinberg A.P. The emerging science of epigenomics. Hum. Mol. Genet. 2006, 15(Spec. No. 1):R95-R101.
69. Orchard S., et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat. Methods 2012, 9:345-350.
70. Qin J., et al. ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor. Nucleic Acids Res. 2011, 39:W430-W436.
71. Guan D., et al. CMGRN: a web server for constructing multilevel gene regulatory networks using ChIP-seq and gene expression data. Bioinformatics 2014.
72. Guan D., et al. PTHGRN: unraveling post-translational hierarchical gene regulatory networks using PPI, ChIP-seq and gene expression data. Nucleic Acids Res. 2014, 42:W130-W136.
73. Yalamanchili H.K., et al. DDGni: dynamic delay gene-network inference from high-temporal data using gapped local alignment. Bioinformatics 2014, 30:377-383.
74. Li M.J., et al. GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2012, 40:D1047-D1054.
75. Wang S., et al. Target analysis by integration of transcriptome and ChIP-seq data with BETA. Nat. Protoc. 2013, 8:2502-2515.