Motif discovery and transcription factor binding sites before and after the next-generation sequencing era

Briefings in Bioinformatics

Volumn 14, Issue 2, 2013, Pages 225-237

  Zambelli, Federico ^a   Pesole, Graziano ^b   Pavesi, Giulio ^a  

^a UNIVERSITY OF MILAN   (Italy)

^b UNIVERSITY OF BARI   (Italy)

Author keywords

ChIP Seq; Chromatin immunoprecipitation; Motif discovery; Transcription factor binding sites

Indexed keywords

DNA; TRANSCRIPTION FACTOR;

ALGORITHM; ANIMAL; ARTICLE; BINDING SITE; BIOLOGY; CHROMATIN IMMUNOPRECIPITATION; CONSENSUS SEQUENCE; GENE EXPRESSION PROFILING; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; METABOLISM; REGULATORY SEQUENCE; STATISTICS;

ALGORITHMS; ANIMALS; BINDING SITES; CHROMATIN IMMUNOPRECIPITATION; COMPUTATIONAL BIOLOGY; CONSENSUS SEQUENCE; DNA; GENE EXPRESSION PROFILING; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; REGULATORY ELEMENTS, TRANSCRIPTIONAL; TRANSCRIPTION FACTORS;

EID: 84875590756     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbs016     Document Type: Article

References (110)

1. Pavesi G, Mauri G, Pesole G. In silico representation and discovery of transcription factor binding sites. Brief Bioinform 2004;5:217-36.
2. Sandve GK, Drablos F. A survey of motif discovery methods in an integrated framework. Biol Direct 2006;1:11.
3. Lemon B, Tjian R. Orchestrated response: a symphony of transcription factors for gene control. Genes Dev 2000;14: 2551-69.
4. Levine M, Tjian R. Transcription regulation and animal diversity. Nature 2003;424:147-51.
5. Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002;32(Suppl):490-5.
6. Schulze A, Downward J. Navigating gene expression using microarrays-a technology review. Nat Cell Biol 2001;3: E190-95.
7. Brazma A, Vilo J. Gene expression data analysis. FEBS Lett 2000;480:17-24.
8. Cordero F, Botta M, Calogero RA. Microarray data analysis and mining approaches. BriefFunctGenomicProteomic 2007;6: 265-81.
9. Collas P, Dahl JA. Chop it, ChIP it, check it: the current status of chromatin immunoprecipitation. Front Biosci 2008; 13:929-43.
10. Pillai S, Chellappan SP. ChIP on chip assays: genome-wide analysis of transcription factor binding and histone modifications. MethodsMol Biol 2009;523:341-66.
11. Mardis ER. ChIP-seq: welcome to the new frontier. Nat Methods 2007;4:613-4.
12. Nomenclature Committee of the International Union of Biochemistry (NC-IUB). Nomenclature for incompletely specified bases in nucleic acid sequences. Recommendations 1984. Proc Natl Acad Sci USA 1986;83:4-8.
13. Crooks GE, Hon G, Chandonia JM, et al. WebLogo: a sequence logo generator. Genome Res 2004;14:1188-90.
14. Stormo GD. DNA binding sites: representation and discovery. Bioinformatics 2000;16:16-23.
15. Mahony S, Benos PV. STAMP: a web tool for exploring DNA-binding motif similarities. Nucleic Acids Res 2007;35: W253-8.
16. Gupta S, Stamatoyannopoulos JA, Bailey TL, et al. Quantifying similarity between motifs. Genome Biol 2007;8:R24.
17. Akutsu T, Arimura H, Shimozono S. On approximation algorithms for local multiple alignment, RECOMB 2000. Tokyo: ACM, 2000, 1-12.
18. Hertz GZ, Hartzell GW, Stormo GD. Identification of consensus patterns in unaligned DNA sequences known to be functionally related. Comput Appl Biosci 1990;6:81-92.
19. Hertz GZ, Stormo GD. Identifying DNA and protein patterns with statistically significant alignments of multiple sequences. Bioinformatics 1999;15:563-77.
20. Bailey TL, Elkan C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell SystMol Biol 1994;2:28-36.
21. Bailey TL, Elkan C. The value of prior knowledge in discovering motifs with MEME. Proc IntConf IntellSystMol Biol 1995;3:21-9.
22. Lawrence CE, Altschul SF, Boguski MS, et al. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 1993;262:208-14.
23. Neuwald AF, Liu JS, Lawrence CE. Gibbs motif sampling: detection of bacterial outer membrane protein repeats. Protein Sci 1995;4:1618-32.
24. Lawrence CE, Reilly AA. An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 1990;7:41-51.
25. Hughes JD, Estep PW, Tavazoie S, et al. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. JMol Biol 2000;296:1205-14.
26. Workman CT, Stormo GD. ANN-Spec: a method for discovering transcription factor binding sites with improved specificity. Pac Symp Biocomput 2000;467-78.
27. Thijs G, Lescot M, Marchal K, et al. A higher-order background model improves the detection of promoter regulatory elements by Gibbs sampling. Bioinformatics 2001;17: 1113-22.
28. Marchal K, Thijs G, De Keersmaecker S, et al. Genome-specific higher-order background models to improve motif detection. TrendsMicrobiol 2003;11:61-6.
29. Narasimhan C, LoCascio P, Uberbacher E. Background rareness-based iterative multiple sequence alignment algorithm for regulatory element detection. Bioinformatics 2003; 19:1952-63.
30. Bailey TL, Williams N, Misleh C, et al. MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res 2006;34:W369-73.
31. Thijs G, Marchal K, Lescot M, et al. A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. JComputBiol 2002;9:447-64.
32. Liu X, Brutlag DL, Liu JS. BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac Symp Biocomput 2001;127-38.
33. Aerts S, Thijs G, Coessens B, et al. Toucan: deciphering the cis-regulatory logic of coregulated genes. Nucleic Acids Res 2003;31:1753-64.
34. Frith MC, Hansen U, Spouge JL, et al. Finding functional sequence elements by multiple local alignment. NucleicAcids Res 2004;32:189-200.
35. Thompson W, Rouchka EC, Lawrence CE. Gibbs Recursive sampler: finding transcription factor binding sites. Nucleic Acids Res 2003;31:3580-5.
36. Down TA, Hubbard TJ. NestedMICA: sensitive inference of over-represented motifs in nucleic acid sequence. Nucleic Acids Res 2005;33:1445-53.
37. Li L. GADEM: a genetic algorithm guided formation of spaced dyads coupled with an EM algorithm for motif discovery. JComputBiol 2009;16:317-29.
38. Fogel GB, Weekes DG, Varga G, et al. Discovery of sequence motifs related to coexpression of genes using evolutionary computation. Nucleic Acids Res 2004;32:3826-35.
39. Defrance M, van Helden J. info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling. Bioinformatics 2009;25:2715-22.
40. Sadler JR, Waterman MS, Smith TF. Regulatory pattern identification in nucleic acid sequences. Nucleic Acids Res 1983;11:2221-31.
41. Waterman MS, Arratia R, Galas DJ. Pattern recognition in several sequences: consensus and alignment. Bull Math Biol 1984;46:515-27.
42. Galas DJ, Eggert M, Waterman MS. Rigorous pattern-recognition methods for DNA sequences. Analysis of promoter sequences from Escherichia coli. JMolBiol 1985;186:117-28.
43. Marsan L, Sagot MF. Algorithms for extracting structured motifs using a suffix tree with an application to promoter and regulatory site consensus identification. J Comput Biol 2000;7:345-62.
44. Pavesi G, Mauri G, Pesole G. An algorithm for finding signals of unknown length in DNA sequences. Bioinformatics 2001;17(Suppl 1):S207-14.
45. Caselle M, Di Cunto F, Provero P. Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes. BMC Bioinformatics 2002;3:7.
46. Cora D, Di Cunto F, Provero P, et al. Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrepresented upstream motifs. BMC Bioinformatics 2004;5:57.
47. van Helden J, Andre B, Collado-Vides J. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. JMolBiol 1998;281:827-42.
48. Shinozaki D, Akutsu T, Maruyama O. Finding optimal degenerate patterns in DNA sequences. Bioinformatics 2003; 19(Suppl 2):II206-14.
49. Sinha S, Tompa M. YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2003;31:3586-8.
50. Valimaki N, Gerlach W, Dixit K, et al. Compressed suffix tree-a basis for genome-scale sequence analysis. Bioinformatics 2007;23:629-30.
51. Pavesi G, Mereghetti P, Mauri G, et al. Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes. NucleicAcidsRes 2004;32: W199-203.
52. Marschall T, Rahmann S. Efficient exact motif discovery. Bioinformatics 2009;25:i356-64.
53. Pevzner PA, Sze SH. Combinatorial approaches to finding subtle signals in DNA sequences. Proc Int Conf Intell SystMol Biol 2000;8:269-78.
54. Zhang S, Li S, Niu M, et al. MotifClick: prediction of cis-regulatory binding sites via merging cliques. BMC Bioinformatics 2011;12:238.
55. Fratkin E, Naughton BT, Brutlag DL, et al. MotifCut: regulatory motifs finding with maximum density subgraphs. Bioinformatics 2006;22:e150-7.
56. Lee NK, Wang D. SOMEA: self-organizing map based extraction algorithm for DNA motif identification with heterogeneous model. BMC Bioinformatics 2011;12 (Suppl 1):S16.
57. Mahony S, Hendrix D, Golden A, etal. Transcription factor binding site identification using the self-organizing map. Bioinformatics 2005;21:1807-14.
58. Sze SH, Gelfand MS, Pevzner PA. Finding weak motifs in DNA sequences. Pac Symp Biocomput 2002;235-46.
59. Buhler J, Tompa M. Finding motifs using random projections. JComput Biol 2002;9:225-242.
60. Tompa M, Li N, Bailey TL, et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005;23:137-44.
61. Li N, Tompa M. Analysis of computational approaches for motif discovery. AlgorithmsMol Biol 2006;1:8.
62. Sandve GK, Abul O, Walseng V, et al. Improved benchmarks for computational motif discovery. BMC Bioinformatics 2007;8:193.
63. Romer KA, Kayombya GR, Fraenkel E. WebMOTIFS: automated discovery, filtering and scoring of DNA sequence motifs using multiple programs and Bayesian approaches. Nucleic Acids Res 2007;35:W217-20.
64. van Heeringen SJ, Veenstra GJ. GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments. Bioinformatics 2011;27:270-1.
65. Kuttippurathu L, Hsing M, Liu Y, et al. CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments. Bioinformatics 2011;27:715-7.
66. Zambelli F, Pesole G, Pavesi G. Pscan: finding over-represented transcription factor binding site motifs in sequences from co-regulated or co-expressed genes. Nucleic Acids Res 2009;37:W247-52.
67. Chiang DY, Moses AM, Kellis M, et al. Phylogenetically and spatially conserved word pairs associated with gene-expression changes in yeasts. Genome Biol 2003; 4:R43.
68. Sauer T, Shelest E, Wingender E. Evaluating phylogenetic footprinting for human-rodent comparisons. Bioinformatics 2006;22:430-7.
69. Dermitzakis ET, Clark AG. Evolution of transcription factor binding sites in Mammalian gene regulatory regions: conservation and turnover. Mol Biol Evol 2002;19:1114-21.
70. Bejerano G, Pheasant M, Makunin I, et al. Ultraconserved elements in the human genome. Science 2004;304:1321-5.
71. Siddharthan R, Siggia ED, van Nimwegen E. PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny. PLoSComput Biol 2005;1:e67.
72. Sinha S, Blanchette M, Tompa M. PhyME: a probabilistic algorithm for finding motifs in sets of orthologous sequences. BMC Bioinformatics 2004;5:170.
73. Odom DT, Dowell RD, Jacobsen ES, et al. Tissue-specific transcriptional regulation has diverged significantly between human and mouse. Nat Genet 2007;39:730-2.
74. Narlikar L, Gordan R, Hartemink AJ. A nucleosomeguided map of transcription factor binding sites in yeast. PLoSComput Biol 2007;3:e215.
75. Bailey TL, Boden M, Whitington T, et al. The value of position-specific priors in motif discovery using MEME. BMC Bioinformatics 2010;11:179.
76. Tang MH, Krogh A, Winther O. BayesMD: flexible biological modeling for motif discovery. JComputBiol 2008;15: 1347-63.
77. Klepper K, Drablos F. PriorsEditor: a tool for the creation and use of positional priors in motif discovery. Bioinformatics 2010;26:2195-7.
78. Mardis ER. The impact of next-generation sequencing technology on genetics. TrendsGenet 2008;24:133-41.
79. Pepke S, Wold B, Mortazavi A. Computation for ChIP-seq and RNA-seq studies. NatMethods 2009;6:S22-32.
80. Krig SR, Jin VX, Bieda MC, et al. Identification of genes directly regulated by the oncogene ZNF217 using chromatin immunoprecipitation (ChIP)-chip assays. J Biol Chem 2007;282:9703-12.
81. Zeller KI, Zhao X, Lee CW, et al. Global mapping of c-Myc binding sites and target gene networks in human B cells. ProcNatl Acad SciUSA 2006;103:17834-9.
82. Loh YH, Wu Q, Chew JL, et al. The Oct4 and Nanog transcription network regulates pluripotency in mouse embryonic stem cells. Nat Genet 2006;38:431-40.
83. Chen X, Xu H, Yuan P, et al. Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell 2008;133:1106-17.
84. Machanick P, Bailey TL. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 2011;27:1696-7.
85. Hu M, Yu J, Taylor JM, et al. On the detection and refinement of transcription factor binding sites using ChIP-Seq data. Nucleic Acids Res 2010;38:2154-67.
86. Reid JE, Wernisch L. STEME: efficient EM to find motifs in large data sets. Nucleic Acids Res 2011;39:e126.
87. Kulakovskiy IV, Boeva VA, Favorov AV, et al. Deep and wide digging for binding motifs in ChIP-Seq data. Bioinformatics 2010;26:2622-3.
88. Liu XS, Brutlag DL, Liu JS. An algorithm for finding protein-DNA binding sites with applications to chromatinimmunoprecipitation microarray experiments. Nat Biotechnol 2002;20:835-9.
89. Ettwiller L, Paten B, Ramialison M, et al. Trawler: de novo regulatory motif discovery pipeline for chromatin immunoprecipitation. NatMethods 2007;4:563-5.
90. Linhart C, Halperin Y, Shamir R. Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets. Genome Res 2008;18: 1180-9.
91. Bailey TL. DREME: motif discovery in transcription factor ChIP-seq data. Bioinformatics 2011;27:1653-9.
92. Sharov AA, Ko MS. Exhaustive search for over-represented DNA sequence motifs with CisFinder. DNA Res 2009;16: 261-73.
93. Georgiev S, Boyle AP, Jayasurya K, et al. Evidence-ranked motif identification. Genome Biol 2010;11:R19.
94. Thomas-Chollier M, Herrmann C, Defrance M, et al. RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets. Nucleic Acids Res 2011, doi: 10.1093/nar/gkr1104.
95. Jothi R, Cuddapah S, Barski A, et al. Genome-wide identification of in vivo protein-DNA binding sites from ChIP-Seq data. Nucleic Acids Res 2008;36:5221-31.
96. Bussemaker HJ, Li H, Siggia ED. Regulatory element detection using correlation with expression. Nat Genet 2001; 27:167-71.
97. Roven C, Bussemaker HJ. REDUCE: An online tool for inferring cis-regulatory elements and transcriptional module activities from microarray data. Nucleic Acids Res 2003;31: 3487-90.
98. Song L, Zhang Z, Grasfeder LL, et al. Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res 2011;21: 1757-67.
99. Ho JW, Bishop E, Karchenko PV, et al. ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis. BMCGenomics 2011;12:134.
100. Narang V, Mittal A, Sung WK. Localized motif discovery in gene regulatory sequences. Bioinformatics 2010;26:1152-9.
101. Cuellar-Partida G, Buske FA, McLeay RC, et al. Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 2012;28:56-62.
102. Pique-Regi R, Degner JF, Pai AA, et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res 2011;21:447-55.
103. Rosenbloom KR, Dreszer TR, Long JC, et al. ENCODE whole-genome data in the UCSC Genome Browser: update 2012. Nucleic Acids Res 2012;40:D912-7.
104. Harbison CT, Gordon DB, Lee TI, et al. Transcriptional regulatory code of a eukaryotic genome. Nature 2004;431: 99-104.
105. Kankainen M, Loytynoja A. MATLIGN: a motif clustering, comparison and matching tool. BMC Bioinformatics 2007;8: 189.
106. Rhee HS, Pugh BF. Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell 2011;147:1408-19.
107. Venters BJ, Wachi S, Mavrich TN, et al. A comprehensive genomic binding map of gene and chromatin regulatory proteins in Saccharomyces. Mol Cell 2011;41:480-92.
108. Smith AD, Sumazin P, Das D, et al. Mining ChIP-chip data for transcription factor and cofactor binding sites. Bioinformatics 2005;21(Suppl 1):i403-12.
109. Mercier E, Droit A, Li L, etal. An integrated pipeline for the genome-wide analysis of transcription factor binding sites from ChIP-Seq. PLoS One 2011;6:e16432.
110. Szalkowski AM, Schmid CD. Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts. Brief Bioinform 2011;12:626-33.