BACKGROUND: ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with high-throughput massively parallel sequencing, is increasingly being used for identification of protein-DNA interactions in vivo in the genome. However, to maximize the effectiveness of data analysis of such sequences requires the development of new algorithms that are able to accurately predict DNA-protein binding sites. RESULTS: Here, we present SIPeS (Site Identification from Paired-end Sequencing), a novel algorithm for precise identification of binding sites from short reads generated by paired-end solexa ChIP-Seq technology. In this paper we used ChIP-Seq data from the Arabidopsis basic helix-loop-helix transcription factor ABORTED MICROSPORES (AMS), which is expressed within the anther during pollen development, the results show that SIPeS has better resolution for binding site identification compared to two existing ChIP-Seq peak detection algorithms, Cisgenome and MACS. CONCLUSIONS: When compared to Cisgenome and MACS, SIPeS shows better resolution for binding site discovery. Moreover, SIPeS is designed to calculate the mappable genome length accurately with the fragment length based on the paired-end reads. Dynamic baselines are also employed to effectively discriminate closely adjacent binding sites, for effective binding sites discovery, which is of particular value when working with high-density genomes.
BACKGROUND: ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with high-throughput massively parallel sequencing, is increasingly being used for identification of protein-DNA interactions in vivo in the genome. However, to maximize the effectiveness of data analysis of such sequences requires the development of new algorithms that are able to accurately predict DNA-protein binding sites. RESULTS: Here, we present SIPeS (Site Identification from Paired-end Sequencing), a novel algorithm for precise identification of binding sites from short reads generated by paired-end solexa ChIP-Seq technology. In this paper we used ChIP-Seq data from the Arabidopsis basic helix-loop-helix transcription factor ABORTED MICROSPORES (AMS), which is expressed within the anther during pollen development, the results show that SIPeS has better resolution for binding site identification compared to two existing ChIP-Seq peak detection algorithms, Cisgenome and MACS. CONCLUSIONS: When compared to Cisgenome and MACS, SIPeS shows better resolution for binding site discovery. Moreover, SIPeS is designed to calculate the mappable genome length accurately with the fragment length based on the paired-end reads. Dynamic baselines are also employed to effectively discriminate closely adjacent binding sites, for effective binding sites discovery, which is of particular value when working with high-density genomes.
Authors: B Ren; F Robert; J J Wyrick; O Aparicio; E G Jennings; I Simon; J Zeitlinger; J Schreiber; N Hannett; E Kanin; T L Volkert; C J Wilson; S P Bell; R A Young Journal: Science Date: 2000-12-22 Impact factor: 47.728
Authors: Gordon Robertson; Martin Hirst; Matthew Bainbridge; Misha Bilenky; Yongjun Zhao; Thomas Zeng; Ghia Euskirchen; Bridget Bernier; Richard Varhol; Allen Delaney; Nina Thiessen; Obi L Griffith; Ann He; Marco Marra; Michael Snyder; Steven Jones Journal: Nat Methods Date: 2007-06-11 Impact factor: 28.547
Authors: Tarjei S Mikkelsen; Manching Ku; David B Jaffe; Biju Issac; Erez Lieberman; Georgia Giannoukos; Pablo Alvarez; William Brockman; Tae-Kyung Kim; Richard P Koche; William Lee; Eric Mendenhall; Aisling O'Donovan; Aviva Presser; Carsten Russ; Xiaohui Xie; Alexander Meissner; Marius Wernig; Rudolf Jaenisch; Chad Nusbaum; Eric S Lander; Bradley E Bernstein Journal: Nature Date: 2007-07-01 Impact factor: 49.962
Authors: Anthony P Fejes; Gordon Robertson; Mikhail Bilenky; Richard Varhol; Matthew Bainbridge; Steven J M Jones Journal: Bioinformatics Date: 2008-07-03 Impact factor: 6.937
Authors: Kelly P Stanton; Jiaqi Jin; Roy R Lederman; Sherman M Weissman; Yuval Kluger Journal: Nucleic Acids Res Date: 2017-12-01 Impact factor: 16.971