Literature DB >> 18599518

FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology.

Anthony P Fejes1, Gordon Robertson, Mikhail Bilenky, Richard Varhol, Matthew Bainbridge, Steven J M Jones.   

Abstract

SUMMARY: Next-generation sequencing can provide insight into protein-DNA association events on a genome-wide scale, and is being applied in an increasing number of applications in genomics and meta-genomics research. However, few software applications are available for interpreting these experiments. We present here an efficient application for use with chromatin-immunoprecipitation (ChIP-Seq) experimental data that includes novel functionality for identifying areas of gene enrichment and transcription factor binding site locations, as well as for estimating DNA fragment size distributions in enriched areas. The FindPeaks application can generate UCSC compatible custom 'WIG' track files from aligned-read files for short-read sequencing technology. The software application can be executed on any platform capable of running a Java Runtime Environment. Memory requirements are proportional to the number of sequencing reads analyzed; typically 4 GB permits processing of up to 40 million reads. AVAILABILITY: The FindPeaks 3.1 package and manual, containing algorithm descriptions, usage instructions and examples, are available at http://www.bcgsc.ca/platform/bioinfo/software/findpeaks Source files for FindPeaks 3.1 are available for academic use.

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Year:  2008        PMID: 18599518      PMCID: PMC2638869          DOI: 10.1093/bioinformatics/btn305

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  8 in total

1.  High-resolution profiling of histone methylations in the human genome.

Authors:  Artem Barski; Suresh Cuddapah; Kairong Cui; Tae-Young Roh; Dustin E Schones; Zhibin Wang; Gang Wei; Iouri Chepelev; Keji Zhao
Journal:  Cell       Date:  2007-05-18       Impact factor: 41.582

2.  Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells.

Authors:  Ryan D Morin; Michael D O'Connor; Malachi Griffith; Florian Kuchenbauer; Allen Delaney; Anna-Liisa Prabhu; Yongjun Zhao; Helen McDonald; Thomas Zeng; Martin Hirst; Connie J Eaves; Marco A Marra
Journal:  Genome Res       Date:  2008-02-19       Impact factor: 9.043

3.  Genomic mapping by fingerprinting random clones: a mathematical analysis.

Authors:  E S Lander; M S Waterman
Journal:  Genomics       Date:  1988-04       Impact factor: 5.736

4.  Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing.

Authors:  Kristen H Taylor; Robin S Kramer; J Wade Davis; Juyuan Guo; Deiter J Duff; Dong Xu; Charles W Caldwell; Huidong Shi
Journal:  Cancer Res       Date:  2007-09-15       Impact factor: 12.701

5.  Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing.

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

6.  Dynamic regulation of nucleosome positioning in the human genome.

Authors:  Dustin E Schones; Kairong Cui; Suresh Cuddapah; Tae-Young Roh; Artem Barski; Zhibin Wang; Gang Wei; Keji Zhao
Journal:  Cell       Date:  2008-03-07       Impact factor: 41.582

7.  Genome-wide mapping of in vivo protein-DNA interactions.

Authors:  David S Johnson; Ali Mortazavi; Richard M Myers; Barbara Wold
Journal:  Science       Date:  2007-05-31       Impact factor: 47.728

8.  Automated generation of heuristics for biological sequence comparison.

Authors:  Guy St C Slater; Ewan Birney
Journal:  BMC Bioinformatics       Date:  2005-02-15       Impact factor: 3.169

  8 in total
  151 in total

1.  AREM: aligning short reads from ChIP-sequencing by expectation maximization.

Authors:  Daniel Newkirk; Jacob Biesinger; Alvin Chon; Kyoko Yokomori; Xiaohui Xie
Journal:  J Comput Biol       Date:  2011-10-28       Impact factor: 1.479

2.  "Calling cards" for DNA-binding proteins in mammalian cells.

Authors:  Haoyi Wang; David Mayhew; Xuhua Chen; Mark Johnston; Robi David Mitra
Journal:  Genetics       Date:  2012-01-03       Impact factor: 4.562

3.  ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data.

Authors:  Lihua J Zhu; Claude Gazin; Nathan D Lawson; Hervé Pagès; Simon M Lin; David S Lapointe; Michael R Green
Journal:  BMC Bioinformatics       Date:  2010-05-11       Impact factor: 3.169

4.  Distinct histone modifications in stem cell lines and tissue lineages from the early mouse embryo.

Authors:  Peter J Rugg-Gunn; Brian J Cox; Amy Ralston; Janet Rossant
Journal:  Proc Natl Acad Sci U S A       Date:  2010-05-17       Impact factor: 11.205

5.  Discovering homotypic binding events at high spatial resolution.

Authors:  Yuchun Guo; Georgios Papachristoudis; Robert C Altshuler; Georg K Gerber; Tommi S Jaakkola; David K Gifford; Shaun Mahony
Journal:  Bioinformatics       Date:  2010-10-21       Impact factor: 6.937

6.  Processing and analyzing ChIP-seq data: from short reads to regulatory interactions.

Authors:  Marion Leleu; Grégory Lefebvre; Jacques Rougemont
Journal:  Brief Funct Genomics       Date:  2010-09-22       Impact factor: 4.241

7.  Effects of oestrogen on microRNA expression in hormone-responsive breast cancer cells.

Authors:  Lorenzo Ferraro; Maria Ravo; Giovanni Nassa; Roberta Tarallo; Maria Rosaria De Filippo; Giorgio Giurato; Francesca Cirillo; Claudia Stellato; Silvana Silvestro; Concita Cantarella; Francesca Rizzo; Daniela Cimino; Olivier Friard; Nicoletta Biglia; Michele De Bortoli; Luigi Cicatiello; Ernesto Nola; Alessandro Weisz
Journal:  Horm Cancer       Date:  2012-06       Impact factor: 3.869

8.  A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

Authors:  Chongzhi Zang; Dustin E Schones; Chen Zeng; Kairong Cui; Keji Zhao; Weiqun Peng
Journal:  Bioinformatics       Date:  2009-06-08       Impact factor: 6.937

Review 9.  Application of 'next-generation' sequencing technologies to microbial genetics.

Authors:  Daniel MacLean; Jonathan D G Jones; David J Studholme
Journal:  Nat Rev Microbiol       Date:  2009-04       Impact factor: 60.633

Review 10.  Genomic location analysis by ChIP-Seq.

Authors:  Artem Barski; Keji Zhao
Journal:  J Cell Biochem       Date:  2009-05-01       Impact factor: 4.429

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