Literature DB >> 21092284

Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing.

Cristian Coarfa1, Fuli Yu, Christopher A Miller, Zuozhou Chen, R Alan Harris, Aleksandar Milosavljevic.   

Abstract

BACKGROUND: Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequence comparison and read mapping and can be employed as a module within diverse configurable analysis pipelines, including ChIP-Seq and methylome mapping by whole-genome bisulfite sequencing.
RESULTS: Pash 3.0 generally matches the accuracy and speed of niche programs for fast mapping of short reads, and exceeds their performance on longer reads generated by a new generation of massively parallel sequencing technologies. By exploiting longer read lengths, Pash 3.0 maps reads onto the large fraction of genomic DNA that contains repetitive elements and polymorphic sites, including indel polymorphisms.
CONCLUSIONS: We demonstrate the versatility of Pash 3.0 by analyzing the interaction between CpG methylation, CpG SNPs, and imprinting based on publicly available whole-genome shotgun bisulfite sequencing data. Pash 3.0 makes use of gapped k-mer alignment, a non-seed based comparison method, which is implemented using multi-positional hash tables. This allows Pash 3.0 to run on diverse hardware platforms, including individual computers with standard RAM capacity, multi-core hardware architectures and large clusters.

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Year:  2010        PMID: 21092284      PMCID: PMC3001746          DOI: 10.1186/1471-2105-11-572

Source DB:  PubMed          Journal:  BMC Bioinformatics        ISSN: 1471-2105            Impact factor:   3.169


  35 in total

1.  dbSNP: the NCBI database of genetic variation.

Authors:  S T Sherry; M H Ward; M Kholodov; J Baker; L Phan; E M Smigielski; K Sirotkin
Journal:  Nucleic Acids Res       Date:  2001-01-01       Impact factor: 16.971

2.  BLAT--the BLAST-like alignment tool.

Authors:  W James Kent
Journal:  Genome Res       Date:  2002-04       Impact factor: 9.043

3.  Good spaced seeds for homology search.

Authors:  Kwok Pui Choi; Fanfan Zeng; Louxin Zhang
Journal:  Bioinformatics       Date:  2004-02-05       Impact factor: 6.937

4.  Detection of large-scale variation in the human genome.

Authors:  A John Iafrate; Lars Feuk; Miguel N Rivera; Marc L Listewnik; Patricia K Donahoe; Ying Qi; Stephen W Scherer; Charles Lee
Journal:  Nat Genet       Date:  2004-08-01       Impact factor: 38.330

5.  Circular binary segmentation for the analysis of array-based DNA copy number data.

Authors:  Adam B Olshen; E S Venkatraman; Robert Lucito; Michael Wigler
Journal:  Biostatistics       Date:  2004-10       Impact factor: 5.899

Review 6.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Authors:  S F Altschul; T L Madden; A A Schäffer; J Zhang; Z Zhang; W Miller; D J Lipman
Journal:  Nucleic Acids Res       Date:  1997-09-01       Impact factor: 16.971

7.  Using the FASTA program to search protein and DNA sequence databases.

Authors:  W R Pearson
Journal:  Methods Mol Biol       Date:  1994

8.  Identification of common molecular subsequences.

Authors:  T F Smith; M S Waterman
Journal:  J Mol Biol       Date:  1981-03-25       Impact factor: 5.469

9.  Pash: efficient genome-scale sequence anchoring by Positional Hashing.

Authors:  Ken J Kalafus; Andrew R Jackson; Aleksandar Milosavljevic
Journal:  Genome Res       Date:  2004-04       Impact factor: 9.043

10.  Putting epigenome comparison into practice.

Authors:  Aleksandar Milosavljevic
Journal:  Nat Biotechnol       Date:  2010-10       Impact factor: 54.908

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  30 in total

Review 1.  Statistical approaches for the analysis of DNA methylation microarray data.

Authors:  Kimberly D Siegmund
Journal:  Hum Genet       Date:  2011-04-26       Impact factor: 4.132

2.  miR-137 Targets p160 Steroid Receptor Coactivators SRC1, SRC2, and SRC3 and Inhibits Cell Proliferation.

Authors:  Vijay Kumar Eedunuri; Kimal Rajapakshe; Warren Fiskus; Chuandong Geng; Sue Anne Chew; Christopher Foley; Shrijal S Shah; John Shou; Junaith S Mohamed; Cristian Coarfa; Bert W O'Malley; Nicholas Mitsiades
Journal:  Mol Endocrinol       Date:  2015-06-12

Review 3.  Analysing and interpreting DNA methylation data.

Authors:  Christoph Bock
Journal:  Nat Rev Genet       Date:  2012-10       Impact factor: 53.242

4.  Allele-specific epigenome maps reveal sequence-dependent stochastic switching at regulatory loci.

Authors:  Vitor Onuchic; Eugene Lurie; Ivenise Carrero; Piotr Pawliczek; Ronak Y Patel; Joel Rozowsky; Timur Galeev; Zhuoyi Huang; Robert C Altshuler; Zhizhuo Zhang; R Alan Harris; Cristian Coarfa; Lillian Ashmore; Jessica W Bertol; Walid D Fakhouri; Fuli Yu; Manolis Kellis; Mark Gerstein; Aleksandar Milosavljevic
Journal:  Science       Date:  2018-08-23       Impact factor: 47.728

5.  Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data.

Authors:  Junko Tsuji; Zhiping Weng
Journal:  Brief Bioinform       Date:  2015-12-01       Impact factor: 11.622

Review 6.  Emerging patterns of epigenomic variation.

Authors:  Aleksandar Milosavljevic
Journal:  Trends Genet       Date:  2011-04-18       Impact factor: 11.639

7.  Interactions between core histone marks and DNA methyltransferases predict DNA methylation patterns observed in human cells and tissues.

Authors:  Kai Fu; Giancarlo Bonora; Matteo Pellegrini
Journal:  Epigenetics       Date:  2019-09-17       Impact factor: 4.528

Review 8.  Methods for cancer epigenome analysis.

Authors:  Raman P Nagarajan; Shaun D Fouse; Robert J A Bell; Joseph F Costello
Journal:  Adv Exp Med Biol       Date:  2013       Impact factor: 2.622

9.  A mostly traditional approach improves alignment of bisulfite-converted DNA.

Authors:  Martin C Frith; Ryota Mori; Kiyoshi Asai
Journal:  Nucleic Acids Res       Date:  2012-03-28       Impact factor: 16.971

10.  BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions.

Authors:  Kasper D Hansen; Benjamin Langmead; Rafael A Irizarry
Journal:  Genome Biol       Date:  2012-10-03       Impact factor: 13.583

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