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Items: 1 to 20 of 85

1.

ZINBA integrates local covariates with DNA-seq data to identify broad and narrow regions of enrichment, even within amplified genomic regions.

Rashid NU, Giresi PG, Ibrahim JG, Sun W, Lieb JD.

Genome Biol. 2011 Jul 25;12(7):R67. doi: 10.1186/gb-2011-12-7-r67.

2.

PatternCNV: a versatile tool for detecting copy number changes from exome sequencing data.

Wang C, Evans JM, Bhagwate AV, Prodduturi N, Sarangi V, Middha M, Sicotte H, Vedell PT, Hart SN, Oliver GR, Kocher JP, Maurer MJ, Novak AJ, Slager SL, Cerhan JR, Asmann YW.

Bioinformatics. 2014 Sep 15;30(18):2678-80. doi: 10.1093/bioinformatics/btu363. Epub 2014 May 29.

3.

Systematic inference of copy-number genotypes from personal genome sequencing data reveals extensive olfactory receptor gene content diversity.

Waszak SM, Hasin Y, Zichner T, Olender T, Keydar I, Khen M, Stütz AM, Schlattl A, Lancet D, Korbel JO.

PLoS Comput Biol. 2010 Nov 11;6(11):e1000988. doi: 10.1371/journal.pcbi.1000988.

4.

A generalized linear model for peak calling in ChIP-Seq data.

Xu J, Zhang Y.

J Comput Biol. 2012 Jun;19(6):826-38. doi: 10.1089/cmb.2012.0023. Epub 2012 Apr 25.

5.

HMCan: a method for detecting chromatin modifications in cancer samples using ChIP-seq data.

Ashoor H, Hérault A, Kamoun A, Radvanyi F, Bajic VB, Barillot E, Boeva V.

Bioinformatics. 2013 Dec 1;29(23):2979-86. doi: 10.1093/bioinformatics/btt524. Epub 2013 Sep 9.

6.

MixClone: a mixture model for inferring tumor subclonal populations.

Li Y, Xie X.

BMC Genomics. 2015;16 Suppl 2:S1. doi: 10.1186/1471-2164-16-S2-S1. Epub 2015 Jan 21.

7.

Broad-Enrich: functional interpretation of large sets of broad genomic regions.

Cavalcante RG, Lee C, Welch RP, Patil S, Weymouth T, Scott LJ, Sartor MA.

Bioinformatics. 2014 Sep 1;30(17):i393-400. doi: 10.1093/bioinformatics/btu444.

8.

OncoSNP-SEQ: a statistical approach for the identification of somatic copy number alterations from next-generation sequencing of cancer genomes.

Yau C.

Bioinformatics. 2013 Oct 1;29(19):2482-4. doi: 10.1093/bioinformatics/btt416. Epub 2013 Aug 7.

PMID:
23926227
9.

rSW-seq: algorithm for detection of copy number alterations in deep sequencing data.

Kim TM, Luquette LJ, Xi R, Park PJ.

BMC Bioinformatics. 2010 Aug 18;11:432. doi: 10.1186/1471-2105-11-432.

10.

Multiscale representation of genomic signals.

Knijnenburg TA, Ramsey SA, Berman BP, Kennedy KA, Smit AF, Wessels LF, Laird PW, Aderem A, Shmulevich I.

Nat Methods. 2014 Jun;11(6):689-94. doi: 10.1038/nmeth.2924. Epub 2014 Apr 13.

11.

BamBam: genome sequence analysis tools for biologists.

Page JT, Liechty ZS, Huynh MD, Udall JA.

BMC Res Notes. 2014 Nov 24;7:829. doi: 10.1186/1756-0500-7-829.

12.

Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions.

McCallum KJ, Wang JP.

Biostatistics. 2013 Jul;14(3):600-11. doi: 10.1093/biostatistics/kxt003. Epub 2013 Feb 20.

PMID:
23428932
13.

Detection of rare genomic variants from pooled sequencing using SPLINTER.

Vallania F, Ramos E, Cresci S, Mitra RD, Druley TE.

J Vis Exp. 2012 Jun 23;(64). pii: 3943. doi: 10.3791/3943.

14.

Nonparametric Tests for Differential Histone Enrichment with ChIP-Seq Data.

Wu Q, Won KJ, Li H.

Cancer Inform. 2015 Jan 27;14(Suppl 1):11-22. doi: 10.4137/CIN.S13972. eCollection 2015 Jan 27.

15.

HiChIP: a high-throughput pipeline for integrative analysis of ChIP-Seq data.

Yan H, Evans J, Kalmbach M, Moore R, Middha S, Luban S, Wang L, Bhagwate A, Li Y, Sun Z, Chen X, Kocher JP.

BMC Bioinformatics. 2014 Aug 15;15:280. doi: 10.1186/1471-2105-15-280.

16.

De novo detection of copy number variation by co-assembly.

Nijkamp JF, van den Broek MA, Geertman JM, Reinders MJ, Daran JM, de Ridder D.

Bioinformatics. 2012 Dec 15;28(24):3195-202. doi: 10.1093/bioinformatics/bts601. Epub 2012 Oct 9.

PMID:
23047563
17.

MochiView: versatile software for genome browsing and DNA motif analysis.

Homann OR, Johnson AD.

BMC Biol. 2010 Apr 21;8:49. doi: 10.1186/1741-7007-8-49.

18.

Using CisGenome to analyze ChIP-chip and ChIP-seq data.

Ji H, Jiang H, Ma W, Wong WH.

Curr Protoc Bioinformatics. 2011 Mar;Chapter 2:Unit2.13. doi: 10.1002/0471250953.bi0213s33.

19.

MEME-ChIP: motif analysis of large DNA datasets.

Machanick P, Bailey TL.

Bioinformatics. 2011 Jun 15;27(12):1696-7. doi: 10.1093/bioinformatics/btr189. Epub 2011 Apr 12.

20.

Impact of sequencing depth in ChIP-seq experiments.

Jung YL, Luquette LJ, Ho JW, Ferrari F, Tolstorukov M, Minoda A, Issner R, Epstein CB, Karpen GH, Kuroda MI, Park PJ.

Nucleic Acids Res. 2014 May;42(9):e74. doi: 10.1093/nar/gku178. Epub 2014 Mar 5.

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