BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Genome Biology

Volumn 18, Issue 1, 2017, Pages

  de Santiago, Ines ^a,b   Liu, Wei ^a,c   Yuan, Ke ^a,d   O'Reilly, Martin ^a   Chilamakuri, Chandra Sekhar Reddy ^a   Ponder, Bruce A J ^a   Meyer, Kerstin B ^a   Markowetz, Florian ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b Seven Bridges Genomics   (United States)

^c UNIVERSITY OF GLASGOW   (United Kingdom)

^d UNIVERSITY OF GLASGOW   (United Kingdom)

Author keywords

Allele frequency; Allele specific binding; Bayesian statistics; Cancer; ChIP sequencing; Copy number change; FAIRE sequencing

Indexed keywords

TRANSCRIPTION FACTOR;

ALLELE; ALLELIC IMBALANCE; ARTICLE; BAYES THEOREM; BREAST CANCER; CHROMATIN IMMUNOPRECIPITATION; CONTROLLED STUDY; COPY NUMBER VARIATION; GENE AMPLIFICATION; GENE FREQUENCY; GENETIC BACKGROUND; GENETIC VARIABILITY; GENOME; HUMAN; HUMAN CELL; PROTEIN BINDING; SEQUENCE ANALYSIS; SIMULATION; SINGLE NUCLEOTIDE POLYMORPHISM; BINDING SITE; BIOLOGY; GENETICS; GENOTYPE; HIGH THROUGHPUT SEQUENCING; METABOLISM; NEOPLASM; PROCEDURES; QUALITY CONTROL; REPRODUCIBILITY; TUMOR CELL LINE; WORKFLOW;

ALLELES; ALLELIC IMBALANCE; BAYES THEOREM; BINDING SITES; CELL LINE, TUMOR; CHROMATIN IMMUNOPRECIPITATION; COMPUTATIONAL BIOLOGY; DNA COPY NUMBER VARIATIONS; GENE AMPLIFICATION; GENOTYPE; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; NEOPLASMS; QUALITY CONTROL; REPRODUCIBILITY OF RESULTS; TRANSCRIPTION FACTORS; WORKFLOW;

EID: 85013821974     PISSN: 14747596     EISSN: 1474760X     Source Type: Journal    
DOI: 10.1186/s13059-017-1165-7     Document Type: Article

References (49)

1. McDaniell R, Lee BK, Song L, Liu Z, Boyle AP, Erdos MR, et al. Heritable individual-specific and allele-specific chromatin signatures in humans. Science. 2010; 328(5975):235-9.
2. Reddy TE, Gertz J, Pauli F, Kucera KS, Varley KE, Newberry KM, et al. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression. Genome Res. 2012; 22(5):860-9.
3. Kasowski M, Kyriazopoulou-Panagiotopoulou S, Grubert F, Zaugg JB, Kundaje A, Liu Y, et al. Extensive variation in chromatin states across humans. Science. 2013; 342(6159):750-2.
4. Kilpinen H, Waszak SM, Gschwind AR, Raghav SK, Witwicki RM, Orioli A, et al. Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription. Science. 2013; 342(6159):744-7.
5. McVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE, Raj A, et al. Identification of genetic variants that affect histone modifications in human cells. Science. 2013; 342(6159):747-9.
6. Degner JF, Marioni JC, Pai AA, Pickrell JK, Nkadori E, Gilad Y, et al. Effect of read-mapping biases on detecting allele-specific expression from RNA-sequencing data. Bioinformatics. 2009; 25(24):3207-12.
7. Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, et al. Alleleseq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011; 7(1):522.
8. Satya RV, Zavaljevski N, Reifman J. A new strategy to reduce allelic bias in RNA-Seq readmapping. Nucleic Acids Res. 2012; 40(16):e127.
9. Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM. A powerful and flexible statistical framework for testing hypotheses of allele-specific gene expression from RNA-Seq data. Genome Res. 2011; 21(10):1728-37.
10. Wei Y, Li X, Wang Q-F, Ji H. iASeq: integrative analysis of allele-specificity of protein-DNA interactions in multiple ChIP-seq datasets. BMC Genomics. 2012; 13(1):681.
11. Mayba O, Gilbert HN, Liu J, Haverty PM, Jhunjhunwala S, Jiang Z, et al. MBASED: allele-specific expression detection in cancer tissues and cell lines. Genome Biol. 2014; 15(8):405.
12. Li G, Bahn JH, Lee JH, Peng G, Chen Z, Nelson SF, et al. Identification of allele-specific alternative mRNA processing via transcriptome sequencing. Nucleic Acids Res. 2012; 40(13):e104.
13. Chen J, Rozowsky J, Galeev TR, Harmanci A, Kitchen R, Bedford J, et al. A uniform survey of allele-specific binding and expression over 1000-genomes-project individuals. Nat Commun. 2016; 7:11101.
14. Almlöf JC, Lundmark P, Lundmark A, Ge B, Pastinen T, Goodall AH, et al. Single nucleotide polymorphisms with cis-regulatory effects on long non-coding transcripts in human primary monocytes. PLoS ONE. 2014; 9(7):e102612.
15. Bailey SD, Virtanen C, Haibe-Kains B, Lupien M. ABC: a tool to identify SVNs causing allele-specific transcription factor binding from ChIP-seq experiments. Bioinformatics. 2015; 31(18):3057-9.
16. Liu Z, Gui T, Wang Z, Li H, Fu Y, Dong X, et al. cisASE: a likelihood-based method for detecting putative cis-regulated allele-specific expression in RNA sequencing data. Bioinformatics. 2016; 32:3291-7. doi: 10.1093/bioinformatics/btw416.
17. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010; 464(7289):768-72.
18. Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: statistical inference of clonal population structure in cancer. Nat Methods. 2014; 11(4):396-8.
19. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012; 489(7414):57-74.
20. R Core Team. R: a language and environment for statistical computing; 2014. http://www.R-project.org/.
21. de Santiago I, Liu W, O'Reilly M, Yuang K, Chilamakuri SRC, Ponder BAJ, et al. BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes. R package version 1.0.0. 2016. https://bioconductor.org/packages/release/bioc/html/BaalChIP.html.
22. Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 2015; 16(1):1.
23. Fujita PA, Rhead B, Zweig AS, Hinrichs AS, Karolchik D, Cline MS, et al. The UCSC genome browser database: update 2011. Nucleic Acids Res. 2011; 39(suppl_1):D876-82.
24. Pickrell JK, Gaffney DJ, Gilad Y, Pritchard JK. False positive peaks in ChIP-seq and other sequencing-based functional assays caused by unannotated high copy number regions. Bioinformatics. 2011; 27(15):2144-6.
25. Carroll TS, Liang Z, Salama R, Stark R, de Santiago I. Impact of artifact removal on chip quality metrics in ChIP-seq and ChIP-exo data. Front Genet. 2014; 5:75.
26. Lappalainen T, Sammeth M, Friedländer MR, AC't Hoen P, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013; 501(7468):506-11.
27. Peiffer DA, Le JM, Steemers FJ, Chang W, Jenniges T, Garcia F, et al. High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res. 2006; 16(9):1136-48.
28. Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, et al. Detection of large-scale variation in the human genome. Nat Genet. 2004; 36(9):949-51.
29. Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, et al. Large-scale copy number polymorphism in the human genome. Science. 2004; 305(5683):525-8.
30. Biedler JL, Helson L, Spengler BA. Morphology and growth, tumorigenicity, and cytogenetics of human neuroblastoma cells in continuous culture. Cancer Res. 1973; 33(11):2643-52.
31. Liang JC, Ning Y, Wang RY, Padilla-Nash HM, Schröck E, Soenksen D, et al. Spectral karyotypic study of the HL-60 cell line: detection of complex rearrangements involving chromosomes 5, 7, and 16 and delineation of critical region of deletion on 5q31. 1. Cancer Genet Cytogenet. 1999; 113(2):105-9.
32. Gimelbrant A, Hutchinson JN, Thompson BR, Chess A. Widespread monoallelic expression on human autosomes. Science. 2007; 318(5853):1136-40.
33. Tang F, Barbacioru C, Nordman E, Bao S, Lee C, Wang X, et al. Deterministic and stochastic allele specific gene expression in single mouse blastomeres. PLoS ONE. 2011; 6(6):21208.
34. Ni Y, Hall AW, Battenhouse A, Iyer VR. Simultaneous SNP identification and assessment of allele-specific bias from ChIP-seq data. BMC Genetics. 2012; 13(1):46.
35. Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. Faire (formaldehyde-assisted isolation of regulatory elements) isolates active regulatory elements from human chromatin. Genome Res. 2007; 17(6):877-85.
36. Michailidou K, Hall P, Gonzalez-Neira A, Ghoussaini M, Dennis J, Milne RL, et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet. 2013; 45(4):353-61.
37. Turnbull C, Ahmed S, Morrison J, Pernet D, Renwick A, Maranian M, et al. Genome-wide association study identifies five new breast cancer susceptibility loci. Nat Genet. 2010; 42(6):504-7.
38. Tuch BB, Laborde RR, Xu X, Gu J, Chung CB, Monighetti CK, et al. Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PLoS One. 2010; 5(2):9317.
39. Ross-Innes CS, Stark R, Teschendorff AE, Holmes KA, Ali HR, Dunning MJ, et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature. 2012; 481(7381):389-93.
40. Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F. Cancer evolution: mathematical models and computational inference. Syst Biol. 2015; 64:1-25. doi: 10.1093/sysbio/syu081.
41. Morgan M, Pagès H, Obenchain V, Hayden N. Rsamtools: Binary alignment (BAM), variant call (BCF), or tabix file import. R package version 1.18.2. 2010. http://bioconductor.org/packages/release/bioc/html/Rsamtools.html.
42. Lawrence M, Huber W, Pages H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges. PLoS Comput Biol. 2013; 9(8):1003118.
43. Langmead B, Trapnell C, Pop M, Salzberg SL, et al. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009; 10(3):25.
44. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001; 29(1):308-11.
45. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60.
46. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010; 20(9):1297-303.
47. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011; 43(5):491-8.
48. Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinforma. 2013; 43:11.10.1-11.10.33.
49. Ward LD, Kellis M. Haploreg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012; 40(D1):930-4.