Universal Count Correction for High-Throughput Sequencing

PLoS Computational Biology

Volumn 10, Issue 3, 2014, Pages

  Hashimoto, Tatsunori B ^a   Edwards, Matthew D ^a   Gifford, David K ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS;

ANALYSIS TOOLS; COUNT DATUM; HIGH-THROUGHPUT SEQUENCING; NONPARAMETRIC METHODS; OVERDISPERSION; PERFORMANCE; UNIVERSAL METHOD;

RNA;

ANALYTIC METHOD; ARTICLE; CHROMATIN IMMUNOPRECIPITATION; COMPUTER PROGRAM; DATA ANALYSIS; DNASE SEQUENCING; GENETIC PROCEDURES; HIGH THROUGHPUT SEQUENCING; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; NONPARAMETRIC TEST; RNA SEQUENCE; SYSTEMATIC ERROR; UNIVERSAL COUNT CORRECTION; VALIDATION PROCESS;

ALGORITHMS; AREA UNDER CURVE; CHROMATIN; CHROMATIN IMMUNOPRECIPITATION; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; DNA; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; K562 CELLS; LIKELIHOOD FUNCTIONS; POISSON DISTRIBUTION; RNA; SEQUENCE ANALYSIS, DNA; SOFTWARE; TRANSCRIPTION FACTORS;

EID: 84897406933     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003494     Document Type: Article

References (39)

1. Wold B, Myers R, et al. (2008) Sequence census methods for functional genomics. Nature Methods 5: 19-21.
2. Park P, (2009) Chip-seq: advantages and challenges of a maturing technology. Nature Reviews Genetics 10: 669-680.
3. Wang Z, Gerstein M, Snyder M, (2009) Rna-seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10: 57-63.
4. Ozsolak F, Milos PM, (2010) Rna sequencing: advances, challenges and opportunities. Nature Reviews Genetics 12: 87-98.
5. Boyle A, Davis S, Shulha H, Meltzer P, Margulies E, et al. (2008) High-resolution mapping and characterization of open chromatin across the genome. Cell 132: 311-322.
6. Neph S, Vierstra J, Stergachis A, Reynolds A, Haugen E, et al. (2012) An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489: 83-90.
7. Anders S, Huber W, (2010) Differential expression analysis for sequence count data. Genome Biology 11: R106.
8. Spyrou C, Stark R, Lynch A, Tavare S, (2009) Bayespeak: Bayesian analysis of chip-seq data. BMC Bioinformatics 10: 299.
9. Ji H, Jiang H, Ma W, Johnson D, Myers R, et al. (2008) An integrated software system for analyzing chip-chip and chip-seq data. Nature Biotechnology 26: 1293-1300.
10. Guo Y, Mahony S, Gifford DK, (2012) High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS computational biology 8: e1002638.
11. Pepke S, Wold B, Mortazavi A, (2009) Computation for chip-seq and rna-seq studies. Nature methods 6: S22-S32.
12. Jones DC, Ruzzo WL, Peng X, Katze MG, (2012) A new approach to bias correction in rna-seq. Bioinformatics 28: 921-928.
13. Skelly DA, Johansson M, Madeoy J, Wakefield J, Akey JM, (2011) A powerful and exible statistical framework for testing hypotheses of allele-specific gene expression from rna-seq data. Genome research 21: 1728-1737.
14. Srivastava S, Chen L, (2010) A two-parameter generalized poisson model to improve the analysis of RNA-seq data. Nucleic acids research 38: e170.
15. Li J, Jiang H, Wong WH, (2010) Modeling non-uniformity in short-read rates in rna-seq data. Genome Biology 11: R50.
16. Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L, (2011) Improving rna-seq expression estimates by correcting for fragment bias. Genome Biology 12: R22.
17. Hansen KD, Brenner SE, Dudoit S, (2010) Biases in illumina transcriptome sequencing caused by random hexamer priming. Nucleic acids research 38: e131-e131.
18. Bullard J, Purdom E, Hansen K, Dudoit S, (2010) Evaluation of statistical methods for normalization and differential expression in mrna-seq experiments. BMC bioinformatics 11: 94.
19. Cheung MS, Down TA, Latorre I, Ahringer J, (2011) Systematic bias in high-throughput sequencing data and its correction by beads. Nucleic acids research 39: e103-e103.
20. Yaffe E, Tanay A, (2011) Probabilistic modeling of hi-c contact maps eliminates systematic biases to characterize global chromosomal architecture. Nature genetics 43: 1059-1065.
21. Li W, Jiang T, (2012) Transcriptome assembly and isoform expression level estimation from biased rna-seq reads. Bioinformatics 28: 2914-2921.
22. Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, et al. (2012) Iterative correction of hi-c data reveals hallmarks of chromosome organization. Nature Methods 9: 999-1003.
23. Benjamini Y, Speed TP, (2012) Summarizing and correcting the gc content bias in high-throughput sequencing. Nucleic acids research 40: e72-e72.
24. Thygesen HH, Zwinderman AH, (2006) Modeling sage data with a truncated gamma-poisson model. BMC bioinformatics 7: 157.
25. Bliss CI, Fisher RA, (1953) Fitting the negative binomial distribution to biological data. Biometrics 9: 176-200.
26. Bulmer M (1974) On fitting the poisson lognormal distribution to species-abundance data. Biometrics: 101-110.
27. Chen Y, Negre N, Li Q, Mieczkowska JO, Slattery M, et al. (2012) Systematic evaluation of factors inuencing ChIP-seq fidelity. Nature Methods 9: 609-614.
28. Wilbanks EG, Facciotti MT, (2010) Evaluation of algorithm performance in ChIP-Seq peak detection. PLoS ONE 5: e11471 doi:10.1371/journal.pone.0011471.
29. Zhang Y, Liu T, Meyer C, Eeckhoute J, Johnson D, et al. (2008) Model-based analysis of chip-seq (macs). Genome Biology 9: R137.
30. Kharchenko PV, Tolstorukov MY, Park PJ, (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnology 26: 1351-1359.
31. Pique-Regi R, Degner JF, Pai AA, Gaffney DJ, Gilad Y, et al. (2011) Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Research 21: 447-455.
32. Khatun J, (2012) An integrated encyclopedia of dna elements in the human genome. Nature 489: 57-74 doi:10.1038/nature11247.
33. Rozowsky J, Euskirchen G, Auerbach R, Zhang Z, Gibson T, et al. (2009) Peakseq enables systematic scoring of chip-seq experiments relative to controls. Nature Biotechnology 27: 66-75.
34. Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, et al. (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research 22: 1813-1831.
35. Cule M, (2010) Theoretical properties of the log-concave maximum likelihood estimator of a mul-tidimensional density. Electronic Journal of Statistics 4: 254-270.
36. Chang GT, Walther G, (2007) Clustering with mixtures of log-concave distributions. Computational Statistics & Data Analysis 51: 6242-6251.
37. Walther G, (2009) Inference and modeling with log-concave distributions. Statistical Science 24: 319-327.
38. Nesterov Y (2003) Introductory Lectures on Convex Optimization: A Basic Course. Springer.
39. Hildreth C, (1954) Point estimates of ordinates of concave functions. Journal of the American Statistical Association 49: 598.