title: Direct ChIP-Seq significance analysis improves target prediction.
abstract: BACKGROUND
  Chromatin immunoprecipitation followed by sequencing of protein-bound DNA fragments (ChIP-Seq) is an effective high-throughput methodology for the identification of context specific DNA fragments that are bound by specific proteins in vivo. Despite significant progress in the bioinformatics analysis of this genome-scale data, a number of challenges remain as technology-dependent biases, including variable target accessibility and mappability, sequence-dependent variability, and non-specific binding affinity must be accounted for.
  
  
  CONCLUSIONS
  We introduce a nonparametric method for scoring consensus regions of aligned immunoprecipitated DNA fragments when appropriate control experiments are available. Our method uses local models for null binding; these are necessary because binding prediction scores based on global models alone fail to properly account for specialized features of genomic regions and chance pull downs of specific DNA fragments, thus disproportionally rewarding some genomic regions and decreasing prediction accuracy. We make no assumptions about the structure or amplitude of bound peaks, yet we show that our method outperforms leading methods developed using either global or local null hypothesis models for random binding. We test prediction performance by comparing analyses of ChIP-seq, ChIP-chip, motif-based binding-site prediction, and shRNA assays, showing high reproducibility, binding-site enrichment in predicted target regions, and functional regulation of predicted targets.
  
  
  CONCLUSIONS
  Given appropriate controls, a direct nonparametric method for identifying transcription-factor targets from ChIP-Seq assays may lead to both higher sensitivity and higher specificity, and should be preferred or used in conjunction with methods that use parametric models for null binding.
authors.forename: Mukesh
authors.initials: M
authors.lastname: Bansal
authors.forename: Geetu
authors.initials: G
authors.lastname: Mendiratta
authors.forename: Santosh
authors.initials: S
authors.lastname: Anand
authors.forename: Ritu
authors.initials: R
authors.lastname: Kushwaha
authors.forename: Ryan
authors.initials: R
authors.lastname: Kim
authors.forename: Manju
authors.initials: M
authors.lastname: Kustagi
authors.forename: Archana
authors.initials: A
authors.lastname: Iyer
authors.forename: Raju S K
authors.initials: RS
authors.lastname: Chaganti
authors.forename: Andrea
authors.initials: A
authors.lastname: Califano
authors.forename: Pavel
authors.initials: P
authors.lastname: Sumazin
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002460:Cell Line; D047369:Chromatin Immunoprecipitation / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence / Q000235:genetics; D004247:DNA / Q000235:genetics; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D018398:Homeodomain Proteins / Q000235:genetics*; D006801:Humans; D000071317:Nanog Homeobox Protein; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D051881:Receptor, Notch1 / Q000235:genetics*; D055748:SOXB1 Transcription Factors / Q000235:genetics*; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D018398:Homeodomain Proteins; C486780:NANOG protein, human; C497761:NOTCH1 protein, human; D000071317:Nanog Homeobox Protein; D051881:Receptor, Notch1; C527782:SOX2 protein, human; D055748:SOXB1 Transcription Factors; D004247:DNA