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5. 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(1):280. PubMed ID: 25128017 [TBL] [Abstract][Full Text] [Related]
6. ChiLin: a comprehensive ChIP-seq and DNase-seq quality control and analysis pipeline. Qin Q; Mei S; Wu Q; Sun H; Li L; Taing L; Chen S; Li F; Liu T; Zang C; Xu H; Chen Y; Meyer CA; Zhang Y; Brown M; Long HW; Liu XS BMC Bioinformatics; 2016 Oct; 17(1):404. PubMed ID: 27716038 [TBL] [Abstract][Full Text] [Related]
7. DROMPA: easy-to-handle peak calling and visualization software for the computational analysis and validation of ChIP-seq data. Nakato R; Itoh T; Shirahige K Genes Cells; 2013 Jul; 18(7):589-601. PubMed ID: 23672187 [TBL] [Abstract][Full Text] [Related]
8. Data exploration, quality control and statistical analysis of ChIP-exo/nexus experiments. Welch R; Chung D; Grass J; Landick R; Keles S Nucleic Acids Res; 2017 Sep; 45(15):e145. PubMed ID: 28911122 [TBL] [Abstract][Full Text] [Related]
9. diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates. Shen L; Shao NY; Liu X; Maze I; Feng J; Nestler EJ PLoS One; 2013; 8(6):e65598. PubMed ID: 23762400 [TBL] [Abstract][Full Text] [Related]
10. Optimizing detection of transcription factor-binding sites in ChIP-seq experiments. Kallio A; Elo LL Methods Mol Biol; 2013; 1038():181-91. PubMed ID: 23872976 [TBL] [Abstract][Full Text] [Related]
11. NEXT-peak: a normal-exponential two-peak model for peak-calling in ChIP-seq data. Kim NK; Jayatillake RV; Spouge JL BMC Genomics; 2013 May; 14():349. PubMed ID: 23706083 [TBL] [Abstract][Full Text] [Related]
12. rMAT--an R/Bioconductor package for analyzing ChIP-chip experiments. Droit A; Cheung C; Gottardo R Bioinformatics; 2010 Mar; 26(5):678-9. PubMed ID: 20089513 [TBL] [Abstract][Full Text] [Related]
13. Statistical Analysis and Quality Assessment of ChIP-seq Data with DROMPA. Nakato R; Shirahige K Methods Mol Biol; 2018; 1672():631-643. PubMed ID: 29043652 [TBL] [Abstract][Full Text] [Related]
15. From binding motifs in ChIP-Seq data to improved models of transcription factor binding sites. Kulakovskiy I; Levitsky V; Oshchepkov D; Bryzgalov L; Vorontsov I; Makeev V J Bioinform Comput Biol; 2013 Feb; 11(1):1340004. PubMed ID: 23427986 [TBL] [Abstract][Full Text] [Related]
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17. BroadPeak: a novel algorithm for identifying broad peaks in diffuse ChIP-seq datasets. Wang J; Lunyak VV; Jordan IK Bioinformatics; 2013 Feb; 29(4):492-3. PubMed ID: 23300134 [TBL] [Abstract][Full Text] [Related]
18. Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites. Wong KC; Peng C; Li Y IEEE/ACM Trans Comput Biol Bioinform; 2015; 12(6):1416-28. PubMed ID: 26671811 [TBL] [Abstract][Full Text] [Related]
19. fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Madrigal P Bioinformatics; 2017 Mar; 33(5):746-748. PubMed ID: 27993776 [TBL] [Abstract][Full Text] [Related]
20. Is this the right normalization? A diagnostic tool for ChIP-seq normalization. Angelini C; Heller R; Volkinshtein R; Yekutieli D BMC Bioinformatics; 2015 May; 16():150. PubMed ID: 25957089 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]