Carolin Walter1, Daniel Schuetzmann1, Frank Rosenbauer1, Martin Dugas1. 1. Institute of Medical Informatics, University of Münster, 48149 Münster, Germany, and Institute of Molecular Tumorbiology, University of Münster, 48149 Münster, Germany.
Abstract
SUMMARY: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. The package processes aligned 4C-seq raw data stored in binary alignment/map (BAM) format and maps the short reads to a corresponding virtual fragment library. Functions are included to create virtual fragment libraries providing chromosome position and further information on 4C-seq fragments (length and uniqueness of the fragment ends, and blindness of a fragment) for any BSGenome package. An optional filter is included for BAM files to remove invalid 4C-seq reads, and further filter functions are offered for 4C-seq fragments. Additionally, basic quality controls based on the read distribution are included. Fragment data in the vicinity of the experiment's viewpoint are visualized as coverage plot based on a running median approach and a multi-scale contact profile. Wig files or csv files of the fragment data can be exported for further analyses and visualizations of interactions with other programs. AVAILABILITY AND IMPLEMENTATION: Basic4Cseq is implemented in R and available at http://www.bioconductor.org/. A vignette with detailed descriptions of the functions is included in the package. CONTACT: Carolin.Walter@uni-muenster.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
SUMMARY: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. The package processes aligned 4C-seq raw data stored in binary alignment/map (BAM) format and maps the short reads to a corresponding virtual fragment library. Functions are included to create virtual fragment libraries providing chromosome position and further information on 4C-seq fragments (length and uniqueness of the fragment ends, and blindness of a fragment) for any BSGenome package. An optional filter is included for BAM files to remove invalid 4C-seq reads, and further filter functions are offered for 4C-seq fragments. Additionally, basic quality controls based on the read distribution are included. Fragment data in the vicinity of the experiment's viewpoint are visualized as coverage plot based on a running median approach and a multi-scale contact profile. Wig files or csv files of the fragment data can be exported for further analyses and visualizations of interactions with other programs. AVAILABILITY AND IMPLEMENTATION: Basic4Cseq is implemented in R and available at http://www.bioconductor.org/. A vignette with detailed descriptions of the functions is included in the package. CONTACT: Carolin.Walter@uni-muenster.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Lan T M Dao; Ariel O Galindo-Albarrán; Jaime A Castro-Mondragon; Charlotte Andrieu-Soler; Alejandra Medina-Rivera; Charbel Souaid; Guillaume Charbonnier; Aurélien Griffon; Laurent Vanhille; Tharshana Stephen; Jaafar Alomairi; David Martin; Magali Torres; Nicolas Fernandez; Eric Soler; Jacques van Helden; Denis Puthier; Salvatore Spicuglia Journal: Nat Genet Date: 2017-06-05 Impact factor: 38.330
Authors: Charles Y Lin; Serap Erkek; Yiai Tong; Linlin Yin; Alexander J Federation; Marc Zapatka; Parthiv Haldipur; Daisuke Kawauchi; Thomas Risch; Hans-Jörg Warnatz; Barbara C Worst; Bensheng Ju; Brent A Orr; Rhamy Zeid; Donald R Polaski; Maia Segura-Wang; Sebastian M Waszak; David T W Jones; Marcel Kool; Volker Hovestadt; Ivo Buchhalter; Laura Sieber; Pascal Johann; Lukas Chavez; Stefan Gröschel; Marina Ryzhova; Andrey Korshunov; Wenbiao Chen; Victor V Chizhikov; Kathleen J Millen; Vyacheslav Amstislavskiy; Hans Lehrach; Marie-Laure Yaspo; Roland Eils; Peter Lichter; Jan O Korbel; Stefan M Pfister; James E Bradner; Paul A Northcott Journal: Nature Date: 2016-01-27 Impact factor: 49.962