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A command line software tool and a web interface, which takes in the raw 4C sequencing data (FASTQ files) as input, performs automated statistical analysis and presents results in a user-friendly manner. Besides providing users with the list of candidate interacting sites/regions, w4CSeq generates figures showing genome-wide distribution of interacting regions, and sketches the enrichment of key features such as TSSs, TTSs, CpG sites and DNA replication timing around 4C sites. w4CSeq is a valuable tool to unveil the functional genome organization.
A suite of programs for analyzing and visualizing 4C-seq data. fourSig can analyze data from a wide variety of 4C-seq protocols, uses the full range of quantitative information captured by 4C-seq, provides variable parameters to adjust for desired resolution and offers a method of peak prioritization to categorize statistically significant genomic interactions. We showed that this method is capable of reproducing well-studied interactions identified by other methods and can be more sensitive in determining significant interactions between large linear distances than some existing methods.
A package dedicated to the analysis of (multiplexed) 4C sequencing data. FourCSeq provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated.
A package for basic filtering, analysis and subsequent near-cis visualization of 4C-seq data. Basic4Cseq 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. 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.
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A hidden-Markov model based analysis that identifies regions throughout the genome that interact with the 4C bait locus. In addition we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.
Allows users to generate 3D models and derive virtual Hi-C (vHi-C) heat maps of genomic loci based on 4C-seq or any kind of 4C-seq-like data. 4Cin can also produce models using 4C-seq-like data coming from recently developed techniques such as NG Capture-C or Capture-C, as long as they are used to capture at least 4 viewpoints within each region(s) of interest. Moreover, it allows the generation of vHi-C maps, or the identification of topologically associating domains (TADs) boundaries.
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