We are analyzing https://link.springer.com/article/10.1186/1745-6150-6-51.

Title:
Fish the ChIPs: a pipeline for automated genomic annotation of ChIP-Seq data | Biology Direct
Description:
Background High-throughput sequencing is generating massive amounts of data at a pace that largely exceeds the throughput of data analysis routines. Here we introduce Fish the ChIPs (FC), a computational pipeline aimed at a broad public of users and designed to perform complete ChIP-Seq data analysis of an unlimited number of samples, thus increasing throughput, reproducibility and saving time. Results Starting from short read sequences, FC performs the following steps: 1) quality controls, 2) alignment to a reference genome, 3) peak calling, 4) genomic annotation, 5) generation of raw signal tracks for visualization on the UCSC and IGV genome browsers. FC exploits some of the fastest and most effective tools today available. Installation on a Mac platform requires very basic computational skills while configuration and usage are supported by a user-friendly graphic user interface. Alternatively, FC can be compiled from the source code on any Unix machine and then run with the possibility of customizing each single parameter through a simple configuration text file that can be generated using a dedicated user-friendly web-form. Considering the execution time, FC can be run on a desktop machine, even though the use of a computer cluster is recommended for analyses of large batches of data. FC is perfectly suited to work with data coming from Illumina Solexa Genome Analyzers or ABI SOLiD and its usage can potentially be extended to any sequencing platform. Conclusions Compared to existing tools, FC has two main advantages that make it suitable for a broad range of users. First of all, it can be installed and run by wet biologists on a Mac machine. Besides it can handle an unlimited number of samples, being convenient for large analyses. In this context, computational biologists can increase reproducibility of their ChIP-Seq data analyses while saving time for downstream analyses. Reviewers This article was reviewed by Gavin Huttley, George Shpakovski and Sarah Teichmann.
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Keywords {🔍}

data, chipseq, article, pubmed, pipeline, authors, analysis, user, python, genome, genomic, google, scholar, annotation, tools, installation, package, number, sequencing, users, single, installed, regions, cas, computational, mac, machine, step, macs, response, central, time, run, scripts, tool, automated, samples, raw, reads, install, peaks, table, research, open, access, unlimited, peak, ucsc, unix, analyses,

Topics {✒️}

dedicated user-friendly web-form snow leopard version perform complete chip-seq chip-seq data makes gov/traces/sra/sra uk/projects/fastqc/] langmead perform chip-seq analyses level chip-seq analysis related subjects ucsc genome browser validate chip-seq data chip-seq data analyses article download pdf snow leopard epigenetic chip-seq dataset manually curated datasets current peak-finder programs genome-wide analysis chip-seq data high-resolution profiling privacy choices/manage cookies analyzing chip-seq chip-seq experiments annotate chip-seq investigate chromatin states chip-chip data enriched region sole-search full access analyzing chip-chip genome-wide profiles chip-seq pipelines single genomic element standalone script integrated analysis program chip-seq validation massively-parallel sequencing multi-parallel sequencing massively parallel sequencing data analysis routines transcriptional start sites low quality bases chip-seq tools expression tiling data sra raw files gene expression lab biomed central article number 51 single dataset analyzed gene interval notator

Questions {❓}

What are the differences in peaks (epigenetic) and target genes (transcription factor) that are assigned by the different packages?
When Python comes bundled with OSX, why does a full version of Python need to be installed on users machines at all?

Schema {🗺️}

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