metaseq Framework for integrated analysis and plotting of ChIP/RIP/RNA/*-seq data
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Python library Python data analyzer rip chip data plotter

metaseq Description

metaseq is a Python framework that makes it easy to work with a combination of ChIP-seq, RNA-seq, or anything-seq.Example use-cases:- Matrix plots: Generate an MxN matrix (e.g., M = genes and N = bins, TSS +/- 1kb) showing the ChIP-seq signal of of up-regulated genes from an RNA-seq experiment compared to down-regulated genes Support for parallel processing, actual number is a balance between your hard drive speed and number of cores. The signal is calculated directly from the IP and input BAM files, and scaling to library size normalizes the data Since the result is a NumPy array, it's trivial to, say, sort by gene expression or to take column averages.- Cluster genes based on the spatial distribution of ChIP-seq peaks around their TSSs. A matrix can be generated using the same interface as for BAM files, using a BED or bigBed file instead, again resulting in a NumPy array for further analysis- Scatter plot of DESeq results (basemeana vs basemeanb) where points are colored according to the number of ChIP peaks in the gene Using callback functions in matplotlib, clicking on a point can print gene information, or can even open a window showing a mini genome-browser view of that gene- Pie charts of where peaks fall within annotated genes -- TSS, poly-A site, intron, exon, etcWhere possible, the inputs are standard formats -- BED, GFF, GTF, BAM, SAM, DESeq results as saved from R, or even arbitrary tab-delimited data files that have a header. If you take the time to convert to bigWig or bigBed, performance will be improvedmetaseq stands on the shoulders of a large body of existing Python packages to provide a flexible, intuitive, and powerful framework. These packages include:- pysam for parsing BAM files- bx-python for access to bigWig and bigBed files- matplotlib for fast, interactive, and extremely flexible plotting- numpy for fast arrays- scikits.learn for clustering (specifically, the minibatch k-means implementation)- pybedtools, an interface to the BEDTools suite, for flexible and powerful "genome algebra" and feature-level manipulation- gffutils, a lightweight database framework for navigating the hierarchy of gene annotations (exon -> transcript -> gene) in a portable format- Cython for implementing computationally intensive code in CIn addition to providing the "glue" between these various packages, metaseq also supplies binning code written in Cython and helpers for parallelizing data access.Product's homepage

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