.. metachip documentation master file, created by sphinx-quickstart on Sat Oct 8 14:09:02 2011. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. include:: README.rst Some use-cases for :mod:`metaseq`: * Create NumPy arrays (which can be plotted as heatmaps) where each row represents a feature (say, TSS +/- 1kb) at fairly high speed. The actual speed is highly dependent on your data, but a rule of thumb is that it takes about 10s for every 10,000 2-kb features. Once you have the heatmap, you can sort, cluster, zoom, and pan to explore interesting groups. See the :ref:`CTCF example` for more on this as well as the :mod:`metaseq.genomic_signal` module. * "Mini-browsers". Instead of tweaking something, then laboriously converting to bedGraph, then bigWig, uploading your tracks to the genome browser, then searching for the region of interest, you can can just connect a mini-browser to your raw data and spawn figures locally. See the :ref:`example_session` for more on this as well as the :mod:`metaseq.minibrowser` API docs. * Cluster genes based on the spatial distribution of ChIP-seq peaks around their TSSs. * Scatter plot of DESeq results (basemeana vs basemeanb) where points are colored according to the number of ChIP peaks in the gene. This, too, can be attached to mini-browsers, enabling you to click on a point to see the genomic signal. See the :mod:`metaseq.results_table` module for more on this. * Pie charts of where peaks fall within annotated genes -- TSS, poly-A site, intron, exon, etc. See the :mod:`metaseq.integration` module for more on this. Where 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 improved. Contents: .. toctree:: :maxdepth: 2 install running-the-examples example_session example_session_2 autodocs changes