{ "info": { "author": "Kwangbom \"KB\" Choi, Ph. D., The Jackson Laboratory", "author_email": "kb.choi@jax.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Natural Language :: English", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7" ], "description": "===============================\nOverview\n===============================\n\n.. image:: https://badge.fury.io/py/emase.png\n :target: http://badge.fury.io/py/emase\n\n.. image:: https://anaconda.org/kbchoi/emase/badges/version.svg\n :target: https://anaconda.org/kbchoi/emase\n\n.. image:: https://travis-ci.org/churchill-lab/emase.png?branch=master\n :target: https://travis-ci.org/churchill-lab/emase\n\n.. image:: https://readthedocs.org/projects/emase/badge/?version=latest\n :target: http://emase.readthedocs.org/en/latest/?badge=latest\n :alt: Documentation Status\n\n\nEMASE: Expectation-Maximization algorithm for Allele Specific Expression \n------------------------------------------------------------------------\nNarayanan Raghupathy, Kwangbom Choi, Steve Munger, and Gary Churchill\n\n* Free software: GNU General Public License v3 (GPLv3)\n* Documentation: https://emase.readthedocs.org.\n\nNote: The documentation for EMASE is still under work.\n\nWhat is EMASE?\n~~~~~~~~~~~~~~\n\nEMASE is a software program written in Python to quantify allele-specific\nexpression and gene expression simultaneously from RNA-seq data. EMASE takes in\nthe diploid transcriptome alignment BAM file and GTF file as inputs and\nestimates expression abundance for each isoforms and each alleles using\nExpectation Maxmization algorithm.\n\nWhy Use EMASE?\n~~~~~~~~~~~~~~\n\nCurrent RNA-seq analysis pipeline employ two steps to quantify gene expression\nand allele-specific expression (ASE); gene expression is estimated from all\nread alignments, while ASE is assessed separately by using only reads that\noverlap known SNP locations.\n\nLarge-scale genome sequencing efforts have characterized millions of genetic\nvariants across in human and model organisms. However development of tools that\ncan effectively utilize this individual/strain-specific variation to inform\nquantitation of gene expression abundance have lagged behind.\n\nEMASE, together with g2gtools (https://github.com/churchill-lab/g2gtools), offers an integrated\nsolution to utilize known genetic variations in quantifying expression abundances at allele \nand gene/isoform level.\n\nIn F1 hybrids from model organisms, EMASE allows us to utilize parental\nstrain-specific genetic variation in RNA-seq analysis to quantify gene\nexpression and allele-specific expression (ASE) simultaneously\n\nIn humans, EMASE allows us to utilize the individual's genetic variation in\ndoing personalized RNA-seq analysis and quantify gene expression and\nallele-specific expression (ASE) simultaneously\n\nBriefly, EMASE: EM for allele-specific expression, uses individualized diploid\ngenomes/transcriptomes adjusted for known genetic variations and quantifies\nallele-specific gene expression and total gene expressionsimultaneously. The EM\nalgorithm employed in EMASE models multi-reads at the level of gene, isoform,\nand allele and apportions them probabilistically.\n\nEarlier, we developed Seqnature to utilize known genetic variations, bot SNPs\nand Indels, to build indivdualized genomes and adjust annotations. One can use\nSeqnature to create individualized diploid transcritpme and align RNA-seq reads\nsimultaneously to the diploid transcriptome and get alignment file in BAM\nformat. This diploid BAM file can be used as input to EMASE.\n\nApplications\n~~~~~~~~~~~~\n\nAllele-specific gene expression in F1 Hybrids from model organisms\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nIf we have F1 hybrids with parental genetic variants information, one can use\nSeqnature to build strain specific genomes and extract diploid transcriptome.\nRNA-seq alignment bam file obtained by aligbning RNA-seq reads to the diploid\ntranscriptome is used as input for EMASE.\n\nPersonalized ASE analysis in Human\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nEMASE can be used to do personalized RNA-seq analysis in human. For this, we use the phased genetic\nvariation (SNP and Indel) information to construct personalized diploid genome and align reads to the diploid transcriptome..\n\nAllele-specific Binding using Chip-Seq in F1 Hybrids\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nAlthough we explained the use of EMASE to quantify Allele-Specific Expression\nfrom RNA-seq data, the tool can be used with other types of sequencing data. We\nhave successfully used EMASE to quantify allele-specific binding from ChIP-seq\ndata. While useing ChIP-seq, one needs to use diploid binding target sequences\ninstead of diploid transcriptome for alignment target sequences.\n\nMining Diploid alignments and alignment probabilities\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nEMASE can be used to glean more information from the alignment, in addition to running EMASE and \nobtaining effective read counts for each allele and isoform. For example, we can use EMASE's count-alignment \nprogram to obtain unique reads at allele-level for every gene, gene unique reads but allele-level multireads, \nand the total number of reads aligned to every gene. Having these alignment statistics at for every isoform and gene \ncan be useful in interpreting expression estimates from EMASE. \n\n\n\nReferences\n~~~~~~~~~~\n\n* EMASE: Expectation-Maximization algorithm for Allele Specific Expression, Narayanan Raghupathy, Kwangbom Choi, Steve Munger, and Gary Churchill, Manuscript in preparation.\n\n* [RNA-Seq Alignment to Individualized Genomes Improves Transcript Abundance Estimates in Multiparent Populations](http://www.genetics.org/content/198/1/59.short) Steven C. Munger, Narayanan Raghupathy,Kwangbom Choi, Allen K. Simons, Daniel M. Gatti, Douglas A. Hinerfeld, Karen L. Svenson, Mark P. Keller, Alan D. Attie, Matthew A. Hibbs, Joel H. Graber, Elissa J. Chesler and Gary A. Churchill. Genetics. 2014 Sep;198(1):59-73. doi: 10.1534/genetics.114.165886.\n\n* [PRDM9 Drives Evolutionary Erosion of Hotspots in Mus musculus through Haplotype-Specific Initiation of Meiotic Recombination](http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004916) Christopher L. Baker, Shimpei Kajita, Michael Walker, Ruth L. Saxl, Narayanan Raghupathy, Kwangbom Choi, Petko M. Petkov, Kenneth Paigen PLOS Genetics: published 08 Jan 2015 | info:doi/10.1371/journal.pgen.1004916\n\n\n\n\n\nHistory\n-------\n\n0.10.16 (05-10-2016)\n~~~~~~~~~~~~~~~~~~~~\n* Modified ``prepare-emase`` so it can process the newest Ensembl gene annotation (Release 84)\n* The script ``prepare-emase`` can process gzipped files\n* Updated documentation\n\n0.10.15 (05-04-2016)\n~~~~~~~~~~~~~~~~~~~~\n* Uploaded to Anaconda.org\n* Updated documentation\n\n0.10.14 (04-25-2016)\n~~~~~~~~~~~~~~~~~~~~\n* Added option to not having rname when loading/saving ``AlignmentPropertyMatrix``\n* Documentation updated to reflect recent changes (e.g., processing paired-end data etc.)\n\n0.10.12 (04-22-2016)\n~~~~~~~~~~~~~~~~~~~~\n* ``run-emase`` report file names changed (effective -> expected)\n* ``run-emase`` report file can have notes\n\n0.10.11 (02-15-2016)\n~~~~~~~~~~~~~~~~~~~~\n* Minor change in documentation\n\n0.10.9 (02-09-2016)\n~~~~~~~~~~~~~~~~~~~\n* Fixed readthedocs compiling fails\n\n0.10.5 (01-20-2016)\n~~~~~~~~~~~~~~~~~~~\n* Added ``pull_alignments_from`` method in ``AlignmentPropertyMatrix`` class\n* Added a script ``pull-out-unique-reads`` that unsets emase pseudo-alignments that are not uniquely aligning\n\n0.10.3 (01-06-2016)\n~~~~~~~~~~~~~~~~~~~\n* Fixed a bug in ``run-emase`` on handling inbred (reference or one haplotype) alignments\n\n0.10.2 (01-04-2016)\n~~~~~~~~~~~~~~~~~~~\n* Added ``get-common-alignments``: To compute intersection between each of paired ends\n\n0.9.10 (01-04-2016)\n~~~~~~~~~~~~~~~~~~~\n* ``AlignmentMatrixFactory`` can handle unmapped reads\n\n0.9.8 (07-31-2015)\n~~~~~~~~~~~~~~~~~~\n* Fixed a bug in ``simulate-reads``: No more duplicate read ID's\n\n0.9.7 (07-28-2015)\n~~~~~~~~~~~~~~~~~~\n* Added ``create-hybrid``: Build hybrid target directly using custom transcripts\n* Added ``simulate-reads``: Four nested models\n\n0.9.6 (06-02-2015)\n~~~~~~~~~~~~~~~~~~\n* ``AlignmentPropertyMatrix`` can represent an equivalence class\n* Fixed a bug in length normalization\n* Swapped Model ID's between 1 and 2\n\n - Model 1: Gene->Allele->Isoform (*)\n - Model 2: Gene->Isoform->Allele (*)\n - Model 3: Gene->Isoform*Allele\n - Model 4: Gene*Isoform*Allele (RSEM model)\n\n0.9.5 (05-17-2015)\n~~~~~~~~~~~~~~~~~~\n* Fixed length normalization: Depth = Count / (Transcript_Length - Read_Length + 1)\n\n0.9.4 (02-23-2015)\n~~~~~~~~~~~~~~~~~~\n* Fixed a bug in ``prepare-emase``\n\n0.9.3 (02-22-2015)\n~~~~~~~~~~~~~~~~~~\n* Fixed a bug in Model 2 of handling multireads\n* ``run-emase`` checks absolute sum of error (in TPM) for termination\n\n0.9.2 (02-17-2015)\n~~~~~~~~~~~~~~~~~~\n* Added three more models of handling multireads\n\n - Model 1: Gene->Isoform->Allele\n - Model 2: Gene->Allele->Isoform\n - Model 3: Gene->Isoform*Allele\n - Model 4: Gene*Isoform*Allele (RSEM model)\n\n0.9.0 (01-31-2015)\n~~~~~~~~~~~~~~~~~~\n* First release on PyPI\n* Only implements RSEM model for handling Multiply-mapping reads (or multireads)", 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