Metadata-Version: 1.1
Name: emase
Version: 0.10.2
Summary: EMASE: Expectation-Maximization algorithm for Allele Specific Expression
Home-page: https://github.com/churchill-lab/emase
Author: Kwangbom "KB" Choi
Author-email: kb.choi@jax.org
License: GPLv3
Description: ===============================
        Overview
        ===============================
        
        .. image:: https://badge.fury.io/py/emase.png
            :target: http://badge.fury.io/py/emase
        
        .. image:: https://pypip.in/d/emase/badge.png
                :target: https://pypi.python.org/pypi/emase
        
        
        EMASE: Expectation-Maximization algorithm for Allele Specific Expression 
        ------------------------------------------------------------------------
        Narayanan Raghupathy, Kwangbom Choi, Steve Munger, and Gary Churchill
        
        * Free software: GNU General Public License v3 (GPLv3)
        * Documentation: https://emase.readthedocs.org.
        
        Note: The documentation for EMASE is still under work.
        
        What is EMASE?
        ~~~~~~~~~~~~~~
        
        EMASE is a software program written in Python to quantify allele-specific
        expression and gene expression simultaneously from RNA-seq data. EMASE takes in
        the diploid transcriptome alignment BAM file and GTF file as inputs and
        estimates expression abundance for each isoforms and each alleles using
        Expectation Maxmization algorithm.
        
        Why Use EMASE?
        ~~~~~~~~~~~~~~
        
        Current RNA-seq analysis pipeline employ two steps to quantify gene expression
        and allele-specific expression (ASE); gene expression is estimated from all
        read alignments, while ASE is assessed separately by using only reads that
        overlap known SNP locations.
        
        Large-scale genome sequencing efforts have characterized millions of genetic
        variants across in human and model organisms. However development of tools that
        can effectively utilize this individual/strain-specific variation to inform
        quantitation of gene expression abundance have lagged behind.
        
        EMASE, together with Seqnature (https://github.com/jaxcs/Seqnature), offers an integrated 
        solution to utilize known genetic variations in quantifying expression abundances at allele 
        and gene/isoform level.
        
        In F1 hybrids from model organisms, EMASE allows us to utilize parental
        strain-specific genetic variation in RNA-seq analysis to quantify gene
        expression and allele-specific expression (ASE) simultaneously
        
        In humans, EMASE allows us to utilize the individual's genetic variation in
        doing personalized RNA-seq analysis and quantify gene expression and
        allele-specific expression (ASE) simultaneously
        
        Briefly, EMASE: EM for allele-specific expression, uses individualized diploid
        genomes/transcriptomes adjusted for known genetic variations and quantifies
        allele-specific gene expression and total gene expressionsimultaneously. The EM
        algorithm employed in EMASE models multi-reads at the level of gene, isoform,
        and allele and apportions them probabilistically.
        
        Earlier, we developed Seqnature to utilize known genetic variations, bot SNPs
        and Indels, to build indivdualized genomes and adjust annotations. One can use
        Seqnature to create individualized diploid transcritpme and align RNA-seq reads
        simultaneously to the diploid transcriptome and get alignment file in BAM
        format. This diploid BAM file can be used as input to EMASE.
        
        Applications
        ~~~~~~~~~~~~
        
        Allele-specific gene expression in F1 Hybrids from model organisms
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        If we have F1 hybrids with parental genetic variants information, one can use
        Seqnature to build strain specific genomes and extract diploid transcriptome.
        RNA-seq alignment bam file obtained by aligbning RNA-seq reads to the diploid
        transcriptome is used as input for EMASE.
        
        Personalized ASE analysis in Human
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        EMASE can be used to do personalized RNA-seq analysis in human. For this,  we use the phased genetic
        variation (SNP and Indel) information to construct personalized diploid genome and align reads to the diploid transcriptome..
        
        Allele-specific Binding using Chip-Seq in F1 Hybrids
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Although we explained the use of EMASE to quantify Allele-Specific Expression
        from RNA-seq data, the tool can be used with other types of sequencing data. We
        have successfully used EMASE to quantify allele-specific binding from ChIP-seq
        data. While useing ChIP-seq, one needs to use diploid binding target sequences
        instead of diploid transcriptome for alignment target sequences.
        
        Mining Diploid alignments and alignment probabilities
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        EMASE can be used to glean more information from the alignment, in addition to running EMASE and 
        obtaining effective read counts for each allele and isoform. For example, we can use EMASE's count-alignment 
        program to obtain unique reads at allele-level for every gene, gene unique reads but allele-level multireads, 
        and the total number of reads aligned to every gene. Having these alignment statistics at for every isoform and gene 
        can be useful in interpreting expression estimates from EMASE. 
        
        
        
        References
        ~~~~~~~~~~
        
        * EMASE: Expectation-Maximization algorithm for Allele Specific Expression, Narayanan Raghupathy, Kwangbom Choi, Steve Munger, and Gary Churchill, Manuscript in preparation.
        
        * [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.
        
        * [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
        
        
        
        
        
        History
        -------
        
        0.10.2 (2016-01-04)
        ~~~~~~~~~~~~~~~~~~
        * Added get-common-alignments: To compute intersection between each of paired ends
        
        
        0.9.10 (2016-01-04)
        ~~~~~~~~~~~~~~~~~~
        * AlignmentMatrixFactory can handle unmapped reads
        
        0.9.8 (2015-07-31)
        ~~~~~~~~~~~~~~~~~~
        * Fixed a bug in simulate-reads: No more duplicate read ID's
        
        0.9.7 (2015-07-28)
        ~~~~~~~~~~~~~~~~~~
        * Added create-hybrid: Build hybrid target directly using custom transcripts
        * Added simulate-reads: Four nested models
        
        0.9.6 (2015-06-02)
        ~~~~~~~~~~~~~~~~~~
        * AlignmentPropertyMatrix can represent Equivalence Classes
        * Fixed a bug in length normalization
        * Swapped Model ID's between 1 and 2
        
          - Model 1: Gene->Allele->Isoform (*)
          - Model 2: Gene->Isoform->Allele (*)
          - Model 3: Gene->Isoform*Allele
          - Model 4: Gene*Isoform*Allele (RSEM model)
        
        
        0.9.5 (2015-05-17)
        ~~~~~~~~~~~~~~~~~~
        * Fixed length normalization: Depth = Count / (Transcript_Length - Read_Length + 1)
        
        0.9.4 (2015-02-23)
        ~~~~~~~~~~~~~~~~~~
        * Fixed a bug in 'prepare-emase'
        
        0.9.3 (2015-02-22)
        ~~~~~~~~~~~~~~~~~~
        * Fixed a bug in Model 2 of handling multireads
        * 'run-emase' checks absolute sum of error (in TPM) for termination
        
        0.9.2 (2015-02-17)
        ~~~~~~~~~~~~~~~~~~
        * Added three more models of handling multireads
        
          - Model 1: Gene->Isoform->Allele
          - Model 2: Gene->Allele->Isoform
          - Model 3: Gene->Isoform*Allele
          - Model 4: Gene*Isoform*Allele (RSEM model)
        
        0.9.0 (2015-01-31)
        ~~~~~~~~~~~~~~~~~~
        * First release on PyPI
        * Only implements RSEM model for handling Multiply-mapping reads (or multireads)
        
Keywords: emase
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
