Metadata-Version: 1.2
Name: amplimap
Version: 0.4.4
Summary: amplicon/smMIP mapping and analysis pipeline
Home-page: https://github.com/koelling/amplimap/
Author: Nils Koelling
Author-email: git@nk.gl
License: Apache License, Version 2.0
Download-URL: https://github.com/koelling/amplimap/archive/v0.4.4.tar.gz
Description: .. image:: https://raw.githubusercontent.com/koelling/amplimap/master/amplimap_logo_400px.png
        	:width: 400px
        
        ==========================================================
        amplimap: amplicon mapping and analysis pipeline
        ==========================================================
        
        amplimap is a command-line tool to automate the processing and analysis of data from targeted next-generation sequencing (NGS) experiments with PCR-based amplicons or capture-based enrichment systems.
        
        From raw sequencing reads, amplimap generates a variety of output files including read alignments, per-basepair nucleotide counts, target coverage data and annotated variant calls.
        
        In addition to its focus on user-friendliness and reproducibility, amplimap supports advanced features such as the generation of consensus base calls for read families based on molecular identifiers/barcodes (UMIs) and the detection of chimeric reads caused by amplification of off-target loci.
        
        Overview
        ----------
        To run amplimap you create a directory containing a small set of input files:
        
        - A subdirectory with FASTQ.GZ or BAM files representing your different samples (tested with Illumina MiSeq, HiSeq and NextSeq)
        
        - Optionally: Files describing the targeted genomic regions, the primers you used or other custom configuration parameters
        
        Then you can run ``amplimap`` to generate a variety of different output files, depending on your experiment.
        These include, for example:
        
        1. A target coverage table, showing you how well-covered each target region was in each sample.
        
        2. A table of germline variants in your samples, annotated with gene, impact, population frequencies, deleteriousness scores, etc.
        
        3. A per-basepair “pileup” table telling you how often each nucleotide was seen in each sample at each position.
        
        Built on top of `Snakemake <https://snakemake.readthedocs.io/>`_ and Python 3, amplimap is entirely
        automated and can be run on a single machine as well as on an HPC cluster
        (e.g. LSF, SGE).
        
        Supported experimental protocols
        ---------------------------------
        amplimap is compatible with most targeted sequencing protocols that generate paired-end short read data.
        
        For protocols utilising PCR or smMIPs each read should start with a known primer (or targeting arm) sequence, followed by the amplified target DNA.
        Reads can optionally contain a unique molecular identifier (UMI) sequence in front of the primer, which can be used to group reads into families.
        Data should be available as demultiplexed FASTQ.GZ files, with each pair of files representing a different sample.
        
        For capture-based protocols data can be provided in FASTQ.GZ or unmapped/mapped BAM format, which may contain UMIs as BAM tags.
        
        Some of the protocols we have analyzed with amplimap include:
        
        - PCR-based targeted resequencing (single/multiplex)
        - `smMIPs with and without UMIs <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3638140/>`_
        - Probe based target enrichment, for example:
           + `IDT xGen Lockdown probes <https://www.idtdna.com/pages/products/next-generation-sequencing/hybridization-capture/custom-probes-panels/xgen-lockdown-probes>`_
           + `Twist Bioscience Custom Panels <https://twistbioscience.com/products/ngs#product-featured-2911>`_
        
        Tutorials
        ---------
        
        - `Calling germline variants in amplicon-based resequencing data <https://github.com/koelling/amplimap/blob/master/tutorials/tutorial_TLK2.ipynb>`_
        - `Identifying low-frequency somatic mutations in FGFR2 with UMI-tagged smMIPs <https://github.com/koelling/amplimap/blob/master/tutorials/tutorial_FGFR2.ipynb>`_
        - `Quantifying allele-specific expression <https://github.com/koelling/amplimap/blob/master/tutorials/tutorial_TLK2_ASE.ipynb>`_
        
        Links
        --------
         
        - Package: https://pypi.org/project/amplimap/
        - Code: https://github.com/koelling/amplimap/
        - Documentation: https://amplimap.readthedocs.io/
        
        
        Basic installation
        -------------------
        ::
        
        	pip install amplimap
        
        Requires Python 3.5+. `More details here. <https://amplimap.readthedocs.io/en/latest/installation.html>`_
        
        amplimap requires an aligner (BWA/Bowtie2/STAR), a variant caller (Platypus/GATK), bedtools, samtools, bcftools and Annovar.
        If you do not have these installed, please install them manually or see the 
        `instructions for installing required tools through conda <https://amplimap.readthedocs.io/en/latest/extended_installation.html>`_.
        
        Citation and License
        --------------------
        Licensed under the Apache License, version 2.0.
        Copyright 2018 Nils Koelling.
        When you use amplimap, please cite it in your work. For example:
        
          Koelling, Nils. "amplimap: amplicon mapping and analysis pipeline". https://github.com/koelling/amplimap/
        
        
Keywords: amplimap amplicon smmip mapping analysis pipeline
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3
