Snp-pipeline

~~~~~~~~~~~~~~~~~~

**Bug fixes:**

* Fixed some Mac OSX incompatibilities.
* Fixed a bug in copy_snppipeline_data.py that caused copy failure when the destination
directory did not exist.
* Fixed alignSampleToReference.sh to properly handle unpaired gzipped fastq files.

**Installation Changes:**

* There is a new dependency on the python psutil package. When you install the SNP Pipeline,
pip will attempt to install the psutil package automatically. If it fails, you may need to
manually install the python-dev package. In Ubuntu, ``sudo apt-get install python-dev``


**Other Changes:**

*Note a possible loss of backward compatibilty for existing workflows using
alignSampleToReference.sh and prepSamples.sh*


* All-in-one script: Added a new script, run_snp_pipeline.sh, to run the entire pipeline either on
a workstation or on a High Performance Computing cluster with the Torque job
queue manager. See :ref:`all-in-one-script-label`.
* Logging: The run_snp_pipeline.sh script adds consistent logging functionality for
workstation and HPC runs. The logs for each pipeline run are stored in a
time-stamped directory under the output directory. See :ref:`logging-label`.
* Timestamp checking: Changed the python scripts (create_snp_list.py, create_snp_pileup.py, create_snp_matrix.py, create_snp_reference.py)
to skip processing steps when result files already exist and are newer than the input
files. If you modify an upstream file, any dependent downstream files will be rebuilt.
You can force processing regardless of file timestamps with the ``-f`` option.
Similar functionality for the shell scripts was previously implemented in release 0.2.0.
* Mirrored input files: The run_snp_pipeline.sh script has the capability to make a mirrored copy
of the input reference and samples to avoid polluting a clean repository. You have the
choice to create copies, soft links, or hard links. See :ref:`mirrored-input-label`.
* Configuration file: Added the capability to customize the behavior of the SNP Pipeline by specifying parameters
either in a configuration file, or in environment variables. You can create a configuration
file with default values pre-set by executing ``copy_snppipeline_data.py configurationFile``
from the command line. Pass the configuration file to the run_snp_pipeline.sh script with
the ``-c`` option. Alternatively, environment variables matching the names of the
parameters in the configuration file can be manually set (be sure to export the variables).
When the run_snp_pipeline.sh script is run, it copies the configuration file for the run into
the log directory for the run. See :ref:`configuration-label`.
* Removed the ``-p INT`` command line option, to specify the number of cpu cores, from the
alignSampleToReference.sh script. You can now control the number of cpu cores used by bowtie2
with the ``-p INT`` option either in the configuration file when running run_snp_pipeline.sh, or
in the ``Bowtie2Align_ExtraParams`` environment variable when running alignSampleToReference.sh
directly. If not specified, it defaults to 8 cpu cores on a HPC cluster, or all cpu cores on
a workstation.
* Removed the ``--min-var-freq 0.90`` varscan mpileup2snp option from the prepSamples.sh script.
This parameter is now specified in the ``VarscanMpileup2snp_ExtraParams`` environment variable
or in the configuration file.
* Listeria monocytogenes data set: Added a Listeria monocytogenes data set. Updated the usage instructions, illustrating
how to download the Listeria samples from NCBI and how to run the SNP Pipeline on the
Listeria data set. The distribution includes the expected result files for the Listeria
data set. Note that due to the large file sizes, the Listeria expected results data set
does not contain all the intermediate output files.
* Added a command reference page to the documentation. See :ref:`cmd-ref-label`.

~~~~~~~~~~~~~~~~~~

**Bug fixes:**

* Version 0.2.0 was missing the Agona data files in the Python distribution. The
GitHub repo was fine. The missing files only impacted PyPi. Add the Agona
data files to the Python distribution file list.

~~~~~~~~~~~~~~~~~~

**Changes Impacting Results:**

* Previously, the pipeline executed SAMtools mpileup twice -- the first pileup across
the whole genome, and the second pileup restricted to those positions where snps
were identified by varscan in *any* of the samples. This release removes the
second SAMtools pileup, and generates the snp pileup file by simply extracting a
subset of the pileup records from the genome-wide pileup at the positions where
variants were found in *any* sample. The consequence of this change is faster run
times, but also an improvement to the results -- there will be fewer missing
values in the snp matrix.
* Changed the the supplied lambda virus expected results data set to match the
results obtained with the pipeline enhancements in this release and now using SAMtools
version 0.1.19. SAMtools mpileup version 0.1.19 excludes read bases with low quality.
As a reminder, the expected results files are fetched with the copy_snppipeline_data.py
script.
* Removed the "<unknown description>" from the snp matrix fasta file.

**Other Changes:**

*Note the loss of backward compatibilty for existing workflows using prepReference.sh,
alignSampleToReference.sh, prepSamples.sh, create_snp_matrix.py*

* Split the create_snp_matrix script into 4 smaller scripts to simplify the code
and improve performance when processing many samples in parallel. Refer to the
:ref:`usage-label` section for the revised step-by-step usage instructions. The
rewritten python scripts emit their version number, arguments, run timestamps,
and other diagnostic information to stdout.
* Changed the default name of the reads.pileup file to reads.snp.pileup. You can
override this on the command line of the create_snp_pileup.py script.
* Added the referenceSNP.fasta file to the supplied lambda virus expected results
data set.
* Updated the usage instructions, illustrating how to download the Agona samples from
NCBI and how to run the SNP Pipeline on the Agona data set.
* Updated the supplied expected result files for the Agona data set. Note that due to
the large file sizes, the Agona expected results data set does not contain all
the intermediate output files.
* Improved the online help (usage) for all scripts.
* The copy_snppipeline_data.py script handles existing destination directories more
sensibly now. The example data is copied into the destination directory if the directory
already exists. Otherwise the destination directory is created and the example data
files are copied there.
* Changed the alignSampleToReference.sh script to specify the number of CPU cores with
the -p flag, rather than a positional argument. By default, all CPU cores are
utilized during the alignment.
* Changed the shell scripts (prepReference.sh, alignSampleToReference.sh, prepSamples.sh)
to expect the full file name of the reference including the fasta extension, if any.
* Changed the shell scripts (prepReference.sh, alignSampleToReference.sh, prepSamples.sh)
to skip processing steps when result files already exist and are newer than the input
files. If you modify an upstream file, any dependent downstream files will be rebuilt.
You can force processing regardless of file timestamps with the ``-f`` option.
* Changed the name of the sorted bam file to reads.sorted.bam.
* Changed the general-case usage instructions to handle a variety of fastq file
extensions (\*.fastq\* and \*.fq\*).

~~~~~~~~~~~~~~~~~~

**Bug fixes:**

* The snp list, snp matrix, and referenceSNP files were incorrectly sorted by
position alphabetically, not numerically.
* The SNP Pipeline produced slightly different pileups each time we ran the pipeline.
Often we noticed two adjacent read-bases swapped in the pileup files. This was
caused by utilizing multiple CPU cores during the bowtie alignment. The output
records in the SAM file were written in non-deterministic order when bowtie ran
with multiple concurrent threads. Fixed by adding the ``--reorder`` option to the
bowtie alignment command line.
* The snp list was written to the wrong file path when the main working directory
was not specified with a trailing slash.

**Other Changes:**

*Note the loss of backward compatibilty for existing workflows using prepSamples.sh*

* Moved the bowtie alignment to a new script, alignSampleToReference.sh, for
better control of CPU core utilization when running in HPC environment.
* Changed the prepSamples.sh calling convention to take the sample directory,
not the sample files.
* prepSamples.sh uses the CLASSPATH environment variable to locate VarScan.jar.
* Changed prepReference.sh to run ``samtools faidx`` on the reference. This
prevents errors later when multiple samtools mpileup processes run concurrently.
When the faidx file does not already exist, multiple samtools mpileup processes
could interfere with each other by attempting to create it at the same time.
* Added the intermediate lambda virus result files (\*.sam, \*.pileup, \*.vcf) to the
distribution to help test the installation and functionality.
* Changed the usage instructions to make use of all CPU cores.
* Log the executed commands (bowtie, samtools, varscan) with all options to stdout.

~~~~~~~~~~~~~~~~~~

* Basic functionality implemented.
* Lambda virus tests created and pass.
* S. Agona tests created -- UNDER DEVELOPMENT
* Installs properly from PyPI.
* Documentation available at ReadTheDocs.