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A bioinformatics pipeline for estimation of relative cell periods.

Project Description
PTR Extraction Software

This bundle of software is a basic implementation of the algorithm for
extracting Peak-to-Trough Ratios from Metagenomic data, as first
described in `(Korem et. al, Science,
2015) <>`__.


Below follows a description of the main scripts in the package.


A submit script for sending a batch job to slurm for parallel processing
on a computing cluster.

**input:** none

**output:** directory structure as specified in "project.conf"

The main build script with commands intended to be executed on the

**input:** none

**output:** temporary paths and files on compute nodes

Traverses the specified directory generated by and
assembles information from each sample into tabular form (eg. averages
origin locations from many samples for a better estimate).

**input:** $OUTPUT\_PATH, $DORIC\_PATH, $REF\_PATH, bin/accLoc.csv

**output:** Abundance.csv, PTR.csv, DoublingTime.csv, Header.csv

Implements the piecewise linear fit and prior checks on the generated
depth files to filter out those instances in which enough data was
generated to produce a reliable coverage signal for estimating
replication origins. This data can be used further on, once those has
been estimated using the full cohort, to produce PTR-vaules for each

**input:** {reference.depth}

**output:** {reference\_OriC.npy}, {reference\_TerC.npy},
{reference\_coverage.png}, {reference\_fit.log}

This utility can be used to download '.fasta' reference files from the
NCBI servers.

**input:** searchStrings.txt,

**output:** {reference.fasta}, {reference.xml}, taxIDs.txt


Make sure that "pip" is the PyPi command of your *python2* installation,


.. code:: bash

git clone
cd PTR-pipeline
pip3 install --user requirements3.txt
pip install --user requirements.txt

This should install the below *python* dependencies. The other
dependencies have to be installed manually (if you have questions about
this I suggest you consult your cluster IT help desk).

Note that this software uses python3 for tasks executed by the user
("./") and python2 for tasks running on the cluster.

The software has been tested on the "hebbe" cluster at
`C3SE <>`__ which uses the "slurm" system for resource
management (thus slurm is the only queueing system currently supported).





`samtools <>`__

`bamtools <>`__

`bowtie2 <>`__

2.0 <>`__
(should be installed by the above pip command but make sure 'pathoscope
ID' is accessible in the shell, ie. is on the system path)

`parallel <>`__

`DoriC <>`__ is a databse of
chromosome origin locations (OriCs) which is a (recommended) optional
dependency for the pipeline. Please visit the link and enter your e-mail
to download.


1. Identify a set of NCBI genome reference accession numbers and put
them in "./searchStrings" (or use the default one which includes a
*minimal* set of references to bacteria common in the human gut).

2. Identify a metagenomic cohort of interest (download manually or add
URLs as described below). Supported input: raw/gzipped/bzipped
".fastq" files.

3. Add information to the ``project.conf`` file.

4. Edit ``bin/`` to include the **python2** module of the
cluster (or comment out the lines if python2 is accessible by

5. Load **python3** in your submit environment. Run
``./ full`` (use "nohup {cmd} &" to keep alive after

6. Wait for job to complete. Run ``./ collect``.


The script is a common utility for all parts of the
pipeline including downloading of references and metagenomic data,
bulding a reference index, setting up the necessary file structure and
submitting to slurm. Hence, all configuration is intended to be set up
in project.conf (please see ``bin/project.conf.example`` for an

The default 'searchStrings' will most probably not fit your purposes but
is only an example. A more comprehensive Reference library will yield
higher coverage and better PTR values. A more comprehensive list of
human gut bacteria is available at'extra/referenceACClong.txt'.

Note that ``./`` is python3 while the actual pipeline
scripts (running on the cluster) are python2. Be sure to install
pathoscope for your python2 environment.

Usage (detailed)

This is a usage without ````.

1 Update and

Most cluster resources load software as modules. Update
and make sure you have the correct module names for python with numpy
and scipy loaded.

2 Download Metagenomic data to your cluster.

Ex. Download sample ERR525688-ERR525787 of cohort ERR525 using four wget

.. code:: bash

let e="787";let s="688";let n="($e-$s+1)/5-1";
seq $s $e | parallel -j 4 wget -r --no-parent -P $DATA_PATH $COHORT_URL/ERR525{}/

3 Download genome references.

Ex. Use '' to download genome references (specified in
referenceACC.txt) as '.fasta' files from NCBI. Use "-t" to also download
taxonomy information used in later steps.

.. code:: bash

./ -e $EMAIL -t True -s extra/referenceACC.txt -d RefSeq

Alternatively download directly from the `ftp
site <>`__.

4 Build bowtie2 index.

Before building the index, taxonomoy information must be added to the
reference files (this is used by Pathoscope for strain specific read

.. code:: bash

while IFS=$'\t' read -r -a arr
sed -i "" "s/^>gi/>ti|${arr[1]}|gi/" "${REF_PATH}/Fasta/${arr[0]}.fasta"
done < ${REF_PATH}/taxIDs.txt

Now build the index:

.. code:: bash

mkdir ${REF_PATH}/Index
bowtie2-build --large-index $(ls -1 --color=none ${REF_PATH}/Fasta | tr '\n' ',') ${REF_PATH}/Index/${REF_NAME}

6 Submit jobscript to cluster

Please note that sbatch *must* be called from within the PTR-Pipeline

.. code:: bash

sbatch --array=0-$n jobscript

Logs are output to
':math:`SCRIPT_PATH/*.stderr' and '`\ SCRIPT\_PATH/\*.stdout'.

7 Collect the output data into dataframes

.. code:: bash


Directory structure (*example*)

With the above usage example the path structure(s) will look something
like below.


├ "Sample01" (eg. ERR525688)
. ├ {sample01_1.fastq.gz}
. └ {sample01_2.fastq.gz} paired metagenomic reads

├ Index
| └ {REF_NAME.*.bt2l} bowtie2 index files
├ Fasta
| └ {accession.fasta}
├ Headers
| └ {accession.xml} xml files containing extra genome references info
└ taxIDs.txt

├ bacteria_record.dat
└ bacteria_seq.fas

├ "Sample01"
. ├ depth
. | └ {accession.depth} coverage files for each reference
. ├ log
| └ {accession.log} output logs from piecewiseFit
├ npy
| └ {accession_OriC_TerC.npy} numpy files with origin/terminus locations
├ png
| └ {accession_fit.png} images of piecewise fit of the smoothed coverage
└ accession-sam-report.tsv Pathoscope2 reassignment report
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