cbcrg/dpa-analysis

Name: dpa-analysis

Owner: Notredame Lab

Description: Nextflow Pipeline for the analysis of Double Progressive Alignment (DPA)

Forked from: skptic/dpa-analysis

Created: 2017-07-11 15:15:13.0

Updated: 2017-07-11 15:15:15.0

Pushed: 2017-07-12 13:51:41.0

Homepage: null

Size: 1854

Language: Python

GitHub Committers

UserMost Recent Commit# Commits

Other Committers

UserEmailMost Recent Commit# Commits

README

dpa-analysis

Quick Start

Make sure you have either docker/singularity installed or the required dependencies listed in the last section.

Install the Nextflow runtime by running the following command:

$ curl -fsSL get.nextflow.io | bash

When done, you can launch the pipeline execution by entering the command shown below:

$ nextflow run skptic/dpa-analysis

By default the pipeline is executed against the provided example dataset. Check the Pipeline parameters section below to see how enter your data on the program command line.

Running Modes

There are 4 running modes which are determined based on the provided input files.

Each mode can be run with the standard MSA proceedure, with the DPA proceesure or with both (see --dpa_align and --std_align).

1: Basic Alignment Mode

Run multiple sequence alignment procedure with/without DPA.

inputs:

outputs:

2: Reference Alignment Mode

Run multiple sequence alignment procedure with/without DPA and score alignment against the reference alignment.

inputs:

outputs:

3: Custom Guide Tree Alignment Mode

Run basic multiple sequence alignment procedure with/without DPA with user provided guide trees in Newick format.

inputs:

outputs:

4: Reference Alignment Mode with Custom Guide Tree

Run basic multiple sequence alignment procedure with/without DPA with user provided guide trees in Newick format and scored against the reference alignment.

inputs:

outputs:

Pipeline parameters
--seqs

Example:

$ nextflow run skptic/dpa-analysis --seqs '/home/seqs/*.fasta'

This will handle each fasta file as a seperate sample.


This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.