cbcrg/paraMSA

Name: paraMSA

Owner: Notredame Lab

Description: Using uncertainty in multiple sequence alignments for phylogenetic trees and bootstrap support values

Forked from: skptic/paraMSA

Created: 2016-09-28 14:12:58.0

Updated: 2016-09-28 14:13:17.0

Pushed: 2016-09-28 14:36:00.0

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README

paraMSA

paraMSA incorporates the information from uncertainty in multiple sequence alignments into the construction of phylogenetic trees and the bootstrap support values. The application generates, combines and evaluates alternative multiple sequence alignments to provide more accurate phylogenetic trees and more descriminative support values.

Alternative alignments are generated through use of different guide trees, alignment orders and gap penalties using the CLUSTALW, MAFFT and PRANK progressive aligners. The alternative alignments are then applied to phylogenetic reconstruction through generation of phylogenetic trees and support trees.

A new phylogenetic tree support value termed the 'parastrap' is presented. The support value is derived from any number of alternative alignments which are in turn bootstrapped using the traditional bootstrap method to create parastrap replicates. Each parastrap replicate can then be used to create a create a replicate tree. Tree and node evaluation versus a known phylogenetic tree is performed using the T-Coffee seq-reformat tools.

Schematic Outline

alt tag

You can find the Nextflow generated directed acyclic graph here

Quick start

Make sure you have all 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/paraMSA

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.

Pipeline parameters
--seq

Example:

$ nextflow run skptic/paraMSA --seq '/home/my_dataset/*.fasta'

This will handle each fasta file as a seperate alignment/tree/bootstrap support

--ref

Example:

$ nextflow run skptic/paraMSA --ref '/home/my_dataset/referenceTree.nwk'
--aligner --alignments

Example:

$ nextflow run skptic/paraMSA --alignments '32'
--straps

Example:

$ nextflow run skptic/paraMSA --straps '100'

NOTE: The total number of parastrap replicates generated is equal to the number of alternative alignments multiplied by the number of bootstraps. Given the defaults of 16 alginments and 16 bootstraps then 256 support replicates/trees are generated.

--alignments_list

Example:

$ nextflow run skptic/paraMSA --alignments_list '1,10,50,100'
--straps_list

Example:

$ nextflow run skptic/paraMSA --alignments_list '1,10,50,100'
--output

Example:

$ nextflow run skptic/paraMSA --output /home/user/my_results
--name

Example:

$ nextflow run skptic/paraMSA --name `my_run_001`
Cluster support

concTree-NF execution relies on Nextflow framework which provides an abstraction between the pipeline functional logic and the underlying processing system.

Thus it is possible to execute it on your computer or any cluster resource manager without modifying it.

Currently the following platforms are supported:

By default the pipeline is parallelized by spanning multiple threads in the machine where the script is launched.

To submit the execution to a SGE cluster create a file named nextflow.config, in the directory where the pipeline is going to be launched, with the following content:

process {
  executor='sge'
  queue='<your queue name>'
}

In doing that, tasks will be executed through the qsub SGE command, and so your pipeline will behave like any other SGE job script, with the benefit that Nextflow will automatically and transparently manage the tasks synchronisation, file(s) staging/un-staging, etc.

Alternatively the same declaration can be defined in the file $HOME/.nextflow/config.

To lean more about the avaible settings and the configuration file read the Nextflow documentation.

Dependencies
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.