Phylogenetic analysis for large size fasta sequences
2
0
Entering edit mode
6.5 years ago
MAPK ★ 2.1k

I am trying to run phylogenetic analysis for maximum likelihood tree. I have a large dataset (fasta sequences) which is just impossible to run in Mega. Is there a better option that I can resort to so I could use as many computing resources as I want. I need to use a minimum of 500gb ram to do this analysis and I was thinking to do this in HPC cluster. Any suggestion on the tool with multi-threading option would be really appreciated.

phylogenetics • 4.1k views
ADD COMMENT
1
Entering edit mode

RaxML ? not sure on the multithreaded though

ADD REPLY
0
Entering edit mode

Muscle, MAFTT, T-Coffee should all be good alternatives assuming you have access to necessary hardware.

Edit: For step one. MSA.

ADD REPLY
0
Entering edit mode

Aren't those designed for sequence alignment?

ADD REPLY
0
Entering edit mode

Creating alignments for very large sequence datasets can be computationally very challenging and that is where MEGA could be struggling. Take a look at this publication to have a new perspective on this topic.

ADD REPLY
0
Entering edit mode

Alignment was rather easy. Mega was struggling with model testing steps and bootstrapping during maximum likelihood analysis.

ADD REPLY
0
Entering edit mode

you can use https://github.com/stamatak/standard-RAxML it works fine for me

ADD REPLY
0
Entering edit mode

Any new tools for this issue?

ADD REPLY
1
Entering edit mode

I recommend IQ-TREE2, which is very fast and you can use AUTO option that IQ-TREE automatically detects how many threats are needed and needs to be use.

ADD REPLY
1
Entering edit mode
4.3 years ago
Shalu Jhanwar ▴ 540

For phylogenetic analysis, MrBayes (http://nbisweden.github.io/MrBayes/) and FastTree (http://www.microbesonline.org/fasttree/#OpenMP) both supports multi-threading/parallelization.

ADD COMMENT
0
Entering edit mode
6.5 years ago
h.mon 35k

ExaML, RAxML-NG or RAxML (from faster to slower). All three are from the same group, and all three support MPI parallelization, making them suitable to HPC clusters.

ADD COMMENT

Login before adding your answer.

Traffic: 2291 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6