Can I automate CLUSTALO and output alignment sequence identity?

Question

I've detected homology between targets of ligands in drugbank and proteins in the proteome of a pathogen. I've parsed the output very rudimentary and calculated my query coverage. This exists in an excel file (see attached)

I'd like to further filter these hits. For query coverages > 70% alone I have 1500+ hits. Some redundancy exists and some hits are for drugbank targets for which no data has been deposited in the PDB.

So there are ways of further filtering these hits, but I'd like to do pairwise alignments first before I do anything else, as I think this is the most rational filter I can think of.

I do not want to filter based on lack of PDB data necessarily, as when I check my high query coverage hits, I'm getting predictions that are actually backed up by in vitro data. This includes high coverage hits lacking x-ray data

My question:

I have CLUSTALO installed on my desktop: How do programmatically send it jobs if I have the Drugbank sequences and the pathogen sequences in the same file? I would also like to parse out the pathogen IDs from the excel file. There’s 1500 and they are a bit scattered throughout the columns.

0.980634         -                    572         sp|P06672|PDC_ZYMMO                         -                     568                               1.2e-263             874.5          0.0            1        1      6.6e-267  1.4e-263   874.2      0.0       5         562    15     570    12     572    0.98   Uncharacterized                         protein                   OS=Lomentospora            prolificans        OX=41688             GN=jhhlp_005678  PE=3             SV=1

0.950704         -                    580         sp|P06672|PDC_ZYMMO                         -                     568                               1.2e-258             858.0          0.0            1        1      6.8e-262  1.5e-258   857.7      0.0       6         546    10     560    7      578    0.98   Uncharacterized                         protein                   OS=Lomentospora            prolificans        OX=41688             GN=jhhlp_005014  PE=3             SV=1

0.950704         -                    691         sp|P06672|PDC_ZYMMO                         -                     568                               8.2e-162             538.3          0.1            1        1      2.4e-164  5.1e-161   535.6      0.1       1         541    89     650    89     658    0.89   Acetolactate                            synthase                  OS=Lomentospora            prolificans        OX=41688             GN=jhhlp_006830  PE=3             SV=1

0.492958         -                    585         sp|P06672|PDC_ZYMMO                         -                     568                               8e-87                290.7          0.0            1        2      3.3e-83   7e-80      267.8      0.0       3         283    2      287    1      292    0.94   Uncharacterized                         protein                   OS=Lomentospora            prolificans        OX=41688             GN=jhhlp_001897  PE=3             SV=1

0.107394         -                    585         sp|P06672|PDC_ZYMMO                         -                     568                               8e-87                290.7          0.0            2        2      1.7e-08   3.6e-05    21.1       0.0       413       474    436    505    376    583    0.72   Uncharacterized                         protein                   OS=Lomentospora            prolificans        OX=41688             GN=jhhlp_001897  PE=3             SV=1

Answer 1

So, as the first reply said, there's sort of multiple questions here.

For filtering things out from a table, I would absolutely use pandas. https://swcarpentry.github.io/python-novice-gapminder/08-data-frames/ Pandas is a way of managing dataframes in python. It looks like what you want from your table is to remove certain rows, if the content of those rows at a certain column matches certain IDs from a list (pathogen IDs). That's the sort of thing the pandas library is good at.

For managing workflows, I use either bash or snakemake. Snakemake is a python thing. It has some advantages, such as being able to start from an intermediary file (for example, if a workflow is "sequences -> multiple alignment -> phylogenetic tree", it can easily start from the multiple alignment). Also deals with branched workflows quite well. Can be easily installed with conda (anaconda is absolutely a lifesaver for bioinformatics work imo). I still use bash for simple workflows, because it's slightly simpler/faster.

For potential filtering - If you want to keep the top hits as they are, but filter the lower-scoring hits based on whether they have a PDB ID or not, I'd personally look into Entrez queries. They're a way of making your search more specific. Here they're shown for WebBlast, but they work quite well for command line blast too. https://www.youtube.com/watch?v=bxx5uaKjMa8

Answer 2

The question is still in numerous parts,

• parsing a blast output
• obtaining sequences
• feeding this as input into clustalo

If I was automating this I would parse the Blast genbank codes [regex either Perl one-liner (scripts aren't trendy ;-( ), or Pythons "re"] and feed them into NCBI's efetch to hook out the sequence data. Then I would create a subprocess that feed the files into clustalo. In Perl you simply issue the linux command wrapped in "`" either side, in Python use

import os
os.system('clustalo -in file.in -out file.out')

Ensuing your 'bin' is correctly configured to execute clustalo

or

import subprocess # use popen

To my knowledge there is no wrapper for clustalo unlike several other phylogenetics programs in BioPython, which enormously simplifies pipelines. Others might recommend using shell rather than e.g. Python.

BTW I personally use Muscle having been a long term user of clustalo, thats a long discussion.

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To be honest you should look at the power of NBCI's online blast utility. The download facilities are certainly powerful particularly if you are pulling down e.g. 1500 sequences from blast hits, because it automatically can splice the hits of interest from their genes/genomes. However, its context specific.