CODEWITHSUNDEEP
pythonpandasdataframebioinformaticsbiopython
Cody Glickman
Cody Glickman

Reputation: 524

Convert a hmmer --tblout output to a pandas dataframe

  1. Is there a way to convert a hmmer output to a pandas dataframe?
  2. I am also unsure how to load a hmmer tblout table into python via the Bio module.

I believe you can call a hmmer format with SeqIO.parse or SeqIO.search.The format of the table appears tab separated however it seems to be a collection of random spaces meaning if I remove the headers and # leaving only the table information there is not easy way to split the table using a tab separator.

A small example of a hmmer --tblout file is below:

#                                                                                       --- full sequence ---- --- best 1 domain ---- --- domain number estimation ----
# target name        accession  query name                                   accession    E-value  score  bias   E-value  score  bias   exp reg clu  ov env dom rep inc description of target
#------------------- ----------                         -------------------- ---------- --------- ------ ----- --------- ------ -----   --- --- --- --- --- --- --- --- ---------------------
3300000568@Draft_10015026@Draft_1001502652 -          Bacteria_NODE_1_length_628658_cov_8.291329_24 -            7.1e-07   29.3   0.0   1.9e-05   24.6   0.0   2.0   1   1   1   2   2   2   2 -
7000000546@SRS019910_WUGC_scaffold_3948@SRS019910_WUGC_scaffold_3948_gene_2890 -          Bacteria_NODE_1_length_628658_cov_8.291329_53 -            1.6e-07   31.7   0.0   0.00051   20.3   0.0   2.2   2   0   0   2   2   2   2 -
#
# Program:         hmmscan
# Version:         3.1b2 (February 2015)
# Pipeline mode:   SCAN
# Query file:      ../Exponential_High_Complexity_Simulation.faa
# Target file:     final_list.hmm
# Option settings: hmmscan --tblout Exponential_Earth.txt -E 1e-5 --cpu 8 final_list.hmm ../Exponential_High_Complexity_Simulation.faa 
# Current dir:     /Strong/home/glickmanc/Programs/EarthVirome
# Date:            Mon Feb 24 10:47:51 2020
# [ok]

Upvotes: 4

Views: 1958

Answers (1)

BioGeek
BioGeek

Reputation: 22907

I would build a dictionary from the attributes you are interested in and make a DataFrame from that dictionary. Say you are interested in the attributes of the hits:

from collections import defaultdict
import pandas as pd
from Bio import SearchIO

filename = 'test.hmmer'

attribs = ['accession', 'bias', 'bitscore', 'description', 'cluster_num', 'domain_exp_num',  'domain_included_num', 'domain_obs_num', 'domain_reported_num', 'env_num', 'evalue', 'id', 'overlap_num', 'region_num']

hits = defaultdict(list)

with open(filename) as handle:
    for queryresult in SearchIO.parse(handle, 'hmmer3-tab'):
      #print(queryresult.id)
      #print(queryresult.accession)
      #print(queryresult.description)
      for hit in queryresult.hits:
        for attrib in attribs:
          hits[attrib].append(getattr(hit, attrib))

pd.DataFrame.from_dict(hits)

Upvotes: 5

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