A transmembrane helix finder.
Project description
Introduction
pyTMHMM is a Python 3.5+ implementation of the transmembrane helix predictor using a hidden Markov model (TMHMM) originally described in:
E.L. Sonnhammer, G. von Heijne, and A. Krogh. A hidden Markov model for predicting transmembrane helices in protein sequences. In J. Glasgow, T. Littlejohn, F. Major, R. Lathrop, D. Sankoff, and C. Sensen, editors, Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pages 175-182, Menlo Park, CA, 1998. AAAI Press. PMID 9783223
Why?
Dan Søndergaard is the original author of this package and his repository is now archived. Dan wrote this code for a few reasons:
- the source code is not available as part of the publication
- the downloadable binaries are Linux-only
- the downloadable binaries may not be redistributed, so it's not possible to put them in a Docker image or a VM for other people to use
- the need to predict transmembrane helices on a large dataset, which rules out the Web service
This Python implementation includes a parser for the undocumented file format used to describe the model and a fast Cython implementation of the Viterbi algorithm used to perform the annotation. The tool will output files similar to the files produced by the original TMHMM implementation.
Incompatibilities
- The original TMHMM implementation handles ambigious characters and gaps in an
undocumented way. However,
pyTMHMM
does not attempt to handle such characters at all and will fail. A possible fix is to replace those characters with something also based on expert/domain knowledge.
Installation
This package supports Python 3.5 or greater. Install with:
$ pip install pyTMHMM
Usage
$ pyTMHMM -h
usage: pyTMHMM [-h] -f SEQUENCE_FILE [-m MODEL_FILE] [-p]
optional arguments:
-h, --help show this help message and exit
-f SEQUENCE_FILE, --file SEQUENCE_FILE
path to file in fasta format with sequences
-m MODEL_FILE, --model MODEL_FILE
path to the model to use
-p, --plot plot posterior probabilies
The -p
/--plot
option will only be available if matplotlib
is installed
and importable.
Say we have the following sequence in FASTA format in a file called test.fa
:
>B9DFX7|1B|HMA8_ARATH Copper-transporting ATPase PAA2, chloroplastic [Arabidopsis thaliana ]
MASNLLRFPLPPPSSLHIRPSKFLVNRCFPRLRRSRIRRHCSRPFFLVSNSVEISTQSFESTESSIESVKSITSDTPIL
LDVSGMMCGGCVARVKSVLMSDDRVASAVVNMLTETAAVKFKPEVEVTADTAESLAKRLTESGFEAKRRVSGMGVAENV
KKWKEMVSKKEDLLVKSRNRVAFAWTLVALCCGSHTSHILHSLGIHIAHGGIWDLLHNSYVKGGLAVGALLGPGRELLF
DGIKAFGKRSPNMNSLVGLGSMAAFSISLISLVNPELEWDASFFDEPVMLLGFVLLGRSLEERAKLQASTDMNELLSLI
STQSRLVITSSDNNTPVDSVLSSDSICINVSVDDIRVGDSLLVLPGETFPVDGSVLAGRSVVDESMLTGESLPVFKEEG
CSVSAGTINWDGPLRIKASSTGSNSTISKIVRMVEDAQGNAAPVQRLADAIAGPFVYTIMSLSAMTFAFWYYVGSHIFP
DVLLNDIAGPDGDALALSLKLAVDVLVVSCPCALGLATPTAILIGTSLGAKRGYLIRGGDVLERLASIDCVALDKTGTL
TEGRPVVSGVASLGYEEQEVLKMAAAVEKTATHPIAKAIVNEAESLNLKTPETRGQLTEPGFGTLAEIDGRFVAVGSLE
WVSDRFLKKNDSSDMVKLESLLDHKLSNTSSTSRYSKTVVYVGREGEGIIGAIAISDCLRQDAEFTVARLQEKGIKTVL
LSGDREGAVATVAKNVGIKSESTNYSLSPEKKFEFISNLQSSGHRVAMVGDGINDAPSLAQADVGIALKIEAQENAASN
AASVILVRNKLSHVVDALSLAQATMSKVYQNLAWAIAYNVISIPIAAGVLLPQYDFAMTPSLSGGLMALSSIFVVSNSL
LLQLHKSETSKNSL
We can then run pyTMHMM
on this file using the following command:
$ pyTMHMM -f test.fa
This produces three files. One is the summary:
$ cat B9DFX7|1B|HMA8_ARATH.summary
0-444: outside
445-467: transmembrane helix
468-820: inside
821-843: transmembrane helix
844-852: outside
853-870: transmembrane helix
871-882: inside
An annotation in FASTA format:
$ cat B9DFX7|1B|HMA8_ARATH.annotation
>B9DFX7|1B|HMA8_ARATH Copper-transporting ATPase PAA2, chloroplastic [Arabidopsis thaliana ]
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOMMMMMMMMMMMMMMMMMMMMMMMiiiiii
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
iiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiMMMMMMMMMMMMMMMMMMMMMMMoooooooooMMMMMMMMMMMMMMMM
MMiiiiiiiiiiii
And finally a file containing the posterior probabilities for each label for plotting.
$ cat B9DFX7|1B|HMA8_ARATH.plot
inside membrane outside
0.20341044516 0.0 0.79658955484
0.210104176071 2.77194446172e-08 0.78989579621
0.189291062167 3.11365191554e-08 0.810708906697
0.253334801857 7.17866017044e-07 0.746664480277
0.126185012808 1.34197873962e-05 0.873801567405
...
If the -p
flag is set a plot in PDF format will also be produced, following
the same naming scheme as the other output files.
API
You can also use pyTMHMM
as a library:
import pyTMHMM
annotation, posterior = pyTMHMM.predict(sequence_string)
This returns the annotation as a string and the posterior probabilities for
each label as a numpy array with shape (len(sequence), 3)
where column 0, 1
and 2 corresponds to being inside, transmembrane and outside, respectively.
If you don't need the posterior probabilities set compute_posterior=False
,
this will save computation:
annotation = pyTMHMM.predict(
sequence_string, compute_posterior=False
)
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