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README.markdown
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README.markdown

inmembrane

inmembrane is a pipeline for proteome annotation to predict if a protein is exposed on the surface of a bacteria.

Installation and Configuration

The .zip package of the latest version of inmembrane is found at https://github.com/boscoh/inmembrane/zipball/master.

This includes examples, data files, docs and a few included libraries (Beautiful Soup and twill.

The parameters and locations for the dependencies of inmembrane are held in a file called inmembrane.config, which is located in the same directory as the main script. The inmemmbrane.py script will always look for inmembrane.config there.

If inmembrane.config is not found, running inmembrane.py with no arguments will generate a default inmembrane.config. Edit the locations of the binaries in the config file to match your system. The options are:

  • the full path location of the binaries for SignalP, LipoP, TMHMM, HMMSEARCH, and MEMSAT.
  • 'organsim' indicates the type of organism - Gram+, Gram- are supported so far
  • 'hmm_profiles_dir': the location of the HMM profiles for peptidoglycan binding motifs for Gram+
  • for HMM, you can set the cutoffs for significance, the E-value 'hmm_evalue_max', and the score 'hmm_score_min'
  • the shortest length of a loop that sticks out of the peptidoglycan layer of a Gram+ bacteria. The SurfG+ determined this to be 50 amino acids for terminal loops, and twice that for internal loops, 100
  • 'helix_programs' you can choose which of the transmembrane-helix prediction programs you want to use

We provide a number of unit tests for inmembrane. As inmembrane has a lot of dependencies, these tests may help to diagnose many possible dependency issues:

 python runtest.py

Execution

inmembrane was written in Python 2.7. It takes a FASTA input file and runs a number of external bioinformatic analysis programs on the sequences. It then collects the output to make the final analysis. It can be run in two modes.

It can be run as a command-line program in the inmembrane directory:

python inmembrane.py your_fasta_file

The other way of running imembrane.py is with a custom script, such as run_example.py. If you open it with a text editor, you will see that the input file and the output file are filled in. The output file will be created for you. You can run it on the command-line:

python run_example.py

Or easier still, on most systems with Python installed, you simply double-click it in a file-manager.

Output format

Given that this is a glue script that runs a lot of programs, there will be many places for error, and as such, to aid in debugging, we print out all the commands run in the script. These lines will be preceeded by the hash # character so that it can easily be commented out.

The output is CSV compatible. You can open it with EXCEL. It is also designed to be whitespace delineated. For instance, if no feature of interest is found for a sequence, then a '.' is put in the third column. The full name, which has variable number of tokens is put at the end. Here's an example:

SPy_0008 , CYTOPLASM    , .                  , "SPy_0008 from AE004092"
SPy_0009 , CYTOPLASM    , .                  , "SPy_0009 from AE004092"
SPy_0010 , PSE          , tmhmm(1)           , "SPy_0010 from AE004092"
SPy_0012 , PSE          , hmm(GW1);signalp   , "SPy_0012 from AE004092"

The first column is the SeqID which is the first token in the identifier line of the sequence in the FASTA file

The second column is the prediction, it is CYTOPLASM, MEMBRANE, PSE, or SECRETED.

The third line is a summary of features picked up by the other program:

  • tmhmm(2) means 2 transmembrane helices were found
  • hmm(GW1) means the GW1 motif was found in HMMER
  • signalp means a secretion signal was found
  • lipop means a Sp II secretion signal found with an appropriate CYS residue at the cleavage site, which will be attached to a phospholipid in the membrane

The rest of the line gives the full name as given in the rest of the identifier line in the FASTA file. This is enclosed by quotation marks "" for CSV compatability.

Installing dependencies

As it is the nature of bioinformatic programs that they are changed and updated severely with different versions, and that stable APIs and output formats are the exception rather than the norm. It is very important that you have the exact version that we have programmed against.

Required dependencies, and their versions:

  • TMHMM 2.0 or MEMSAT3
  • SignalP 4.0
  • LipoP 1.0
  • HMMER 3.0

These instructions have been tailored for Debian-based systems, in particular Ubuntu 11.10. Each of these dependencies are licensed free to academic users.

TMHMM 2.0

Only one of TMHMM or MEMSAT3 are required, but users that want to compare transmembrane segment predictions can install both.

SignalP 4.0

HMMER 3.0

  • Download HMMER 3.0 from http://hmmer.janelia.org/software.
  • The HMMER user guide describes how to install it. For the pre-compiled packages, this is as simple as putting the binaries on your PATH.

LipoP 1.0

MEMSAT3

Note the the 'runmemsat' script refers to PSIPRED v2, but it means MEMSAT3 - PSIPRED is not required.

Modification guide

As inmembrane is a glue script that runs a lot of external dependencies, we have tried to make the parsing code for each program as concise as possible. The program is written to use as much of the standard Python library as possible, and specifically uses native data structures which provide an incredible amount of flexibility. Plugins that wraps an individual external program or web service can be found in the inmembrane/plugins directory. Protocols which embody a particular high level workflow are found in inmembrane/protocols.

It is a fact of life for bioinformatics that new versions of basic tools changes output formats and API. We believe that it is an essential skill to rewrite parsers to handle the subtle but significant changes in different versions. Hopefully inmembrane makes this as simple as possible.

inmembrane development style guide:

Here are some guidelines for understanding and extending the code.

  • Confidence: Plugins that wrap an external program should have at least one high level test which is executed by run_tests.py. This allows new users to immediately determine if their dependencies are operating as expected.
  • Interface: A plugin that wraps an external program must receive a params data structure (derived from inmembrane.config) and a proteins data structure (which is a dictionary keyed by sequence id). Plugins should return a 'proteins' object.
  • Flexibility: Plugins should have a 'force' boolean argument that will force the analysis to re-run and overwrite output files.
  • Efficiency: All plugins should write an output file which is read upon invocation to avoid the analysis being re-run.
  • Documentation: A plugin must have a Python docstring describing what it does, what parameters it requires in the params dictionary and what it adds to the proteins data structure. See the code for examples.
  • Anal: Unique sequence ID strings (eg gi|1234567) are called 'seqid'. 'name' is ambiguous. 'prot_id' is reasonable, however conceptually a 'protein' is not the same thing as a string that represents it's 'sequence' - hence the preference for 'seqid'.
  • Anal: All file handles should be closed when they are no longer needed.
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