BioContainer helper

BioContainer helper

DSLs for executing commands in the BLAST biocontainer Generated and tested for the tag blast:2.2.31--pl526h3066fca_3

Docker helper

HMMER - Biological sequence analysis using profile hidden Markov models (http://hmmer.org/) Functions providing an API for the HMMER 3 biocontainer

!! Currently many parameters are not implemented!

From the Last docs at http://last.cbrc.jp:

LAST finds similar regions between sequences.

LAST can:
 
Handle big sequence data, e.g:

     - Compare two vertebrate genomes

     - Align billions of DNA reads to a genome

Indicate the reliability of each aligned column.

Use sequence quality data properly.

Compare DNA to proteins, with frameshifts.

Compare PSSMs to sequences

Calculate the likelihood of chance similarities between random sequences.

Do split and spliced alignment.

Train alignment parameters for unusual kinds of sequence (e.g. nanopore).

moFF is an OS independent tool designed to extract apex MS1 intensity using a set of identified MS2 peptides. It currently uses a Thermo library to directly extract data from Thermo Raw spectrum files,
eliminating the need for conversions from other formats. Moreover, moFF also allows to work directly with mzML files.
Input Data: moFF requires two types of input for the quantification procedure
Thermo RAW file or mzML file -and- MS2 identified peptide information.
The MS2 identified peptides can be presented as a tab-delimited file containing mimimal (mandatory) annotation for each peptide.
The tab-delimited file must contain the following information for all the peptides:
'peptide' : peptide-spectrum-match sequence
'prot' : protein ID
'mod_peptide' : peptide-spectrum-match sequence that contains also possible modification (i.e NH2-MLTKFESK-COOH )
'rt': peptide-spectrum-match retention time (i.e the retention time contained in the mgf file; The retention time must be specified in second)
'mz' : mass over charge
'mass' : mass of the peptide
'charge' : charge of the ionized peptide.
 ------ Argentini et al. Nature Methods. 2016 12(13):964–966.

TMHMM 2.0c predicts transmembrane helices in proteins