aMoF is a collection of Python scripts developed to to provide a full featured 'motif finder' tool focused on the analysis of APTAMERS selection results. Classical motifs (or kmers) finding tools take as input long bio-sequences like entire genomes or sets of genes. aMoF instead, focuses on sets of short sequences that are the typical results of an aptamer selection experiment. aMoF is meant to be user friendly and "dummy proof" so the code is compatible with both Python2 and Python3 and it relies ONLY on the Python Standard Library.
##Some background info Aptamers are short sequence of nucleotides or aminoacids (DNA, RNA, peptides, circular peptides and small proteins) that bind to a specific target molecule. They can be used for both basic research and clinical purposes as macromolecular drugs or as "atibody-like-particles" for molecular detection. Aptamers are naturally present in living organisms where they often act as molecular switches to regulate many different biological pathways. In molecular biology, there are two main methods to select aptamers: SELEX (for DNA and RNA) and Phage Display (for peptides). Both methods rely on the analysis of sequences to discriminate specifically enriched aptamers from background noise.
To know more about aptamers here are some links ;)
- http://en.wikipedia.org/wiki/Aptamer
- http://en.wikipedia.org/wiki/Riboswitch
- http://en.wikipedia.org/wiki/Phage_display
- http://en.wikipedia.org/wiki/Systematic_Evolution_of_Ligands_by_Exponential_Enrichment
##Disclaimer Since I am not a professional programmer you may find the code structure "not conventional", you are free to comment and suggest improvements.
##Overview and How-To-Use #####aMoF.py It is the core program. It contains the "motifs finder" algorithm and the main variables.
#####seqPrep.py It prepares an appropriate formatted file to serve as input to aMof.py . It reads all the fas files (obtained by sequencing) contained in the folder FASfiles and it extracts each sequence and the relative id and writes them in a file. This program allow the user to better define from which kind of selection experiment the sequences are coming from.
#####aMoF webapp A Flask-based application with a minimalistic user interface for aMoF - In progress at http://beta-amof.rhcloud.com/
You can chose among this type of experiments
- M13KE - p3 N-term display - NEB PhD-12 - Completed!
- M13KE - p3 N-term display - NEB PhD-7 - Completed!
- M13KE - p3 N-term display - NEB PhD-C7C - Completed!
- M13KE - p8 N-term display - custom - Completed!
- T7 Phage Display - In progress...
- Ribosome Display - In progress...
- DNA SELEX - In progress...
- RNA SELEX - In progress...
With this information program will automatically slice out the sequence of interest from the whole sequencing result and it translate it if needed. For example, in case of M13 Phage Display PhD-C7C, a classic sequencing output, using company provided sequencing primers, looks like this:
| original sequencing output |
|---|
| >12826357.seq - ID: CB8 - Phage Display - PhDC7C - Acid Elution 3 - Plate 2_A01-Amply M13 pIII CS - R on 2014/1/16-0:53:50 automatically edited with PhredPhrap, start with base no.: 41 Internal Params: Windowsize: 20, Goodqual: 19, Badqual: 10, Minseqlength: 50, nbadelimit: 1 |
| agTTTTgTCGTCTTTcCagACGTTAGTAAATGAATTTTCtGtAtGGgattTTGCTAAACAACTTTCAACAGTTTCGGCCGAACCTCCACCGCACTCATAAGGCGAACCAGTATTACAAGCAGAGTGAGAATAGAAAGGTACCACTAAAGGAATTGCGAATAATAATTTTTTCACGTTGAAAATCTCCAAAAAAAAGGCTCCAAAAGGAGCCTTTAATTGTATCGGTTTATCAGCTTGCTTTCGAGGTGAATTTCTTAAACAGCTTGATACCGATAGTTGCGCCgACnATGACAACAACCATCGCCCACGCATAACCGATATATTCGGTCGCTGAGGCTTGCAGGGAGTTAAAGGCCGCTTTTGCGGGATCGTCACCCTCAGCAGCGAaagAcAGCATCGGAACGAGGGTAGCAACGGCTACAGAGGCTTTGAGGACTAAAGACTTTTTCATGAGGAAGTTTCCATTAAACGGGTAAAATACgTAATGCCACTACGAAGGCACCaaCCTAAAACgAaagAGGCAaaanAATACactAaaACACTCATCTTTGACCCCCAgcGATTATACCAAGCGCGAAACAAAGtACAACGGAGATTTGTATCATCGCCTGATAAATTGTGTCgAAnTCCGCGAcCTGCTCCATGTTACTTAgCCGGAACGAGGCGCAGACGGnCAATcannnnGGAaCCGAaCTGACCaACtTTGAAAgAggacnnAngAaCGGTGTACAGACCAGGCGCATAGGCTGGCTGAnCtTtCaTcaAnAgtAnnctTGAcAAGAACCGGATATTCATTAcCCnAAnTCaacGtACnaAGCTnGCtCaTTccannGAAnnAAGGctTGcCCTGAcgAgAAACacCananngAgtAGTAAATTGGgcTTtGAgATGgnnttattTCaaCTTTaatCaTTGTGAATTACcttntGCgAtTTnanAAcTGGCTcatta |
| after using seqPrep.py |
|---|
| >A01 |
| NTGSPYE |
Note that the repeated sequence at N-ter (AC) and C-term (CGGGS), distinctive of any PhD-C7C sequence, has been sliced out to exclude them from future statistical analysis