Sequence-level differences to consider
- Point mutations
- translational stability (re: codon bias)?
- Deletions (and insertions)
- frame shift
- downstream effects?
- Add more here...
- Coding vs nonCoding seq
- We should be able to determine AUG and stop codons...
- Copy-number variants
- This can be viewed as a type of insertion or deletion.
General coding questions / ideas
- How should we handle file input?
- How should we format the output?
- Generation of a set of test inputs.
- How can the identification of a class of differences be identified with a set of implementable conditions (ie how do you seperate out a duplication or an insertion from a rearrangement.
Using clustalw for sequence alignment
- clustalw can be used for performing multiple sequence alignments
- BioPython provides a wrapper for clustalw through the Bio.Clustalw package. However, clustalw needs to be downloaded and installed before it is accessible using your biopython scripts.
- As Zsun noticed, the BioPython cookbook has some outdated example code for accessing clustalw. An online Python Course in Bioinformatics by Katja Schuerer and Catherine Letondal has several better examples with correct code.
- Once you have clustalw installed and accessible via python, download an example apoe.fasta and try running the following code.
#!/usr/bin/env python import os from Bio import Clustalw cline = Clustalw.MultipleAlignCL(os.path.join(os.curdir, 'apoe.fasta')) cline.set_output('test.aln') alignment = Clustalw.do_alignment(cline)
- This should give you an output file, test.aln, which you should inspect.
- Try modifying apoe.fasta, and upload new versions as you improve it to include all of the test cases enumerated above.
- Note from Kay: If you are creating/modifying your fasta file in Python, you must close the file before attempting to pass it into Clustalw. Otherwise, it won't load properly.
Extracting information from an alignment
- Look at Bio/Align/Generic.py for ideas about what you can do with an alignment
- Once you have obtained an alignment, one way to parse it is to look at each column and check for differences
- Here is some initial code to give you some ideas.
#!/usr/bin/env python import os from Bio import Clustalw from Bio.Align import AlignInfo from sets import Set cline = Clustalw.MultipleAlignCL(os.path.join(os.curdir, 'apoe.fasta')) cline.set_output('test.aln') alignment = Clustalw.do_alignment(cline) for i in range(alignment.get_alignment_length()): col = alignment.get_column(i) s = Set() # create a new set for c in range(len(col)): s.add(col[c]) # add each column element to the set if len(s) > 1: # multiple elements in s indicate a mismatch print i, col # Here is another way to locate sequence differences # it is still necessary to look at columns to determine type diffs =  stars = alignment._star_info for i in range(len(stars)): if not stars[i] == '*': diffs.append(i) print diffs
- You may elaborate on this code to start handling specific cases of sequence mismatches.
- Try to think about how differences in the alignment correspond to the cases enumerated above.
- One thing I (katie) noticed you can do is generate a consensus sequence pretty easily. Click here and scroll down to find out how.
More on extracting alignment info
One thing I (Xiaodi) have found to be helpful is to programmatically break down the problem into comparing each sequence to the reference itemwise, and using the returned, aligned data to find insertions and deletions. It allows for a more intuitive, I think, manner of handling the problem than aligning things vertically across more than two sequences and making determinations that way. Don't know if others will see it that way, but the code in my notebook works on this premise.