Talk:Harvard:Biophysics 101/2007/02/13: Difference between revisions
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*You may elaborate on this code to start handling specific cases of sequence mismatches. | *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. | *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 [http://www.bioinformatics.org/bradstuff/bp/tut/Tutorial003.html#toc16 | * One thing I (katie) noticed you can do is generate a consensus sequence pretty easily. Click [http://www.bioinformatics.org/bradstuff/bp/tut/Tutorial003.html#toc16 here] and scroll down to find out how. |
Revision as of 20:34, 19 February 2007
Sequence-level differences to consider
- Point mutations
- silent/synonymous
- missense
- nonsense
- 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...
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- As Zsun noticed, the BioPython cookbook has some outdated example code for accessing clustalw.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.
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
- 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.