Cbcb:Pop-Lab:Ted-Report: Difference between revisions

From Cbcb
Jump to navigation Jump to search
(→‎Comparative Network Analysis pt. 2: Added some thoughts on my network alignment project)
Line 18: Line 18:


=== Comparative Network Analysis pt. 2 ===
=== Comparative Network Analysis pt. 2 ===
* Probably need to follow up with Ben
* Meeting with Volker this Friday to discuss how best to apply network alignment to what he's doing
* I'm simultaneously trying to find a way to apply my network alignment technique to predicting genes in metagenomic samples
** I've been trying to find a way to get beyond the restriction that my current program requires genes to be annotated with an EC number. A potentially interesting next step may be to use BioPython to BLAST the sequence of each enzyme annotated in every micro-organism in KEGG against a metagenomic library.
*** The results would be stretches of linked reactions that have been annotated in KEGG pathways.
*** This method could be applied to contigs just as easily as finished sequences. In a scenario where perhaps there was low coverage, it could be used to identify genes which are probably there but just weren't sampled by showing the presence of the rest pathway. In short, this could finally accomplish what Mihai asked me to work on when I showed up.
*** The major theoretical shortcoming of this approach is that it could only identify relatively well characterized pathways.
*** The practical shortcoming of this approach will start by obtaining a fairly complete copy of KEGG (which as we've learned is a mess to parse locally and unusably slow to call through the API), and will continue to the computational challenge of such a large scale BLAST operation.

Revision as of 22:57, 13 January 2010

Older Entries

2009

January 15, 2010

Minimus Documentation

Presently, the only relevant Google hit for "minimus" on the first page of results is the sourceforge wiki. The only example on this page is incomplete and appears to be an early draft made during development.

Ideally, it should be easy to find a complete guide with the general format:

  • Simple use case
    • Necessary tools for set up (toAmos)
  • Option 1
  • Option 2
  • etc

The description found on the Minimus/README page (linked to from the middle of the starting page) is more appropriate, but features use cases that may no longer be common and references another required tool (toAmos) without linking to it or describing how to access it. A description of this tool can be found on Amos File Conversion Utilities page (again, linked to from the starting page), but it is less organized than what I've come to expect from a project page and it is easy to get lost or distracted by the rest of the Amos documentation while trying to peace together the necessary steps for a basic assembly.

Comparative Network Analysis pt. 2

  • Meeting with Volker this Friday to discuss how best to apply network alignment to what he's doing
  • I'm simultaneously trying to find a way to apply my network alignment technique to predicting genes in metagenomic samples
    • I've been trying to find a way to get beyond the restriction that my current program requires genes to be annotated with an EC number. A potentially interesting next step may be to use BioPython to BLAST the sequence of each enzyme annotated in every micro-organism in KEGG against a metagenomic library.
      • The results would be stretches of linked reactions that have been annotated in KEGG pathways.
      • This method could be applied to contigs just as easily as finished sequences. In a scenario where perhaps there was low coverage, it could be used to identify genes which are probably there but just weren't sampled by showing the presence of the rest pathway. In short, this could finally accomplish what Mihai asked me to work on when I showed up.
      • The major theoretical shortcoming of this approach is that it could only identify relatively well characterized pathways.
      • The practical shortcoming of this approach will start by obtaining a fairly complete copy of KEGG (which as we've learned is a mess to parse locally and unusably slow to call through the API), and will continue to the computational challenge of such a large scale BLAST operation.