A beta release for Biopython 1.51 is now available for download and testing.

In the two months since Biopython 1.50 was released, we have introduced support for writing features in GenBank files using Bio.SeqIO, extended SeqIO’s support for the FASTQ format to include files created by Illumina 1.3+, and added a new set of application wrappers for alignment programs, and made numerous tweaks and bug fixes.

All the new features have been tested by the dev team but it’s possible there are cases that we haven’t been able to foresee and test, especially for the GenBank feature writer (as there as just so many possible odd fuzzy feature locations).

Note that as previously announced, Biopython no longer supports Python 2.3, and our deprecated parsing infrastructure (Martel and Bio.Mindy) has been removed.

Source distributions and Windows installers are available from the downloads page on the Biopython website (biopython.org).

We are interested in getting feedback on the beta release as a whole, but especially on the new features and the Biopython Tutorial and Cookbook (PDF).

So, gather your courage, download the release, try it out and let us know what works and what doesn’t through the mailing lists (or bugzilla).

Constructing complicated NCBI Entrez searches can be tricky, but it turns out one of the Entrez Programming Utilities called Entrez EInfo can help.

For example, suppose you want to search for mitochondrial genomes from a given taxa – either just in the Entrez web interface, for use in a script with EFetch).

I knew from past experience about using name[ORGN] in Entrez to search for an organism name – but how would you specify just mitochondria? I actually worked this out from the NCBI help and exploring the Entrez website’s advanced search – but it took a while.

There is an easier way to find out the search fields available in Entrez! Just recently I came across an interesting blog post from Neil Saunders (written a couple of weeks ago) showing how Entrez EInfo provides information about the search fields in XML format, and how you can use Ruby to process this.

Biopython can do this too of course – using Bio.Entrez this took just a few lines of Python:

>>> from Bio import Entrez
>>> data = Entrez.read(Entrez.einfo(db="genome"))
>>> for field in data["DbInfo"]["FieldList"] :
... print "%(Name)s, %(FullName)s, %(Description)s" % field
...
ALL, All Fields, All terms from all searchable fields
UID, UID, Unique number assigned to each sequence
FILT, Filter, Limits the records
WORD, Text Word, Free text associated with record
TITL, Title, Words in definition line
KYWD, Keyword, Nonstandardized terms provided by submitter
AUTH, Author, Author(s) of publication
JOUR, Journal, Journal abbreviation of publication
VOL, Volume, Volume number of publication
ISS, Issue, Issue number of publication
PAGE, Page Number, Page number(s) of publication
ORGN, Organism, Scientific and common names of organism, and all higher levels of taxonomy
ACCN, Accession, Accession number of sequence
PACC, Primary Accession, Does not include retired secondary accessions
GENE, Gene Name, Name of gene associated with sequence
PROT, Protein Name, Name of protein associated with sequence
ECNO, EC/RN Number, EC number for enzyme or CAS registry number
PDAT, Publication Date, Date sequence added to GenBank
MDAT, Modification Date, Date of last update
SUBS, Substance Name, CAS chemical name or MEDLINE Substance Name
PROP, Properties, Classification by source qualifiers and molecule type
SQID, SeqID String, String identifier for sequence
GPRJ, Genome Project, Genome Project
SLEN, Sequence Length, Length of sequence
FKEY, Feature key, Feature annotated on sequence
RTYP, Replicon type, Replicon type
RNAM, Replicon name, Replicon name
ORGL, Organelle, Organelle


That gives us a list of all the fields we can currently search on in the Genome database (and you could use the same code for any of the other NCBI databases in Entrez – they probably all have different searchable fields). Very handy! The ones in bold are discussed below.

So for my particular search, using “ORGL” to filter on organelle looks sensible, and after a bit of trial and error on the website I ended up with mitochondrion[ORGL] as a useful filter (not mitochondrial, or mitochondria).

I already knew about using “ORGN” to filter on the organism, either by species name or with a suitably formatted NCBI taxon ID (which you can get by searching or browsing the Entrez taxonomy database), e.g. txid9443[ORGN] gives primates.

Putting these together, to get all the primate mitochondria in the Entrez genome database you could use:

txid9443[ORGN] AND mitochondrion[ORGL]

Note that you have to use “AND” in upper case.

I think we’ll have to add something along these lines to the Biopython Tutorial and Cookbook (PDF)… Update: That’s done now and will be included with our next release

Entrez rocks! (although their documentation could use a few more examples).

Peter

The BOSC 2009 schedule of speakers with links to abstracts has been posted at http://www.open-bio.org/wiki/BOSC_2009_Schedule.

• BOSC Update: Ruttenberg, Hanmer confirmed as Keynotes, Early Registration Deadline Friday: Alan Ruttenberg of Sc.. http://tinyurl.com/p3wy9a #

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Alan Ruttenberg of Science Commons and Robert Hanmer of the Hillside Group have been confirmed as Keynote Speakers for BOSC 2009.  For more information, see the BOSC 2009 web site at http://www.open-bio.org/wiki/BOSC_2009. 

Abstract acceptances went out today–stay tuned for the schedule, which will be posted once the speakers have confirmed their invitations.

The early registration deadline for BOSC is Friday, May 15; don’t forget to take advantage of the discounted fee for early registrants at http://www.iscb.org/ismbeccb2009/registration.php.

• Dropping Python 2.3 Support: As announced here, any last minute requests to postpone dropping support for Python.. http://tinyurl.com/daypom #

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As announced here, any last minute requests to postpone dropping support for Python 2.3 from the next release of Biopython must be posted to the main Biopython mailing list no later than Friday, May 8.

• Biopython projects chosen for Google Summer of Code: Congratulations to Nick Matzke and Eric Talevich who have h.. http://tinyurl.com/cx9f34 #
• Introducing (and expanding) the Biopython Cookbook: Hi all,
  You may have noticed we’re trying out using th.. http://tinyurl.com/c7ef6h #

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Hi all,

You may have noticed we’re trying out using the wiki for Biopython cookbook entries. It’s a new idea so at the moment there are only a few ‘recipes’ on offer. If you have some tricks you find yourself using time and again to solve a problem why not share them? Similarly, if you find yourself coming up against a problem you can’t seem to solve easily with Biopython’s tools send a message to one of the mailing lists proposing it as a cookbook example and someone just might solve it for you!

There are also several short examples in the main “Biopython Tutorial and Cookbook” (pdf version) which might be worth copying/moving to the wiki. What would you pick from here?

Feedback from Biopython newcomers would be especially valuable!

Congratulations to Nick Matzke and Eric Talevich who have had Biopython projects accepted for this year’s Google Summer of Code. Both projects were accepted as part of The National Evolutionary Synthesis Center’s (NESCent) involvement as a mentoring organisation with the program.

Nick will spend his summer working on modules that access locality data from biodiversity databases and incorporate this information in biogeographical and phylogenetic analyses (Nick’s abstract) while Eric will be building a parser for the emerging PhyloXML format for storing and sharing phylogenetic trees (Eric’s abstract).

Congratulations to Nick and Eric and to their mentors Stephen Smith and Brad Chapman (who has his own post on this topic) and thanks to Google and NESCent for their support of these projects.