Category: Bioinformatics

Metaxa updated

Just a short note; Metaxa has been updated to version 1.0.1. This incremental version brings two small new features, and a minimal bug fix.

  • Added the option to select whether HMMER’s heuristic filtering should be used or not. This can be configured using the –heuristics option:
    –heuristics {T or F} : Selects whether to use HMMER’s heuristic filtering, off (F) by default
  • Removed some redundant information written to the screen, as output to the screen was a bit cluttered.

Bug fix:

  • Fixed a rare bug affecting detection sensivity of some SSU sequences.

Of course I would recommend it to every Metaxa user as it fixes a small bug, but the update is not in anyway critical for normal use.  The updated version can be downloaded using this link.

Metaxa released

I proudly announce that today Metaxa has been officially released. Metaxa is a a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequence datasets. We have been working on Metaxa for quite some time, and it has now been in beta for about two months. However, it seems to be stable enough for public consumption. In addition, the software package is today presented in a talk at the SocBiN conference in Helsinki.

A more thorough post on the rationale behind Metaxa, and how it works will follow when I am not occupied by the SocBiN conference. A paper on Metaxa is to be published in the journal Antonie van Leeuwenhoek. The  software can be downloaded from here.

Pfam + Wikipedia – finally!

Browsing the Pfam web site today, I discovered that the database finally has launched its Wikipedia co-ordination efforts.

This has happened along with the 25th release of the Pfam database (released 1st of April), and basically means that Wikipedia articles will be linked to Pfam families. Gradually, this will (hopefully) improve the annotation of Pfam families, which has in many cases been rather poor. The Xfam blog post related to Pfam release 25 says the change will be happening gradually, which might actually be good thing, given the quirks that might pop up.

(…) a major change is that Pfam annotation is now beginning to be co-ordinated via Wikipedia. Unlike Rfam, where every entry has a Wikipedia entry, we expect this to be a more gradual transition for Pfam, so not all entries currently have a corresponding Wikipedia article. For a more detailed discussion, check the help page.  We actively encourage the addition of new/updated annotations via Wikipedia as they will appear far quicker than waiting for a Pfam release.  If there are articles in Wikipedia that you think correspond to a family, then please mail us!

I have awaited this change for a long time, and is very happy that Pfam has finally taken this step. Congratulations and my sincerest thanks to the Pfam team! Now, let’s go editing!

Software reorganisation

I have reorganised my Software page a little bit, putting the smaller scripts on a separate page, to make the main software page tidier. The content of the pages is the same, and you still find bloutminer and metaorf on the main software page.

BLAST-mining software: bloutminer

I have put some “new” software online. I have had this piece of code lying around for some time but never got to upload it as I didn’t view it as “finished”. It is still not finished, but I would nevertheless like to share it with a wider audience. So, today I introduce bloutminer – the BLAST output mining script I have been using lately. bloutminer allows you to specify e.g. an E-value cutoff, a length cutoff and a percent identity cutoff, and extract a list of the hits satisfying these cutoffs. It takes table output (blastall option -m 8 ) as input. This is the software I used for the BLAST visualisation I have discussed earlier.

I normally use an E-value cutoff of 10 for my BLAST searches, and then extracts hits with bloutminer, allowing me to change the cutoffs at a later stage without redoing the whole BLAST search. You can also “pool” sequences into groups, based on their sequence tags. bloutminer is work in progress, and may contain nasty bugs. It can be found on the Software page. Please improve it at will.

Thesis presentation

I will present my master thesis “Metagenomic Analysis of Marine Periphyton Communities”, on Tuesday the 22nd of March, at 13.00. The presentation will take place in the room Folke Andreasson at Medicinaregatan 11 in Gothenburg. The presentation is open for everyone, but the number of seats are limited.

Assemblathon

There is currently an interesting competition going on organised by UCSC called the Assemblathon. The idea is that participating research groups will try to assemble simulated short-reads to a simulated genome, with the winner being the group doing it “best” (by some criteria set up by the evaluation team at the UC Davis Genome Center). The complete set of rules can be found here. The whole thing will culminate in a Genome Assembly Workshop at UC Santa Cruz in mid-March.

I think the competition is an interesting initiative, hopefully inspiring new, more efficient, sequence assembly ideas. Those are desperately needed in these times of ever-incresing DNA sequence generation. In addition, there are numerous already existing genome assembly programs, but (as noted on the Assemblathon site) it is not obvious which one is the best in a given situation. Hopefully the competition can shed some light on that too. The deadline for participation is the sixth of February, and even though I am not myself competent enough to participate, I hope the ones who do are successful in their work.

BLAST Visualization

Perhaps because of my roots in systems biology (or the cause of going there in the first place), I have always had an interest in creating visually appealing images of data, many times in the form of networks. I find that often in bioinformatics, one of the hardest problems is to make information understandable. For example, a BLAST output might say very little about how the genes or proteins are connected to each other, at least to the untrained eye.

Therefore, during the last weeks I have fiddled around with various ways of viewing interesting portions of BLAST reports. By making all-against-all BLAST searches, and outputting the data in table format (blastall option -m 8), I have been able to extract the hits I am interested in and export them into a Cytoscape compatible format, with some accompanying metadata (scores, e-values, alignment length, etc.). The results are many times pretty unparsable by the eye, rendering them a bit meaningless, but have been more and more interesting as I have put more effort into the extraction script. Just as an example, I here provide a simple map of the best all-against-all matches in the Saccharomyces cerevisiae genome, as a Cytoscape network (click for full size):

The largest circle consists of transposable elements (jumping DNA which inserts itself at multiple locations in the genome, no surprise there is a lot of them, and that these are pretty conserved). The circle to the left of the transposon circle consists of genes located inside the telomeric regions. Why they show such high similarity I do not know, but it seems plausible that the telomere thing could play a role here. The third circle contain mostly members of the seripauperin multigene family, which is also located close to the telomeres. At the bottom you found the gene pairs, that match to each other. You could go on with all the smaller structures as well, but I am no yeast expert, so I will stop here, letting this serve as an example of what a BLAST report really look like.

For this image, I have used a blastn report of all yeast ORFs (taken from yeastgenome.org) as input to my extraction tool, selected Cytoscape compatible output, and used a maximal e-value of 0.00001 and an alignment length of at least 50 nts as criteria to be extracted. I have also pooled the sequences according to chromosome number. The pooling was used to color code the nodes in Cytoscape. The edge width is connected to alignment score, a high score renders a thick line, and a low score causes the line to be thin.

I am still working on the extraction tool and will not provide any code yet. Input would, however, be appreciated. My personal opinion is that in the near future, the overload of newly produced DNA and protein sequences will choke us if do not come up with more intuitive ways of displaying data. I don’t think that the network above is there yet. Still, it conveys information I would not have been able to understand from just looking at the BLAST output. The first attempts to come around the sequence overload problem won’t be the best ones. But we got to start working on visualization methods today, so that we do not end up with sequences over our shoulders in just a few years. Besides, a network image seems much more impressive than a number of lines of text…

Blastgrep updated

I have fixed two small bugs in the blastgrep tool (see below), and the version number has been increased to 1.0.2. This update is recommended to everybody who downloaded the previous version of blastgrep. The new version of blastgrep can be downloaded using this link.

Version 1.0.2 fixes:
  • Fixed a bug with extracting information from queries without any matches
Version 1.0.1 fixes:
  • Fixed an inconsistency bug while using “-o count”

Software: blastgrep

I have added some software I have written to this page (see link to Software at the top of the page). Among these is the useful little Unix/Linux utility blastgrep, which functions as a grep adopted for extracting useful information from BLAST-reports. I wrote it recently as I increasingly use complicated combinations of piped Unix-commands to do the same thing. blastgrep makes it all more easy. Use it as you wish, and if you do, please tell me about its bugs (hopefully none…)