Those attending the Metagenomics lab (part of the basic NGS course for PhD students given at GU this week), can find the material for the lab on this page:
Of course, the page is open for anyone else as well, although you won’t get the support that the GU students are given.
Yesterday, our paper on Megraft – a software tool to graft ribosomal small subunit (16S/18S) fragments onto full-length SSU sequences – became available as an accepted online early article in Research in Microbiology. Megraft is built upon the notion that when examining the depth of a community sequencing effort, researchers often use rarefaction analysis of the ribosomal small subunit (SSU/16S/18S) gene in a metagenome. However, the SSU sequences in metagenomic libraries generally are present as fragmentary, non-overlapping entries, which poses a great problem for this analysis. Megraft aims to remedy this problem by grafting the input SSU fragments from the metagenome (obtained by e.g. Metaxa) onto full-length SSU sequences. The software also uses a variability model which accounts for observed and unobserved variability. This way, Megraft enables accurate assessment of species richness and sequencing depth in metagenomic datasets.
The algorithm, efficiency and accuracy of Megraft is thoroughly described in the paper. It should be noted that this is not a panacea for species richness estimates in metagenomics, but it is a huge step forward over existing approaches. Megraft shares some similarities with EMIRGE (Miller et al., 2011), which is a software package for reconstruction of full-length ribosomal genes from paired-end Illumina sequences. Megraft, however, is set apart in that it has a strong focus on rarefaction, and functions also when the number of sequences is small, which is often the case in 454 and Sanger-based metagenomics studies. Thus, EMIRGE and Megraft seek to solve a roughly similar problem, but for different sequencing technologies and sequencing scales.
Bengtsson, J., Hartmann, M., Unterseher, M., Vaishampayan, P., Abarenkov, K., Durso, L., Bik, E.M., Garey, J.R., Eriksson, K.M., Nilsson R.H. (2012). Megraft: A software package to graftribosomal small subunit (16S/18S) fragments onto full-length sequences for accurate species richness and sequencing depth analysis in pyrosequencing-length metagenomes and similar environmental datasets. Research in Microbiology, doi: 10.1016/j.resmic.2012.07.001.
- Miller, C. S., Baker, B. J., Thomas, B. C., Singer, S. W., & Banfield, J. F. (2011). EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biology, 12(5), R44. doi:10.1186/gb-2011-12-5-r44
I am extremely happy to announce that Metaxa 1.1 (first announced back in July) has finally left the beta stage, and is now designated as a feature complete 1.1 update. We consider this update stable for production use. The 1.1 update utilize hmmsearch instead of hmmscan for higher extraction speeds and better accuracy. This clever trick was inspired by a blog post by HMMER’s creator Sean Eddy on hmmscan vs hmmsearch (http://selab.janelia.org/people/eddys/blog/?p=424). As the speedup comes from the extraction step, the speed increase will be largest for huge data sets with only a small proportion of actual SSU sequences (typically large 454 metagenomes).
What took so long, you might ask, as I promised an imminent release already in August. Well, during testing a difference in scoring was discovered. This difference did not have any implications for long sequences (> ~350 bp), but caused Metaxa to have problems on short reads (most evident on ~150 bp and shorter). Therefore, the scoring system had to be redesigned, which in turn required more extensive testing. Now, however, Metaxa 1.1 has a fine-tuned scoring system, which by default is based on scores instead of E-values, and in some instances have even better detection accuracy than the old Metaxa version. We encourage everyone to try out this new version of Metaxa (although the 1.0.2 version will remain available for download). It should be bug free, but we cannot ensure 100% compatibility in all usage scenarios. Therefore, we are happy if you report any bugs or inconsistencies to the e-mail address: metaxa (at] microbiology [dot) se.
The new version of Metaxa can be downloaded here: https://microbiology.se/software/metaxa/ Please note that the manual has not yet been updated yet, so use the help feature for the up-to-date options. Happy SSU detecting!
I’m working on an update to Metaxa that will bring at least double speed to the program (and even more when run on really large data sets on many cores). While there is still no real release version of this update (version 1.1), I have today posted a public “beta”, which you can use for testing purposes. Do not use this version for anything important (e.g. research) as it contains at least one known bug (and maybe even more I haven’t discovered yet). I would appreciate, if you are interested, that you download this version and e-mailed any bugs or inconsistencies found to me (firstname.lastname[at]microbiology.se).
Note that to install this version, you first need to download and install the current version of Metaxa (1.0.2). Then the new version can be used with the old’s databases.
I listened to a great talk by Alex Bateman (one of the guys behind Pfam and Rfam, as well as involved in HMMER development) at FEBS yesterday. In addition to talking about the problems of increasing sequence amounts, Alex also provided some reflections on co-operativity and knowledge-sharing – not only among fellow researchers, but also to a wider audience. The starting point of this discussion is Rfam, where the annotation of RNA families is entirely based on a community-driven wiki, tightly integrated with Wikipedia. This means that to make a change in the Rfam annotation, the same change is also made at the corresponding Wikipedia page for this RNA family. And what’s the use of this? Well, as Alex says, for most of the keywords in molecular biology (and I would guess in all of science), the top hit on Google will be a Wikipedia entry. If not, the Wikipedia entry will be in the top ten list of hits, if a good Wiki page exists. This means that Wikipedia is the primary source of scientific information for the general public, as well as many scientists. Wikipedia – not scientific journals.
The consequence of this is that to communicate your research subject, you should contribute to its Wikipedia page. In fact, Bateman argues, we have a responsibility as scientists to provide accurate and correct information to the public through the best sources available, which in most cases would be Wikipedia. To put this in perspective (and here I once again borrow Alex’ words), if somebody told you ten years ago that there would be one single internet site that everybody would visit to find scientific information, and where discussion and continuous improvement would be allowed, encouraged and performed, most people would have said that was too good to be true. But that’s what Wikipedia offers. It is time to get rid of the Wiki-sceptisism, and start improving it.
And so, what about the future of publishing? Bateman has worked hard to form an agreement with the journal RNA Biology to integrate the publishing into the process of adding to the easily accessible public information. To have an article on a new RNA family published under the journal’s RNA families track, the family must not only be submitted to the Rfam database, but the authors must also provide a Wikipedia formatted article, which undergo the same peer-review process as the journal article. This ensures high-quality Wikipedia material, as well as making new scientific discoveries public.
I don’t think there’s a long stretch to guess that in the future, more journals and/or funding agencies will take on similar approaches, as researchers and decision-makers discover the importance of correct, publicly available information. The scientific world is slowly moving towards being more open, also for non-scientists. This openness is of extremely high importance in these times of climate scepticism, GMO controversy, extinction of species, and nuclear power debate. For the public to make proper decisions and send a clear message to the politicians, scientists need to be much better at communicating the current state of knowledge, or what many people prefer to call “truth”.