Tag: DNA sequencing

An update to Metaxa2 that has long remained in internal testing has been deemed bug-free (as far as we can tell) and has been uploaded to the Metaxa2 web site. The update brings a slightly improved classifier, and is the first release that we declare full stable, although we have found no problems with the previously available version (release candidate 3). This also means that we take a jump directly from version 2.0, release candidate 3 to version 2.0.1 without passing a final 2.0 release. The update is available here.

Another paper I have made a contribution to have just recently been published in Molecular Ecology Resources. The paper (1), which is lead-authored by Xin-Cun Wang and Chang Liu at the Institute of Medicinal Plant Development in Beijing, investigates the usability of the ITS1 and ITS2 as separate barcodes across the Eukaryotes. The study is a large scale meta-analysis comparing available high-quality sequence data in as many taxonomic groups at possible from three different aspects: PCR amplification, DNA sequencing efficiency and species discrimination ability. Specifically, we have looked for the presence of DNA barcoding gaps, species discrimination efficiency, sequence length distribution, GC content distribution and primer universality, using bioinformatic approaches. We found that the ITS1 had significantly higher efficiencies than the ITS2 in 17 of 47 families and 20 of 49 investigated genera, which was markedly better than the performance of ITS2. We conclude that, in general, ITS1 represents a better DNA barcode than ITS2 for a majority of eukaryotic taxonomic groups. This of course doesn’t mean that using the ITS2 or the ITS region in its entirety should be dismissed, but our results can serve as a ground for making informed decisions about which region to choose for your amplicon sequencing project. The results complement what have previously been observed for e.g. fungi, where the difference between ITS1 and ITS2 were much less pronounced (2).

References:

1. Wang X-C, Liu C, Huang L, Bengtsson-Palme J, Chen H, Zhang J-H, Cai D, Li J-Q: ITS1: A DNA barcode better than ITS2 in eukaryotes? Molecular Ecology Resources. Early view. doi: 10.1111/1755-0998.12325 [Paper link]
2. Blaalid R, Kumar S, Nilsson RH, Abarenkov K, Kirk PM, Kauserud H: ITS1 versus ITS2 as DNA metabarcodes for fungi. Molecular Ecology Resources. Volume 13, Issue2, Page 218-224. doi: 10.1111/1755-0998.12065 [Paper link]

Another paper I have co-authored related to the UNITE database for fungal rDNA ITS sequences is now published as an Online Early article in Fungal Diversity. The paper describes an effort to improve the annotation of ITS sequences from fungal plant pathogens. Why is this important? Well, apart from fungal plant pathogens being responsible for great economic losses in agriculture, the paper is also conceptually important as it shows that together we can accomplish a substantial improvement to the metadata in sequence databases. In this work we have hunted down high-quality reference sequences for various plant pathogenic fungi, and re-annotated incorrectly or insufficiently annotated ITS sequences from the same fungal lineages. In total, the 59 authors have made 31,954 changes to UNITE database data, on average 540 changes per author. While one, or a few, persons could not feasibly have made this effort alone, this work shows that in larger consortia vast improvements can be made to the quality of databases, by distributing the work among many scientists. In many ways, this relates to proposals to “wikify” GenBank, and after Rfam and Pfam it might now be time to take the user-contribution model to, at least, the RefSeq portion of GenBank, which despite its description as being “comprehensive, integrated, non-redundant, [and] well-annotated” still contains errors and examples of non-usable annotation. More on that at a later point…

Paper reference:

Nilsson RH, Hyde KD, Pawlowska J, Ryberg M, Tedersoo L, Aas AB, Alias SA, Alves A, Anderson CL, Antonelli A, Arnold AE, Bahnmann B, Bahram M, Bengtsson-Palme J, Berlin A, Branco S, Chomnunti P, Dissanayake A, Drenkhan R, Friberg H, Frøslev TG, Halwachs B, Hartmann M, Henricot B, Jayawardena R, Jumpponen A, Kauserud H, Koskela S, Kulik T, Liimatainen K, Lindahl B, Lindner D, Liu J-K, Maharachchikumbura S, Manamgoda D, Martinsson S, Neves MA, Niskanen T, Nylinder S, Pereira OL, Pinho DB, Porter TM, Queloz V, Riit T, Sanchez-García M, de Sousa F, Stefaczyk E, Tadych M, Takamatsu S, Tian Q, Udayanga D, Unterseher M, Wang Z, Wikee S, Yan J, Larsson E, Larsson K-H, Kõljalg U, Abarenkov K: Improving ITS sequence data for identification of plant pathogenic fungi. Fungal Diversity Online early (2014). doi: 10.1007/s13225-014-0291-8 [Paper link]

Science for Life Laboratories (SciLifeLab) in Stockholm will host a metagenome data analysis workshop on May 21-23, in which I will participate as a tutorial assistant. Additionally, our group leader Joakim Larsson will be giving a lecture about how we use metagenomics to assess the environmental reservoir of antibiotic resistance genes (much of my recent work will likely go into that). I hope to meet you there, so don’t forget to register!

Confirmed speakers:
Lex Nederbragt, Oslo University, Norway
Saskia Smits, Erasmus University Rotterdam, Netherlands
Joakim Larsson, Göteborg University, Sweden
Paul Wilmes, University of Luxembourg, Luxembourg
Anders Andersson, SciLifeLab, Sweden
Noan Le Bescot, UPMC (Tara expedition), France

The workshop is part of the AllBio Bioinformatics initiative.

If you are thinking about doing a PhD and think that bioinformatics and antibiotic resistance is a cool subject, then now is your chance to come and join us for the next four years! There is a PhD position open i Joakim Larsson’s group, which means that if you get the job you will work with me, Joakim Larsson, Erik Kristiansson, Ørjan Samuelsen and Carl-Fredrik Flach on a super-interesting project relating to discovery of novel beta-lactamase genes (NoCURE). The project aims to better understand where, how and under what circumstances these genetic transfer events take place, in order to provide opportunities to limit or delay resistance development and thus increase the functional lifespan of precious antibiotics. The lion’s share of the work will be related to interpreting large-scale sequencing data generated by collaborators within the project; both genome sequencing and metagenomic data.

This is a great opportunity to prove your bioinformatics skills and use them for something urgently important. Full details about the position can be found here.

It’s been a while since the PETKit got any attention from me. Partially, that has been due to a nasty bug that could produce no output for one of the read files in Pefcon when using FASTA input files, but mostly it has simply been due to lack of time to continue development on the package. Now, I have finally put all threads together (bug fixes, new features, documentation) and today the 1.1 version is released! The new features are:

• A new tool has been added – peacat – that can be used to e.g. stitch contigs together that have been separated for one reason or another in an assembly
• Another tool – pemap – has been added that can be used to determine whether an assembled contig is from a circular DNA element
• The default offset value for FASTQ files has been set to 33 (as in Sanger and Illumina 1.8+ PHRED format)
• The documentation has been vastly improved (but is still rather inferior)

If you’re looking for a PhD position in bioinformatics, working with antibiotic resistance, there’s an opening in Erik Krisiansson’s (best bioinformatician in Gothenburg? I think so) group. To apply you need to have a master’s level degree in bioinformatics, mathematical statistics, mathematics, computer science, physics, molecular biology or any equivalent topic, obtained latest June 2014. If you’re a master student and want to join us, this is your chance! You can read more and apply for the position here.

A new year has begun, and it brings with it a few updates on the website. I have added a summary of the year 2013 from my perspective, and (as you may recognize) updated my picture on the front page. Briefly, this year will bring lots of exciting stuff. Personally, I am quite excited to finally be able to share the new version of Metaxa – Metaxa2 – which will be released to the public late this Winter (or early Spring). Additionally, I look forward to wrap up some manuscript on metagenomics and antibiotic resistance, which I have been working with for more than 2.5 years now. Also, we look forward to some super-intersting technology developments in DNA sequencing, with PacBio finally finding proper usage scenarios, Nano-pore sequencing around the corner, and super-multiplexing on the Illumina instruments. We’re in for a treat with DNA sequencing in 2014!

Those of you attending the Swedish Bioinformatics Workshop, this year given in Skövde, will have a chance seeing me talk about how sequencing depth influences the picture we get of the environmental resistance gene diversity. I think the topic is very urgent and interesting, and will likely come back to it in a more thorough blog post later. There are also a few other very interesting talks, for example about metagenomic gene quantification, and en masse sequencing of E. coli and H. pylori isolates. I think all attendants are in for a treat! See you there!

I am happy to inform you that our paper on ITSx now is out online in Methods in Ecology and Evolution issue 4.10. Meanwhile, I am slowly getting my stuff together on an update that will bring some minor requested features. The publication brings the proper citation of the ITSx paper to be:

Bengtsson-Palme, J., Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., De Wit, P., Sánchez-García, M., Ebersberger, I., de Sousa, F., Amend, A. S., Jumpponen, A., Unterseher, M., Kristiansson, E., Abarenkov, K., Bertrand, Y. J. K., Sanli, K., Eriksson, K. M., Vik, U., Veldre, V., Nilsson, R. H. (2013), Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution, 4: 914–919. doi: 10.1111/2041-210X.12073