Category: Bioinformatics

After almost a year in different stages of review and revision, in which the paper (but not the software) saw a total transformation, I am happy to announce that the paper describing Metaxa2 has been accepted in Molecular Ecology Resources and is available in a rudimentary online early form. The figures in this version are not that pretty, but those who wants to read the paper asap, you have the possibility to do so.

This means that if you have been using Metaxa2 for a publication, there is now a new preferred way of citing this, namely:

Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved Identification and Taxonomic Classification of Small and Large Subunit rRNA in Metagenomic Data. Molecular Ecology Resources (2015). doi: 10.1111/1755-0998.12399

The paper (1), apart from describing the new Metaxa version, also brings a very thorough evaluation of the software, compared to other tools for taxonomic classification implemented in QIIME (2). In short, we show that:

• Metaxa2 can make trustworthy taxonomic classifications even with reads as short as 100 bp
• Generally, the performance is reliable across the entire SSU rRNA gene, regardless of which V-region a read is derived from
• Metaxa2 can reliably recapture species composition from short-read metagenomic data, comparable with results of amplicon sequencing
• Metaxa2 outperforms other popular tools such as Mothur (3), the RDP Classifier (4), Rtax (5) and the QIIME implementation of Uclust (6) in terms of proportion of correctly classified reads from metagenomic data
• The false positive rate of Metaxa2 is very close to zero; far superior to many of the above mentioned tools, many of which assume that reads must derive from the rRNA gene

Metaxa2 can be downloaded here. We have already used it for around two years internally, and it forms the base of the taxonomic classifications in e.g. our recently published paper on antibiotic resistance in a polluted Indian lake (7).

References

1. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved Identification and Taxonomic Classification of Small and Large Subunit rRNA in Metagenomic Data. Molecular Ecology Resources (2015). doi: 10.1111/1755-0998.12399 [Paper link]
2. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
3. Schloss PD, Westcott SL, Ryabin T et al.: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537–7541 (2009).
4. Wang Q, Garrity GM, Tiedje JM, Cole JR: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 5261–5267 (2007).
5. Soergel DAW, Dey N, Knight R, Brenner SE: Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. The ISME Journal, 6, 1440–1444 (2012).
6. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
7. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014).

My colleague Henrik Nilsson has been interviewed by the ResearchGate news team about the recent effort to better annotate ITS data for plant pathogenic fungi. It’s an interesting read, and I think Henrik nicely underscores why large-scale efforts for improving and correcting sequence annotations are important. You can read the interview here, and the paper they talk about is referenced below.

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, Volume 67, Issue 1 (2014), 11–19. doi: 10.1007/s13225-014-0291-8 [Paper link]

A minor bug in the “its1.full_and_partial.fasta” file has been fixed in a minor update to ITSx (1.0.11) released to day. The bug occasionally caused newline characters at the end of a sequence to be skipped and the next entry to begin at the same row. The bug only manifested itself when ITSx was used with the --partial option and only in the above mentioned FASTA file. If you have been affected by the bug, you should have noticed as the resulting FASTA file would be considered corrupted by most bioinformatics software. The updated version of ITSx can be downloaded here.

If you’re looking for super-interesting jobs within bioinformatics, you don’t need to look any further. Instead, you should apply for a position at 1928 Diagnostics here in Gothenburg and join them in the fight against antibiotic resistant bacteria. The position is in the development team and the deadline for application is December 19. All the details can be found here.

Our paper describing the bacterial community of a polluted lake in India has now been typeset and appears in its final form in Frontiers in Microbiology. If I may say so, I think that the paper turned out to be very goodlooking and it is indeed nice to finally see it in print. The paper describes an unprecedented diversity and abundance of antibiotic resistance genes and genes enabling transfer of DNA between bacteria. We also describe a range of potential novel plasmids from the lake. Finally, the paper briefly describes a new approach to targeted assembly of metagenomic data — TriMetAss — which can be downloaded here.

Reference:
Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648

One of the highlights of the Swedish Bioinformatics Workshop 2014 was of course the dinner entertainment, a song specially crafted for the event. It has now, fortunately, been put online. For anyone who might not catch all the words, here’s the complete lyrics for the song (which is based on the song “Java Jive” in the Manhattan Transfer arrangement):

The Bioinformatics ABC

Grab your coffee
Grab your tea
Put down your spoon now and listen to me
For the bioinformatics ABC
Wake up, wake up, wake up, wake up, wake up
(Boy)

A for ABYSS
B for BLAST
And C for Clustal, though it’s not that fast
Alternatives are Muscle and MAFFT
ABYSS and BLAST and Clustal, Muscle, MAFFT
(Yeah)

D count reads with DESeq or E for EdgeR
And F for FastQC and G for GLIMMER
H for HMMER using Markov Chains
(Explain)
Hidden hidden Markov model

I for Inchworm;
Jellyfish
Add Chrysalis and Butterfly and wish
Assemble fast with a sound that goes swish
Contig, contig, contig, contig, transcript
(Girl)

KBASE
Lasergene
And the ton of tools for metagenomics
MEGAN, Megraft,
MetaPhlan, MG-RAST, Meta-GeneMark
And that’s just mentioning a few of them
(Talk it boy)

N for Newbler, old-school it is
If you’re still using 454 it’s a bliss
O for Oases, P for PyroNoise
45-45-45-454-454

Q is for Quake for that great quality
And R is for all those neat statistics
S for the Spades assembler, oh yeah

T for TopHat
U for Uclust
V for Velvet

There is Wham to align
XMatchView to review
And YASS to pursue
(But do you)
Know any tools beginning with Z?
What?
Yeah, Zorro, Zorro, Zorro
Oh yeah

With the publication of my latest paper last week (1), I also would like to highlight some of the software underpinning the findings a bit. To get around the problem that extremely common resistance genes could be present in multiple contexts and variants, causing assembler such as Velvet (2) to perform sub-optimally, we have written a software tool that utilizes Vmatch (3) and Trinity (4) to iteratively construct contigs from reads associated with resistance genes. This could of course be used in many other situations as well, when you want to specifically assemble a certain portion of a metagenome, but suspect that that portion might be found in multiple contexts.

TriMetAss is a Perl program, employing Vmatch and Trinity to construct multi-context contigs. TriMetAss uses extracted reads associated with, e.g., resistance genes as seeds for a Vmatch search against the complete set of read pairs, extracting reads matching with at least 49 bp (by default) to any of the seed reads. These reads are then assembled using Trinity. The resulting contigs are then used as seeds for another search using Vmatch to the complete set of reads, as above. All matches (including the previously matching read pairs) are again then used for a Trinity assembly. This iterative process is repeated until a stop criteria is met, e.g. when the total number of assembled nucleotides starts to drop rather than increase. The software can be downloaded here.

References:

1. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648
2. Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18, 821–829 (2008). doi:10.1101/gr.074492.107
3. Kurtz S: The Vmatch large scale sequence analysis software (2010). http://vmatch.de/
4. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al.: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644–652 (2011). doi:10.1038/nbt.1883

After a long delay-time in testing ITSx version 1.0.10 has been made public. The new version patches a bug causing the 3′ anchor not being properly written to file when using the “--anchor hmm” option. If a number was used for the “--anchor” option, this bug did not apply. Thus, if you have not been using the “--anchor” option together with “hmm”, you have not been affected in any way by this bug. Nevertheless, I encourage updating in case you would use the “--anchor hmm” option in the future. The update can be downloaded here. Happy barcoding!

I would like to sincerely apologize for that I have been terrible at responding to support issues pertaining to ITSx, Metaxa, Atosh etc. lately. I am currently on 50% parental leave and at the same time I am wrapping up three first-author papers, organizing a workshop and preparing a talk. Thus, support issues has been lagging a bit behind the last weeks to be able to cope with everything else. I have been ticking off most (all?) of my support questions the last couple of days, but if I have any remaining issues that I have missed to reply to, please re-send them to me!

I will try to improve response times, but it is hard when I am working less than usual (also, note that I (strangely) don’t get paid for supporting software, so I have to do this on my “sparetime”). My aim is to respond within a few days, so if I have not done so, please resend your e-mail with a friendly reminder that you are waiting for my response. Reminding me will very likely put your question up the priority pile.

So, my advice to becoming dads is: Do take paternal leave. Do take a lot of it. Share responsibilities with your partner. Because what you get back is awesome. (And also you get a good reason not to answer support questions in time.) But finally, don’t plan to wrap up the last couple of year’s worth of work and arrange a conference at the same time as you take out paternal leave. That will only make you feel insufficient at all fronts.

Keep the spirit high!

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]