Tag: DNA sequencing

ITSx – a software tool for detection and extraction of ITS1 and ITS2 sequences

For a couple of years, I have been working with microbial ecology and diversity, and how such features can be assessed using molecular barcodes, such as the SSU (16S/18S) rRNA sequence (the Metaxa and Megraft packages). However, I have also been aiming at the ITS region, and how that can be used in barcoding (see e.g. the guidelines we published last year). It is therefore a great pleasure to introduce my next gem for community analysis; a software tool for detection and extraction of the ITS1 and ITS2 regions of ITS sequences from environmental communities. The tool is dubbed ITSx, and supersedes the more specific fungal ITS extractor written by Henrik Nilsson and colleagues. Henrik is once more the mastermind behind this completely rewritten version, in which I have done the lion’s share of the programming. Among the new features in ITSx are:

  • Robust support for the Cantharellus, Craterellus, and Tulasnella genera of fungi
  • Support for nineteen additional eukaryotic groups on top of the already present support for fungi (specifically these groups: Tracheophyta (vascular plants), Bryophyta (bryophytes), Marchantiophyta (liverworts), Chlorophyta (green algae), Rhodophyta (red algae), Phaeophyceae (brown algae), Metazoa (metazoans), Oomycota (oomycetes), Alveolata (alveolates), Amoebozoa (amoebozoans), Euglenozoa, Rhizaria, Bacillariophyta (diatoms), Eustigmatophyceae (eustigmatophytes), Raphidophyceae (raphidophytes), Synurophyceae (synurids), Haptophyceae (haptophytes) , Apusozoa, and Parabasalia (parabasalids))
  • Multi-processor support
  • Extensive output options
  • Virtually zero false-positive extractions

ITSx is today moved from a private pre-release state to a public beta state. No code changes has been made since February, indicative of that the last pre-release candidate is now ready to fly on its own. As far as our testing has revealed, this version seems to be bug free. In reality though, researchers tend to find the most unexpected usage scenarios. So please, if you find any unexpected behavior in this version of ITSx, send me an e-mail and make us aware of the potential shortcomings of our software.

We expect this open-source software to boost research in microbial ecology based on barcoding of the ITS region, and hope that the research community will evaluate its performance also among the eukaryote groups that we have less experience with.

PETKit updated – Critical bug fix

Some good and some bad news regarding the PETKit. Good news first; I have written a fourth tool for the PETKit, which is included in the latest release (version 1.0.2b, download here). The new tool is called Pesort, and sorts input read pairs (or single reads) so that the read pairs occur in the same order. It also sorts out which reads that don’t have a pair and outputs them to a separate file. All this is useful if you for some reason have ended up with a scrambled read file (pair). This can e.g. happen if you want to further process the reads after running Khmer or investigate the reads remaining after mapping to a genome.

Then the bad news. There’s a critical bug in PETKit version 1.0.1b. This bug manifest itself when using custom offsets for quality scores (using the –offset option), and makes the Pearf and Pepp tools too strict – leading to that they discard reads that actually are of good quality. This does not affect the Pefcon program. If you use the PETKit for read filtering or ORF prediction, and have used custom offset values, I recommend that you re-run your data with the newly released PETKit version (1.0.2b), in which this bug has been fixed. If you have only used the default offset setting, your safe. I sincerely apologize for any inconveniences that this might have caused.

Introducing the PETKit

You know the feeling when your assembler supports paired-end sequences, but your FASTQ quality filterer doesn’t care about what pairs that belong together? Meaning that you end up with a mess of sequences that you have to script together in some way. Gosh, that feeling is way too common. It is for situations like that I have put together the Paired-End ToolKit (PETKit), a collection of FASTQ/FASTA sequence handling programs written in Perl. Currently the toolkit contains three command-line tools that does sequence conversion, quality filtering, and ORF prediction, all adapted for paired-end sequences specifically. You can read more about the programs, which are released as open source software, on the PETKit page. At the moment they lack proper documentation, but running the software with the “–help” option should bring up a useful set of options for each tool. This is still considered beta-software, so any bug reports, and especially suggestions, are welcome.

Also, if you have an idea of another problem that is unsolved or badly executed for paired-end sequences, let me know, and I will see if I can implement it in PETKit.

Published paper: Guidelines for DNA quality checking

I have co-authored a paper together with, among others, Henrik Nilsson that was published today in MycoKeys. The paper deals with checking quality of DNA sequences prior to using them for research purposes. In our opinion, a lot of the software available for sequence quality management is rather complex and resource intensive. Not everyone have the skills to master such software, and in addition computational resources might also be scarce. Luckily, there’s a lot that can be done in quality control of DNA sequences just using manual means and a web browser. This paper puts these means together into one comprehensible and easy-to-digest document. Our targeted audience is primaily biologists who do not have a strong background in computer science, and still have a dataset requiring DNA sequence quality control.

We have chosen to focus on the fungal ITS barcoding region, but the guidelines should be pretty general and applicable to most groups of organisms. In very short our five guidelines spells:

  1. Establish that the sequences come from the intended gene or marker
    Can be done using a multiple alignment of the sequences and verifying that they all feature some suitable, conserved sub-region (the 5.8S gene in the ITS case)
  2. Establish that all sequences are given in the correct (5’ to 3’) orientation
    Examine the alignment for any sequences that do not align at all to the others; re-orient these; re-run the alignment step; and examine them again
  3. Establish that there are no (at least bad cases of) chimeras in the dataset
    Run the sequences through BLAST in one of the large sequence databases, e.g. at NCBI (or in the ITS case, use the UNITE database), to verify that the best match comprises more or less the full length of the query sequences
  4. Establish that there are no other major technical errors in the sequences
    Examine the BLAST results carefully, particularly the graphical overview and the pairwise alignment, for anomalies (there are some nice figures in the paper on how it should and should not look like)
  5. Establish that any taxonomic annotations given to the sequences make sense
    Examine the BLAST hit list to see that the species names produced make sense

A much more thorough description of these guidelines can be found in the paper itself, which is available under open access from MycoKeys. There’s simply no reason not to go there and at least take a look at it. Happy quality control!

Nilsson RH, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch CL, Nylander JAA, Bergsten J, Porter TM, Jumpponen A, Vaishampayan P, Ovaskainen O, Hallenberg N, Bengtsson-Palme J, Eriksson KM, Larsson K-H, Larsson E, Kõljalg U: Five simple guidelines for establishing basic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys. Issue 4 (2012), 37–63. doi: 10.3897/mycokeys.4.3606 [Paper link]

ISME14 begins today

I am on my way to Copenhagen for the ISME14 conference that begins today. I’m myself quite excited about this event, and will present three posters (two as first author), and give a short talk on antibiotic resistance gene identification and metagenomics. My talk will be in the Bioinformatics in Microbial Ecology session on Thursday afternoon (at 13.30).

If you’d like to talk about Metaxa and Megraft, I will present an SSU-oriented poster in the Monday afternoon poster section (board number 267A). My antibiotic resistance gene poster will be presented on Thursday afternoon (board number 002A), and I really encourage everyone interested in metagenomics (especially metagenomic assembly) to come talk to me then! Finally, I am also partially responsible for a poster on periphyton metagenomics with Martin Eriksson as its main author. This poster is also presented on Monday, in the Microbial Dispersion and Biogeography session (board number 021A).

I hope to be able to make another post later tonight on what are the “essential” sessions for me on this conference. Hope to see you there soon!

New paper accepted: Megraft

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.

Megraft is available for download here, and the paper can be read here.

  1. 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 graft
  2. 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

Presentation at SocBiN 2012

For those of you who like to listen to (or look at) me, I will be giving a presentation at this year’s SocBiN conference in Stockholm. My presentation has the long and quite informative title: Comprehensive Analysis of Antibiotic Resistance Genes in River Sediment, Well Water and Soil Microbial Communities Using Metagenomic DNA Sequencing. The talk is scheduled in the Using Next generation sequence data session, right after Jeroen Raes and Christopher Quince… It’s a short talk, so I will probably need to keep it simple, but it will be the first time I present results generated in relation with my present position, which I personally feel is very nice. We’re moving forward!

Pfam team aims at cleaning erroneous protein families

The guys at Pfam recently introduced a new database, called AntiFam, which will provide HMM profiles for some groups of sequences that seemingly formed larger protein families, although they were not actually real proteins. For example, rRNA sequences could contain putative ORFs, that seems to be conserved over broad lineages; with the only problem being that they are not translated into proteins in real life, as they are part of an rRNA [1].

With this initiative the Xfam team wants to “reduce the number of spurious proteins that make their way into the protein sequence databases.” I have run into this problem myself at some occasions with suspicious sequences in GenBank, and I highly encourage this development towards consistency and correctness in sequence databases. It is of extreme importance that databases remain reliable if we want bioinformatics to tell us anything about organismal or community functions. The Antifam database is a first step towards such a cleanup of the databases, and as such I would like to applaud Pfam for taking actions in this direction.

To my knowledge, GenBank are doing what they can with e.g. barcoding data (SSU, LSU, ITS sequences), but for bioinformatics and metagenomics (and even genomics) to remain viable, these initiatives needs to come quickly; and automated (but still very sensitive) tools for this needs to get our focus immediately. For example, Metaxa [2] could be used as a tool to clean up SSU sequences of misclassified origin. More such tools are needed, and a lot of work remains to be done in the area of keeping databases trustworthy in the age of large-scale sequencing.


  1. Tripp, H. J., Hewson, I., Boyarsky, S., Stuart, J. M., & Zehr, J. P. (2011). Misannotations of rRNA can now generate 90% false positive protein matches in metatranscriptomic studies. Nucleic Acids Research, 39(20), 8792–8802. doi:10.1093/nar/gkr576
  2. Bengtsson, J., Eriksson, K. M., Hartmann, M., Wang, Z., Shenoy, B. D., Grelet, G.-A., Abarenkov, K., et al. (2011). Metaxa: 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 sequencing datasets. Antonie van Leeuwenhoek, 100(3), 471–475. doi:10.1007/s10482-011-9598-6

Antibiotic resistance driving virulence?

It seriously worries me that a number of indications recently have pointed to that the heavy use of antibiotics does not only drive antibiotic resistance development, but also the development towards more virulent and aggressive strains of pathogenic bacteria. First, the genome sequencing of the E. coli strain that caused the EHEC outbreak in Germany in May revealed not only antibiotic resistance genes, but also is also able to make Shiga toxin, which is causes the severe diarrhoea and kidney damage related to the haemolytic uremic syndrome (HUS). The genes encoding the Shiga toxin are not originally bacterial genes, but instead seem to originate from phages. When E. coli gets infected with a Shiga toxin-producing phage, it becomes a human pathogen [1]. David Acheson, managing director for food safety at consulting firm Leavitt Partners, says that exposure to antibiotics might be enhancing the spread of Shiga toxin-producing phage. Some antibiotics triggers what is referred to as the SOS response, which induces the phage to start replicating. The replication of the phage causes the bacteria to burst, releasing the phages, and with them the toxin [1].

Second, there is apparently an ongoing outbreak of scarlet fever in Hong Kong. Kwok-Yung Yuen, microbiologist at the University of Hong Kong, has analyzed the draft sequence of the genome, and suggests that the bacteria acquired greater virulence and drug resistance by picking up one or more genes from bacteria in the human oral and urogenital tracts. He believes that the overuse of antibiotics is driving the emergence of drug resistance in these bacteria [2].

Now, both of these cases are just indications, but if they are true that would be an alarming development, where the use of antibiotics promotes the spread not only of resistance genes, impairing our ability to treat bacterial infections, but also the development of far more virulent and aggressive strains. Combining increasing untreatability with increasing aggressiveness seems to me like the ultimate weapon against our relatively high standards of treatment of common infections. Good thing hand hygiene still seems to help [3].


  1. Phage on the rampage (http://www.nature.com/news/2011/110609/full/news.2011.360.html), Published online 9 June 2011, Nature, doi:10.1038/news.2011.360
  2. Mutated Bacteria Drives Scarlet Fever Outbreak (http://news.sciencemag.org/scienceinsider/2011/06/mutated-bacteria-drives-scarlet.html?etoc&elq=cd94aa347dca45b3a82f144b8213e82b), Published online 27 June 2011.
  3. Luby SP, Halder AK, Huda T, Unicomb L, Johnston RB (2011) The Effect of Handwashing at Recommended Times with Water Alone and With Soap on Child Diarrhea in Rural Bangladesh: An Observational Study. PLoS Med 8(6): e1001052. doi:10.1371/journal.pmed.1001052 (http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001052)