Microbiology, Metagenomics and Bioinformatics

Johan Bengtsson-Palme, University of Gothenburg | Wisconsin Institute for Discovery

Browsing Posts tagged Metagenomics

I am happy to announce that our Viewpoint article on strategies for improving sequence databases has now been published in the journal Proteomics. The paper (1) defines some central problems hampering genomic, proteomic and metagenomic analyses and suggests five strategies to improve the situation:

  1. Clearly separate experimentally verified and unverified sequence entries
  2. Enable a system for tracing the origins of annotations
  3. Separate entries with high-quality, informative annotation from less useful ones
  4. Integrate automated quality-control software whenever such tools exist
  5. Facilitate post-submission editing of annotations and metadata associated with sequences

The paper is not long, so I encourage you to read it in its entirety. We believe that spreading this knowledge and pushing solutions to problems related to poor annotation metadata is vastly important in this era of big data. Although we specifically address protein-coding genes in this paper, the same logic also applies to other types of biological sequences. In this way the paper is related to my previous work with Henrik Nilsson on improving annotation data for taxonomic barcoding genes (2-4). This paper was one of the main end-results of the GoBiG network, and the backstory on the paper follows below the references…


  1. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, Early view (2016). doi: 10.1002/pmic.201600034
  2. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TT, Bengtsson-Palme J, Callaghan TM, Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I, Schüßler A, Senés C, Smith ME, Suija A, Taylor DE, Telleria MT, Weiß M, Larsson KH: Towards a unified paradigm for sequence-based identification of Fungi. Molecular Ecology, 22, 21, 5271–5277 (2013). doi: 10.1111/mec.12481
  3. 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, 67, 1, 11–19 (2014). doi: 10.1007/s13225-014-0291-8
  4. Nilsson RH, Tedersoo L, Ryberg M, Kristiansson E, Hartmann M, Unterseher M, Porter TM, Bengtsson-Palme J, Walker D, de Sousa F, Gamper HA, Larsson E, Larsson K-H, Kõljalg U, Edgar R, Abarenkov K: A comprehensive, automatically updated fungal ITS sequence dataset for reference-based chimera control in environmental sequencing efforts. Microbes and Environments, 30, 2, 145–150 (2015). doi: 10.1264/jsme2.ME14121

In June 2013, the Gothenburg Bioinformatics Group for junior scientists (GoBiG) arranged a workshop with two themes: “Parallelized quantification of genes in large metagenomic datasets” and “Assigning functional predictions to NGS data”. The following discussion on how to database quality influenced results and what could be done to improve the situation was rather intense, and several good ideas were thrown around. I took notes from the meeting, and in the evening I put them down during a warm summer night at the balcony. In fact, the notes were good enough to be an early embryo for a manuscript. So I sent it to some of the most active GoBiG members (Kaisa Thorell and Fredrik Boulund), who were positive regarding the idea to turn it into a manuscript. I wrote it together more properly and we decided that everyone who contributed with ideas at the meeting would be invited to become co-authors. We submitted the manuscript in early 2014, only to see it (rather brutally) rejected. At that point most of us were sucked up in their own projects, so nothing happened to this manuscript for over a year. Then we decided to give it another go, updated the manuscript heavily and changed a few parts to better reflect the current database situation (at this point, e.g., UniProt had already started implementing some of our suggested ideas). Still, some of the proposed strategies were more radical in 2013 than they would be now, more than three years later. We asked the Proteomics editors if they would be interested in the manuscript, and they turned out to be very positive. Indeed, the entire experience with the editors at Proteomics has been very pleasant. I am very thankful to the GoBiG team for this time, and to the editors at Proteomics who saw the value of this manuscript.

Late last year, we introduced FARAO – the Flexible All-Round Annotation Organizer – a software tool that allows visualization of annotated features on contigs. Today, the Applications Note describing the software was published as an advance access paper in Bioinformatics (1). As I have described before, storing and visualizing annotation and coverage information in FARAO has a number of advantages. FARAO is able to:

  • Integrate annotation and coverage information for the same sequence set, enabling coverage estimates of annotated features
  • Scale across millions of sequences and annotated features
  • Filter sequences, such that only entries with annotations satisfying certain given criteria will be outputted
  • Handle annotation and coverage data produced by a range of different bioinformatics tools
  • Handle custom parsers through a flexible interface, allowing for adaption of the software to virtually any bioinformatic tool not supported out of the box
  • Produce high-quality EPS output
  • Integrate with MySQL databases

I have previously used FARAO to produce annotation figures in our paper on a polluted Indian lake (2), as well as in a paper on sewage treatment plants (which is in press and should be coming out any day now). We hope that the tool will find many more uses in other projects in the future!


  1. Hammarén R, Pal C, Bengtsson-Palme JFARAO: The Flexible All-Round Annotation Organizer. Bioinformatics, advance access (2016). doi: 10.1093/bioinformatics/btw499 [Paper link]
  2. 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 [Paper link]

So, on Thursday (May 26th) I will defend my thesis, titled “Antibiotic resistance in the environment: a contribution from metagenomic studies”. I will not dwell into this by writing a novel text, but will instead shamelessly reproduce the press release, which should give a reasonable overview of what I have been doing:

More and more people are infected with antibiotic resistant bacteria. But how do bacteria become resistant? A doctoral thesis from the Centre for Antibiotic Resistance Research at University of Gothenburg has investigated the role of the environment in the development of antibiotic resistance.

“An important question we asked was how low concentrations of antibiotics that can favour the growth of resistant bacteria in the environment”, says Johan Bengtsson-Palme, author of the thesis.

“Based on our analyses, we propose emission limits for 111 antibiotics that should not be exceeded in order to avoid that environmental bacteria become more resistant.”

A starting point to regulate antibiotic pollution
A recent report, commissioned by the British Prime Minister David Cameron, proposes that the emission limits suggested in Johan’s thesis should be used as a starting point to regulate antibiotic pollution from, for example, pharmaceutical production – globally.

“Many people are surprised that such regulations are not already in place, but today it is actually not a crime to discharge wastewater contaminated with large amounts of antibiotics, not even in Europe”, says Johan Bengtsson-Palme.

Resistance genes
In one of the studies in the thesis, the researchers show that resistance genes against a vast range of antibiotics are enriched in an Indian lake polluted by dumping of wastewater from pharmaceutical production.

“It’s scary. Not only do the bacteria carry a multitude of resistance genes. They are also unusually well adapted to share those genes with other bacteria. If a disease-causing bacterium ends up in the lake, it may quickly pick up the genes it needs to become resistant. Since the lake is located close to residential areas, such spread of resistant bacteria to humans is not hard to imagine”, says Johan Bengtsson-Palme.

Spreading by travelers
The thesis also shows that resistant bacteria spread in the intestines of travelers who have visited India or Central Africa, even if the travelers themselves have not become sick.

“That resistant bacteria spread so quickly across the planet highlights that we must adopt a global perspective on the resistance problem”, says Johan Bengtsson-Palme. “Furthermore, it is not enough to reduce the use of antibiotics in healthcare. We must also reduce the use of antibiotics for animals, and try to limit the releases of antibiotics into the environment to try to get control over the growing antibiotic resistance problem before it is too late”.

The thesis Antibiotic resistance in the environment: a contribution from metagenomic studies will be defended on a dissertation on May 26th.

Today marks the five year anniversary for the Metaxa software’s initial release. Much has happened to the software since; Metaxa started off as an rRNA extraction utility for metagenomic data (1), including coarse classification to organism/organelle type. Since it has gained full-scale taxonomic classification ability better or on par with other software packages (2), much greater speed, support for the LSU gene, gained a range of related software tools (3), and spurred development of other tools such as ITSx (4). I have also been involved in no less than four peer-reviewed publications directly related to the software (1-3,5).

But it does not end here; these five years were just the beginning. We are – in different constellations – working on further enhancements to Metaxa2, including support for more genes, an updated classification database, and better customization options. I am very much still devoted to keep Metaxa2 alive and relevant as a tool for taxonomic analysis of metagenomes, applicable whenever accuracy is a key parameter. Thanks for being part of the community for these five years!


  1. Bengtsson J, Eriksson KM, Hartmann M, Wang Z, Shenoy BD, Grelet G, Abarenkov K, Petri A, Alm Rosenblad M, Nilsson RH: 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 (2011). doi:10.1007/s10482-011-9598-6. [Paper link]
  2. 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, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]
  3. Bengtsson-Palme J, Thorell K, Wurzbacher C, Sjöling Å, Nilsson RH: Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2. Ecological Informatics, 33, 45–50 (2016). doi: 10.1016/j.ecoinf.2016.04.004 [Paper link]
  4. Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, De Wit P, Sánchez-García M, Ebersberger I, de Souza F, Amend AS, Jumpponen A, Unterseher M, Kristiansson E, Abarenkov K, Bertrand YJK, Sanli K, Eriksson KM, Vik U, Veldre V, Nilsson RH: Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for use in environmental sequencing. Methods in Ecology and Evolution, 4, 10, 914–919 (2013). doi: 10.1111/2041-210X.12073 [Paper link]
  5. Bengtsson-Palme J, Hartmann M, Eriksson KM, Nilsson RH: Metaxa, overview. In:Nelson K. (Ed.) Encyclopedia of Metagenomics: SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg (2013). doi: 10.1007/978-1-4614-6418-1_239-6 [Link]

Yesterday, Ecological Informatics put our paper describing Metaxa2 Diversity Tools online (1). Metaxa2 Diversity Tools was introduced with Metaxa2 version 2.1 and consists of

  • metaxa2_dc – a tool for collecting several .taxonomy.txt output files into one large abundance matrix, suitable for analysis in, e.g., R
  • metaxa2_rf – generates resampling rarefaction curves (2) based on the .taxonomy.txt output
  • metaxa2_si – species inference based on guessing species data from the other species present in the .taxonomy.txt output file
  • metaxa2_uc – a tool for determining if the community composition of a sample is significantly different from others through resampling analysis

At the same time as I did this update to the web site, I also took the opportunity to update the Metaxa2 FAQ to better reflect recent updates to the Metaxa2 software.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 (3) has been the ability to make simple analyses from the data after classification. The Metaxa2 Diversity Tools included in Metaxa2 2.1 is a seed for such an effort (although not close to a full-fledged community analysis package comparable to QIIME (4) or Mothur (5)). It currently consist of four tools.

The Metaxa2 Data Collector (metaxa2_dc) is the simplest of them (but probably the most requested), designed to merge the output of several *.level_X.txt files from the Metaxa2 Taxonomic Traversal Tool into one large abundance matrix, suitable for further analysis in, for example, R. The Metaxa2 Species Inference tool (metaxa2_si) can be used to further infer taxon information on, for example, the species level at a lower reliability than what would be permitted by the Metaxa2 classifier, using a complementary algorithm. The idea is that is if only a single species is present in, e.g., a family and a read is assigned to this family, but not classified to the species level, that sequence will be inferred to the same species as the other reads, given that it has more than 97% sequence identity to its best reference match. This can be useful if the user really needs species or genus classifications but many organisms in the studied species group have similar rRNA sequences, making it hard for the Metaxa2 classifier to classify sequences to the species level.

The Metaxa2 Rarefaction analysis tool (metaxa2_rf) performs a resampling rarefaction analysis (2) based on the output from the Metaxa2 classifier, taking into account also the unclassified portion of rRNAs. The Metaxa2 Uniqueness of Community analyzer (metaxa2_uc), finally, allows analysis of whether the community composition of two or more samples or groups is significantly different. Using resampling of the community data, the null hypothesis that the taxonomic content of two communities is drawn from the same set of taxa (given certain abundances) is tested. All these tools are further described in the manual and the recent paper (1).

The latest version of Metaxa2, including the Metaxa2 Diversity Tools, can be downloaded here.


  1. Bengtsson-Palme J, Thorell K, Wurzbacher C, Sjöling Å, Nilsson RH: Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2. Ecological Informatics, 33, 45–50 (2016). doi: 10.1016/j.ecoinf.2016.04.004 [Paper link]
  2. Gotelli NJ, Colwell RK: Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379–391 (2000). doi:10.1046/j.1461-0248.2001.00230.x
  3. 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]
  4. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
  5. 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).

After a long wait (1) Sara Lundström’s paper establishing minimal selective concentrations (MSCs) for the antibiotic tetracycline in complex microbial communities (2), of which I am a co-author, has gone online. Personally, I think this paper is among the finest work I have been involved in; a lot of good science have gone into this publication. Risk assessment and management of antibiotics pollution is in great need of scientific data to underpin mitigation efforts (3). This paper describes a method to determine the minimal selective concentrations of antibiotics, and investigates different endpoints for measuring those MSCs. The method involves a testing system highly relevant for aquatic communities, in which bacteria are allowed to form biofilms in aquaria under controlled antibiotic exposure. Using the system, we find that 1 μg/L tetracycline selects for the resistance genes tetA and tetG, while 10 μg/L tetracycline is required to detect changes of phenotypic resistance. In short, the different endpoints studied (and their corresponding MSCs) were:

  • CFU counts on R2A plates with 20 μg/mL tetracycline – MSC = 10 μg/L
  • MIC range – MSC ~ 10-100 μg/L
  • PICT, leucine uptake after short-term TC challenge – MSC ~ 100 μg/L
  • Increased resistance gene abundances, metagenomics – MSC range: 0.1-10 μg/L
  • Increased resistance gene abundances, qPCR (tetA and tetG) – MSC ≤ 1 μg/L
  • Changes to taxonomic diversity – no significant changes detected
  • Changes to taxonomic community composition – MSC ~ 1-10 μg/L

This study confirms that the estimated PNECs we reported recently (4) correspond well to experimentally determined MSCs, at least for tetracycline. Importantly, the selective concentrations we report for tetracycline overlap with those that have been reported in sewage treatment plants (5). We also see that tetracycline not only selects for tetracycline resistance genes, but also resistance genes against other classes of antibiotics, including sulfonamides, beta-lactams and aminoglycosides. Finally, the approach we describe can be used for improved in risk assessment for (also other) antibiotics, and to refine the emission limits we suggested in a recent paper based on theoretical calculations (4).

References and notes

  1. Okay, seriously: how can a journal’s production team return the proofs for a paper within 24 hours of acceptance, and then wait literally five weeks before putting the final proofs online? Nothing against STOTEN, but I honestly wonder what was going on beyond the scenes here.
  2. Lundström SV, Östman M, Bengtsson-Palme J, Rutgersson C, Thoudal M, Sircar T, Blanck H, Eriksson KM, Tysklind M, Flach C-F, Larsson DGJ: Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Science of the Total Environment, 553, 587–595 (2016). doi: 10.1016/j.scitotenv.2016.02.103 [Paper link]
  3. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunstrom B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science and Technology (2015). doi:10.1021/acs.est.5b00302
  4. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015
  5. Michael I, Rizzo L, McArdell CS, Manaia CM, Merlin C, Schwartz T, Dagot C, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Research, 47, 957–995 (2013). doi:10.1016/j.watres.2012.11.027

TriMetAss has today been updated to version 1.2. The new version addresses a number of minor issues, some of which I thought was fixed with the previous version. The update can be found here.

The main problem with the previous version of TriMetAss was that the Trinity developers had changed many options in the Trinity software, which rendered more recent versions of Trinity incompatible with TriMetAss. TriMetAss was not the only external software using Trinity that was affected by these changes. As far as my testing goes, these incompatibilities should now be fixed, by improved Trinity version determination in TriMetAss. This is still not a guarantee for future changes though, so just to make sure, use one of the Trinity versions tested with TriMetAss (versions v2.1.1 or trinityrnaseq_r2013_08_14).

This time I would like to thank Artemis Louyakis at the Univesity of Florida and Tatsuya Unno at the Jeju National University (Korea) for their input on TriMetAss.

I have today uploaded an updated version of Metaxa2 (version 2.1.2). This update primarily improves the memory performance of the Metaxa2 Diversity Tools. The core Metaxa2 programs remain the same as for the previous Metaxa2 versions.

New features and bug fixes in this update:

  • Dramatically improved memory performance of metaxa2_uc
  • Added the 'min' option to the -s flag in metaxa2_uc, which will cause the program to sample the number of entries present in the smallest sample from each sample
  • Fixes a bug that disregarded the level specified by the -l option in metaxa2_si
  • Minor updates and improvements on the manual

The updated version of Metaxa2 can be downloaded here.
Happy barcoding!

A problem with annotating contigs from genomic and metagenomic projects is that there are few tools that allow the visualization of the annotated features, particularly if those features come from different sources. To alleviate this problem, I have (with assistance from Rickard Hammarén and Chandan Pal) over the last years developed a new annotation and read coverage visualization package – FARAO – which we today introduce to the public. FARAO has been used to produce the basis for the the contig annotation figures in my paper on the polluted Indian lake. Storing and visualizing annotation and coverage information in FARAO has a number of advantages. FARAO is able to:

  • Integrate annotation and coverage information for the same sequence set, enabling coverage estimates of annotated features
  • Scale across millions of sequences and annotated features
  • Filter sequences, such that only entries with annotations satisfying certain given criteria will be outputted
  • Handle annotation and coverage data produced by a range of different bioinformatics tools
  • Handle custom parsers through a flexible interface, allowing for adaption of the software to virtually any bioinformatic tool
  • Produce high-quality EPS output
  • Integrate with MySQL databases

FARAO is today moved from a private pre-release state to a public beta state. It is still possible that this version contains bug that we have not discovered in our testing. Please send me an e-mail and make us aware of the potential shortcomings of our software if you find any unexpected behavior in this version of FARAO.

I got a very nice little e-mail yesterday evening, which made me realize that when I posted the Metaxa 2.1 update, I forgot to thank and credit the wonderful Metaxa/Metaxa2 community who have contributed with input on which Metaxa2 features that they would like to see implemented. Particularly, I would like to thank Thomas Haverkamp who suggested the reference option, Åsa Sjöling who brainstormed what led to the metaxa2_uc tool with me, and everyone who have suggested various downstream analysis tricks that have got baked into the Metaxa2 Diversity Tools.

Within the Metaxa team I would like to specifically thank Kaisa Thorell (particularly for the --split_pairs option) and Martin Hartmann (who said that the software should obviously be able to detect which BLAST version that was installed), who keep pushing for features and ideas to make the software better. Thanks a lot to all of you, and have a nice weekend!