Microbiology, Metagenomics and Bioinformatics

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

Browsing Posts tagged DNA sequencing

MycoKeys today put a paper online which I was involved in. The paper describes the results of a workshop in May, when we added and refined annotations for fungal ITS sequences according to the MIxS-Built Environment annotation standard (1). Fungi have been associated with a range of unwanted effects in the built environment, including asthma, decay of building materials, and food spoilage. However, the state of the metadata annotation of fungal DNA sequences from the built environment is very much incomplete in public databases. The workshop aimed to ease a little part of this problem, by distributing the re-annotation of public fungal ITS sequences across 36 persons. In total, we added or changed of 45,488 data points drawing from published literature, including addition of 8,430 instances of countries of collection, 5,801 instances of building types, and 3,876 instances of surface-air contaminants. The results have been implemented in the UNITE database and shared with other online resources. I believe, that distributed initiatives like this (and the ones I have been involved in in the past (2,3)) serve a very important purpose for establishing better annotation of sequence data, an issue I have brought up also for sequences outside of barcoding genes (4). The full paper can be found here.

References

  1. Abarenkov K, Adams RI, Laszlo I, Agan A, Ambrioso E, Antonelli A, Bahram M, Bengtsson-Palme J, Bok G, Cangren P, Coimbra V, Coleine C, Gustafsson C, He J, Hofmann T, Kristiansson E, Larsson E, Larsson T, Liu Y, Martinsson S, Meyer W, Panova M, Pombubpa N, Ritter C, Ryberg M, Svantesson S, Scharn R, Svensson O, Töpel M, Untersehrer M, Visagie C, Wurzbacher C, Taylor AFS, Kõljalg U, Schriml L, Nilsson RH: Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden). MycoKeys, 16, 1–15 (2016). doi: 10.3897/mycokeys.16.10000
  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. 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

After a long wait (1), Science of the Total Environment has finally decided to make our paper on selection of antibiotic resistance genes in sewage treatment plants (STPs) available (2). STPs are often suggested to be “hotspots” for emergence and dissemination of antibiotic-resistant bacteria (3-6). However, we actually do not know if the selection pressures within STPs, that can be caused either by residual antibiotics or other co-selective agents, are sufficiently large to specifically promote resistance. To better understand this, we used shotgun metagenomic sequencing of samples from different steps of the treatment process (incoming water, treated water, primary sludge, recirculated sludge and digested sludge) in three Swedish STPs in the Stockholm area to characterize the frequencies of resistance genes to antibiotics, biocides and metal, as well as mobile genetic elements and taxonomic composition. In parallel, we also measured concentrations of antibiotics, biocides and metals.

We found that only the concentrations of tetracycline and ciprofloxacin in the influent water were above those that we predict to cause resistance selection (7). However, there was no consistent enrichment of resistance genes to any particular class of antibiotics in the STPs, neither for biocide and metal resistance genes. Instead, the most substantial change of the bacterial communities compared to human feces (sampled from Swedes in another study of ours (8)) occurred already in the sewage pipes, and was manifested by a strong shift from obligate to facultative anaerobes. Through the treatment process, resistance genes against antibiotics, biocides and metals were not reduced to the same extent as fecal bacteria were.

Worryingly, the OXA-48 beta-lactamase gene was consistently enriched in surplus and digested sludge. OXA-48 is still rare in Swedish clinical isolates (9), but provides resistance to carbapenems, one of our most critically important classes of antibiotics. However, taken together metagenomic sequencing did not provide clear support for any specific selection of antibiotic resistance. Rather, since stronger selective forces affect gross taxonomic composition, and thereby also resistance gene abundances, it is very hard to interpret the metagenomic data from a risk-for-selection perspective. We therefore think that comprehensive analyses of resistant vs. non-resistant strains within relevant species are warranted.

Taken together, the main take-home messages of the paper (2) are:

  • There were no apparent evidence for direct selection of resistance genes by antibiotics or co-selection by biocides or metals
  • Abiotic factors (mostly oxygen availability) strongly shape taxonomy and seems to be driving changes of resistance genes
  • Metagenomic and/or PCR-based community studies may not be sufficiently sensitive to detect selection effects, as important shifts towards resistant may occur within species and not on the community level
  • The concentrations of antibiotics, biocides and metals were overall reduced, but not removed in STPs. Incoming concentrations of antibiotics in Swedish STPs are generally low
  • Resistance genes are overall reduced through the treatment process, but far from eliminated

References and notes

  1. Okay, those who takes notes know that I have already complained once before on Science of the Total Environment’s ridiculously long production handling times. But, seriously, how can a journal’s production team return the proofs for after three days of acceptance, and then wait seven weeks before putting the final proofs online? I still wonder what is going on beyond the scenes, which is totally obscure because the production office also refuses to respond to e-mails. Not a nice publishing experience this time either.
  2. Bengtsson-Palme J, Hammarén R, Pal C, Östman M, Björlenius B, Flach C-F, Kristiansson E, Fick J, Tysklind M, Larsson DGJ: Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics. Science of the Total Environment, in press (2016). doi: 10.1016/j.scitotenv.2016.06.228 [Paper link]
  3. Rizzo L, Manaia C, Merlin C, Schwartz T, Dagot C, Ploy MC, Michael I, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for antibiotic resistant bacteria and genes spread into the environment: a review. Science of the Total Environment, 447, 345–360 (2013). doi: 10.1016/j.scitotenv.2013.01.032
  4. Laht M, Karkman A, Voolaid V, Ritz C, Tenson T, Virta M, Kisand V: Abundances of Tetracycline, Sulphonamide and Beta-Lactam Antibiotic Resistance Genes in Conventional Wastewater Treatment Plants (WWTPs) with Different Waste Load. PLoS ONE, 9, e103705 (2014). doi: 10.1371/journal.pone.0103705
  5. Yang Y, Li B, Zou S, Fang HHP, Zhang T: Fate of antibiotic resistance genes in sewage treatment plant revealed by metagenomic approach. Water Research, 62, 97–106 (2014). doi: 10.1016/j.watres.2014.05.019
  6. Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015). doi: 10.1038/nrmicro3439
  7. 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
  8. Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrobial Agents and Chemotherapy, 59, 10, 6551–6560 (2015). doi: 10.1128/AAC.00933-15
  9. Hellman J, Aspevall O, Bengtsson B, Pringle M: SWEDRES-SVARM 2014. Consumption of antimicrobials and occurrence of antimicrobial resistance in Sweden. Public Health Agency of Sweden and National Veterinary Institute, Solna/Uppsala, Sweden. Report No.: 14027. Available from: http://www.folkhalsomyndigheten.se/publicerat-material/ (2014)

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…

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

Backstory
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!

References

  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]

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.

References

  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).

Metaxa2 has been updated again today to version 2.1.3. This update adds a few features to the Metaxa2 Diversity Tools (metaxa2_uc and metaxa2_rf). The core Metaxa2 programs remain the same as for the previous Metaxa2 versions. The new features were suggested as part of the review process of a Metaxa2-related manuscript, and we thank the anonymous reviewers for their great suggestions!

New features and bug fixes in this update:

  • Added the Chao1, iChao1 and ACE estimators in addition to the original species abundance (“Bengtsson-Palme”) model in metaxa2_rf
  • Added the Raup-Crick dissimilarity method to the metaxa2_uc tool
  • Added a warning message when data is highly skewed for metaxa2_uc
  • Improved robustness of the ‘model’ mode of metaxa2_uc for highly skewed sample groups
  • Fixed a bug causing miscalculation of Euclidean distances on binary data in metaxa2_uc

The updated version of Metaxa2 can be downloaded here.

Happy barcoding!

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

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.

Yesterday, a paper I co-authored with my colleagues Chandan Pal, Erik Kristiansson and Joakim Larsson on the co-occurences of resistance genes against antibiotics, biocides and metals in bacterial genomes and plasmids became published in BMC Genomics. In this paper (1) we utilize the publicly available, fully sequenced, genomes and plasmids in GenBank to investigate the co-occurence network of resistance genes, to better understand risks for co-selection for resistance against different types of compounds. In short, the findings of the paper are that:

  • ARGs are associated with BMRG-carrying bacteria and the co-selection potential of biocides and metals is specific towards certain antibiotics
  • Clinically important genera host the largest numbers of ARGs and BMRGs and those also have the highest co-selection potential
  • Bacteria isolated from human and domestic animal origins have the highest co-selection potential
  • Plasmids with co-selection potential tend to be conjugative and carry toxin-antitoxin systems
  • Mercury and QACs are potential co-selectors of ARGs on plasmids, however BMRGs are common on chromosomes and could still have indirect co-selection potential
  • 14 percent of bacteria and more than 70% of the plasmids completely lacked resistance genes

This analysis was possible thanks to the BacMet database of antibacterial biocide and metal resistance genes, published about two years ago (2). The visualization of the plasmid co-occurence network we ended up with can be seen below. Note the strong connection between the mercury resistance mer operon and the antibiotic resistance genes to the right.

On a side note, it is interesting to note that the underrepresentation of detoxification systems in marine environments we noted last year (3) still seems to hold for genomes (and particularly plasmids), supporting the genome streamlining hypothesis (4).

References:

  1. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential. BMC Genomics, 16, 964 (2015). doi: 10.1186/s12864-015-2153-5 [Paper link]
  2. Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DGJ: BacMet: Antibacterial Biocide and Metal Resistance Genes Database. Nucleic Acids Research, 42, D1, D737-D743 (2014). doi: 10.1093/nar/gkt1252 [Paper link]
  3. Bengtsson-Palme J, Alm Rosenblad M, Molin M, Blomberg A: Metagenomics reveals that detoxification systems are underrepresented in marine bacterial communities. BMC Genomics, 15, 749 (2014). doi: 10.1186/1471-2164-15-749 [Paper link]
  4. Giovannoni SJ, Cameron TJ, Temperton B: Implications of streamlining theory for microbial ecology. ISME Journal, 8, 1553-1565 (2014).