Microbiology, Metagenomics and Bioinformatics

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

I was recently involved as an adviser in a report by the County Administrative Board in Västra Götaland (Länsstyrelsen) which has now been published [1]. [UPDATE: The PDF link at Länsstyrelsen's page does not seem to work, but leads to another report in Swedish. I have reported this error to the web admin, we'll see what happens. Once again, the PDF seems to work.] The report aims to identify gaps in the current monitoring system of hazardous substances in the Swedish environment. The report deals with effect based monitoring tools and their usefulness for predicting and/or observing effects of hazardous substances in the environment. The overall conclusion of the report is that there are several gaps in both knowledge and techniques, and a need for developing new resources. However, Sweden still has a good potential to adapt the monitoring system to fill the needs. I have been involved in one of the last chapters, describing the use of metagenomics if study ecosystem function (chapter 30.3). For people with an interest in environmental monitoring, the report is an interesting read in its entirety. For those more interested in applications for metagenomics I recommend turning to page 285 and continue to the end of the report (it’s only five pages on metagenomics, so you’ll manage).

  1. Länsstyrelsen i Västra Götalands län. (2012). Swedish monitoring of hazardous substances in the aquatic environment (No. 2012:23). (A.-S. Wernersson, Ed.) Current vs required monitoring and potential developments (pp. 1–291). Länsstyrelsen i Västra Götalands län, vattenvårdsenheten.

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.

References

  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

The newly formed bioinformatics network for PhD students in Gothenburg (GoBiG), will have an introductory meeting next week, on thursday the 26th at Chalmers. See this page for more info.

Finally I have gotten around to finish my reply to Amy Pruden, who gave me some highly relevant and well-balanced critique of my previous post on antibiotic resistance genes as pollutants, back in early March. Too much came in between, but now I am more or less content with my answer.

First of all I would like to thank Amy for her response to my post on antibiotic resistance genes as pollutants. Her reply is very well thought-through, and her criticism of some of my claims is highly appropriate. For example, I have to agree on that the extracellular DNA pool is vastly uncharacterized, and that my statement on this likely not being a source of resistance transmission is a bit of a stretch. The role of “free-floating” DNA in gene transfer must be further elucidated, and currently we do not really know whether it is important or not; and if so, to what extent it contributes.

However, I still maintain my view that there are problems with considering resistance genes pollutants, mainly because the blurs the line between cause and effect. If we for example consider photosynthetic microbial communities exposed to the photosynthesis inhibitor Irgarol, the communities develop (or acquires) tolerance towards the compound over time (Blanck et al 2009). The tolerance mechanism has been attributed to changes in the psbA gene sequence (Eriksson et al. 2009). If we address this issue from a “resistance-genes-as-pollutants” perspective, would these tolerance-conveying psbA genes be considered pollutants? It would make sense to do so as they are unwanted in weed control circumstances; much like antibiotic resistance genes are unwanted in clinical contexts. It could be argued here that in these microbes such tolerance-associated psbA genes do not cause any harm. But consider for a moment that they did not occur microbes, but in weeds, would they then be considered pollutants? In weeds they would certainly cause (at least economic) harm. Furthermore, say that the tolerance-conveying psbA genes have the ability to spread (which is possible at least in marine settings assisted by phages (Lindell et al 2005)), would that make these tolerance genes pollutants? It is quite of a stretch but as plants can take up genetic material from bacteria (c.f. Clough & Bent 1998, although this is not my area of expertise), there could be a spreading potential to weeds of these tolerance-conveying psbA genes.

What I am trying to say is that if we start viewing antibiotic resistance genes as pollutants per se, instead of looking at the chemicals (likely) causing resistance development, we start blurring the line between cause and effect. Resistance genes in the environment provide resilience to communities (at least to some species – the issue of ecosystem function responses to toxicants is a highly interesting area one as well). However, in this case the resilience itself is the problem, because we think it can spread into human and animal pathogens. But from my point of view, the causes are still use, overuse, misuse and inappropriate release of antibiotics. Therefore, I maintain that we should be careful with pointing out resistance genes by themselves as pollutants – if we do not have very good reasons to do so.

Nevertheless, that does not mean that I think Pruden, and many other prominent authors, are wrong when they refer to resistance genes as pollutants. All I want to point out is that the statement in itself is a bit dangerous, as it might draw attention towards mitigating the effect of pollution, instead of mitigating the source of pollution itself. The persistence of resistance genes in bacterial genomes is alarming (Andersson & Hughes 2011), as it means that removal of selection pressures may have less effect on resistance gene abundance than anticipated. However, the only way I see out of this darkening scenario is to:

  1. Minimize the selection pressure for resistance genes in the clinical setting
  2. Immediately reduce environmental release of antibiotics, both from manufacturing and use. This primarily has to be done using better treatment technologies
  3. Find the routes that enable environmental bacteria to disseminate resistance genes to clinically relevant species and strains – and close them
  4. Develop antibiotics exploiting new mechanisms to eliminate bacteria

Lastly, I would like to thank Amy for taking my critique seriously – I think we agree on a lot more than we differ on, and I look forward to have this discussion in person at some point. I think we both agree that regardless of our standpoint, the terminology used in this context deserves to be discussed. Nevertheless, the terminology is quite unimportant compared to the values that are at stake – our fundamental ability to treat diseases and perform modern health care.

References

  1. Andersson, D.I. & Hughes, D., 2011. Persistence of antibiotic resistance in bacterial populations. FEMS Microbiology Reviews, 35(5), pp.901–911.
  2. Blanck, H., Eriksson, K. M., Grönvall, F., Dahl, B., Guijarro, K. M., Birgersson, G., & Kylin, H. (2009). A retrospective analysis of contamination and periphyton PICT patterns for the antifoulant irgarol 1051, around a small marina on the Swedish west coast. Marine pollution bulletin, 58(2), 230–237. doi:10.1016/j.marpolbul.2008.09.021
  3. Clough, S. J., & Bent, A. F. (1998). Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. The Plant journal : for cell and molecular biology, 16(6), 735–743.
  4. Eriksson, K. M., Clarke, A. K., Franzen, L.-G., Kuylenstierna, M., Martinez, K., & Blanck, H. (2009). Community-level analysis of psbA gene sequences and irgarol tolerance in marine periphyton. Applied and Environmental Microbiology, 75(4), 897–906. doi:10.1128/AEM.01830-08
  5. Lindell, D., Jaffe, J. D., Johnson, Z. I., Church, G. M., & Chisholm, S. W. (2005). Photosynthesis genes in marine viruses yield proteins during host infection. Nature, 438(7064), 86–89. doi:10.1038/nature04111

I received some well-formulated and very much relevant critique on my post Why viewing antibiotic resistance genes as a pollutant is a problem, which I wrote in January. To encourage the debate on this issue, I have asked the author – Amy Pruden – for her permission to republish it here, to give it the visibility it deserves. I intend to follow up on her comments in a forthcoming post, but I have not had time to formulate my answer yet. Until then, please read and contemplate both the original post by me, and Amy’s highly relevant answer below. I hope that we can continue this discussion in the same fruitful manner!

First of all I thank Johan Bengtsson for initiating a lively and much needed discussion on which pollutant we should precisely be targeting, antibiotics or antibiotic resistance genes (ARGs), in our important war against the spread of antibiotic resistance. As Bengtsson correctly alludes, my perspective comes from that of environmental science and engineering. At the core of these disciplines is defining and predicting the fate of pollutants in the environment, as well as designing appropriate means for their control. For these purposes, the definition of the pollutant of interest is of central importance. In general they may be defined as “undesired or harmful constituents within an environmental matrix, usually of human origin.” Pollutants may be classified in all shapes and sizes, including conservative (i.e., not subject to degradation or growth), non-conservative, biotic, abiotic, dissolved, and suspended (i.e., not dissolved). Thus, the first point, regarding the nature by which ARGs are spread disqualifying them from being considered as pollutants, is inaccurate.

At the same time, I recognize and agree that ARGs are indeed a natural and important aspect of the natural ecosystem. I commend recent work revealing the vast “antibiotic-resistome” in ancient environments (D’Costa et al. 2011; Allen et al. 2009), as it provides an essential understanding of the baseline antibiotic resistance in the pre-antibiotic era, which may serve as contrast for observations in the current antibiotic era. Thus, I agree that not all ARGs are pollutants, rather, anthropogenic sources of ARGs are the agents of interest. Perhaps I and others are guilty of not making this distinction more clear. It should also be pointed out that likewise, the vast majority of antibiotics in use today are derived from natural compounds, yet I agree that they can also serve as important environmental pollutants of concern. Thus, it is not necessarily whether the constituent is naturally occurring that defines the pollutant, rather its magnitude and distribution, as influenced by human activities.

It is agreed that viewing ARGs as contaminants does pose technical challenges. They may amplify within a host, or attenuate due to degradation or diminished selection pressure. However, with appropriate understanding of the mechanisms of transport and persistence, accurate models may be developed. I do contend that the jury is still out regarding the relative importance of extracellular and intracellular ARGs. The pool of extracellular DNA remains vastly uncharacterized, and some studies suggest that it is more extensive than previously thought (Wu et al. 2009; Corinaldesi et al. 2005). Other studies have specifically demonstrated the capability of extracellular ARGs to persist under certain environmental conditions and maintain its integrity for host uptake (Cai et al. 2007). While focusing attention on individual resistant strains of bacteria has merit in some instances, this approach is also greatly limited by the unculturability of the vast majority of environmental microbes. As we have now entered the metagenomic era, we now have the tools to tackle the complexity of resistance elements in the environment and precisely define the human influence. Distribution of ARGs may also be considered in parallel with key genetic elements driving their horizontal gene transfer, such as plasmids, transposons, and integrons.

Regarding the antibiotics themselves, clearly they are important. The direct relationship between clinical use and increasing rates of antibiotic resistance is well-documented and certainly continued vigilance in promoting their appropriate use and disposal is called for. What remains much foggier is the exact role of environmental antibiotics in enabling selection once released into the environment. There is good evidence that even sub-inhibitory levels of antibiotics can stimulate various functions in the cell, especially horizontal gene transfer, as reviewed recently by Aminov (2011). However, environmentally-relevant concentrations driving selection of resistant strains are largely unknown. Further, at what point along a discharge pathway from wastewater treatment plant or livestock lagoon do ARGs persist independently of ambient antibiotic conditions? Indeed, some studies have noted correlations between antibiotics and ARGs in environmental matrices while others have noted an absence of such a correlation. In either case, it appears that ARGs persist and are transported further along pathways than antibiotics, suggesting distinct factors governing transport (McKinney et al. 2010; Peak et al. 2007). Research is needed to better understand the mechanisms at play, such as antibiotics other selectors (e.g. metals and other toxins), in leaving a human foot-print on environmental reservoirs of resistance. Nonetheless, a reasonable approach for mitigating risk seems to be focusing attention on developing appropriate technologies for eliminating both antibiotics and genetic material from wastestreams.

Thanks again for opening this discussion- I hope to meet you at a conference sometime in the future!

References
1. Allen, H.K., Moe, L.A., Rodbumrer, J., Gaarder, A., & Handelsman, J., 2009. Functional metagenomics reveals diverse b-lactamases in a remote Alaskan soil. ISME 3, pp. 243-251.
2. Aminov, R.I., 2011. Horizontal gene exchange in environmental microbiota. Front. Microbiol. 2,158 doi:10.3389/fmicb.2011.00158.
3. Corinaldesi, C., Danovaro, R. & Dell‘Anno, A., 2005. Simultaneous recovery of intracellular and extracellular DNA suitable for molecular studies from marine sediments. Appl. Environ. Microbiol. 71, pp. 46-50.
4. D’Costa, V.M., McGrann, K.M., Hughes, D.W., & Wright, G.D., 2006. Sampling the antibiotic resistome. Science 311, pp. 374-377.
5. McKinney, C.W., Loftin, K.A., Meyer, M.T., Davis, J.G., & Pruden, A., 2010. tet and sul antibiotic resistance genes in livestock lagoons of various operation type, configuration, and antibiotic occurrence. Environ. Sci. Technol. 44 (16), pp. 6102-6109.
6. Peak, N., C.W. Knapp; R.K. Yang; M.M. Hanfelt; M.S. Smith, D.S. Aga, & Graham, D. W., 2007. Abundance of six tetracycline resistance genes in wastewater lagoons at cattle feedlots with different antibiotic use strategies. Environ. Microbiol. 9 (1), pp. 143–151.
7. Wu, J. F. & Xi, C. W., 2009. Evaluation of different methods for extracting extracellular DNA from the biofilm matrix. Appl. Environ. Microbiol. 75, pp. 5390-5395.

Michael BartonPierre Lindenbaum, and Rob Syme are currently running a survey on what it is like to be a bioinformatician today. The survey has a history since back in 2008, and I think everyone who’s doing bioinformatics should take it. It aims “to understand the field of bioinformatics by surveying the people whom work in it,” which I think is a nice objective for running a survey. It will be interesting to see what comes out of it. Take the survey, and read more about it at: http://bioinfsurvey.org/

It is not uncommon that scientists, especially researchers active within the environmental field, view antibiotic resistance genes (ARGs) as pollutants (e.g. Pruden et al. 2006). While there are practical benefits of doing so, especially when explaining the threat of antibiotic resistance to politicians and the public, this generalization is a little bit problematic from a scientific view. There are several reasons why this view is not as straightforward as one might think.

The first is that ARGs does not spread the same way as pollutants do. ARGs are carried in bacteria. This means that ARGs cannot readily be transferred into, e.g. the human body by themselves. They need to be carried by a bacterial host (ARGs present on free DNA floating around is of course possible, but likely not a major source of ARG transmission into new systems). Therefore, when we find resistance genes in an environment, that is an extremely strong indication of that we also have resistant bacteria. Also, finding ARGs is not necessarily an indication of high levels of antibiotics, as the resistance genes can remain present in the bacterial genome for extended periods of time after exposure (Andersson & Hughes 2011).

The second reason why ARGs should not be viewed as pollutants is that they are not. If anything, the ARGs contribute to the resilience of the ecosystem towards the actual toxicants, which are the antibiotics themselves. Having a resistance gene is an insurance that you will survive antibiotic perturbations. Calling ARGs pollutants just deflects attention from the real problem to nature’s response to our contaminant.

What we have to do is not to try to defeat the resistance itself, but to try to minimize the spread of it. This means that we need to constantly monitor our usage and possible emissions of antibiotics and try to reduce risk environments as much as possible. Emissions from sewage treatment plants (Karthikeyan & Meyer 2006; Lindberg et al. 2007), hospitals (Lindberg et al. 2004), production facilities (Larsson et al. 2007; Fick et al. 2009) and food production (Davis et al. 2011) are obvious starting points, but we need to continuously monitor sources of antibiotic pollutions. Of course, this is only my view of the problem, but I believe that while the problem for our society lies within the resistance genes, the cause lies within the actual pollutants – the antibiotics we use and abuse.

References

  1. Andersson, D.I. & Hughes, D., 2011. Persistence of antibiotic resistance in bacterial populations. FEMS Microbiology Reviews, 35(5), pp.901–911.
  2. Davis, M.F. et al., 2011. An ecological perspective on U.S. industrial poultry production: the role of anthropogenic ecosystems on the emergence of drug-resistant bacteria from agricultural environments. Current Opinion in Microbiology, 14(3), pp.244–250.
  3. Fick, J. et al., 2009. Contamination of surface, ground, and drinking water from pharmaceutical production. Environmental toxicology and chemistry / SETAC, 28(12), pp.2522–2527.
  4. Karthikeyan, K.G. & Meyer, M.T., 2006. Occurrence of antibiotics in wastewater treatment facilities in Wisconsin, USA. The Science of the total environment, 361(1-3), pp.196–207.
  5. Larsson, D.G.J., de Pedro, C. & Paxeus, N., 2007. Effluent from drug manufactures contains extremely high levels of pharmaceuticals. Journal of hazardous materials, 148(3), pp.751–755.
  6. Lindberg, R. et al., 2004. Determination of antibiotic substances in hospital sewage water using solid phase extraction and liquid chromatography/mass spectrometry and group analogue internal standards. Chemosphere, 57(10), pp.1479–1488.
  7. Lindberg, R.H. et al., 2007. Environmental risk assessment of antibiotics in the Swedish environment with emphasis on sewage treatment plants. Water research, 41(3), pp.613–619.
  8. Pruden, A. et al., 2006. Antibiotic resistance genes as emerging contaminants: studies in northern Colorado. Environmental Science & Technology, 40(23), pp.7445–7450.

Merry Christmas

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I just want to wish everybody a merry Christmas and a happy new year, from the sunny town of Stellenbosch in South Africa. I will have the pleasure of spending Christmas here this year. As some holiday reading I have provided a longer peace on metagenomics, and I hope to be able to provide a shorter one on resistance genes as well. Happy holidays!

One thing that I find slightly annoying is when people do not get the basic concepts right – or when debatable concepts are used without discussion of their implications. This further annoys me when it is done by senior scientists, who should know better. Sometimes, I guess this happens out of ignorance, and sometimes to be able to stick your subject to a certain buzzword concept. Neither is good, even though the former reason is little more forgivable then the latter. One area where this problem becomes agonizingly evident is when molecular biologists or medical scientists moves into ecology, as has happened with the advent of metagenomics. When the study of the human gut microflora turned into a large-scale sequencing effort, people who had previously studied bacteria grown on plates started facing a world of community ecology. However, I get the impression that way too often these people do not ask ecologists for advice, or even read up on the ecological literature. Which, I suppose, is the reason why medical scientists can talk about how the human gut microflora can “evolve” into a stable community a couple years after birth, even though words such as “development” or “succession” would be much more accurate to describe this change.

The marker gene flaw

To set what I mean straight, let us compare the human gut to a forest. If an open field is left to itself, larger plants will slowly inhabit it, and gradually different species will replace each other, until we have a fully developed forest. Similarly, the human gut microflora is at birth rather unstable, but stabilizes relatively quickly and within a few years we have a microbial community with “adult-like” characteristics. To arrive at this conclusion, scientists generally use the 16S (small sub-unit) genetic marker to study the bacterial species diversity. This works in pretty much the same way as going out into the forest and count trees of different kinds.

Now, if I went out into the forest once and counted the tree species, waited for 50 years and then did the same thing again, I would presumably see that the forest species composition had changed. However, if I called this “evolution”, fellow scientists would laugh at me. Raspberry bushes do not evolve into birches, and birches do not evolve into firs. Instead, ecologists talk about “succession”; a progressive transformation of a community, going on until a stable community is formed. The concept of succession seems well-suited also to describe what is happening in the human gut, and should of course also be used in that setting. The most likely driver of the functional community changes is not that some bacterial species have evolved new functions, but rather that bacterial species performing these new functions have outcompeted the once previously present.

In fact, I would argue that it is impossible to study evolution through a genetic marker such as the 16S gene (except in the rare case when you study evolution of the 16S gene itself). Instead, the only thing we could assess using a marker gene is how the copy number of the different gene variants change over time (or space, or conditions). The copy number tells us about the species composition of the community at a given time, which can be used to measure successional changes. However, evolutionary changes would require heritable changes in the characteristics of biological populations, i.e. that their genetic material change in some way. Unless that change happens in the marker gene of choice, we cannot measure it, and the alterations of composition we measure will only reflect differences in species abundances. These differences might have arisen from genetic (i.e. evolutionary) changes, but we cannot assess that.

What are we studying with metagenomics?

This brings us to the next problem, which is not only a problem of semantics and me getting annoyed, but a problem with real implications. What are we really studying using metagenomics? When we apply an environmental sequencing approach to a microbial community, we get a snapshot of the genetic material at a given time and site; at specific conditions. Usually, we aim to characterize the community from a taxonomic or functional perspective, and we often have some other community which we want to compare to. However, if we only collect data from different communities at one time point, or if we only study a community before and after exposure, we have no way of telling if differences stem from selective pressures or from more a random succession progress. As most microbial habitats are not as well studied as the human gut, we know little about microbial community assembly and succession.

Also, in ecology a disturbance to a particular community is generally considered as a starting point for a new succession process. This process may, or may not, return the community to the same stable state. However, if the disturbance was of permanent nature, the new community will have to adapt to the new conditions, and the stable state will likely not have the same species distribution. Such an adaption could be caused by genetic changes (which would clearly be an evolutionary process), or by simple replacement of sensitive species with tolerant ones. The latter would be a selective process, but not necessarily an evolutionary one. If the selection does not alter the genetic material within the species, but only the species composition, I would argue that this is also a case of succession.

Complications with resistance

This complicates the work with metagenomic data. If we study antibiotic resistance genes, and say that bacteria in an environment have evolved antibiotic resistance, we base that assertion on that genes responsible for resistance have either evolved within the present bacteria, or have (more likely) been transferred into the genomes of the bacteria via horizontal gene transfer. However, if the resistance profile we see is simply caused by a replacement of sensitive species with resistant ones, we have not really discovered something new evolving, but are only witnessing spread of already resistant bacteria. In the gut, this would be a problem by itself, but say that we do the same study in the open environment. We already know that environmental bacteria have contained resistance genes for ages, so the real threat to human health here would be a spread from naturally resistant bacteria to human pathogens. However, as mentioned earlier, without extremely well thought-through methodology we cannot really see such transmissions of resistance genes. Here, the search for mobile elements, and large-scale takes on community composition vs. resistance profiles in contaminated and non-polluted areas can play a huge role in shedding light on the question of spreading. However, this will require larger and better planned experiments using metagenomics than what is generally performed at the moment. The questions of microbial community assembly, dispersal, succession and adaption are still largely unanswered, and our metagenomic and environmental sequencing approaches have just started to tinker around with the lid of the jar.

I am extremely happy to announce that Metaxa 1.1 (first announced back in July) has finally left the beta stage, and is now designated as a feature complete 1.1 update. We consider this update stable for production use. The 1.1 update utilize hmmsearch instead of hmmscan for higher extraction speeds and better accuracy. This clever trick was inspired by a blog post by HMMER’s creator Sean Eddy on hmmscan vs hmmsearch (http://selab.janelia.org/people/eddys/blog/?p=424). As the speedup comes from the extraction step, the speed increase will be largest for huge data sets with only a small proportion of actual SSU sequences (typically large 454 metagenomes).

What took so long, you might ask, as I promised an imminent release already in August. Well, during testing a difference in scoring was discovered. This difference did not have any implications for long sequences (> ~350 bp), but caused Metaxa to have problems on short reads (most evident on ~150 bp and shorter). Therefore, the scoring system had to be redesigned, which in turn required more extensive testing. Now, however, Metaxa 1.1 has a fine-tuned scoring system, which by default is based on scores instead of E-values, and in some instances have even better detection accuracy than the old Metaxa version. We encourage everyone to try out this new version of Metaxa (although the 1.0.2 version will remain available for download). It should be bug free, but we cannot ensure 100% compatibility in all usage scenarios. Therefore, we are happy if you report any bugs or inconsistencies to the e-mail address: metaxa (at] microbiology [dot) se.

The new version of Metaxa can be downloaded here: http://microbiology.se/software/metaxa/ Please note that the manual has not yet been updated yet, so use the help feature for the up-to-date options. Happy SSU detecting!