Tag: Species classification

In the very first episode of the Bengtsson-Palme lab podcast, a crew consisting of Johan Bengtsson-Palme, Emil Burman, Haveela Kunche and Anna Abramova discusses the origin of the novel coronavirus, interactions between influenza and the respiratory tract microbiome, resistant bacteria in glaciers, pathway analysis methods, a new genus of bacteria discovered in Gothenburg, as well as life in research during a global pandemic.

The specific papers discussed in the pod (with approximate timings) are as follows:

• 10:15 – Andersen, K.G., Rambaut, A., Lipkin, W.I., Holmes, E.C., Garry, R.F., 2020. The proximal origin of SARS-CoV-2. Nature Medicine 26, 450–452. https://doi.org/10.1038/s41591-020-0820-9
• 17:30 – Zhou, P., et al., 2020. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273. https://doi.org/10.1038/s41586-020-2012-7
• 19:30 – https://www.fli.de/en/press/press-releases/press-singleview/novel-coronavirus-sars-cov-2-fruit-bats-and-ferrets-are-susceptible-pigs-and-chickens-are-not/
• 20:45 – Kadioglu, O., Saeed, M., Greten, H.J., Efferth, T, 2020. Identification of novel compound against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Bulletin of the World Health Organization. https://doi.org/10.2471/BLT.20.255943
• 21:45 – Cheng, V.C.C., Lau, S.K.P., Woo, P.C.Y., Yuen, K.Y., 2007. Severe Acute Respiratory Syndrome Coronavirus as an Agent of Emerging and Reemerging Infection. Clinical Microbiology Reviews 20, 660–694. https://doi.org/10.1128/CMR.00023-07
• 22:15 – Fan, Y., Zhao, K., Shi, Z.-L., Zhou, P., 2019. Bat Coronaviruses in China. Viruses 11, 210. https://doi.org/10.3390/v11030210
• 29:15 – Zhang, L., et al., 2020. Characterization of antibiotic resistance and host-microbiome interactions in the human upper respiratory tract during influenza infection. Microbiome 8. https://doi.org/10.1186/s40168-020-00803-2
• 39:15 – Makowska, N., et al., 2020. Occurrence of integrons and antibiotic resistance genes in cryoconite and ice of Svalbard, Greenland, and the Caucasus glaciers. Science of The Total Environment 716, 137022. https://doi.org/10.1016/j.scitotenv.2020.137022
• 49:45 – Bengtsson-Palme, J., Boulund, F., Fick, J., Kristiansson, E., Larsson, D.G.J., 2014. Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in microbiology 5, 648. https://doi.org/10.3389/fmicb.2014.00648
• 58:45 – Gillings, M.R., 2014. Integrons: past, present, and future. Microbiology and molecular biology reviews : MMBR 78, 257–277. https://doi.org/10.1128/MMBR.00056-13
• 60:45 – Moradi, E., Marttinen, M., Häkkinen, T., Hiltunen, M., Nykter, M., 2019. Supervised pathway analysis of blood gene expression profiles in Alzheimer’s disease. Neurobiology of Aging 84, 98–108. https://doi.org/10.1016/j.neurobiolaging.2019.07.004
• 62:15 – Johnson, W.E., Li, C., Rabinovic, A., 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127. https://doi.org/10.1093/biostatistics/kxj037
• 72:15 – Marathe, N.P., et al., 2019. Scandinavium goeteborgense gen. nov., sp. nov., a New Member of the Family Enterobacteriaceae Isolated From a Wound Infection, Carries a Novel Quinolone Resistance Gene Variant. Frontiers in Microbiology 10. https://doi.org/10.3389/fmicb.2019.02511
• 76:00 – Boulund, F., et al., 2017. Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics 18, 438. https://doi.org/10.1186/s12864-017-4064-0

The podcast was recorded on April 9, 2020. If you want to reach out to us with comments, suggestions or other feedback, please send an e-mail to podcast at microbiology dot se or contact @bengtssonpalme via Twitter. The music that can be heard on the pod is composed by Johan Bengtsson-Palme and is taken from the album Cafe Phonocratique.

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On Friday, Molecular Ecology Resources put online Christian Wurzbacher‘s latest paper, of which I am also a coauthor. The paper presents three sets of general primers that allow for amplification of the complete ribosomal operon from the ribosomal tandem repeats, covering all the ribosomal markers (ETS, SSU, ITS1, 5.8S, ITS2, LSU, and IGS) (1). This paper is important because it introduces a technique to utilize third generation sequencing (PacBio and Nanopore) to generate high‐quality reference data (equivalent or better than Sanger sequencing) in a high‐throughput manner. The paper shows that the quality of the Nanopore generated sequences was 99.85%, which is comparable with the 99.78% accuracy described for Sanger sequencing.

My main contribution to this paper is the consensus sequence generation script – Consension – which is available from my software page. Importantly, there are huge gaps in the reference databases we use for taxonomic classification and this method will facilitate the integration of reference data from all of the ribosomal markers. We hope that this work will stimulate large-scale generation of ribosomal reference data covering several marker genes, linking previously spread-out information together.

Reference

1. Wurzbacher C, Larsson E, Bengtsson-Palme J, Van den Wyngaert S, Svantesson S, Kristiansson E, Kagami M, Nilsson RH: Introducing ribosomal tandem repeat barcoding for fungi. Molecular Ecology Resources, Accepted article (2018). doi: 10.1111/1755-0998.12944 [Paper link]

Last week, I uploaded a new database to the Metaxa2 Database Repository, called DAIRYdb. DAIRYdb (1) is a manually curated reference database for 16S rRNA amplicon sequences from dairy products. Significant efforts have been put into improving annotation algorithms, such as Metaxa2 (2), while less attention has been put into curation of reliable and consistent databases (3). Previous studies have shown that databases restricted to the studied environment improve unambiguous taxonomy annotation to the species level, thanks to consistent taxonomy, lack of blanks and reduced competition between different reference taxonomies (4-5). The usage of DAIRYdb in combination with different classification tools allows taxonomy annotation accuracy of over 90% at species level for microbiome samples from dairy products, where species identification is mandatory due to the affiliation to few closely related genera of most dominant lactic acid bacteria.

The database can be added to your Metaxa2 (version 2.2 or later) installation by using the following command:

metaxa2_install_database -g SSU_DAIRYdb_v1.1.2

Further adaptations of the DAIRYdb can be found on GitHub and the preprint has been deposited in BioRxiv (1). DAIRYdb was developed by Marco Meola, Etienne Rifa and their collaborators, who also provided most of the text for this post. Thanks Marco for this excellent addition to the database collection!

References

1. Meola M, Rifa E, Shani N, Delbes C, Berthoud H, Chassard C: DAIRYdb: A manually curated gold standard reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products. bioRxiv, 386151 (2018). doi: 10.1101/386151
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
3. Edgar RC: Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ, 6, e4652 (2018). doi: 10.7717/peerj.4652
4. Ritari J, Salojärvi J, Last L, de Vos WM: Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genomics, 16, 1, 1056 (2015). doi: 10.1186/s12864-015-2265-y
5. Newton ILG, Roeselers G: The effect of training set on the classification of honey bee gut microbiota using the naïve bayesian classifier. BMC Microbiology, 12, 1, 221 (2012). doi: 10.1186/1471-2180-12-221

One of the questions I have received regarding Metaxa2 is if it is possible to use it on other DNA barcodes. My answer has been “technically, yes, but it is a very cumbersome process of creating a custom database for every additional barcode”. Not anymore, the newly introduced Metaxa2 Database Builder makes this process automatic, with the user just supplying a FASTA file of sequences from the region in question and a file containing the taxonomy information for the sequences (in GenBank, NSD XML, Metaxa2 or SILVA-style formats). The preprint (1) has been out for some time, but today Bioinformatics published the paper describing the software (2).

The paper not only details how the database builder works, but also shows that it is working on a number of different barcoding regions, albeit with different results in terms of accuracy. Still, even with seemingly high misclassification rates for some DNA barcodes, the software performs better than a simple BLAST-based taxonomic assignment (76.5% vs. 41.4% correct classifications for matK, and 76.2% vs. 45.1% for tnrL). The database builder has already found use in building a COI database for anthropods (3), and we envision a range of uses in the near future.

As the paper is now published, I have also moved the Metaxa2 software (4) from beta-status to a full-worthy version 2.2 update. Hopefully, this release should be bug free, but my experience is that when the community gets their hands of the software they tend to discover things our team has missed. I would like to thank the entire team working on this, particularly Rodney Richardson (who initiated this entire thing) and Henrik Nilsson. The software can be downloaded here. Happy barcoding!

References

1. Bengtsson-Palme J, Richardson RT, Meola M, Wurzbacher C, Tremblay ED, Thorell K, Kanger K, Eriksson KM, Bilodeau GJ, Johnson RM, Hartmann M, Nilsson RH: Taxonomic identification from metagenomic or metabarcoding data using any genetic marker. bioRxiv 253377 (2018). doi: 10.1101/253377 [Link]
2. Bengtsson-Palme J, Richardson RT, Meola M, Wurzbacher C, Tremblay ED, Thorell K, Kanger K, Eriksson KM, Bilodeau GJ, Johnson RM, Hartmann M, Nilsson RH: Metaxa2 Database Builder: Enabling taxonomic identification from metagenomic and metabarcoding data using any genetic marker. Bioinformatics, advance article (2018). doi: 10.1093/bioinformatics/bty482 [Paper link]
3. Richardson RT, Bengtsson-Palme J, Gardiner MM, Johnson RM: A reference cytochrome c oxidase subunit I database curated for hierarchical classification of arthropod metabarcoding data. PeerJ Preprints, 6, e26662v1 (2018). doi: 10.7287/peerj.preprints.26662v1 [Link]
4. 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]

Due to an extremely embarrassing for-loop error in the classifier of the most recent Metaxa2 beta (beta 8), which was released a few weeks ago, the classifier often would (on certain platforms and configurations) enter an endless loop and hang. I apologize for this mistake, which has been corrected in the new beta 9 released today, available from this download link. No other changes have been made since the previous version. Thanks for your patience (and thanks Kaisa Thorell for first bringing my attention the error!)

I am very happy to announce that a first public beta version of Metaxa2 version 2.2 has been released today! This new version brings two big and a number of small improvements to the Metaxa2 software (1). The first major addition is the introduction of the Metaxa2 Database Builder, which allows the user to create custom databases for virtually any genetic barcoding region. The second addition, which is related to the first, is that the classifier has been rewritten to have a more solid mathematical foundation. I have been promising that these updates were coming “soon” for one and a half years, but finally the end-product is good enough to see some real world testing. Bear in mind though that this is still a beta version that could contain obscure bugs. Here follows a list of new features (with further elaboration on a few below):

• The Metaxa2 Database Builder
• Support for additional barcoding genes, virtually any genetic region can now be used for taxonomic classification in Metaxa2
• The Metaxa2 database repository, which can be accessed through the new metaxa2_install_database tool
• Improved classification scoring model for better clarity and sensitivity
• A bundled COI database for athropods, showing off the capabilities of the database builder
• Support for compressed input files (gzip, zip, bzip, dsrc)
• Support for auto-detection of database locations
• Added output of probable taxonomic origin for sequences with reliability scores at each rank, made possible by the updated classifier
• Added the -x option for running only the extraction without the classification step
• Improved memory handling for very large rRNA datasets in the classifier (millions of sequences)
• This update also fixes a bug in the metaxa2_rf tool that could cause bias in very skewed datasets with small numbers of taxa

The new version of Metaxa2 can be downloaded here, and for those interested I will spend the rest of this post outlining the Metaxa2 Database Builder. The information below is also available in a slightly extended version in the software manual.

The major enhancement in Metaxa2 version 2.2 is the ability to use custom databases for classification. This means that the user can now make their own database for their own barcoding region of choice, or download additional databases from the Metaxa2 Database Repository. The selection of other databases is made through the “-g” option already existing in Metaxa2. As part of these changes, we have also updated the classification scoring model for better stringency and sensitivity across multiple databases and different genes. The old scoring system can still be used by specifying the –scoring_model option to “old”.

There are two different main operating modes of the Metaxa2 Database Builder, as well as a hybrid mode combining the features of the two other modes. The divergent and conserved modes work in almost completely different ways and deal with two different types of barcoding regions. The divergent mode is designed to deal with barcoding regions that exhibit fairly large variation between taxa within the same taxonomic domain. Such regions include, e.g., the eukaryotic ITS region, or the trnL gene used for plant barcoding. In the other mode – the conserved mode – a highly conserved barcoding region is expected (at least within the different taxonomic domains). Genes that fall into this category would be, e.g., the 16S SSU rRNA, and the bacterial rpoB gene. This option would most likely also be suitable for barcoding within certain groups of e.g. plants, where similarity of the barcoding regions can be expected to be high. There is also a third mode – the hybrid mode – that incorporates features of both the other. The hybrid mode is more experimental in nature, but could be useful in situations where both the other modes perform poorer than desired.

In the divergent (default) mode, the database builder will start by clustering the input sequences at 20% identity using USEARCH (2). All clusters generated from this process are then individually aligned using MAFFT (3). Those alignments are split into two regions, which are used to build two hidden Markov models for each cluster of sequences. These models will be less precise, but more sensitive than those generated in the conserved mode. In the divergent mode, the database builder will attempt to extract full-length sequences from the input data, but this may be less successful than in the conserved mode.

In the conserved mode, on the other hand, the database builder will first extract the barcoding region from the input sequences using models built from a reference sequence provided (see above) and the Metaxa2 extractor (1). It will then align all the extracted sequences using MAFFT and determine the conservation of each position in the alignment. When the criteria for degree of conservation are met, all conserved regions are extracted individually and are then re-aligned separately using MAFFT. The re-aligned sequences are used to build hidden Markov models representing the conserved regions with HMMER (4). In this mode, the classification database will only consist of the extracted full-length sequences.

In the hybrid mode, finally, the database builder will cluster the input sequences at 20% identity using USEARCH, and then proceed with the conserved mode approach on each cluster separately .

The actual taxonomic classification in Metaxa2 is done using a sequence database. It was shown in the original Metaxa2 paper that replacing the built-in database with a generic non-processed one was detrimental to performance in terms of accuracy (1). In the database builder, we have tried to incorporate some of the aspects of the manual database curation we did for the built-in database that can be automated. By default, all these filtration steps are turned off, but enabling them might drastically increase the accuracy of classifications based on the database.

To assess the accuracy of the constructed database, the Metaxa2 Database Builder allows for testing the detection ability and classification accuracy of the constructed database. This is done by sub-dividing the database sequences into subsets and rebuilding the database using a smaller (by default 90%), randomly selected, set of the sequence data (5). The remaining sequences (10% by default) are then classified using Metaxa2 with the subset database. The number of detections, and the numbers of correctly or incorrectly classified entries are recorded and averaged over a number of iterations (10 by default). This allows for obtaining a picture of the lower end of the accuracy of the database. However, since the evaluation only uses a subset of all sequences included in the full database, the performance of the full database actually constructed is likely to be slightly better. The evaluation can be turned on using the “–evaluate T” option.

Metaxa2 2.2 also introduces the database repository, from which the user can download additional databases for Metaxa2. To download new databases from the repository, the metaxa2_install_database command is used. This is a simple piece of software but requires internet access to function.

References

1. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved Identification and Taxonomic Classification of Small and Large Subunit rRNA in Metagenomic Data. Molecular Ecology Resources (2015). doi: 10.1111/1755-0998.12399 [Paper link]
2. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
3. Katoh K, Standley DM: MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution, 30, 772–780 (2013).
4. Eddy SR: Accelerated profile HMM searches. PLoS Computational Biology, 7, e1002195 (2011).
5. Richardson RT, Bengtsson-Palme J, Johnson RM: Evaluating and Optimizing the Performance of Software Commonly Used for the Taxonomic Classification of DNA Sequence Data. Molecular Ecology Resources, 17, 4, 760–769 (2017). doi: 10.1111/1755-0998.12628

Yesterday, Molecular Ecology Resources put online an unedited version of a recent paper which I co-authored. This time, Rodney Richardson at Ohio State University has made a tremendous work of evaluating three taxonomic classification software – the RDP Naïve Bayesian Classifier, RTAX and UTAX – on a set of DNA barcoding regions commonly used for plants, namely the ITS2, and the matK, rbcL, trnL and trnH genes.

In the paper (1), we discuss the results, merits and limitations of the classifiers. In brief, we found that:

• There is a considerable trade-off between accuracy and sensitivity for the classifiers tested, which indicates a need for improved sequence classification tools (2)
• UTAX was superior with respect to error rate, but was exceedingly stringent and thus suffered from a low assignment rate
• The RDP Naïve Bayesian Classifier displayed high sensitivity and low error at the family and order levels, but had a genus-level error rate of 9.6 percent
• RTAX showed high sensitivity at all taxonomic ranks, but at the same time consistently produced the high error rates
• The choice of locus has significant effects on the classification sensitivity of all tested tools
• All classifiers showed strong relationships between database completeness, classification sensitivity and classification accuracy

We believe that the methods of comparison we have used are simple and robust, and thereby provides a methodological and conceptual foundation for future software evaluations. On a personal note, I will thoroughly enjoy working with Rodney and Reed again; I had a great time discussing the ins and outs of taxonomic classification with them! The paper can be found here.

References and notes

1. Richardson RT, Bengtsson-Palme J, Johnson RM: Evaluating and Optimizing the Performance of Software Commonly Used for the Taxonomic Classification of DNA Sequence Data. Molecular Ecology Resources, Early view (2016). doi: 10.1111/1755-0998.12628 [Paper link]
2. This is something that several classifiers also showed in the evaluation we did for the Metaxa2 paper (3). Interestingly enough, Metaxa2 is better at maintaining high accuracy also as sensitivity is increased.
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, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]

I am very happy to announce that Metaxa2 version 2.1 has been released today. This new version brings a lot of important improvements to the Metaxa2 software (1), in particular by the introduction of the Metaxa2 Diversity Tools. This is the list of new features (further elaboration follows below):

• The Metaxa2 Diversity Tools:
  • 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 rarefaction curves 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
• Added a new detection mode for detection of multiple rRNA in the same sequence, e.g. a genome
• Added the --reference option to improve the use of Metaxa2 as a tool to sort out host rRNA sequences from a dataset
• Added the --split_pairs option causing Metaxa2 to output paired-end sequences into two separate files, which is nice for further analysis of rRNA reads
• The default setting for the --align option has been changed to ‘none‘
• Automatic detection of which BLAST package that is installed
• Fixed a bug causing the last read of paired-end FASTA input to be ignored
• Fixed an occasionally occurring BLAST+ related warning message
• Fixed a bug that could cause the classifier to crash on highly divergent BLAST matches

The new version of Metaxa2 can be downloaded here, and for those interested I will spend the rest of this post outlining the new features.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 is the ability to further make simple analysis 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-fledge community analysis package compared to QIIME (2) or Mothur (3)). The set 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 rarefaction analysis 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.

The genome mode
Metaxa2 has long been said not to be useful for predicting rRNA in longer sequences, such as full genomes or chromosomes, since it has traditionally only looked for a single rRNA hit. With Metaxa2 2.1, it is now possible to use Metaxa2 on longer sequences to detect multiple rRNA occurrences. To do this, you need to change the operating mode using the new --mode option to either ‘auto‘ or ‘genome‘. The auto mode will treat sequences longer than 2500 bp as “genome” sequences and look for multiple matches in these.

The reference mode
Another feature request that has been addressed in the new Metaxa2 version is the ability to filter out certain sequences from the data set. For example, you may want to exclude all rRNA sequences that are derived from to host organism, but keep the analysis of all other rRNA reads. This is now possible by supplying a file of reference rRNA sequences to exclude in FASTA format to the --reference option.

Experimental Usearch support
Finally, we have toyed around with support for Usearch (4) instead of BLAST (5) as the search algorithm for the classification step. However, this is far from fine-tuned and it is included as an experimental feature that you may use on your own risk! We recommend that you not use it for classification of data for publication yet. However, we are interested in how this works for you, so if you like you may test to run the Usearch algorithm in parallel with your BLAST-based analysis and compare the results and send me your input on how it works. You can read more about using Usearch at the end of the Metaxa2 manual.

References

1. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved Identification and Taxonomic Classification of Small and Large Subunit rRNA in Metagenomic Data. Molecular Ecology Resources (2015). doi: 10.1111/1755-0998.12399 [Paper link]
2. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
3. Schloss PD, Westcott SL, Ryabin T et al.: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537–7541 (2009).
4. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
5. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 25, 3389–3402 (1997).

Metaxa2 has been updated to version 2.0.2 and can be downloaded from the Metaxa2 web site. The 2.0.2 update fixes two minor bugs; one causing the “.graph” file to display incorrect or no names for the regions of the LSU regions, and one causing misreporting of the number of sequences in single-end FASTQ files (paired-end files were reported correctly). The update also brings a slightly improved classifier. Thanks to Marco Severgnini for reporting the FASTQ file issue! The update is available here.

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

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

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

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

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

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

References

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