The 11th annual meeting of PhD students and Postdoc researchers in Bioinformatics in Sweden will take place in Lund the 29-30 September. The workshop is an opportunity for young researchers to meet, exchange ideas, and keep up to date with the growing body of knowledge. I will go there, and you should be there too! Besides, it’s free for PhD students and Postdocs! All info can be found at the workshop website. The last time I was there (2010) really fueled some interesting discussions, and I am really looking forward to the event this year. Hope to meet you there, fellow Swedish bioinformaticians!
Finally, the Metaxa FAQ is ready! If you have any other questions, please mail them to metaxa [at] microbiology [dot] se, and I will include them in the FAQ at some later point. I would like to thank anyone who has contributed with questions, suggestions, comments and other types of feedback so far. It really helps improving this software. The FAQ is found here.
You may also wonder what has happened to the stable version of the 1.1 Metaxa speedup I promised in July. It is still on the way, but due to a minor computer failure and other CPU-heavy tasks being of higher priority the software still has not been fully tested. As we want to release a truly stable and functional update, we need to hold back on the package for some more time. Be patient, or try out the beta that is already available.
Phil Goetz at JCVI recently posted his reflections from the Summit of Systems Biology. I was not there, but I read his summary with interest. Now, what strikes me as interesting is the notion that “there were no talks on metagenomics. This also struck me as odd; bacterial communities seem like a natural systems biology problem.” Having been working with microbial communities for a while, I am surprised that the modeling perspective that is so prevalent in macro-organism ecosystems ecology have not yet really come to fruition in microbial ecology. With the tremendous amounts of sequences that are pouring over us from microbial communities, and with the plethora of functional metagenomics annotation that is made, how come that there has been so little research in the actual interactions between microorganisms within e.g. biofilms?
The problem is also connected to the lack of time-series data from community research. To be able to understand how a system behaves under changing conditions, we need to measure its reactions to various parameter changes over time. Instead of pooling metagenomes to reduce temporal “noise” we need to be better at identifying the changing parameters and then use the temporal differences to look for responses to the parameter changes. By applying a functional metagenomics perspective at each sample point, combining this with measured changes in community species structure (as measured e.g. by 16S or some other marker gene), and correlating this with changes in the parameters, we should be able to build a model of how the ecosystem responds to changing environments. With the large-scale sequencing technologies available today, and the possibilities given by metatranscriptomics, these ideas should be challenging but not impossible.
I am not saying that any of these things have not been done. But it has been done to a surprisingly small extent. I would highly appreciate reading a paper trying to build a mathematical model of how the ecosystem functions in bacterial communities shift in response to an environmental stressor. Because when someone builds such a model we suddenly have a tool to take microbial community research from an explorative perspective to an applied one. The applied perspective will be useful for actually protecting environments and ecosystem services, as well as for understanding how to manipulate microbial ecosystems to maximize the outtake beneficial to society. Also, the understanding the ecosystem dynamics of microbial systems could be carried over to macro-ecosystems and provide a small-scale ecosystem laboratory for all ecosystem research. Such a shift towards applied microbial community systems biology will be more or less necessary to be able to argue for more resources and time being spent on e.g. metagenomics. And I believe that we will soon be there, because the step is shorter than we might imagine.
I’m working on an update to Metaxa that will bring at least double speed to the program (and even more when run on really large data sets on many cores). While there is still no real release version of this update (version 1.1), I have today posted a public “beta”, which you can use for testing purposes. Do not use this version for anything important (e.g. research) as it contains at least one known bug (and maybe even more I haven’t discovered yet). I would appreciate, if you are interested, that you download this version and e-mailed any bugs or inconsistencies found to me (firstname.lastname[at]microbiology.se).
Note that to install this version, you first need to download and install the current version of Metaxa (1.0.2). Then the new version can be used with the old’s databases.
One potential use for Metaxa (paper) is to include it in a pipeline for classification of SSU rRNA in metagenomic data (or other environmental sequencing sets). However, as Metaxa is provided from this site, it only classifies SSUs to the domain level (archaea, bacteria and eukaryotes, with the addition of chloroplasts and mitochondria). It is also able to do some (pretty rough) species guesses using the “
--guess_species T” option. An easy solution to implement would be to pass the Metaxa output, e.g. “metaxa_output.bacteria.fasta” to BLAST, and compare all these sequences to the sequences in e.g. the SILVA or GreenGenes database. There is, however, a way to improve this, which uses Metaxa’s ability to compares sequences to custom databases. In this tutorial, I will show you how to achieve this.
Before we start, you will of course need to download and install Metaxa, and its required software packages (BLAST, HMMER, MAFFT). When you have done this, we can get going with the database customization. I will in this tutorial use the SILVA database for SSU classification. However, the basic idea for the tutorial should be easily applicable to GreenGenes and other rRNA databases as well.
- Visit SILVA through this link, and download the file named “SSURef_106_tax_silva.fasta.tgz”. The file is pretty big so it may take a while to download it. If you’re running Metaxa on a server, you’ll have to get the SILVA-file to the server somehow.
- Unzip and untar the file (Mac OS X makes this neatly by doubleclicking the file, on linux you can do it on the command line by typing “
tar -xvzf SSURef_106_tax_silva.fasta.tgz“). This will give you a FASTA-file.
- The FASTA-file needs to be prepared a bit for Metaxa usage. First, we need to give Metaxa identifiers it can understand. Metaxa identifies sequences’ origins by the last character in their identifier, e.g. “>A16379.1.1496.B”. Here, “.B” indicates that this is a bacterial sequence. We are now going to use the unix command sed to process the file and insert the appropriate identifiers.
- We begin with the archaeal sequences. To get those straight, we type:
sed "s/ Archaea;/.A - Archaea;/" SSURef_106_tax_silva.fasta > temp1
Notice that we direct the output to a temporary file. It is bad practice to replace the input file with the output file, so we work with two temp-files instead.
- The next step is also easy, now we find all eukaryote sequences and add E:s to the identifiers:
sed "s/ Eukaryota;/.E - Eukaryota;/" temp1 > temp2
- Now it becomes a little more complicated, as SILVA classes mitochondrial and chloroplast SSU sequences as subclasses of bacteria. However, there is a neat little trick we can use. First we do the same with the bacterial sequences as with the archaeal and eukaryote:
sed "s/ Bacteria;/.B - Bacteria;/" temp2 > temp1
- Now, we can use two a little more complicated commands to annotate the mitochondrial and chloroplast sequences:
sed "s/\.B - \(Bacteria;.*;[Mm]itochondria;\)/.M - \1/" temp1 > temp2
sed "s/\.B - \(Bacteria;.*;[Cc]hloroplast;\)/.C - \1/" temp2 > temp1
- We also need to get “rid” of the unclassified sequences, by assigning them to the “other” origin (O):
sed "s/ Unclassified;/.O - Unclassified;/" temp1 > temp2
- We begin with the archaeal sequences. To get those straight, we type:
- That wasn’t too complicated, was it? We can now check the number of different sequences in the file by typing the pretty complicated command:
grep ">" temp2 | cut -f 1 -d " " | rev | cut -f 1 -d "." | sort | uniq -c
If you have been working with the same files as me, you should now see the following numbers:
- At this stage, we need to remove the full taxonomy from the FASTA headers, as Metaxa cannot handle species names of this length. We do this by typing:
sed "s/ - .*;/ - /" temp2 > temp1
- We can now change the temp-file into a FASTA file, and delete the other temp-file:
mv temp1 SSURef.fasta
- We now need to configure Metaxa to use the database. First, we format a BLAST-database from the FASTA-file we just created:
formatdb -i SSURef.fasta -t "SSURef Metaxa DB" -o T -p F
- With that done, we can now run Metaxa using this database instead of the classification database that comes with the program. By specifying that we want to guess the species origin of sequences, we can get (as accurate as SILVA lets us be) which species each sequence in our set come from. We do this by using the
metaxa -i test.fasta -d SSURef.fasta -o TEST --guess_species T --cpu 2
The input in this case was the test file that comes with Metaxa. Note also that we’re using two CPUs to get multithreaded speeds. Remember that you must provide the full (or relative) path to the database files we just created, if you are not running Metaxa from the same directory as the database resides in.
- The output should now look like this (taken from the bacterial file):
>coryGlut_Bielefeld_dna Bacterial 16S SSU rRNA, best species guess: Corynebacterium glutamicum
>gi|116668568:792344-793860 Bacterial 16S SSU rRNA, best species guess: Arthrobacter sp. J3.40
>gi|117927211:c1399163-1397655 Bacterial 16S SSU rRNA, best species guess: Acidothermus cellulolyticus
And so on. As you can see the species names are now located at the end of each definition line, and can easily be extracted using sed, e.g. “
grep ">" TEST.bacteria.fasta | sed "s/.*: //"“.
And that’s it. It’s pretty simple, and can easily be scripted. In fact, I have already made the bash script for you. That means that the short version is, download the script, download the sequence file from SILVA, move into the directory you have downloaded the file to and run the script by typing:
A few notes at the end. The benefit of using this approach is that we maintain the sorting capabilities, marking of uncertain sequences and error checking of Metaxa, but we don’t have to add another BLAST step after Metaxa has finished. However, as this database we create is a lot bigger than the database that comes with Metaxa, the running time of the classification step will be substantially longer. This is in most cases acceptable, as that time is the same as the time it would have taken to run BLAST on the Metaxa output. It should also be noted that this approach limits Metaxa’s ability of classifying 12S sequences, as there are no such sequences in SILVA. Good luck with classifying your metagenome SSUs (and if you use Metaxa in your research, remember to cite the paper)!
It seriously worries me that a number of indications recently have pointed to that the heavy use of antibiotics does not only drive antibiotic resistance development, but also the development towards more virulent and aggressive strains of pathogenic bacteria. First, the genome sequencing of the E. coli strain that caused the EHEC outbreak in Germany in May revealed not only antibiotic resistance genes, but also is also able to make Shiga toxin, which is causes the severe diarrhoea and kidney damage related to the haemolytic uremic syndrome (HUS). The genes encoding the Shiga toxin are not originally bacterial genes, but instead seem to originate from phages. When E. coli gets infected with a Shiga toxin-producing phage, it becomes a human pathogen . David Acheson, managing director for food safety at consulting firm Leavitt Partners, says that exposure to antibiotics might be enhancing the spread of Shiga toxin-producing phage. Some antibiotics triggers what is referred to as the SOS response, which induces the phage to start replicating. The replication of the phage causes the bacteria to burst, releasing the phages, and with them the toxin .
Second, there is apparently an ongoing outbreak of scarlet fever in Hong Kong. Kwok-Yung Yuen, microbiologist at the University of Hong Kong, has analyzed the draft sequence of the genome, and suggests that the bacteria acquired greater virulence and drug resistance by picking up one or more genes from bacteria in the human oral and urogenital tracts. He believes that the overuse of antibiotics is driving the emergence of drug resistance in these bacteria .
Now, both of these cases are just indications, but if they are true that would be an alarming development, where the use of antibiotics promotes the spread not only of resistance genes, impairing our ability to treat bacterial infections, but also the development of far more virulent and aggressive strains. Combining increasing untreatability with increasing aggressiveness seems to me like the ultimate weapon against our relatively high standards of treatment of common infections. Good thing hand hygiene still seems to help .
- Phage on the rampage (http://www.nature.com/news/2011/110609/full/news.2011.360.html), Published online 9 June 2011, Nature, doi:10.1038/news.2011.360
- Mutated Bacteria Drives Scarlet Fever Outbreak (http://news.sciencemag.org/scienceinsider/2011/06/mutated-bacteria-drives-scarlet.html?etoc&elq=cd94aa347dca45b3a82f144b8213e82b), Published online 27 June 2011.
- Luby SP, Halder AK, Huda T, Unicomb L, Johnston RB (2011) The Effect of Handwashing at Recommended Times with Water Alone and With Soap on Child Diarrhea in Rural Bangladesh: An Observational Study. PLoS Med 8(6): e1001052. doi:10.1371/journal.pmed.1001052 (http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001052)
So Metaxa has gone into the wild, which means that I start to get feedback from users using it in ways I have not foreseen. This is the best and the worst thing about having your software exposed to real-world usage; it makes it possible to improve it in a variety of ways, but it also gives you severe headaches at times. I could luckily fix a smaller bug in the Metaxa code within a matter of hours and issue an update to version 1.0.2. The interesting thing here was that I would never have discovered the bug myself, as I never would have called the Metaxa program in the way required for the bug to happen. But once I saw the command given, and the output, which the user kindly sent me, I pretty quickly realized what was wrong, and how to fix it. Therefore, I would like to ask all out you who use Metaxa to send me your questions, problems and bug reports. The feedback is highly appreciated, and I can (at least currently) promise to issue fixes as fast as possible. We are really committed to make Metaxa work for everyone.
If you have suggestions for improvements, those are welcome as well (though it will take significantly more time to implement new features than to fix bugs). I am currently compiling a FAQ, and all questions are welcome. Finally, I would like to thank everybody who has downloaded and tried the Metaxa package. I can see in the server logs that there are quite many of you, which of course makes us happy.
I was informed by a Metaxa user of a bug in the current Metaxa version (1.0.1). This bug caused problems when Metaxa-output was directed to another directory than the current directory Metaxa was run from. I have fixed this issue as fast as I could, as this could cause problems when Metaxa is included in larger analysis pipelines. The update to 1.0.2 is therefore strongly recommended for all Metaxa users. The update to 1.0.2 also introduces better handling of input files created in Windows environments, as well as improving the handling of extremely long sequence identifiers. The update can be downloaded using this link.
- Improves import of sequence sets from Windows environments.
- Fixed a bug causing trouble with sequences with extremely long identifiers.
- Fixed an output-related bug causing problems with output directed to another directory.
It is a pleasure to annonce that the paper on Metaxa is now available as an Online early article in Antonie van Leeuwenhoek. In short, the paper describes a software tool that is able to extract small subunit (SSU) rRNA sequences from large data sets, such as metagenomes and environmental PCR libraries, and classify them according to bacterial, archaeal, eukaryote, chloroplast or mitochondrial origin. The program makes it easy to distinguish between e.g. the bacterial SSU sequences you like to analyze, and the SSU sequences you would like to remove prior to the analysis (e.g. mitochondrial and chloroplast sequences). This task is particularly important in metagenomics, where sequences can potentially derive from a variety of origins, but bacterial diversity often is the desired target for analysis. The software can be downloaded here, and the article can be read here. I would like to thank all the co-authors on this paper for a brilliant collaboration, and hope to be working with them again.
- Bengtsson J, Eriksson KM, Hartmann M, Wang Z, Shenoy BD, Grelet G, Abarenkov K, Petri A, Alm Rosenblad M, Nilsson RH: Metaxa: A software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets. Antonie van Leeuwenhoek Journal of Microbiology, 2011, doi:10.1007/s10482-011-9598-6.
A random sample of things from this week’s scientific news I think are worth sharing:
Britain is apparently shutting down many of its climate change outreach efforts. I find this very saddening, and see it as an indication of our extreme short-sightedness. We need to put more effort and funding into preserving the environment – not less. In addition, the economic benefits of taking care of the nature around us will probably be much larger than the small sums we save in the short term by not doing anything. We clearly need better incentives to look beyond the next budget and the next election.
The editorial of Nature Reviews Microbiology points the torch on the need for research within basic microbiology, pointing out that “the functions of many genes in the genomes of even the best studied organisms, such as Escherichia coli and Bacillus subtilis, remain unknown. Often these genes do not resemble other, characterized, genes in the databases, allowing for the possibility that interesting new pathways remain to be discovered. (…) if we want to understand how life works at the molecular level, it is crucial to continue and expand basic microbiology research.” I would like to add that a more complete understanding of at least one model organism would drastically increase the accuracy of genome (and metagenome) annotation in new sequencing projects, which today is patchy, to say the least.