A virtual machine, for those unfamiliar with the concept, is a software-based computer. It runs within another program, a so-called “hypervisor” or virtualisation framework, on a real, physical computer. The framework provides everything a virtual computer needs to operate, from a CPU and memory to disk space and a network interface, all of it controllable and customisable in software. The virtual machine boots like an ordinary desktop computer and can then run an operating system and, on top of that, whatever software you want to install. Wikipedia has a description of virtual machines with links to all of the background and related information you could possibly want.

Virtualisation has become a very useful tool in data-centres around the world, the Sanger Institute’s included, allowing more efficient management of computer resources, power and floor/rack space and an incredible degree of flexibility in how resources are deployed. Companies like Amazon have taken virtualisation to a whole new level, building massive “cloud” infrastructures that allow companies, groups and individuals to commission and use potentially vast compute and storage resources on-demand and for only the length of time that they need them.

For us, the advantage of a virtual machine is that we can install (and partially configure) everything needed for the website and then bundle it up and make it available as a single downloadable file. The process of deploying the VM is as simple as importing it into whatever virtualisation framework you have at hand and there are now lots of alternatives out there, both free and paid-for. By reducing the installation process to that one step, we’ve removed the pain of slogging through the installation of a raft of interdependent Perl modules, operating system packages and Pfam code, leaving only the final step of configuring the website to suit your environment.

If you’re interested, you can find the virtual machine on our FTP area. There’s more information about the package, as well as documentation on installing and configuring it, in the Pfam website.

If you decide to download and try the VM, we would be very interested to hear about your experiences with it, but please bear in mind that this is our first attempt at packaging the website like this: there will be problems and bugs, some of them undoubtedly major ! You can mail us at pfam-help@sanger.ac.uk. Let us know what goes wrong and we’ll do our best to help.

Posted by John.

As pointed out by one of our users:

Pfam v26 includes, in addition to DDE_Tnp_1, the following new families:

DDE_Tnp_1_2
DDE_Tnp_1_3
DDE_Tnp_1_4
DDE_Tnp_1_5
DDE_Tnp_1_6
DDE_Tnp_1_7

These extra new families with the name_2, name_3, name_4 etc, have been constructed to increase the coverage of Pfam.  Many of our existing large diverse families are not well modelled by a single HMM and there are many true members that are not matched. So by building multiple models we can match more things.  Each of these models will be in the same Pfam clan, the RNaseH clan in this case.  For the most part these models do not represent any particular subfamily or classification group.  Essentially you should think of a match to any of the above seven DDE_TnP_1 families as being the same thing.  Because of the way  Pfam is built any particular region of a protein may only belong to one of these families.  We have a step in building clans called competition which means that if a region of a protein matches to both DDE_Tnp_1 and DDE_Tnp_1_2 for example then the region will be assigned to the family with the highest score.  This means that a match to DDE_Tnp_1 in release 25.0 may now end up in a different family such as DDE_Tnp_1_2.  You shouldn’t read too much into these changes.

The reason that many of these new families are appearing in Pfam release 26.0 is due to a change in strategy in how we are building many new Pfam families.  The new strategy consists of taking complete genomes and taking each protein that does not match Pfam and using it as a starting point for a Jackhmmer search.  Jackhmmer is an iterative search tool like PSI-blast.  If we find that the Jackhmmer search finds lots of homologues but has some overlaps with an existing family then we may build one of these new additional families to increase coverage of known sequences. Rather than give these families completely new names we simply call them the same as the existing family and append a number to them to show that they are closely related to each other.

Posted by Alex

You’ll still be able to access all the non-Wikipedia content – that is, all the covariance models and HMMs describing families, domain graphics, full and seed alignments, as well as our species trees.

Posted by Sarah and Alex

As you surely have noted the highly anticipated new Pfam paper is out as part of the 2012 NAR database issue! We were delighted to be listed as a featured article. The paper covers the new release 26.0 (more on this from Rob soon) and presents some novel analysis that may be of interest to Pfam addicts like you. We quite extensively discuss our use of family-specific bit score gathering thresholds (GAs), hoping to bring clarity to an issue that seems to have been a source of confusion in the past (a.k.a. stop sending us tickets asking what GAs are and how to use them! ). Also, we extend and update the analysis of DUF families that was presented in a previous publication hoping to push more people into the de-DUF a DUF game. So, enjoy reading the paper and send us comments and suggestions, your support and advice is as always invaluable to us!!

Posted by Marco

Lately I have been wondering about that missing 20%. I see three possibilities:

1. Sequences should be part of new families
2. Sequences are missing members of existing families
3. Sequences are incorrect gene predictions and never expressed

To investigate this one can take all the proteins in a genome that do not already hit Pfam. We can then use each of these sequences as a seed for an iterative Jackhmmer search. Some proteins find thousands of hits many of which belong to existing families (case 2), and many of them have tens to hundreds of matches and do not match to any known family (case 1). Some of the searches find only the sequence itself and perhaps these are candidates for spurious proteins (case 3). The graph below shows the results of such an analysis for one bacterial genome.

Graph showing the number of Jackhmmer hits (y axis) against the unmatched proteins in a genome. Proteins are ordered along X-axis with those whose searches hit the most proteins at the right.

On the right hand side of the graph we can see the 300 or so proteins that should be parts of existing families. But the large majority of proteins do not appear to be part of existing families and only consist of a red bar. That suggests that we are still far from getting a complete list of all known protein families (if such a thing is possible). So the search continues … and the golf course must wait.

Posted by Alex

References

Chothia C. Proteins. One thousand families for the molecular biologist. Nature. 1992;357:543-544.

The hub file is available on our ftp site, and by following the instructions at the UCSC Genome Browser Custom Hub page, you can visualise Rfam annotations for the majority of species for which genomes are provided by the UCSC Genome Browser. Clicking on a match will give you exact start and stop positions, as well as links to the Rfam annotation page here at the Sanger. At the moment, bit scores or E-values for a given match aren’t yet available directly through the UCSC Genome Browser, though we’re working on it. Happy browsing!

Rfam types for Genome annotation

Xfam (in the forms of Sarah and Rob) attended the NIH Genome Annotation Workshop last week, and it was a great insight into the trials and tribulations of coming up with common standards that everyone’s happy with. It was also nice to hear that Rfam is being used exensively to annotate ncRNA features. However, there’s been some confusion amongst annotators when converting between Rfam types (such as CD-Box) and the ncRNA_classes required by INSDC under the ncRNA feature key. The ncRNA feature key is intended to describe non-coding RNAs that aren’t ribosomal or transfer RNAs; these use the rRNA and tRNA feature keys respectively.

To use the ncRNA feature key, annotators are required to supply an appropriate ncRNA_class, and this is where confusion arises, as there’s no perfect overlap between the Rfam entry types and the ncRNA classes. To reduce this, here at Rfam we’ve put together a handy translation guide to make it easy to know what ncRNA class you should apply if you are using an Rfam family to annotate a genome. There are also some cases where an INSDC type is more specific than the Rfam type; for example, we don’t have a specific telomerase RNA type, whereas there is a ncRNA_class called telomerase_RNA. Therefore any annotation to RF00025 can use the telomerase_RNA ncRNA_class category. You can find our table of Rfam types and their INSDC equivalents here.

You can also find out all you ever wanted to know about the feature tables used for genome annotation here, and here.

Rfam now has 1973 families, 528 more families than the 10.0 release. We are just one prokaryotic RNA-seq project away from hitting 2000 families! In fact, we have passed 2000 in terms of Rfam accession (RF02031 is the Escherichia coli sRNA, tpke11). My selfish attempt to claim the coveted RF02000 accession was snatched from my grasp by Chris Boursnell who added RF02000 which now corresponds to the rice microRNA MIR1846 from the miRBase database. I did claim RF01999 and RF02001 with two
sub-types of Group II catalytic intron domains 1-4 that Zasha Weinberg kindly provided to Rfam.

The new families included nearly 100 novel elements inferred by Zasha Weinberg and colleagues in his recent Genome Research article [1]. Zasha kindly provides Rfam with the alignments and writes Wikipedia articles for each notable element, greatly easing the burden on Rfam for incorporating these into the database.

Our new recruit, Ruth Eberhardt, originally from the UniProt group at EBI, has also made a significant mark on the new release. Ruth has been busily incorporating “domains” derived from long messenger-like non-coding RNAs (a.k.a. lncRNAs). These are regions within each transcript that are unusually well conserved and there is some evidence that secondary structure within the regions is evolutionarily constrained. The new families include: MEG3, MALAT1, MIAT, PRINS, Xist, TUG1, HSR-omega, Evf1, HOTAIR, KCNQ1OT1, SOX2OT, NEAT1, EGOT, H19 and HOTAIRM1.

This summer we had the pleasure of hosting another talented summer student, Ben Moore. Ben is a prolific Wikipedian and rapidly made his mark on the RNA Wikipedia entries and continues to do so while working on a MRes in Computational Biology at the University of York. One stunning feat Ben accomplished was passing the article for “Toxin-antitoxin system” through Wikipedia’s peer-review process for “good articles”. This process appears to be at least as rigorous as scientific peer-review and is quite an achievement. He also built a number of families for RNA anti-toxins including Sok, RNAII, IstR, RdlD, FlmB, Sib, RatA, SymR and PtaRNA1. PtaRNA1 is a newly discovered RNA antitoxin that was first published by Sven Findeiss and colleagues in the RNA families track at RNA Biology. This track has provided very useful updates and expansions for Rfam directly from the RNA community.

A guest Rfam rogue, Chris Boursnell, who has been visiting from Andrew Firth’s group has also been busy building new families. With permission from the good people at the Recode-2 database [3] Chris has added a number of new frame-shift elements and has also updated a number of microRNA families based on the latest release of miRBase [4].

An enormous achievement for the database is the inclusion of full-length small subunit ribosomal RNA families. Previously Rfam had just one truncated model that covered all three kingdoms of life. Thanks to the hard work of Eric Nawrocki and colleagues in Sean Eddy’s lab on the Infernal software and related package ssu-align which can now deal with much larger datasets than were previously possible. The three new alignments now cover bacteria, archaea and eukaryotes. These are all derived from the highly accurate and excellent alignments from the work of Robin Gutell and colleagues who run the Comparative RNA Website.

Thanks to the exciting work by Stefan Washietl and colleagues on the RNAcode software package we now have good evidence that the “RNA” family, C0343 (RF00120), is in fact protein-coding and most-likely is not functioning as a RNA other than in a mRNA-sense [5]. Therefore C0343 has been removed for the 10.1 release.

Our SRP families have all been rebuilt and supplemented with additional families thanks to the work of Magnus Rosenblad and colleagues [6]. This is another excellent contribution to the RNA families track at RNA Biology. Based on this work the existing two SRP families were replaced and supplemented by 7 new families: Metazoa_SRP (RF00017), Bacteria_small_SRP (RF00169), Fungi_SRP (RF01502), Bacteria_large_SRP (RF01854), Plant_SRP (RF01855), Protozoa_SRP (RF01856) and Archaea_SRP (RF01857). These new models should improve the specificity of Rfam annotations and reduce the number of pseudogenes incorporated.

We have continued to work on the Rfam clans and have added 3 new clans. These are U3 (CL00100), Cobalamin (CL00101) and group-II-D1D4 (CL00102). Also, the membership of the clans tRNA (CL00001), RNaseP (CL00002) and SNORA62 (CL00040) have been updated.

Finally, several problematic microRNA families mir-544 (RF01045), mir-1302 (RF00951), mir-1255 (RF00994), mir-548 (RF01061), mir-649 (RF01029), mir-562 (RF00998) and spliceosomal U13 (RF01210) were rethresholded to remove the excessive number of pseudogene annotations in the full alignments. This rethresholding along with the rebuilding of our SSU models have removed approximately 600,000 annotations from Rfam.

There are countless other changes that have made, if I’ve forgotten to include any that are significant to you or to mention your name then I apologise profusely.

This release could not have happened without the invaluable help of Jen Daub and John Tate who have worked tirelessly and enthusiastically on this release. This was made particularly challenging by the fact that I have recently relocated to my homeland in New Zealand to take up a position as a Rutherford discovery fellow and senior lecturer at the University of Canterbury in Christchurch. I hope to continue to contribute to Rfam and the wider RNA community from here. This is also a good moment to welcome the new Czars of Rfam, Sarah Burge and Eric Nawrocki who will now face the exciting and challenging task of managing the day-to-day work of maintaining Rfam. I wish them the best of luck in their new roles. I hope they enjoy it as much as I have.

Paul Gardner.

References

[1] Weinberg Z, Wang JX, Bogue J, Yang J, Corbino K, Moy RH, Breaker RR. (2010) Comparative genomics reveals 104 candidate structured RNAs from bacteria, archaea, and their metagenomes. Genome Biology. 11(3):R31.

[2] Findeiss S, Schmidtke C, Stadler PF, Bonas U (2010). A novel family of plasmid-transferred anti-sense ncRNAs. RNA Biology. 7 (2): 120–4.

[3] Bekaert M, Firth AE, Zhang Y, Gladyshev VN, Atkins JF, Baranov PV. (2010) Recode-2: new design, new search tools, and many more genes. Nucleic Acids Res. 38(Database issue):D69-74.

[4] Kozomara A, Griffiths-Jones S. (2011) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Research. Jan;39(Database issue):D152-7.

[5] Washietl S, Findeiss S, Müller SA, Kalkhof S, von Bergen M, Hofacker IL, Stadler PF, Goldman N. (2011) RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA. 17(4):578-94.

[6] Rosenblad MA, Larsen N, Samuelsson T, Zwieb C. (2009) Kinship in the SRP RNA family. RNA Biology. 2009 Nov-Dec;6(5):508-16.

The increase in coverage has in part come from the use of jackhmmer, which is now used routinely when curating new Pfam entries. Jackhmmer, a program that is new to HMMER3, allows the iterative searching of a sequence database, starting from a single sequence, similar to PSI-BLAST.  Using this approach, the curators have been systematically going through proteomes, trying to plug the gaps not covered by Pfam.  Many of the new families are being added to clans, as the existing clan members just do not cover the entire protein space for that ‘superfamily’.

We have changed the copyright from GNU GPL to the creative commons zero (CC0) license.  Without getting in to the details, this basically removes any restrictions on the use of Pfam and does away with the viral GPL license.  Pfam is for the public domain and we waive any rights!

So what’s new?

Well, the answer is not that much, but a major change is that Pfam annotation is now beginning to be co-ordinated via Wikipedia. Unlike Rfam, where every entry has a Wikipedia entry, we expect this to be a more gradual transition for Pfam, so not all entries currently have a corresponding Wikipedia article. For a more detailed discussion, check the help page.  We actively encourage the addition of new/updated annotations via Wikipedia as they will appear far quicker than waiting for a Pfam release.  If there are articles in Wikipedia that you think correspond to a family, then please mail us!

As well as updating the help documentation and bringing back tools such as the domain graphic generator, we have also developed a new way for visualizing the species distribution for a family.  Rather than using a tree like visualization, we have used a radial layout, termed sunburst.  The concentric rings of the representation indicate different taxomomic levels, with species being represented in the outer ring. Using this approach means that all families, regardless of size, are displayed in the same amount of space.  This representation is also much quicker to render than the previous tree view, even for large trees, which we hope will make the species tree information more usable..

Why has it taken so long to get this release out?

Both Jaina Mistry and myself have moved on to pastures new.  We both were working on the release up until we departed in July 2010 and if it had not been for two power outages that wiped out the Sanger compute farm, we might have completed the release before we left.  Since leaving Sanger, I have been setting up the HMMER web servers at Janelia Farm, which allow sequence searches (phmmer, hmmscan and hmmsearch) via a web interface… but more about that in another post.  The move across the pond and settling in to life in the US with my family and the new job start took precedence – hence the delay.  In the last month John Tate and I have had a big push to get the release out!  We think that we have got everything in place, but if you notice anything is broken, then please mail us.

What next?

Well, we will pause to catch our breath, but we will endeavour to get another release out in the not too distant future -  the sequence database that Pfam 25.0 is running on is already 10 months old and our curators, Penny Coggill and Ruth Eberhardt are screaming out for more sequences.  For this release, the HMM data files are already on the FTP site but the database dump files are not yet available there.  We’ll be adding them as soon as possible.  Marco Punta has now joined the group in Sanger as Pfam team leader, and he and Alex Bateman are going to lead a more focused curation effort from Pfam-UK, while I will continue to be responsible for making the releases from Pfam-US.

Posted by Rob (and John)

Here’s the first one:

What turned you on to science in the first place and proteins/RNA in particular?

An interest in all living things

What did you study and where?

Biological Sciences at Lancaster University, followed by a PhD in Molecular Enzymology at the University of Cambridge

Where did you start work, continue studies/work, were you last before joining xfam?

I started work as a research assistant at the Animal Health Trust, before leaving to do my PhD. I then worked at the Department of Veterinary Medicine in Cambridge, before leaving laboratories behind. I spent 9 years as a curator at EBI, firstly in the ENA group and then spending many years in UniProt.

How long have you been with the group?

Three months

Why Xfam?

I have always been interested in proteins, and in finding out more about them.

What is your favourite aspect of  the job?

The detective work involved in trying to discover the function of a protein.

Word or LaTex?

Word

Programming/spoken languages?

Only English

What is your most surprising skill?

When we were thinking of questions for this blog one member of our group suggested the question “Can you shear a sheep”, my reply to this was “No, but I can clip a horse”. We eventually decided a broader question should be “what is you most surprising skill”. So although it is not a particularly surprising skill given my hobby I feel I should list this here.

Favourite music/books/composers?

My favourite book is The Hitchhiker’s Guide to the Galaxy. My favourite music depends on what mood I am in. Mostly 70s/80s/90s rock/pop. If I had to make a choice it would be Queen.

Hobbies?

Horse riding, particularly dressage, walking and gardening.

Hates?

Curry!

And the second one:

What turned you on to science in the first place and proteins/RNA in particular?

I was better at science than other subjects so I naturally gravitated into that subject. In terms of proteins I was delighted that one could understand their function in the most detailed way from looking at their structure.  I spent many hours in the university library reading about the amazingly complex yet beautiful structures of proteins.

What did you study and where?

Newcastle University – BSc Biochemistry, Origami and Juggling (the last two were study delay tactics)

Where did you start work, continue studies/work, were you last before joining xfam?

After an undergraduate degree in Biochemistry in Newcastle I realised that experimental biology was not my forte.  But I loved protein structure. So I went to work for my PhD with Cyrus Chothia at the MRC Laboratory for Molecular Biology in Cambridge.  I worked on the evolution, structure and function of the immunoglobulin superfamily.  I got distracted along many different paths including structure prediction with Alexey Murzin.  But my real passion was sequence analysis and the identification of new protein domains.

How long have you been with the group? / Why xfam?

It has been my dream job since I first arrived at the Sanger Centre in 1997 to work as a Post-doc with Richard Durbin in October 1997.

What is your favourite aspect of the job?

I really enjoy the detective work involved in building a new family of protein domains and then deducing the possible function.  It is a real buzz when you finally work out the puzzle!

Word or LaTex?

Word

Programming/spoken languages?

Perl, English

What is your most surprising skill?

Perhaps origami.

Favourite music/books/composers?

In terms of books I like to read biographies of sports people and sci-fi/fantasy fiction. I don’t really listen to music very often.

Hobbies?

I generally like making things.  I have been folding paper for the last twenty years. See examples at:

http://www.flickr.com/photos/42673512@N00/sets/72157594493635506/

More recently I’ve taken up woodcarving and started to create some molecular biological models, including DNA and a beta-barrel membrane protein.

Hates?

Not having enough time to do everything I would like to.

In this publication we discuss the success of the relationship between Wikipedia and Rfam. This includes a fun analysis of the degree of vandalism the RNA pages have received with respect to the number of useful edits. We also discuss the new clans that explicitly link families that share an evolutionary relationship yet are too divergent to be sensibly aligned, the latest “decimal” release and our future plans.

While you are there, check out the latest miRBase paper and the new miRBase blog:
[2] Kozomara A, Griffiths-Jones S. (2010) miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res.

Our friends over at the EBI have 2 papers describing the all-important nucleotide sequence archives:
[3] Cochrane G, Karsch-Mizrachi I, Nakamura Y; On behalf of the International Nucleotide Sequence Database Collaboration. (2010) The International Nucleotide Sequence Database Collaboration. Nucleic Acids Res.

[4] Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A, Cheng Y, Cleland I, Faruque N, Goodgame N, Gibson R, Hoad G, Jang M, Pakseresht N, Plaister S, Radhakrishnan R, Reddy K, Sobhany S, Ten Hoopen P, Vaughan R, Zalunin V, Cochrane G. (2010) The European Nucleotide Archive. Nucleic Acids Res.

The ENSEMBL series of genome databases has had an update:
[5] Flicek P et al. (2010) Ensembl 2011. Nucleic Acids Res.

Also, see the very useful nomenclature efforts of the “(Human Genome Organisation) Gene Nomenclature Committee”:
[6] Seal RL, Gordon SM, Lush MJ, Wright MW, Bruford EA. (2010) genenames.org: the HGNC resources in 2011. Nucleic Acids Res.