101 questions with a bioinformatician #20: Roy Chaudhuri

This post is part of a series that interviews some notable bioinformaticians to get their views on various aspects of bioinformatics research. Hopefully these answers will prove useful to others in the field, especially to those who are just starting their bioinformatics careers.

Roy Chaudhuri is a Lecturer in Bioinformatics in the Department of Molecular Biology and Biotechnology at the University of Sheffield, and is part of the Sheffield Bioinformatics Hub. Roy's expertise concerns the comparative genomics and phylogenetics of bacterial pathogens and in a previous life he helped set up the coliBASE and xBASE databases. In a previous-previous life he was also a pioneering website designer (I shouldn't judge: people in glass houses… and all that).

He claims that his current duties involve "research, teaching, publishing, and trying to convince people to give me money". If you would like to give Roy money (perhaps a £1 donation towards his Eccles Cake fund?), you can get in contact with him via the Sheffield Bioinformatics Hub website. You can also find out more about Roy by following him on twitter (@RoyChaudhuri)…but be warned, he is a non-stop tweeter! And now, on to the 101 questions...



001. What's something that you enjoy about current bioinformatics research?

I like that after 16 years as a bioinformatician, I'm still learning new things every day, and that there's no shortage of cool datasets and interesting problems to keep me busy. I also like how far it's possible to get by knowing a little bit of biology and a little Perl.

010. What's something that you don't enjoy about current bioinformatics research?

I worry that too much community effort has been devoted to dealing with problems that are specific to short-read data. I'd like to think that in five years time sequencing will just work, and we will be able to devote our time to dealing with biological quirks rather than technical ones. I'm pretty sure I said the same thing five years ago, though.



011. If you could go back in time and visit yourself as a 18 year old, what single piece of advice would you give yourself to help your future bioinformatics career?

Most of my advice wouldn't be work-related, but I'd certainly mention that the clock starts ticking on potential fellowship opportunities as soon as you get your PhD. I definitely missed the starting gun on that one.



100. What's your all-time favorite piece of bioinformatics software, and why?

I'll go for Prokka, because it does an astonishingly good job at annotating bacterial genomes (better than many manual attempts...), because Torsten wrote the book (well, blog post) on creating usable command-line bioinformatics tools. I particularly like that it checks for its dependencies at the start, rather than choking half-way through, and because it sometimes finishes with a quote from the Hitchhiker's Guide to the Galaxy.

Other than that, I'm a big fan of MUMmer, and I'm always impressed by how many different things it's possible to achieve by stringing two or three SAMtools commands together. If non-bioinformatics-specific software counts, then I'd also mention GNU Parallel, Perl and UNIX itself.



101. IUPAC describes a set of 18 single-character nucleotide codes that can represent a DNA base: which one best reflects your personality, and why?

M, because it's Not K.

Searching for sausage rolls: using Google Scholar to look at the popularity of British culinary delights

Sometimes it can be fun to search Google Scholar for words or phrases that you might not expect to ever appear in the title of an academic article. So last night, I conducted an important scientific study and looked at the popularity of various quintessential items of Britsh cuisine:

Updated: 2014-12-10: includes addition of 'Spotted Dick' thanks to reader @MattBashton.

B-O-G-U-S

There's a lot of it about at the moment…

  • MAGeCK: Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout

Slightly bogus.

  • PrEMeR-CG: Probabilistic Extension of Methylated Reads at CpG resolution

Somewhat bogus.

  • ODIN: One-stage DIffereNtial peak caller

Bogus.

  • Gustaf: Generic mUlti-SpliT Alignment Finder

Definitely bogus.

  • iPro54-PseKNC: Predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition

I'm not sure if 'PseKNC' meant to be pronounced 'sequence'? I'm also not sure if the 'Pro' refers to prokaryote' or 'promoters'. Likewise, I'm not sure if the 'i' is for 'identifying' or is just an Apple-style brand prefix. Finally, I'm not even sure if this is meant to be an acronym at all. But it looks bogus to me.

Tales of drafty genomes: part 1 — The Human Genome

One of my recent blog posts discussed this new paper in PLOS Computational Biology:

There has also been a lot of chatter on twitter about this paper. Here is just part of an exchange that I was involved in yesterday:


The issue of what is or isn’t a draft genome — and whether this even matters — is something on which I have much to say. It’s worth mentioning that there are a lot of draft genomes out there: Google Scholar reports that there are 1,440 artices that mention the phrase ‘draft genome’ in their title [1]. In the first part of this series, I’ll take a look at one of the most well-studied genome sequences in existence…the human genome.

The most famous example of a draft genome is probably the ‘working draft’ of the human genome that was announced — with much fanfare — in July 2000 [2]. At this time, the assembly was reported as consisting of “overlapping fragments covering 97 percent of the human genome”. By the time the working draft was formally published in Nature in January 2001, the assembly was reported as covering “about 94% of the human genome” (incidentally, this Nature paper seems to be first published use of the N50 statistic).

On April 14, 2003 the National Human Genome Research Institute and the Department of Energy announced the “successful completion of the Human Genome Project” (emphasis mine). This was followed by the October 2004 Nature paper that discussed the ongoing work in finishing the euchromatic portion of the human genome[3]. Now, the genome was being referred to as ‘near-complete’ and if you focus on the euchromatic portion, it was indeed about 99% complete. However, if you look at the genome as a whole, it was still only 93.5% complete [4].

Of course the work to correctly sequence, assemble, and annotate the human genome has never stopped, and probably will never stop for some time yet. As of October 14, 2014, the latest version of the human genome reference sequence is GRCh38.p1[5] lovingly maintained by the Genome Reference Consortium (GRC). The size of the human genome has increased just a little bit compared to the earlier publications from a decade ago[6], but there is still several things that we don’t know about this ‘complete/near-complete/finished’ genome. Unknown bases still account for 5% of the total size (that’s over 150 million bp). Furtheremore, there are almost 11 million bp of unplaced scaffolds that are still waiting to be given a (chromosomal) home. Finally, there remains 875 gaps in the genome (526 are spanned gaps and 349 unspanned gaps[7]).

If we leave aside other problematic issues in deciding what a reference genome actually is, and what it should contain[8], we can ask the simple question is the current human genome a draft genome? Clearly I think everyone would say ‘no’. But what if I asked is the current human genome complete? I’m curious how many people would say ‘yes’ and how many people would ask me to first define ‘complete’.

Here are some results for how many hits you get when Googling for the following phrases:

Scientists and journalists don’t help the situation by maybe being too eager to overhype the state of completion of the human genome[9]. In conclusion, the human genome is no longer a draft genome, but it is still just a little bit drafty. More on this topic of drafty genomes in part 2!

  1. There are 1,570 if you don’t require the words ‘draft’ and ‘genome’ to be together in the article title.  ↩

  2. The use of the ‘working draft’ as a phrase had been in use since at least late 1998.  ↩

  3. There is also the entire batch of chromosome-specific papers published between 2001 and 2006.  ↩

  4. This percentage is based on the following line in the paper: “The euchromatic genome is thus ~2.88 Gb and the overall human genome is ~3.08 Gb”  ↩

  5. This is the 1st patched updated to version 38 of the reference sequence  ↩

  6. There are 3,212,670,709 bp in the latest assembly  ↩

  7. The GRC defines the two categories as follows:

    Spanned gaps are found within scaffolds and there is some evidence suggesting linkage between the two sequences flanking the gap. Unspanned gaps are found between scaffolds and there is no evidence of linkage.  ↩

  8. Remember, human genomes are diploid and not only vary between individuals but can also vary from cell-to-cell. The idea of a ‘reference’ sequence is therefore a nebulous one. How much known variation do you try to represent (the GRC represents many alternative loci)? How should a reference sequence represent things like ribosomal DNA arrays or other tandem repeats?  ↩

  9. Jonathan Eisen wrote a great blog post on this: Some history of hype regarding the human genome project and genomics  ↩

Recent changes to the ACGT blog

There's probably some published rules for how to write blog posts and I imagine that one of those rules might be don't blog about your blog. Oh well…

Over the last few months I've been making lots of tweaks to this site. Most of them have been subtle changes in order to make the pages less cluttered and more aesthetically pleasing — e.g. did you notice that I moved the 'About Blog Contact' menu bar ~20 pixels closer to the top of the page as it was just a little bit too lonely where it used to be? Aside from these minor cosmetic alterations, I've also made some more substantial changes:

  • You can no longer see tags for individual blog posts (but I have kept the tag cloud on the About page so you can still find all posts that share the same tag).
  • On the same About page, I added an option to let people subscribe to the blog via email (maximum one email per day).
  • Just about every post listed on the main page of this site used to have a 'Read More' link which you needed to click in order to read the entire article. I'm now restricting this only to my longer posts.
  • I've disabled comments from the blog; partly because I wasn't getting a lot of them but mostly because of the many excellent reasons stated on the @avoidcomments Twitter account.
  • I've started including occasional 'link blog' posts. These are short posts — often just a paragraph or two — that typically comment on a paper or on another blog post. The titles of link blog posts are themselves links to the source paper/blog post, and include a little arrow to indicate this. E.g. see here, here, or here.

As a final note, I'd like to thank everyone who has tweeted or otherwise spread the word about this blog. Aside from churning out more posts about bioinformatics tools with bogus names, I do want to make a bit more of an effort to write about some more substantive issues in bioinformatics. Stay tuned!

Ten recommendations for software engineering in research →

The list of recommendations in this new GigaScience paper by Janna Hastings et al. is not aimed at bioinformatics in particular, but many bioinformaticians would benefit from reading it. I can particularly relate to this suggestion:

Document everything
Comprehensive documentation helps other developers who may take over your code, and will also help you in the future. Use code comments for in-line documentation, especially for any technically chal- lenging blocks, and public interface methods. However, there is no need for comments that mirror the exact detail of code line-by-line.

3 word summary of new PLOS Computational Biology paper: genome assemblies suck

Okay, I guess a more accurate four word summary would be 'genome assemblies sometimes suck'.

The paper that I'm referring to, by Denton et al, looks at the problem of fragmentation (of genes) in many draft genome assemblies:

As they note in their introduction:

Our results suggest that low-quality assemblies can result in huge numbers of both added and missing genes, and that most of the additional genes are due to genome fragmentation (“cleaved” gene models).

One section of this paper looks at the quality of different versions of the chicken genome and CEGMA is one of the tools they use in this analysis. I am a co-author of CEGMA, and reading this paper brought back some memories of when we were also looking at similar issues.

In our 2nd CEGMA paper we tried to find out why 36 core genes were not present in the v2.1 version of the chicken genome (6.6x coverage). Turns out that there were ESTs available for 29 of those genes, indicating that they are not absent from the genome, just from the genome assembly. This led us to find pieces of these missing genes in the unanchored set of sequences that were included as part of the set of genome sequences (these often appear as a 'ChrUn' FASTA file in genome sequence releases).

Something else that we reported on in our CEGMA paper is that, sometimes, a newer version of a genome assembly can actually be worse than what it replaces (at least in terms of genic content). Version 1.95 of the Ciona intestinalis genome contained several core genes that subsequently disappeared in the v2.0 release.

In conclusion — and echoing some of the findings of this new paper by Denton et al.:

  1. Many genomes are poorly assembled
  2. Many genomes are poorly annotated (often a result of the poor assembly)
  3. Newer versions of genome assemblies are not always better

101 questions with a bioinformatician #19: Valerie Schneider

This post is part of a series that interviews some notable bioinformaticians to get their views on various aspects of bioinformatics research. Hopefully these answers will prove useful to others in the field, especially to those who are just starting their bioinformatics careers.

Valerie Schneider is a Staff Scientist at the NCBI in charge of the teams that provide bioinformatics support for the Genome Reference Consortium (GRC). Her teams develop web resources and databases for the analysis and visualization of genomic data, including Map Viewer, various genome browsers and the NCBI Genome Remapping Service.

She tells me that the work of her group focuses on "providing tools that enable researchers to take advantage of the wealth of genomic data available in public databases". Valerie also wanted to mention the following:

The Genome Reference Consortium always appreciates feedback on the human, mouse and zebrafish reference assemblies. If you think the genome looks wrong, or have questions, about it, please let us know.

And now, on to the 101 questions...

001. What's something that you enjoy about current bioinformatics research?

I’m excited by the current push for the sharing of big data. Projects like the Global Alliance for Genomics and Health are bridging the gap between bioinformatics and clinical research. At the NCBI, we’re actively developing new resources that will help researchers navigate and use all this data effectively. I’m also really excited by the effect that longer read sequencing technologies are having on software development. For many years, we’ve been attacking questions and developing software tools based on short read data. Long reads make it possible to investigate genomic regions, such as segmental duplications and other repetitive regions that were previously largely inaccessible, and should also result in more contiguous assemblies. I’m looking forward to seeing what will likely be a variety of new tools to manipulate this data.



010. What's something that you don't enjoy about current bioinformatics research?

The ability to reproduce somebody else’s results is integral to good science. Unfortunately, this is often a challenge in current bioinformatics research. This is not because the science is unreliable or the results wrong. On the contrary, it can be hard because software and datasets aren’t always in public databases that are maintained for the long-term, software versions change (and may not be explicitly noted in publications) or software uses non-standard file formats or works as part of a specific tool chains. As someone who works for an informatics repository, I’m well aware that for most researchers, data management isn’t as exciting as the data. But if it’s not well-managed, science suffers in the long term because we can’t easily reanalyze the data in light of new findings. As bioinformatics-based analyses work their way into journals that haven’t traditionally dealt with big data, this becomes more and more a challenge. Journals are looking at ways to get it right, and archive resources are storing data and tools in more forms than ever, but researchers must also make this a priority when submitting and reviewing publications.



011. If you could go back in time and visit yourself as a 18 year old, what single piece of advice would you give yourself to help your future bioinformatics career?

Don’t over-specialize in any one area of biology (and take more programming classes!). Bioinformatics can lead you all over the genome. A solid grounding in genetics, population biology, structural biology and evo/devo, is helpful not only in defining your own interests, but will prepare you to follow the data, whatever it reveals.



100. What's your all-time favorite piece of bioinformatics software, and why?

I’m going to put in a shameless plug for the NCBI Coordinate Remapping service. Between last year’s release of an updated human reference assembly and the growth in the number of genome assemblies, it’s critical that researchers be able to translate their data between coordinate systems. The Remapping service is based upon assembly-assembly alignments (which are also available); the remapping is done as a base-by-base comparison. It has a notion of first and second pass alignments that can be useful for identifying duplicate sequences. It also stands out because not only does it let you map between chromosomes, you can map between chromosomes and alt loci in GRC



101. IUPAC describes a set of 18 single-character nucleotide codes that can represent a DNA base: which one best reflects your personality, and why?

C (cytosine). I appreciate its propensity for change, in its ability to spontaneously convert to Uracil, and I like the way it can take alternate methylated forms. The former reflects the way I work, tackling a wide variety of projects every day and switching between biological and technical discussions. The latter reflects the fact that I wear multiple professional hats: scientist and scuba instructor.

Buy one bogus bioinformatics acronym, get one free!

New in the journal Bioinformatics:

So the software is called AdvISER-M-PYRO and this is (presumably) derived from Amplicon identification using SparsE representation of multiplex PYROsequencing. However, I don't understand why 'S', 'E', and 'PYRO' get capitalized but not the 'I' of 'identification' or the 'M' of 'multiplex'? Also, I like how they sneak in two letters ('d' and 'v') that don't occur in the name of the software (at least not before the 'I' of 'identification').

This paper is a 2-for-1 type of deal, because if you read a bit more of the abstract you will see:

In parallel, the nucleotide dispensation order was improved by developing the SENATOR (‘SElecting the Nucleotide dispensATion Order’) algorithm.

This second bogus acronym also lacks some clarity. Is the 'R' of 'SENATOR' derived from the first or second 'r' of 'the word 'Order'???