If you are reading this, and you are in Boston on those dates, consider showing up, it is a great experience. There will also be a codefest on the two days before the meeting. This year’s topic is cloud computing for bioinformatics. If you like using AWS for bioinformatics or if you want to learn more, this is your chance. Amazon have provided a grant towards this codefest. (Thanks!) Biopython, Bioperl, Biojava and Bioruby developers will all be there, tailoring code to the cloud.

Which brings me to the latest poll: if you are a bioinformatics programmer, which of the Bio* packages  are you using in your programming, if any? If more than one, check the one you use most frequently. Poll answers on the right. As with all Internet polls, you must be crazy if you take it at all seriously.

Stephen Colbert has something to say about it; but in this case, although he is his usual facetiously hilarious self, he seemed to confuse the ACLU, who was one of the plaintiffs. Actually, he confused me too. His arguments for the patent invalidation seem a tad self-defeating, rather unusual for Colbert. See for yourself.

If you don’t know which Ubuntu version you are running, type:

$ lsb_release -a

No more than fifteen minutes after I posted my Q to the amos-help mailing list, Florent Angly came through with a solution. I am posting his email here.

Hi,

This issue was fixed in the development version of AMOS. See below for instructions on how to install this version on Ubuntu:

Download either the regular or development version of AMOS. As of April 4, 2010,
Minimo is only available from the development version of AMOS.
i/ The regular AMOS version is available from http://sourceforge.net/projects/amos/files/, e.g.:
$ wget http://sourceforge.net/projects/amos/files/amos/2.0.8/amos-2.0.8.tar.gz/download
ii/ The development version of AMOS is in a CVS repository. To get it, run:
$ cvs -z3 -d:pserver:anonymous@amos.cvs.sourceforge.net:/cvsroot/amos co -P AMOS

In the directory where the AMOS file are located, run the following to install
the prerequisites:
$ sudo aptitude install ash coreutils gawk gcc automake mummer mummer-doc libboost-dev

For the Hawkeye component of AMOS, you need Qt3:
$ sudo aptitude install libqt3-headers

For the standard version of AMOS, skip to next step, but for the CVS development version, first, run:
$ ./bootstrap

Then regardless of the version:
$ ./configure –with-Qt-dir=/usr/share/qt3 –prefix=/usr/local/AMOS
$ make
$ make check
$ sudo make install
$ sudo ln -s /usr/local/AMOS/bin/* /usr/local/bin/

Now all the programs shipped in AMOS should be available from the command-line.
For example try:
$ Minimo -h
Regards,

Florent

You will need the AMOS development version for Ubuntu 9.10 (and above, presumably), but the regular version for 9.04 (and below). If you are getting the development version, you will also need to install cvs on your machine:

$ sudo aptitude install cvs

Hope this helps anyone struggling with installing AMOS on Ubuntu or other Linux platforms.

• Day 1
• Day 2
• Day 3
• Day 4

About intergenic regions: intergenic regions are as interesting and sometimes even more interesting than the genes themselves: when you are interested in promoters, transcription factor binding sites or almost any other transcription regulation mechanism. Here’s a simple script to find intergenic regions. It reads a genbank formatted file and uses the information there to extract the intergenic regions. The sequences are written to a FASTA file.

#!/usr/bin/env python
import sys
import Bio
from Bio import SeqIO, SeqFeature
from Bio.SeqRecord import SeqRecord
import os

# Copyright(C) 2009 Iddo Friedberg & Ian MC Fleming
# Released under Biopython license. http://www.biopython.org/DIST/LICENSE
# Do not remove this comment
def get_interregions(genbank_path,intergene_length=1):
    seq_record = SeqIO.parse(open(genbank_path), "genbank").next()
    cds_list_plus = []
    cds_list_minus = []
    intergenic_records = []
    # Loop over the genome file, get the CDS features on each of the strands
    for feature in seq_record.features:
        if feature.type == 'CDS':
            mystart = feature.location._start.position
            myend = feature.location._end.position
            if feature.strand == -1:
                cds_list_minus.append((mystart,myend,-1))
            elif feature.strand == 1:
                cds_list_plus.append((mystart,myend,1))
            else:
                sys.stderr.write("No strand indicated %d-%d. Assuming +\n" %
                                  (mystart, myend))
                cds_list_plus.append((mystart,myend,1))

    for i,pospair in enumerate(cds_list_plus[1:]):
        # Compare current start position to previous end position
        last_end = cds_list_plus[i][1]
        this_start = pospair[0]
        strand = pospair[2]
        if this_start - last_end >= intergene_length:
            intergene_seq = seq_record.seq[last_end:this_start]
            strand_string = "+"
            intergenic_records.append(
                  SeqRecord(intergene_seq,id="%s-ign-%d" % (seq_record.name,i),
                  description="%s %d-%d %s" % (seq_record.name, last_end+1,
                                                        this_start,strand_string)))
    for i,pospair in enumerate(cds_list_minus[1:]):
        last_end = cds_list_minus[i][1]
        this_start = pospair[0]
        strand = pospair[2]
        if this_start - last_end >= intergene_length:
            intergene_seq = seq_record.seq[last_end:this_start]
            strand_string = "-"
            intergenic_records.append(
                  SeqRecord(intergene_seq,id="%s-ign-%d" % (seq_record.name,i),
                  description="%s %d-%d %s" % (seq_record.name, last_end+1,
                                                        this_start,strand_string)))
    outpath = os.path.splitext(os.path.basename(genbank_path))[0] + "_ign.fasta"
    SeqIO.write(intergenic_records, open(outpath,"w"), "fasta")

if __name__ == '__main__':
    if len(sys.argv) == 2:
         get_interregions(sys.argv[1])
    elif len(sys.argv) == 3:
         get_interregions(sys.argv[1],int(sys.argv[2]))
    else:
         print "Usage: get_intergenic.py gb_file [intergenic_length]"
         sys.exit(0)

What are we seeing here?

Lines 11-16 are the preamble: we read the GenBank file using Biopython’s genbank parser in line 12.  Beacuse we expect a genome file, which contains a single record, this is a one-time read. Note that this is a rate limiting step, and can take a couple of seconds. Took me ~2secs to read the full E. coli genome on my Linux box.  We prepare one list for the + strand intergenic regions (13), another one for the minus strand intergenic regions (14) and one for all the records (line 15).

The rest of the code are three loop blocks: lines 16-28 I loop over the genbank features, extracting the coordinated of the genes themselves. Line 32-41 I find the intergenic regions on the + strand. Lines 42-52 I do the same for the “-” strand.

Now for a philosophical interlude: although there is a way to read all the intergenic regions in a single pass, I subscribe to the “code simple” doctrine of research software writing. Code performance optimization is a low priority for me. I’d much rather have something that is simple to write,read and modify. I also don’t want to spend too much time coding and elegant script for elegance’s sake, especially if I may not use it too much. Historically, scientific code written for research is mostly extinct: thrown away after a short lived hypothesis was tested and ended its days. Research coding is mostly throwaway glue code. Very rarely it matures into a product. Then, and only then, can you apply all those fine software engineering you learned in college. Before that, write fast and simple.

But I digress. Line 17 loops over the features in the genome file. Line 18 we identify if it is a coding sequence (CDS). If so, we identify the start position, and position and the strand the CDS is on. The list cds_list_minus is a list of 2-tuples. Each 2-tuple is the start and end positions of a CDS on the minus strand. (If you would like to go over the genes, as opposed to coding sequences, change line 18 to:

if feature.type == 'gene':

(or better yet, pass an argument that defines it.)

cds_list_plus, is, yes, the same as cds_list_minus, only for the plus strand (line 24).

Sometime, a CDS does not contain information on which strand it is. With genome files, that is usually the case with single stranded viral genomes. Therefore, we put in the default assumption that if there is no strand indication, then the feature is is on the plus strand. We generate a warning message nevertheless (lines 25-28).

Lines 30-41 we loop over the plus  strand list, and identify the coordinates between the genes. Python’s enumerate function is very useful here. The enumerate function allows us to iterate over a list, but at the same time keep track of which index we are in when looping over the list. So in line 30, pospair receives the start and end coordinates of a CDS as a 2-tuple, while i receives the actual number if the index in the plus strand CDS list. In that way, we can look back to the previous list member, find the coordinates where that CDS ends, and where the current CDS begins. The two coordinates make up the beginning and end of the intergenic regions between those two genes on that strand. In line 35 we check if the intergenic region length is equal to or larger than a threshold: suppose we are only interested in those intergenic regions that are longer than 100 bases? (The default value is 1, see line 11.) In lines 38-39 we build a biopython sequence object that contains an informative header, and the sequence of the intergenic region. The description which goes in the sequence header contains the start and end coordinates of the intergenic region, and the locus ID of the CDS directly downstream from it. The sequence object is appended to a list, which will eventually get written (lines 40-41).

Lines 42-52 are a repeat of lines 30-41, only for the minus strand.  Lines 53 & 54: the list that contains all the intergenic region sequence objects gets written to its own fasta file.

Finally, line 56-63 are boilerplate code, that make this script runnable from the command line. Have fun looking at intergenic regions. Let me know of you find something interesting.

The most exciting phrase to hear in science, the one that heralds new
discoveries, is not ‘Eureka!’, but ‘That’s funny…’ -Isaac Asimov

Thanks to Ruchira Datta for pointing out this one.

Science is many things to many people, but any lab-rat will tell you that research is mainly long stretches of frustration, interspersed with flashes of satisfying success. The best laid schemes of mice and men gang aft agley. A scientist’s path contains leads to blind alleys more than anything else, and meticulous experimental preparation only serves to somehow mitigate the problem, if you’re lucky. This doesn’t work, that doesn’t work either and this technique worked perfectly in Dr. X’s lab, why can’t I get this to work for me?  My experiment was invalidated by my controls; my controls didn’t work the way the controls were supposed to work in the first place. I keep getting weird results from this assay. I can’t explain my latest results in any coherent way… these statements are typical of daily life in the lab.

This stumped and stymied day-to-day life is not the impression of science we get from reading a research paper, when listening to a lecture, or when watching a science documentary show. When science is actually presented, it seems that the path to discovery was carefully laid out, planned and  flawlessly executed, a far cry from the frustrating, bumbling mess that really led to the discovery. There are three chief reasons for the disparity between how research is presented, as opposed to what really goes on. First, no one wants to look like an idiot, least of all scientists whose part of their professional trappings is strutting their smarts. Second, there are only so many pages to write a paper, one hour to present a seminar or one hour for a documentary: there is no time to present all the stuff that did not work. Third, who cares about what didn‘t work? Science is linked to progress, not to regress. OK, you had a hard time finding this out, we sympathize and thank you for blazing the trail for the rest of us. Make a note for yourself not to go into those blind alleys that held you back for years and move on. We’re not interested in your tales of woe.

Only maybe these tales of woe should be interesting to other people. If you make your negative results public, that could help others avoid the same pitfalls you had. If you share the limits of a technique, a protocol or software then someone can avoid using it in a way that does not work. A lab’s publications are actually the tip of the sum total of its accumulated knowledge.Every lab has its own oral tradition of accumulated do’s and dont’s. Not oral in the literal sense: they may even be written down for internal use, but never published. UPDATE (2-FEB-2010): most peer-reviewed journals don’t like stuff that does not work. Thanks to Mickey Kosloff for pointing out the Journal of Negative Results in Biomedicine and The Journal of Negative Results – Ecology and Evolutionary Biology.

Until now.

The Journal of Serendipitous and Unexpected Results aims to help us examine the sunken eight-ninths of the scientific knowledge iceberg, in life science and in computer science. (So an additional field over JNRB and JNREEB). From JSUR’s homepage:

Help disseminate untapped knowledge in the Computational or Life Sciences

Can you demonstrate that:

* Technique X fails on problem Y.
* Hypothesis X can’t be proven using method Y.
* Protocol X performs poorly for task Y.
* Method X has unexpected fundamental limitations.
* While investigating X, you discovered Y.
* Model X can’t capture the behavior of phenomenon Y.
* Failure X is explained by Y.
* Assumption X doesn’t hold in domain Y.
* Event X shouldn’t happen, but it does.

The problem with the JSUR model, and the nature of discovery

I expect JSUR will be a great way to comment on  methods and techniques. Indeed it will codify a trend that has been going on for some time: public protocol knowledge sharing. Many sites like openwetware,  seqanswers or the UC Davis bioinformatics wiki have been doing this for a while. Not to mention a plethora of blogs. Scientists are willing to share their experience with working protocols and procedures, and if this sharing of knowledge can be now monetized to that all-important coin of academia, the  peer-reviewed publication, all the better.

So where is the problem? The problem lies with discovery, and credit given towards it. It would be very hard to get anyone to share awkward, unexpected or yet-uninterpreted results. First, as I said, no one wants to look like an idiot. Second, unexpected or yet uninterpreted results are often viewed as a precursor to yet another avenue of exploration. A scientist would rather pursue that avenue, with the hope of  the actual meaningful discovery occurring in the lab. At most, there will be a consultation with a handful of trusted colleagues in a closed forum. If the results are made public, someone else might take the published unexpected and uninterpreted results, interpret them using complementary knowledge gained in their lab, and publish them as a bona-fide research paper. The scientist who catalyzed the research paper with his JSUR publication receives, at best, secondary credit. The story of Rosalind Franklin’s under-appreciated contribution to the discovery of the structure of DNA comes to mind. Watson and Crick used the X-ray diffraction patterns generated by Franklin to solve the three dimensional structure of the DNA molecule. Yet she was not given a co-authorship on the paper. (And she did not even make the results public, they were shared without her knowledge.) Unexpected results are viewed either as an opportunity or an embarrassment, and given the competitive nature of science, no on wants to advertise either: the first due to the fear of getting scooped, the second for fear of soiling a reputation. I expect JSUR would have a harder time filling in the odd-results niche, but I hope I am wrong.

But if you have protocols you are willing to share…what are you waiting for? Get those old lab notebooks, 00README files, forum posts  and start editing them to a paper. You are sitting on a goldmine of publishable data and you did not even realize it.

Finally, here are two scientists who never declined sharing their unexpected results.

This post has been slashdotted. Exercise extreme caution.

The voices in the song are sampled from this KFC employee training tape. The video won a prize in machinima.com. So if you like World of Warcraft, bacteria, KFC, sampled music, or any combination of the above, you’re gonna love this.

Google flew the green-starred flag of hope yesterday, in celebration of the 150th birthday of a man who constructed a whole language based upon hope. He called himself Doctor Hopeful, and he wanted that the language he created would help break down national barriers. He made it easy to learn, so that people would be motivated to learn it as their second language. They would then speak the Language of Hope, understand each other, and not be so insular. As isolation breed suspicion, and suspicion breeds hostility and ultimately violence.

Unfortunately, neither his language nor his vision of a more understanding and tolerant mankind caught on. One hundred and fifty years after his birth, and 122 after the publication of his book, the world is no friendlier nor tolerant than it was when Ludwig Zamenhof set to correct it by publishing his book International Language: Foreword And Complete Textbook under the pseudonym of Doktoro Esperanto.

English has become the second language of choice for many.  The increasing dominance of English speaking powers throughout the last 200 years resulting in English as the lingua franca is interpreted by many that English was adopted as an imposition from above. English is perceived by many who wish to preserve their non-Anglo cultures as  overwhelming, a threat to their local culture, which would be diluted to extinction through constant bombardment by English speaking movies, TV shows, and Internet provided content. Zamenhof would not have liked that, as Esperanto was intended to be an adoption of choice, without carrying any threatening cultural baggage.

The Internet itself is  hailed by many as a medium to strike down barriers to knowledge and help communications.  But  national firewalls, traffic monitoring, crackdowns on content sharing, criminal abuse and vilification in the popular media cause many to see it more as a threat to their own society, rather than a promise for all societies. And let us not forget that it is still mostly  a developed world’s medium, with most of the content and cultural narrative originating from rich countries.

Neither a world-wide communication technology nor a globally dominant language seem to have brought us closer to the peaceful, understanding and egalitarian world that Zamenhof envisioned. We should be mindful of that, and of Esperanto. The Esperanto language is viewed as a curiosity at best. Esperantists as people with a quaint hobby.  Happily, Esperantists do not view themselves as such. They are continuing the mission of Zamenhof for a more understanding humankind. Esperanto is kept alive by the two million who speak it, by national and  international organizations, by books, magazines, and even music. Happy Birthday Doctor Hopeful.

Martin Weise of the Swedish Esperanto-singing Band Persone. from his Solo Album “more than nothing” Pli ol nenio.

WordPress (like, duh).

Wikipedia (default for looking up new stuff)

Wikis in general (great lab management tool. Don’t need LIMS)

Open Access Publishing and Creative Commons licensing.

FLOSS licensing (90% of the software I use, and 100% of what I write)

Science Bloggers (too numerous to link)

Science tweeters and FriendFeeders (too numerous to link. That’s how I keep up with things)

BLAST (Sometimes it feels like bioinformatics is should be renamed to blastology)

LaTeX (Wrote my dissertation in LaTeX, and never looked back)

OpenOffice.org (because not everyone uses LaTeX).

CiteULike (Keeping my reference library up to date and in good order)

Delicious (Keeping my bookmarks up to date and in good order)

Gmail (because finding that document you sent me a month ago would be impossible otherwise)

Google Scholar (For standing on the toes of Hobbits. Or something like that)

GIS (for blogging and making class slides)

Vim (because emacs blows)

Python (ease & power)

Biopython (OK, conflict of interest here, since I contributed a bit)

Friendly colleagues (They certainly are!)

Good students (gotta make my lab page).

Goulash for dinner. Can’t stand oven Turkey.

Music. Especially the latest song that is going around in my head:

One problem that I am facing is convincing colleagues of the utility of an Open Access publication. The usual arguments: more visibility, retention of the right to re-use material, the Greater Good, taxpayer access to taxpayer-funded research and so on don’t stick very well when faced with a $1500-$2500 or higher publication fee. These can be very big expenses if one is working on medium to small size grants, and where publication fees are sought, in part, from the College. Note: in many case the OA fees are not unaffordable; one would not request, in good faith, that the fees be waived or discounted by the publisher. But if one can use this money to pay the summer salary of a couple of more students, go to a conference, or upgrade / repair equipment, then the utility of shelling out this money for a publication seems marginal and pying this money for publication fees seems almost frivolous. In the US, funding agencies require, at most, that publications resulting from their funding would, be available on Pubmed Central within a certain time period and many non-OA publications comply, or they would lose the ability to publish a large chunk of NIH/NSF funded research projects. But doing so is not really timely OA. The bottom line is, if the grant is smaller than R01 size, many applicants would rather budget the expected $8000 of OA fees for the 3-4 year grant period for other line items that have a more palpable payoff, so to speak.

I don’t really have a point to this post, other than raising a problem that seems to be ignored, or marginalized, by many OA advocates. Not everyone operates on large grants. Many lab budgets leave very little room to buy a new laptop, let alone pay for an OA publication (typically the price of two of said laptops).

We are a few years into the future. Whole human genomes can be sequenced relatively cheaply and accurately. Direct to Consumer Genomics companies offer true genomic analyses now, not just marker analyses. They BLAST^* your sequence against known genotype & disease databases, looking for known genotypic associations.  Furthermore, individuals who are “bioinformatics savvy” can analyze their own genome. We hear of the first life-saving BLAST: a person found an association between one of his SNPs and pancreatic cancer, and managed to undergo a life-saving operation in time. We also hear, tragically, of the first BLAST related murder: a molecular biologist  killed her infant child and herself after she discovered on her own she and her son are both destined to have Huntington’s chorea.  Another, similar suicide took place, but in that second case the person misdiagnosed himself. In a few US states as well as in Italy, the police have successfully subpoenaed  DNA sequences from DTC genomics companies. In Singapore, a mandatory database of the genome of all citizens has been announced.

Credit: Adrian Cousins, Wellcome Images

Worldwide, calls for legislation abound that would limit individuals’ access to their own genomic data. At the same time, a loose coalition of political activists, scientists and journalists advocate a “Genomic Freedom Movement”  to legislate a governmental and insurance company “hands off” policy. Finally, insurance companies (not just health), financial companies and employers are all interested in the new field of “genomic personality studies”, or “Tarot card genomics” as those studies are called by their opponents.  With the advent of many complete human genomes, there has been an explosion of studies that tie personality traits, life-expectancy, lifestyle, earning power, accident prone-ness and even sexual prowess to genomic  data. These studies, some of questionable quality, are gaining strong public attention. Cosmopolitan has just published “Is He Right for You?: how to Get his Genome and What you can Learn From It”. A whole industry of “compatibility genomics” for couples to be married is flourishing.  The Leubavitcher Hassidim are maintaining a “shidduch” genomic database for eligible singles.

The future of genomic data, who can access it and for what reasons seems murky at best. Under those conditions will you have your own genome sequenced? Note that there is no company that will give up that data (you can have your DNA sequence file, but they wish to keep it too, although they promise complete anonymity and privacy).

So will you have your genome sequenced?

——

(*) BLAST is used, as a generic name for any sequence based database searching software. We may have something else that rules the roost 5 years from now.