Do we need naming regulations for computer software?

Well, just saw this new paper: BMC Bioinformatics | Full text | Bellerophon: a hybrid method for detecting interchromo-somal rearrangements at base pair resolution using next-generation sequencing data.  Seems potentially interesting.  But one part of it struck me as very awkward.  You see, there already is a Bellerophon software program used by many in my field: Bellerophon: a program to detect chimeric sequences in multiple sequence alignments.  Seems like a very bad idea to have a new program with the same name as an existing (and still used) one in a similar general field (DNA sequence analysis).

This leads me to the following question - do we need some sort of naming guidelines or regulations for computer software?  We have all sorts of naming regulations and conventions for genes, for species, for other groups of taxa, and more.  Why not software tools?  But seriously, I don't think we need such a thing - we just need people to use Google and to do a little searching before they invent / publish a software package in case it's name is, well, already used.

Gordon and Betty Moore Foundation hiring fellow for Marine Microbiology program #bioinformatics

Interesting Job Opportunity: Program Fellow, Marine Microbiology Initiative - Gordon and Betty Moore Foundation

See key details of the ad below:

The Bioinformatics Fellow position will be a 1-2 year term. 

The Program Fellow will: 

• Contribute to developing strategy and implementation plans for the bioinformatics portfolio within the Marine Microbiology Initiative.  The fellow will prepare needs assessment for cyberinfrastruture to support research and discovery by marine microbial ecologists.  The fellow will also coordinate bioinformatics-related activities within MMI. (60% time effort)  
• Help convene, facilitate and participate in meetings about cyberinfrastructure related to the MMI community to gather and disseminate knowledge, and produce meeting reports or white papers. (30% effort)  
• Collaborate with MMI Program Officers on grants management related to bioinformatics and data management. (10% effort)  

Attention all metagenomicists: put your pinky in the corner of your mouth & say "1 million dollars"

Already posted this to Twitter and Facebook but had to post here too.  This is wild.  DTRA has announced a $1 million prize for metagenomic analysis: DTRA Algorithm Challenge | Landing Page.  From their page

The Prize:
As nth generation DNA sequencing technology moves out of the research lab and closer to the diagnostician's desktop, the process bottleneck will quickly become information processing. The Defense Threat Reduction Agency (DTRA) and the Department of Defense are interested in averting this logjam by fostering the development of new diagnostic algorithms capable of processing sequence data rapidly in a realistic, moderate-to-low resource setting. With this goal in mind, DTRA is sponsoring an algorithm development challenge. 

The Challenge:
Given raw sequence read data from a complex diagnostic sample, what algorithm can most rapidly and accurately characterize the sample, with the least computational overhead?

My instinct is to keep this to myself because, well, I want to win.  But my sharing side of things won out and I am posting here.  Maybe we (i..e, the community) can develop an open, collaborative project to do this?  Just a thought ...

Guest post on "CHANCE" ChIP-seq QC and validation software

Guest post by Aaron Diaz from UCSF on a software package called CHANCE which is for ChIP-seq analyses.  Aaron wrote to me telling me about the software and asking if I would consider writing about it on my blog.  Not really the normal topic of my blog but it is open source and published in an open access journal and is genomicy and bioinformaticy in nature.   So I wrote back inviting him to write about it.  Here is his post:

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CHANCE: A comprehensive and easy-to-use graphical software for ChIP-seq quality control and validation

Our recent paper presents CHANCE a user-friendly software for ChIP-seq QC and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user's data with ENCODE's large collection of published datasets, performs multi-sample normalization, checks against qPCR-validated control regions, and produces publication ready graphical reports. CHANCE can be downloaded here.

An overview of ChIP-seq: cross-

linked chromatin is sheared,

enriched for a transcription factor

or epigenetic mark of interest

using an antibody, purified and

sequenced.

Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq) is a powerful tool for constructing genome wide maps of epigenetic modifications and transcription factor binding sites. Although this technology enables the study of transcriptional regulation with unprecedented scale and throughput interpreting the resulting data and knowing when to trust the data can be difficult. Also, when things go wrong it is hard to know where to start when troubleshooting. CHANCE provides a variety of tests to help debug library preparation protocols.


One of the primary uses of CHANCE is to check the strength of the IP. CHANCE produces a summary statement which will give you an estimate of the percentage of the IP reads which map DNA fragments pulled down by the antibody used for the ChIP. In addition to the size of this signal component within the IP CHANCE reports the fraction of the genome these signal reads cover, as well as the statistical significance of the genome wide percentage enrichment relative to control in the form of a q-value (positive false discovery rate). CHANCE has been trained on CHIP-seq experiments from the ENCODE repository by making over 10,000 Input to IP and Input to replicate Input comparisons. The q-value reported gives then the fraction of comparisons between Input sample techinical replicates that report an enrichment for signal in one sample compared to another equal to the user provided sample or greater. CHANCE identifies insufficient sequencing depth, PCR amplification bias in library preparation, and batch effects.

CHANCE identifies biases in sequence content and quality, as well as cell-type and laboratory-dependent biases in read density. Read-density bias reduces the statistical power to distinguish subtle but real enrichment from background noise. CHANCE visualizes base-call quality and nucleotide frequency with heat maps. Furthermore, efficient techniques borrowed from signal processing uncover biases in read density caused by sonication, chemical digestion, and library preparation.

Three talks, 1.5 days at #ISMB ... phylogeny, phylogenomics, open science and more

Gave three talks in 1.5 days here in Long Beach as part of the satellite meetings associated with the "Intelligent Systems for Molecular Biology" (ISMB) 2012 Conference. I will write more about the meeting and the craziness of giving three very different talks in 1.5 days. But for now I wanted to at least get my talks posted here since I posted the slides to slideshare and recorded the audio in synch with the slides and posted these "slideshows" to YouTube. Here are the talks below:

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Talk 1 for the "Bioinformatics Open Source Conference" BOSC2012.  Was asked to talk about Open Science ... so ... I did ...

Slides: Jonathan Eisen talk on "Open Science" at #BOSC2012 #ISMB

View more presentations from Jonathan Eisen

Slideshow with audio:
 

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Talk 2 for the Student Council Symposium SCS2012. Sort of supposed to be a career guidance discussion so I geared my talk on the lines of "lessons learned" ...

Slides:Jonathan Eisen talk for #SCS2012 at #ISMB "Networks in genomics and bioinformatics: from phylogeny to Twitter"

View more presentations from Jonathan Eisen

Slideshow with audio:

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Talk 3 for the "Automated function prediction" AFP2012 satellite meeting.  I decided to talk about phylogenetic and phylogenomics approaches to functional prediction ...

Slides:Jonathan Eisen talk "Phylogneomic approaches to functional prediction"a #AFP2012 #ISMB

View more presentations from Jonathan Eisen

Slideshow with audio:

Summary of responses to question about metrics for density in phylogenetic trees

I posted a question to Twitter and Facebook about metrics for assessing density in a phylogenetic tree. Here is a "Storification" of the responses. Thanks for the help all.
Any other suggestions welcome in comments ...

Draft post cleanup #11: Tree Hugging

Yet another post in my "draft blog post cleanup" series.  Here is #11 from September.

Just a quick one.  In August a nice review paper came out on phylogenetic analysis software: Learning to Become a Tree Hugger | The Scientist.  By Amy Maxmen it is a "A guide to free software for constructing and assessing species relationships".  Definitely worth checking out.

Draft blog post cleanup #1: Divide and Conquer to Find Orthologs

OK - I am cleaning out my draft blog post list.  I start many posts and don't finish them and then they sit in the draft section of blogger.  Well, I am going to try to clean some of that up by writing some mini posts.  Here is the first ---

Saw an interesting paper worth checking out:
PLoS ONE: Calculating Orthologs in Bacteria and Archaea: A Divide and Conquer Approach

Playing around with CloVR - cloud computing bioinformatics system

Nice new tool/resource available out there for metagenomic and genomic analysis called CloVR: CloVR: A virtual machine for automated and portable sequence analysis from the desktop using cloud computing

It is available at http://clovr.org and it should be useful to many people out there doing genomics and metagenomics if you want to make use of cloud computing resources.

CloVR is brought to us by Florian Fricke and Owen White and Sam Angiuoli and others from the University of Maryland (full disclosure - many of the authors are ex-colleagues of mine from TIGR).

Not only is Clovr available openly and freely but they even have a Clovr blog: http://clovr.org/category/blog/ ... though it does not seem to be heavily used.  Kudos to this team for producing and releasing this software for others to use.  And kudos to NSF, USDA and NIH for funding its development -- I have a feeling many people will use it.

New paper from my lab (& the Facciotti lab): Mauve Assembly Metrics #Halophiles #Genomics

Just a quick post here. A new paper from my lab has come out in Bioinformatics. The paper is relatively simple. Titled "Mauve Assembly Metrics" it reports work of Aaron Darling and Andrew Tritt (with some minor contributions from me and Marc Facciotti). Aaron wrote the program Mauve when he was a student in Nicole Perna's lab at Wisconsin: Mauve: multiple alignment of conserved genomic sequence with rearrangements. Over the years he (and others) have continued to develop the program and written a few papers too including for example, the development of progressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. This new paper reports basically a system/scripts to measure assembly quality. Here is the abstract:

High throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high quality reference genome, and measuring assembly accuracy using some metrics. We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms.

Check out the paper: Mauve Assembly Metrics. Download the scripts/code http://ngopt.googlecode.com and Mauve and play around and let me know what you think.

Note this paper was supported by a grant from the National Science Foundation (ER 0949453). That grant is focused on comparative genomics (sequencing and analysis) of halophlic archaea. Stay tuned for more on that project as we are writing up a series of papers ....

Some related links:

• Genome Evolution Laboratory - Download Mauve
• Mauve | Download Mauve software for free at SourceForge.net
• http://ngopt.googlecode.com

Yes, I am a #RedSox & #PLoS fan; & this video sort of is proof #BenFranklinAward #OpenScience

Just saw this posted on Youtube.  Did not know it was coming ... but am happy they recorded it



And here are the slides I used.  Will try to synch

Ben Franklin Award Slides

View more presentations from Jonathan Eisen

For more on this award see

• UC Davis' Jonathan Eisen Wins 2011 Benjamin Franklin Award - Bio ...
• Back in Boston, Eisen Celebrates 2011 Benjamin Franklin Award ...
• BioMed Central Blog : Jonathan Eisen: 2011 Benjamin Franklin Laureate
• Boston, Bioinformatics & Ben Franklin Award wrap up from #BioIT11

Boston, Bioinformatics & Ben Franklin Award wrap up from #BioIT11

Photo by Mark Gabrenya

Well, just got back from Boston where I went to the BioIT World convention to pick up the "Benjamin Franklin Award" for contributions to Open Science from Bioinformatics.Org.  A quick round up of the trip:

Tracing the evolutionary history of Sarah Palin: links to a parasitic nematode and the pathogenic fungus Botryotinia fuckeliana

You see, as a total sequence analysis dork, when I see names, I frequently ask whether the letters in the name include only letters which are used as amino acid abbreviations. I started this game when the brilliant notes/letters came out in Science in the early 90s about whether ELVIS was overrepresented in protein sequences. Of course, despite being 20 years old, Science still keeps these under wraps requiring registration to see them (see for example the Stevens letter).

Anyway, alas, three of the major candidates for the US election have names that do not use traditional amino acid abbreviations so I am stuck with analyzing Sarah Palin. But that is OK because of her professed aversion to evolution and support to Creationism (and since sequence analysis is inherently an evolutionary study).

So - I took here name and went to the NCBI Blast page and did some searches. And what came up? Well, here are some of the top hits from the blastp searches (which I used to compare the pretend peptide "SARAHPALIN" with all the peptides in the non redundant collection at Genbank).

>ref|XP_001545292.1|  hypothetical protein BC1G_16161 [Botryotinia fuckeliana B05.10]
gb|EDN25226.1|  predicted protein [Botryotinia fuckeliana B05.10]
Length=383

GENE ID: 5425746 BC1G_16161 | hypothetical protein
[Botryotinia fuckeliana B05.10]

Score = 26.9 bits (56),  Expect =   189
Identities = 8/9 (88%), Positives = 8/9 (88%), Gaps = 0/9 (0%)

Query  1    SARAHPALI  9
            SARA PALI 
Sbjct  209  SARAQPALI  217

>ref|YP_061725.1|  homoserine dehydrogenase [Leifsonia xyli subsp. xyli str. CTCB07]
gb|AAT88620.1|  homoserine dehydrogenase [Leifsonia xyli subsp. xyli str. CTCB07]
Length=451

GENE ID: 2939000 thrA | homoserine dehydrogenase
[Leifsonia xyli subsp. xyli str. CTCB07] (10 or fewer PubMed links)

Score = 26.9 bits (56),  Expect =   189
Identities = 8/9 (88%), Positives = 8/9 (88%), Gaps = 0/9 (0%)

Query  1    SARAHPALI  9
            SAR HPALI
Sbjct  267  SARVHPALI  275

>ref|ZP_02031476.1|  hypothetical protein PARMER_01474 [Parabacteroides merdae ATCC
43184]
gb|EDN87136.1|  hypothetical protein PARMER_01474 [Parabacteroides merdae ATCC
43184]
Length=299

Score = 26.1 bits (54),  Expect =   340
Identities = 7/8 (87%), Positives = 8/8 (100%), Gaps = 0/8 (0%)

Query  3    RAHPALIN  10
            RAHPAL+N 
Sbjct  170  RAHPALVN  177
>ref|XP_567332.1|  hypothetical protein CNJ01520 [Cryptococcus neoformans var. neoformans
JEC21]
ref|XP_773201.1|  hypothetical protein CNBJ1950 [Cryptococcus neoformans var. neoformans
B-3501A]
gb|EAL18554.1|  hypothetical protein CNBJ1950 [Cryptococcus neoformans var. neoformans
B-3501A]
gb|AAW45815.1|  hypothetical protein CNJ01520 [Cryptococcus neoformans var. neoformans
JEC21]
Length=437

GENE ID: 3254188 CNJ01520 | hypothetical protein
[Cryptococcus neoformans var. neoformans JEC21] (10 or fewer PubMed links)

Score = 26.1 bits (54),  Expect =   340
Identities = 8/9 (88%), Positives = 8/9 (88%), Gaps = 0/9 (0%)

Query  1    SARAHPALI  9
            SAR HPALI 
Sbjct  415  SARQHPALI  423

>ref|YP_001626035.1|  citrate synthase [Renibacterium salmoninarum ATCC 33209]
gb|ABY24621.1|  citrate synthase [Renibacterium salmoninarum ATCC 33209]
Length=386

GENE ID: 5822379 RSal33209_2898 | citrate synthase 
[Renibacterium salmoninarum ATCC 33209]

Score = 25.7 bits (53),  Expect =   456
Identities = 9/11 (81%), Positives = 9/11 (81%), Gaps = 2/11 (18%)

Query  1    SARAHP--ALI  9
            SARAHP  ALI 
Sbjct  218  SARAHPYAALI  228

>ref|YP_001817256.1|  integral membrane sensor hybrid histidine kinase [Opitutus terrae
PB90-1]
gb|ACB73656.1|  integral membrane sensor hybrid histidine kinase [Opitutus terrae
PB90-1]
Length=936

GENE ID: 6208547 Oter_0366 | integral membrane sensor hybrid histidine kinase
[Opitutus terrae PB90-1]

Score = 25.2 bits (52),  Expect =   611
Identities = 7/7 (100%), Positives = 7/7 (100%), Gaps = 0/7 (0%)

Query  3    RAHPALI  9
            RAHPALI 
Sbjct  256  RAHPALI  262

>ref|YP_001757871.1|  putative anti-sigma regulatory factor, serine/threonine protein
kinase [Methylobacterium radiotolerans JCM 2831]
gb|ACB27188.1|  putative anti-sigma regulatory factor, serine/threonine protein
kinase [Methylobacterium radiotolerans JCM 2831]
Length=331

GENE ID: 6141303 Mrad2831_5232 | putative anti-sigma regulatory factor,
serine/threonine protein kinase [Methylobacterium radiotolerans JCM 2831]

Score = 25.2 bits (52),  Expect =   611
Identities = 7/8 (87%), Positives = 8/8 (100%), Gaps = 0/8 (0%)

Query  2    ARAHPALI  9
            ARAHPAL+
Sbjct  299  ARAHPALV  306

>ref|ZP_01466013.1|  hydrolase, TatD family [Stigmatella aurantiaca DW4/3-1]
gb|EAU63211.1|  hydrolase, TatD family [Stigmatella aurantiaca DW4/3-1]
Length=209

Score = 25.2 bits (52),  Expect =   611
Identities = 7/7 (100%), Positives = 7/7 (100%), Gaps = 0/7 (0%)

Query  3   RAHPALI  9
           RAHPALI 
Sbjct  79  RAHPALI  85

>ref|YP_001558323.1|  glycosyl transferase group 1 [Clostridium phytofermentans ISDg]
gb|ABX41584.1|  glycosyl transferase group 1 [Clostridium phytofermentans ISDg]
Length=357

GENE ID: 5743305 Cphy_1206 | glycosyl transferase group 1
[Clostridium phytofermentans ISDg]

Score = 25.2 bits (52),  Expect =   611
Identities = 8/10 (80%), Positives = 8/10 (80%), Gaps = 0/10 (0%)

Query  1    SARAHPALIN  10
            S RAHP LIN 
Sbjct  113  SERAHPLLIN  122

There does not appear to be a perfect match in the NCBI NR protein database. But take a close look at the #1 scoring hit. That is right, it is from and organism called Botryotinia fuckeliana. No comment on the appropriateness of this name, but it does contain a term I will probably use a lot if she gets elected.

Of course, anybody who has heard me blather on and on about evolution knows that I am always talking about how blast top hits are not a good measure of relatedness per se (see my NAR paper where I first talked about this in 1995). So - I decided to build a tree of Sarah Palin. I used the NCBI Distance Tree option which you can do from blast searches.










Since most likely you cannot see that in enough detail - here is a zoom in.








That one did not come through on the Blog so well either so I decided to output the tree in Newick format and then I searched for a program that could draw a better figure on the web (we have tools in my lab to do this but I am trying to do this all on the web as an exercise). And I found a web site that makes drawtree available. And I plugged in the Newick format and it made a nicer one.




Though making trees from really short sequences is not ideal, in this tree, Sarah Palin is shown to be at the root of a branch including a protein from the parasitic nematode Brugia malayi. So if we take an evolutionary interpretation it seems that this causative agent of filariasis (well, a protein from this agent) is descended from SarahPalin. In other words, she seems to be ancestral to this parasite.

So in conclusion - by similarity - SarahPalin is closest to a plant pathogen with an unusual name. And by phylogeny SarahPalin is ancestral to a parasitic nematode. Sounds about right.

Connection between Video Games and Bioinformatics?

The Scientist Magazine has a nice piece on one of my favorite people in all of Science - Sean Eddy. In the article, they discuss how Sean is one of those bioinformatics folks who does not just hack together some code to do something but actually writes really good code for his programs. For those of you who do not know, Sean has made a whole collection of software tools for biologists (see his web site here). Perhaps the most widely used is HMMER, which is designed for making and using hidden markov models. But there are some other good ones he has put out. My favorite is Forester, which was made by Christian Zmasek in his lab and is supposed to be available here, although the site is not working right now (NOTE - Christian has posted a new link for it in the comments). I like this because, well, it is software for "phylogenomic" analysis.

Anyway - it is a nice article about Sean, especially the parts talking about how his background in video games contributed to his success in bioinformatics. Back to something I said above, Sean is without a doubt one of my favorite people in science. There are many reasons for this but here are a few.

• He is very open with ideas.
  
  Once, at a conference, I gave a talk on this bizarre new pattern we had found when we were comparing the genomes of E. coli and V. cholerae. We had found that when we did genome-level alignments of these species there was an X-like pattern (see our paper on this here). Anyway, in the talk I said something to the effect of "we have no friggin idea how these X-like alignments could be generated" And Sean, I think in the quesiton session, pointed out that in another paper of ours we had seen what appeared to be symmetric inversions occurring around the origin of replication and that could create the X-alignment. And lo and behold he was right. We got the paper, but in a large part it was his push that got us looking at the inversions sooner than we would have.
  
  
• He is very open with science.
  
  Most of Sean's work is on the open side of science. Open Source software. Open Access publications. Open everything. And I should point out that it was a talk by Sean that catalyzed my conversion into an Open Science supporter. I was attending a meeting in Ft. Lauderdale to discuss data release policies for genome projects. This meeting led to the "Ft Lauderdale Agreement" on data release, by the way. A the meeting there were many genomics players like Eric Lander and Francis Collins who were trying to push for not completely open data release policies where genome centers could release data but there would be constraints placed on the use of the data so that the genome centers would be the first to be able to publish genome scale analysis of an organisms genome sequence.
  
  At the time I was working at TIGR and I supported this notion of basically letting people search for a few genes of interest but preventing them from doing genome analyses. And then Sean got up and gave a talk and, well, blew my mind. I am sure I have notes somewhere from the meeting but basically what he said was - the genome projects whole point is to generate genome data for people to do genome-level analysis. So how on earth can we justify preventing exactly the type of analysis that the projects were designed to generate. He was not saying that we should not somehow protect the genome centers. What he was saying was that for the benefit of science, we need to find a way to allow people to do genome-level analyses immediately on the data. And he also said that the risks of releasing ones data with no restrictions are much less than everyone claims. I think he convinced many people that genome centers needed to open up their data release policies a bit more. And he convinced me.
  
  And so I went home from that meeting and decided to release the data from as many of my genome projects as I could, with NO restrictions (e.g., this is what we did with Tetrahymena). And also, this new found belief in openness helped pave the way for my conversion to being an Open Access publishing supporter.

Anyway, glad to see Sean getting positive press. It is well deserved. Now off to play some video games.

Tony Hey visits U. C. Davis

Just got back from a dinner with Tony Hey, who was visiting UC. Davis to give a talk and meet with various people. Hey is currently VP for technical computing at some place called Microsoft. Hey has done some pretty interesting things in his career but what I know him from is his time as the head of the "E-science" initiative in the UK. Before I blather on about this ... check out Timo Hannay's blog about Hey's visit to Nature which has a pseudo outline of his talk he gave there.

It is interesting to see Microsoft getting into collaborative science --- I hope they stay serious about it because we need more "top down" types of efforts are big places like Microsoft. Whether Microsoft could make much money out of contributing to science I do not know, but if they put 1/1000 of the effort into this as they do into games and Office, science would almost certainly benefit. Many years ago when I was at TIGR, some Microsoft folks came to visit (when genome-stocks were going crazy) and expressed an interest in getting more involved in bioinformatics and genomics. Looks like that did not go anywhere. Maybe now is the time to try to get them doing this again?

I know Microsoft is viewed as Evil incarnate by many academics but hey (no pun intended), given the cool stuff being done by the Gates Foundation in various areas of science, maybe Microsoft will move a little more into science if only to support Gates Foundation efforts. Certainly, Tony Hey's background suggests that they have the potential to do some interesting stuff.