Percent of pancreatic cancer patients diagnosed when the disease is still localized: 8

Their 5-year relative survival: 21.5%

Percent of pancreatic cancer patients diagnosed when the disease has metastasized: 53

Their 5-year relative survival: 1.8%

Mean survival rate after diagnosis: < 1 year

Ranking in cause of cancer-related deaths worldwide: 4

Number of effective screening procedures for early detection of pancreatic cancer: 0

Number of people who will be diagnosed with pancreatic cancer during their lifetime: 1 in 71.

Estimated number of people in the US who were diagnosed with pancreatic cancer in 2010: 43,140

Estimated number of people in the US who died from  pancreatic cancer in 2010: 36,800

Median age of death from pancreatic cancer in the US, 2003-2007: 73

Number of National Institutes of Health funded grants having the words “pancreatic  AND (adenocarcinoma OR carcinoma OR cancer)” in their abstracts: 477

Estimated annual  funding for these grants: $34,897,600

Estimated cost of Kim Kardashian’s wedding: >$18,000,000

Estimated total annual health care costs in the US for treating pancreatic pancer: $2,600,000,000

Average cost of treating a pancreatic cancer patient in the US: $87,784

Average cost of treating a pancreatic cancer patient in Sweden: $21,899

Total annual costs of pancreatic cancer to the US economy, including lost work days, lost wages: $4,900,000,000

Percent of pancreatic cancer patients diagnosed with major depression: 76

People near me who I loved and died of pancreatic cancer: 1^*

Sources:

http://seer.cancer.gov/statfacts/html/pancreas.html

http://www.cancer.org/Cancer/PancreaticCancer/DetailedGuide/pancreatic-cancer-detection

http://projectreporter.nih.gov/reporter.cfm

http://www.wikeez.com/en/people/pictures-kim-kardashian%E2%80%99s-extravagant-wedding-12871

http://www.medscape.com/viewarticle/409001_2

http://www.asco.org/ascov2/Meetings/Abstracts?&vmview=abst_detail_view&confID=40&abstractID=33928

http://www.ncbi.nlm.nih.gov/pubmed/21850604

http://www.medscape.com/viewarticle/468138_6

The trouble with genomic sequencing, is that it is too cheap. Anyone that has a bit of extra cash laying around, you can scrape the bugs off your windshield, sequence them, and write a paper. Seriously?

Yes, seriously now: as we sequence more and more genomes, our annotation tools cannot keep up with them. It’s like unearthing thousands of books at some vast archaeological dig of an ancient library, but being able to read only a few pages here and there. Simply put: what do all these genes do? The gap between what we do know and what we do not know is constantly growing. We are unearthing more and more books (genomes) at an ever-increasing pace, but we cannot keep up with the influx of new and strange words (genes) of this ancient language. Many genes are being tested for their function experimentally in laboratories. But the number of genes whose function we are determining using experiments is but a drop in the ocean compared to the number of genes we have sequenced and whose whose function is not known We may be sitting on the next drug target for cancer or Alzheimer’s disease, but those proteins are labeled as “unknown function” in the databases.

The red line is the growth of protein sequences deposited in TrEMBL, a comprehensive protein sequence database. The blue line illustrates the growth proteins in TrEMBL whose function is know, or at least can be predicted with some reasonable accuracy. The green line is the growth in the proteins whose 3D structure has been solved. Note the logarithmically increasing gap between what we know (blue) and what we do not know (red). Image courtesy of Predrag Radivojac.

Enter bioinformatics. CPU hours are cheaper than high throughput screening assays. And if the algorithms are good, software can do the work of determining function much cheaper than experiments. But therein lies the rub: how do we know how well function prediction algorithms perform? How do we compare their accuracy? Which method performs best, and are different methods better for different types of function predictions? This is important because most of the functional annotations in the databases come from bioinformatic prediction tools, not from experimental evidence. We need to know how accurate these tools are. Think about it this way: even an increase of 1% in accuracy  would means that hundreds of thousands of sequence database entries are better annotated, which in turn means a lot less time in the lab or in high throughput screening labs going after false drug leads.

So a few of us got together and decided to run an experiment to compare the performance of different function prediction software tools.  We call our initiative the CAFA challenge: Critical Assessment of Function Annotation. There are many research groups that are developing algorithms for gene and protein function prediction, but those have not been compared on a large scale, yet. OK then: let’s have some fun. We, the CAFA challenge organizers, will release the sequences of some 50,000 proteins whose functions are unknown. The various research groups will predict their functions using their own software. By January 2011 all the predictions should be submitted to the CAFA experiment website. Over the net few months, some of these proteins will get annotated experimentally. Not many, probably no more than a few hundred judging by the slow growth of the experimental annotations in the databases. But we don’t need that many to score the predictions. A few dozen will do.

On July 15, 2011 we will all meet in Vienna, and hold the first-ever CAFA meeting as a satellite meeting of ISMB 2011. This will be the fifth Automated Function Prediction meeting we have been holding since 2005. Only this time, there won’t just be the usual talks and posters, there will be the results of a very interesting experiment. The International Society for Computational Biology is generously hosting our meeting, and judging by the response we are getting so far, we will need one of the larger halls.

Learn more at http://biofunctionprediction.org If computational protein function prediction is your thing, join the CAFA challenge. If you are just an interested observer, keep an eye on the site. In any case, please spread the word.  Finally, if your company wants some publicity, get in touch! We could use the sponsorship ^_^

Acknowledgements: I would like to thank the CAFA co-organizers, Michal Linial and Predrag Radivojac. The CAFA steeering committee: Burkhard Rost, Steven Brenner, Patsy Babbitt and Christine Orengo for supporting us, keeping us on the straight and narrow and for incredibly useful and insightful suggestions.  Sean Mooney and Amos Bairoch for hashing out the assessment.  Tal Ronnen-Oron and the rest of Sean Mooney’s group for setting up the CAFA website. The International Society for Computational Biology for sponsoring us. The community of computational function predictors that have participated in and supported past meetings on computational function prediction, the research groups that have registered to CAFA so far, and those that will register soon   Finally, Inbal Halperin-Landsberg for coining the name CAFA. I apologize in advance if I left someone out.

GO CAFA!

Fast forward to July 1, 2011: the meeting is so on. We have 38 teams participating in the challenge, with over 50 algorithms. We have about 600 proteins whose functions have been predicted. We have a great line up of speakers including Janet Thornton the Director of the European Bioinformatics Institute and Amos Bairoch, the founder of SwissProt. The full program is available. The Radivojac, Mooney and Friedberg labs have been working very hard, and we do have some really great results to report, and some surprising activities. We are also grateful to the NIH and the US Department of Energy for their support.

So if you are planning on being at ISMB this year, drop by  the AFP/CAFA SIG. We are messing with something brand-new, which is what science is all about.

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:

@NIH protein catalytic site evolution health implic: good for drug design.
Young scientist, need $0.7M 2010-2013, 2xpostdocs+cluster+travel. 
Prev pubs: http://is.gd/vnPN; kthxbi

I hope I won’t get killed at the renewal…

Credit: n4sim on Flickr

Following  Shirley Wu’s excellent post on the stimulus money at the NIH, I decided to do my bit, and post some bioinformatically relevant programs from the  Challenge Grants. I am defining bioinformatics rather narrowly here, and excluding most biomedical informatics, imaging technologies, clinical data management, etc. Also, many other topics would be supported to some extent with bioinformatics. I picked those that seem to have bioinformatics as the core applicable field.

(03) Biomarker discovery and validation

03-AA-103        Molecular Markers of Alcohol-induced Tissue Injury. High-throughput
bioinformatic investigations of alcohol’s impact on, for example, the epigenome,
transcriptome, proteome, metabolome, etc. are needed to inform our understanding of themechanisms involved in alcohol-induced injury to adult and fetal tissues. Additionally, these approaches have the potential to reveal candidate biomarkers of alcohol-induced
pathology and alcohol exposure. Research is sought to develop diagnostic biomarker
signatures of alcohol consumption and alcohol-induced organ damage. Contact: Dr. Dale
Hereld, 301-443-0912, hereldd@mail.nih.gov or Dr. Kathy Jung, 301-443-8744,
jungma@mail.nih.gov

03-CA-106 Utilizing data from the TCGA and TARGET projects to support a large scale bioinformatics effort to identify biomarkers that lie within a pathway or are epi-pathway indicators of tumor formation or progression. Epi-pathway markers lie outside of typical pathways but can be identified as indicators when statistically significant numbers of tumors are characterized as is being done in these projects. Potential markers would be validated under other funding mechanisms. Contact: Dr. Joseph Vockley, 301-435-3881, vockleyj@mail.nih.gov

(06) Enabling Technologies

06-GM-103* Development of predictive methods for molecular structure, recognition, and ligand interaction. Studies to more precisely predict molecular structure and interactions between molecules and ligands to lay the foundation for a new generation of therapeutics and drug design. Powerful predictive methods will require the acquisition of experimentally derived constraints and breakthrough computational methods. Reliable, high-throughput predictive methods would create a more comprehensive resource for understanding molecular interaction that would eventually replace the use of slower, empirical determinations. Contacts: Dr. Peter Preusch, 301-594-0828,preuschp@nigms.nih.gov; Dr. Warren Jones, 301-594-3827, jonesw@nigms.nih.gov

06-HG-101* New computational and statistical methods for the analysis of large data sets from next-generation sequencing technologies. The introduction of new methods for DNA sequencing has opened new avenues, including large-scale sequencing studies, metagenomics, transcriptomics, genetic network analysis, and determination of the relationship of sequence variation and phenotypes to disease, to address heretofore unapproachable problems in biomedical research. However, since the large amounts (terabases) of data generated overwhelm existing computational resources and analytic methods, urgent action is needed to enable the translation of this rich new source of genomic information into medical benefit. Contact: Dr. Lisa Brooks, 301 496-7531, brooksl@mail.nih.gov

06-GM-114 Microbial sequence annotation. Development of new approaches to the rapid and comprehensive annotation of microbial sequences resulting from metagenomics and other high-capacity outputs. Approaches may combine high-throughput experimental methods with innovative data mining algorithms and model building. Contact: Dr. James Anderson, 301-594-0943, andersoj@nigms.nih.gov

06-GM-115 High-end computing software. Upgrading of biomedical computing software to high-end computing (HEC). This developmental effort will seek to expand the domain areas to the macromolecular, cell, tissue, organ, whole-organism, and population levels. The program would support grants to upgrade and port software to run and perform experiments on new generation HEC supercomputers. Contact: Dr. Peter Lyster, 301-594-3928, lysterp@mail.nih.gov

06-AG-106 New computational and statistical methods for the analysis of large data sets from genome-wide association studies (GWAS) and the use of next-generation sequencing technologies. Develop new tools to enable the translation of vast amounts of genomic information into medical benefit to address large amounts of data generated (e.g., terabases of sequence) that overwhelm existing computational resources and analytic methods. These new approaches include very large-scale genotyping and sequencing studies, metagenomics, transcriptomics, and genetic network analysis. Contact: Dr. Marilyn Miller, 301-496-9350, MillerM@nia.nih.gov

06-CA-110 In Silico Cancer Drug Medicine. For years, researchers have explored the myriad wonders of the construction of virtual proteins based on gene and protein sequence alignments and the screening of virtual compounds against a database of drug targets. But as is so often the case in drug development, most of these virtual compounds fail to achieve their lofty goals when synthesized and exposed to the harshness of the real world and the complexity of the human body. This obstacle now negatively impacts translation of new chemical entities into the market. Today, an opportunity exists for the NIH to implement a concerted effort that develops transformative tools (virtual and physical) that test drugs in real-world scenarios, while still in the virtual phase of human physiology. Contact: Dr. Henry Rodriguez, 301-496-1550, rodriguezh@mail.nih.gov

06-CA-111 Integrative analysis of genomic data sets generated by TCGA and TARGET. Methods for the unsupervised analysis of large and varied data sets that are predictive of cancer formation and can determine regulatory points in pathways and circuits. Contact: Dr. Joseph Vockley, 301-435-3881, vockleyj@mail.nih.gov

06-CA-112 Development of high throughput mechanisms for genomic analysis. This includes methods to improve the throughput of next gen methods for genomic analysis. Methods could be either laboratory based or bioinformatics based improvements with the goal of decreasing the amount of time it takes to analyze a sample. Contact: Dr. Joseph Vockley, 301-435-3881, vockleyj@mail.nih.gov

06-DA-109 Developing new computational approaches to Information Retrieval. Development of computational approaches which query multiple data sources and types relevant to basic neuroscience and behavioral addiction research, and which (1) employ or add to the Neurolex vocabulary (http://www.neurolex.org) of the NIH Blueprint Neuroscience Information Framework and (2) focus on enabling user-friendly complex queries based on concepts, anatomical coordinates, and other query parameters relevant to addiction research, that return source data elements directly within a format and context that makes them easily interpreted and accessible. Contact: Dr. Karen Skinner, 301-435-0886, ks79x@nih.gov

06-GM-101* Structural analysis of macromolecular complexes. Development of new approaches, technologies, and reagents that would facilitate functional and/or structural analysis of macromolecular complexes. Contacts: Dr. Ravi Basavappa, 301-594-0828, basavapr@nigms.nih.gov; Dr. Laurie Tompkins, 301-594-0943, tompkinl@nigms.nih.gov

06-GM-107 Metal ion binding and function. Development of high-throughput methods for the prediction of metal ion binding and function in proteins at the structural, redox, and/or catalytic levels. Contact: Dr. Vernon Anderson, 301-594-3827, andersonve@nigms.nih.gov

06-HL-108 Develop new informatics techniques for integrative analysis of genomic and epigenomic data. Much of the complex interplay between genetic and environmental risk factors for disease likely occurs through the interactive regulation of gene expression by both genotype and epigenetic markings of the genome. Epigenetic tags such as cytosine methylation and histone tail modifications, which modulate chromatin structure and function thereby affecting gene expression, are associated with environmental toxicities and are well documented. An integrated analysis of gene expression regulation, with simultaneous consideration of both genetic and epigenetic characteristics and of the interactions between these factors, is essential for understanding the complex pathobiology of chronic heart, lung, and blood diseases. New computational and informatics techniques are needed to allow such analyses. Contact: Dr. Robert Smith, 301-435-0202, smithra3@nhlbi.nih.gov

(The one below is not exactly bioinformatics, but if you are wasting time on Second Life and Web 2.0 in general,  why not do something for humanity and get the NIH to pay you?)

06-RR-101* Virtual environments for multidisciplinary and translational research. Virtual networking environments like Science Commons, Facebook, and Second Life, create platforms that can eliminate many barriers in scientific collaborations. These environments integrate fragmented information sources, enable “one-click” access to research resources, and assist in re-use of scientific workflows. Funded projects would develop and implement virtual collaborative environments to facilitate biomedical and translational research, e.g. addressing issues of privacy, technology transfers, and sharing resources. Contact: Dr. Olga Brazhnik, 301-435-0758, brazhnik@mail.nih.gov; NIDA Contact: Dr. David Thomas, 301-435-1313, dthomas1@nida.nih.gov

(08) Genomics

08-AI-101 Explore the utilization and integration of available “omic” datasets to assess pathogen-host biological networks: Challenge Grant studies in this area can facilitate alternative and innovative approaches for the development of new prevention and therapeutic options. Contact: Dr. Valentina Di Francesco, 301-496-1888, difrancesco@mail.nih.gov

08-CA-107 Bioinformatic pipeline for rapid genomic analysis. Development of bioinformatics tools and analytical pipelines that will significantly decrease the amount of time it takes to analyze data from TCGA, TARGET and other high throughput projects. Contact: Dr. Joseph Vockley, 301-435-3881, vockleyj@mail.nih.gov

08-DA-102 Improved Bioinformatics Analysis for Deep Sequencing. The current estimate of sequencing an entire human genome is $5000 and can be accomplished in a few months. However, current bioinformatic and analytic capabilities are inadequate to analyze the volumes of data that would be generated by deep sequencing many individuals. Specifically, RC1 applications are sought to (1) optimize base calls from next-generation sequencing machines, (2) develop and improve optimal alignment/mapping methods that tackle uncertainty and multiple potential placements, (3) identify methods for SNP calling from multiple reads and multiple samples, (4) identify copy-number variation calling from next-generation sequencing data, and (5) develop automated methods for searching sequence databases that could be used to give probabilities that a variant is real. Contact: Dr. Jonathan D. Pollock, 301-435-1309, jpollock@mail.nih.gov

08-DK-102 Beyond GWAS. Use methods such as ‘deep’ sequencing, exon sequencing, high-throughput genotyping and comparative genome hybridization to identify structural variations to pinpoint causal variants associated with NIDDK-relevant diseases or phenotypes, especially those identified in GWAS. Contact: Dr. Rebekah Rasooly, 301-594-6007, rasoolyr@mail.nih.gov (Note: GWAS: Genome Wide Association Studies).

08-HL-102 Develop methods to integrate and analyze data from two or more different ‘omics approaches (e.g., GWAS, sequencing, epigenetics, metabolomics, transcriptomics) to capitalize on existing heart, lung, and blood data sets. Considerable resources have been expended in developing ‘omics technologies and applying them to heart, lung, and blood studies. However, the diverse ‘omics technologies each generate multiple data types. Limitations in our ability to combine and analyze data across various ‘omics studies have constrained their use in efforts to elucidate the molecular mechanisms underlying heart, lung, and blood disorders. To obtain full value from those data will require new and improved tools to:

• Integrate data across two or more ‘omics data sets.
• Analyze integrated data sets using improved statistical tools and approaches necessary to handle the challenges inherent in the complex integrated data sets.

Contact: Dr. Deborah Applebaum-Bowden, 301-435-0513, applebad@nhlbi.nih.gov

08-OD-101 Computational approaches for epigenomic analysis. Technologies such as ultra-high-throughput sequencing allow one to perform epigenomic analyses that were previously impossible. However one of the major remaining challenges is the lack of effective tools for the analysis and integration of epigenomic data. The development of computational or statistical tools to analyze epigenomic data and integrate it with other data types (multiple epigenetic marks, gene expression data, DNA sequence, comparison to diseased cell types etc) would allow epigeneticists to overcome this challenge and make it significantly easier for researchers to investigate the epigenomic basis of disease states. Contact: Dr. Joni Rutter (NIDA), 301-435-0298, jrutter@mail.nih.gov.

(10) Information Technology for Processing Health Care Data

Everything here is biomedical, some bioinformatic, hard to say where one ends and the other begins. Look for yourselves. Lots of database integration and other infrastructure work.

If you are doing ontologies, this one seems like a great one to look at:

10-CA-103 Cell Behavior Ontology. Descriptions of various processes and behaviors of cells are still crudely described and quantified. This type of description makes it difficult to compare and integrate this type of research into various aspects of biological research. Approaches and nomenclatures are desperately needed to better understand, describe, and utilize the vast amount of information about these critical processes in the transforming environment. Contact: Dr. Jerry Li, 301-435-5226, jiayinli@mail.nih.gov

(15) Translational Science

15-AR-102 Link Genomics, Proteomics, Bioinformatics and Systems Biology To Clinically Relevant Outcomes in Autoimmune Diseases. The objective is to develop new, cost effective and accurate tools that will be used to predict, prevent and monitor autoimmune diseases. Define assays that are effective at monitoring disease activity and that predict the development of specific complications. Contact: Dr. Susana Serrate-Sztein, 301-594-5032, NIAMShelp-NIHChallengeGrants@mail.nih.gov

15-OD-102 Analysis of PubChem data sets. The Molecular Libraries Probe Production Centers Network (MLPCN) implements high throughput screens for a number of biological targets and develops probe compounds from the results. The emphasis is on finding useful probes for a wide variety of targets rather than on an in depth investigation of each target or the interactions between them. The NIH will support projects based on MLPCN data available through Pub Chem (http://pubchem.ncbi.nlm.nih.gov) that combine informatics, chemical synthesis and non-high-throughput biological testing to enable the scientific community to take full advantage of the ML resources. Contact: Dr. Ajay (NHGRI), 301-594-7108, ajaydr@mail.nih.gov.

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