Remember when we said we had big news to share? It’s almost here! As we get closer to launching our public site, share in our excitement by entering the contests and prize drawings we’ll be announcing in this space over the next few weeks.
Join us every single week for another chance to win, or just drop by and participate when you like- there’s no obligation necessary (Either way, don’t miss the Official Rules first). And once our public site is up, come back for our biggest contest of the year: the NextBio Genomics Innovation Challenge.
At NextBio, we like to look at data with fresh eyes. We know that integrating diverse kinds of information with strong data mining strategies can yield fresh hypotheses, new insights and much more. With the data accessible and the most intuitive, user-friendly tools possible, brand new genomic discoveries are just waiting to be made. The NextBio Genomic Innovation Challenge is your chance to do it. And if intellectual satisfaction isn’t thrilling enough for you, there are big prizes to be won as well. Watch this space to find out more. And as you wait for the public site and bigger prizes, why not try your hand at a few other contests too?
For starters, we’ll keep it really simple- all you have to do is keep in touch! First, like us on Facebook and second, drop us a line. Leave us a comment either on this blog post or on our Facebook Wall, telling us about your research interests. Do both (Follow us on Facebook AND leave a comment) by
August 5, 2011 August 10, 2011 and your name will be entered in a drawing for one of three gift cards ($10.00 each) from Starbucks, Amazon or iTunes (your choice!).
Independent of advances in sequencing and data mining technologies, nearly all translational and integrative genomic research relies on understanding mutations to explain biological phenomena. Despite being the most fundamental unit of genetic change, different mutations can have profoundly different effects. In the first of a three-part series on the biology underlying phenotypic diversity, we describe how insertions/deletions and repeat regions can change local mutation rates in the genomes of species ranging from bacteria to humans.
How insertions and deletions shape genome evolution
Mutations are the heart of genetic diversity and changes in DNA sequence eventually drive evolution. Can this diversity in genomes and protein structure be influenced more by one kind of mutation than others? A series of research over three years examines two conflicting ideas about the importance of insertion/deletion mutations, or “indels”, in increasing sequence diversity in species ranging from bacteria to humans.
Though there’s no clear reason why, certain regions called “genome hotspots” are known to be more prone to errors and increased mutation rates than others. Two potential explanations were examined in a Nature paper in 2008: (i) the “regional instability” hypothesis, which proposes that certain configurations of DNA bases are more unstable than others, and (ii) the “mutagenic indel” hypothesis, based on the idea that the presence of indels might increase surrounding instability to create genomic hotspots of diversity. (full text paper here)
“Because of the way data is scaling up, we need to build systems focusing on high-throughput computational tools that are also biologically relevant.”
Mimicking the current data explosion in biology, a molecular biologist who began his career working on a single bacterial gene now handles terabytes of data from genomes, gene expression, and much more on a daily basis. After ten years in biotechnology research with Affymetrix, Venugopal Valmeekam moved to NextBio’s Biocomputing team, where members work to develop pipelines to handle curated data.
Next in our series on data analysis and life at NextBio, read on to find out more about what the Biocomputing team does!
NB: What is your background and how did you start working in this field?
I’m actually a biologist originally. I finished a Ph.D in Molecular Biology and worked at Cold Spring Harbor as a post-doctoral researcher after I graduated. Then I moved to the position of resident scientist at Affymetrix, where they were looking for someone with a biology background who wasn’t afraid of computers to build applications to analyze genomic data.
Distinguishing driver and hitchhiker mutations for targeted chemotherapy
Cancer genomes are complex, with multiple changes in gene sequence, expression profiles and epigenetic changes to regulatory regions. Some of these changes, termed ‘driver’ mutations, are significant to tumor formation and cancer progression, while others are simply ‘hitchhikers’ of little (known) clinical relevance.
As pharmacogenomics progresses towards identifying genetic profiles for stratified medicine strategies, distinguishing driver and hitchhiker mutations becomes essential. Two recent reports from the Translational Genomics Research Institute (TGEN), presented at the 14th World Conference on Lung Cancer, examine signaling pathways in lung cancer to identify novel treatment strategies and potential drug targets.
Two broad categories of lung cancer exist: small cell lung cancer (SCLC), causing about 15% of all lung cancers, and non-small cell lung cancer (NSCLC). Of the three sub-forms of NSCLC, adenocarcinomas are the most prevalent, accounting for more than 50% of all lung cancer cases.
The Cancer Genome Atlas maps genomic changes in ovarian adenocarcinoma
Popping an aspirin cures headaches for nearly everyone regardless of their DNA, but some drugs only work on patients with specific genetic profiles. Herceptin, for example, is effective only in breast cancer patients whose tumors over-express the HER-2 gene, and Gleevec is specifically designed to inhibit an altered enzyme form in leukemia. Several other such drugs, designed for specific gene forms or expression patterns, are currently in use. Understanding the genetic profile of complex diseases like cancer is the first step towards designing more effective therapies.
“We have to bring “genome-drug” interactions to (physicians’) attention just as we currently bring “drug-drug” interactions to their attention.”
Adverse drug events account for over 700,000 deaths each year, and nearly 30% of these are attributed to interactions of drug combinations. Public databases curate hundreds of thousands of gene variants linked to disease risks every year. Mining these diverse sources could help us learn how genetic variations, drug targets and clinical parameters come together to influence human health. Using computational tools to utilize this wealth of scientific data effectively is something we’ve discussed on the blog earlier as well.
Beginning at the “intersection of molecular biology and medical informatics” over ten years ago, Russ Altman is the founder of PharmGKB (PharmacoGenomics Knowledge Base), a database that curates and disseminates information about gene-drug-disease relationships. The professor of bio-engineering, genetics and medicine at Stanford University is also on the Scientific Advisory Board at NextBio, and spoke to us about genomics and the future of medicine.
Genome editing treats hemophilia in mice
Though we’re speed-reading individual genomes with next-generation sequencing technologies, gene-based clinical therapy remains largely anecdotal. Even for single-gene Mendelian diseases, gene therapy is yet to become common practice. Much of this lag is due to technical concerns with the process itself, such as the random insertion of viral vectors (causing leukemia in some cases), unexpected changes in gene expression, and even a fatal immune response (in the case of Jesse Gelsinger).
A new technique called “genome editing” circumvents some of these technical problems by simply correcting an existing faulty gene, rather than inserting an entirely new version. In a recent report in Nature, researchers explored the promise of genome editing in a mouse model of hemophilia, successfully curing the disease by using zinc-finger nucleases to correct a faulty version of the gene hF9.
“The interesting thing with biological data is that using new [software] technologies makes such a difference to what you can do with the data.”
Programming at NextBio could mean using software tools named after toy elephants or occasionally bribing the Curation team with chocolate. But working behind the scenes is still serious business. As Dan Grammas, Senior Software Engineer says, “I’m not just working to protect someone’s computer from a virus. The work we do here is relevant to people’s lives- researchers, clinicians, patients.”
Curation scientists keep track of all the data that’s published and import it to the NextBio pipeline. Software engineers process curated data, sorting and validating it so results can be accurately scored and categorized. Dan’s been programming for several decades now, and now develops APIs and pipelines to validate data imported into NextBio. Here’s what he has to say about where data goes when it ‘vanishes’ behind a progress bar that says “Processing”.
Integrative approaches identify molecular component essential for pumping calcium into mitochondria
Mammalian lung mitochondria, from Wikimedia Commons
A mysterious pump transports calcium ions across the mitochondrial membrane inside cells, literally controlling every breath we take and every move we make. This ‘calcium uniporter’ drives energy fluxes by controlling the rate of the Krebs cycle. For decades, scientists have researched the physics and kinetics of this molecular transporter, but never managed to assign a genetic identity to the functions they so extensively studied. At the end of a 50-year search, Vamsi Mootha and colleagues finally pinned a gene to the elusive transporter, thanks to some ingenious data sleuthing and classical molecular biology.
Using the internet for open science
A few days ago, PLoS hosted a talk by Michael Nielsen at their San Francisco offices. Nielsen, author of the book “Reinventing discovery”, due late this year from Princeton Press, is a strong-voiced proponent of the need for a change in the way we share data.
The Polymath project, his opening story, is one of the best examples of how and why open science works. Tim Gowers, a Fields medalist, posted a famous mathematical problem on his blog, an open invitation to anyone interested to try their hand at solving it. For the first 70 hours, nothing happened. Then a math professor left a comment, quickly followed by a high school teacher, another Fields medalist and so on. In the span of 37 days, over 800 comments collectively solved the problem. How many conferences and scientific papers, peer reviews boards and editorial revisions would it have taken to even get these diverse minds thinking together in the same space? Nielsen describes it as the difference between “driving and pushing your car”.