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Sequencing a Human Genome In a Week
blackbearnh writes "The Human Genome Project took 13 years to sequence a single human's genetic information in full. At Washington University's Genome Center, they can now do one in a week. But when you're generating that much data, just keeping track of it can become a major challenge. David Dooling is in charge of managing the massive output of the Center's herd of gene sequencing machines, and making it available to researchers inside the Center and around the world. He'll be talking about his work at OSCON, and gave O'Reilly Radar a sense of where the state of the art in genome sequencing is heading. 'Now we can run these instruments. We can generate a lot of data. We can align it to the human reference. We can detect the variance. We can determine which variance exists in one genome versus another genome. Those variances that are cancerous, specific to the cancer genome, we can annotate those and say these are in genes. ... Now the difficulty is following up on all of those and figuring out what they mean for the cancer. ... We know that they exist in the cancer genome, but which ones are drivers and which ones are passengers? ... [F]inding which ones are actually causative is becoming more and more the challenge now.'"
Data handling and analysis is becoming a big problem for biologists generally. Techniques like microarray (or exon array) analysis can tell you how strongly a set of genes (tens of thousands, with hundreds of thousands of splice variants) are being expressed under given conditions. But actually handling this data is a nightmare, especially as a lot of biologists ended up there because they love science but aren't great at maths. Given a list of thousands of genes, teasing out the statistically significantly different genes from the noise is only the first step. Then you have to decide what's biologically important (e.g. what's the prime mover and what's just a side-effect), and then you have a list of genes which might have known functions but more likely have just a name or even a tag like "hypothetical ORF #3261", for genes that are predicted by analysis of the genome but have never been proved to actually be expressed. After this, there's the further complication that these techniques only tell you what's going on at the DNA or RNA level. The vast majority of genes only have effects when translated into protein and, perhaps, further modified, meaning that you cant's be sure that the levels you're detecting by the sequencing (DNA level) or expression analysis chips (RNA level) actually reflects what's going on in the cell.
One of the big problems studying expression patterns in cancer specifically is the paucity of samples. The genetic differences between individuals (and tissues within individuals) means there's a lot of noise underlying the "signal" of the putative cancer signatures. This is especially true because there are usually several genetic pathways that a given tissue can take to becoming cancerous: you might only need mutations in a small subset of a long list of genes, which is difficult to spot by sheer data mining. While cancer is very common, each type of cancer is much less so; therefore the paucity of available samples of a given cancer type in a given stage makes reaching statistical significance very difficult. There are some huge projects underway at the moment to collate all cancer labs' samples for meta-analysis, dramatically increasing the statistical power of the studies. A good example of this is the Pancreas Expression Database, which some pacreatic cancer researchers are getting very excited about.