Informatics
Figure 1
A logarithmic plot of historical trends in storage prices versus DNA sequencing costs. The advent of next-generation sequencing (NGS) in 2004 causes an inflection (red) in the exponential curve associated with DNA
sequencing costs (yellow) to a doubling time of less than six months. (Source: Stein, Genome Biology 2010)
Data and lab information management for NGS Today, next-generation genomics labs can easily produce more data than they can effectively man- age or analyse. Industry analysis that once focused on the costs associated with sequencing genome data now focus on the challenges of managing it. In a JP Morgan report conducted in 2010, lab directors cited data storage, data management and informatics as the biggest collective hurdle to expanding NGS operations. This hurdle, however, is more of steeplechase than a single, easily cleared obstacle because the bottlenecks continually shift. Storage was the first critical concern for most organisations as they con- fronted the reality that new machines running at capacity could generate in a single year more infor- mation than was deposited in GenBank by the beginning of 2008 (Figure 2). Some labs changed their initial data handling strategies midstream to free up space; image files, for instance, are by far the bulkiest data types produced by sequencing, and some labs have opted not to store these files types. Other labs, realising that processing and storage power are relatively cheap, have opted to store everything and figure out afterwards what they need for analysis.
The ‘store everything’ approach, though, shifts the bottleneck to analysis, which explains why analysis costs remain high even as the total cost of sequencing a human genome has significantly
74
decreased. The most generous estimates put analy- sis at half again as much as the cost of sequencing. Researchers at the National Center for Genome Resources said that the bulk of the costs in a quar- ter-million dollar sequencing project in 2009 com- prised analysis expenses. “An awful lot of manual analysis is required”, according to this report. “It’s a very large amount of human effort”. Clearing the analysis hurdle requires more than an investment in hardware, infrastructure and bioinformatics expertise. Organisations must com- pletely revamp the workflows that support sequencing, many of which are based on manual, one-at-a-time processes and information stored in disconnected silos such as spreadsheets, emails or document-based communications and paper lab notebooks. Sample preparation often emerges as a critical area of emphasis for organisations seeking to streamline operations. The most prestigious grants and research projects often require labs to be able to guarantee sample traceability – it is essential when dealing with the often limited DNA supplies associated with certain clinical sample cohorts. Nevertheless, busy labs often struggle to ensure that samples received from clients and collaborators are appropriately labelled and that all vital experimen- tal context is passed on efficiently and accurately to bioinformaticians. Clear sample taxonomy, tracked from the moment a sample enters a lab to the point at which results are reported, makes it easier for research scientists and bioinformaticians to set up
Drug Discovery World Summer 2011
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92