Monday, 14 July 2014
After about a decade from the first appearance if NGS sequencing we have seen incredible improvements in throughput, accuracy and analysis methods and sequencing is now more diffused and easy to achieve also for small labs. Researchers have produced tons of sequencing data and the new technology allowed us to investigate DNA and human genomic variations at unprecedent scale and precision.
However, beside the milestones achieved, we have now to deal with new challenges that were largely underestimated in the early days of NGS.
MassGenomics has a nice blog post underlining the main ones, that I reported here:
Where do we put all those data from large genomic sequencing projects? Can we afford the cost of store everything or we have to be more selectively on what to keep in our hard drives?
GWAS studies have showed us that large numbers, in the order of 10 thousands of samples, are needed to achieve statistical significance for association studies, particularly for common diseases. Even when you consider the present low price of 1,000$ / genome it will require around 10 millions $ for such a sequencing project. So we can reduce our sample size (and thus significance) or create mega consortium with all the managing issues.
Samples became precious resources.
In the present scenario sequencing power is not longer a limitation. The real matter is find enough well-characterized samples to sequence!
Whole genome and whole exome approaches let researchers to rapidly identify new variants potentially related to phenotypes. But which of them are truly relevant? Our present knowledge do not allow for a confident prediction of functional impact of genetic variation and thus functional studies are often needed to assess the actual role of each variants. These studies, based on cellular models or animal models, could be expensive and complicated.
With large and increasing amount of genomic data available to the community and studies showing that people ancestry and living location could be traced using them (at least in a proportion of cases), there are concerns about how "anonymous" these kind of data could really be. This is going to became a real problem has more and more genomes are sequenced.
Friday, 4 July 2014
This tool mines the "genomic" literature for your gene of interest and reports a list of interactions with other genes, specifying also the kind of the relation (inhibit, activate, regulate...). It can also search for a SNP and find phenotypes associated to it by GWAS. You can then filter the results and also report if the listed interactions are actually real or not.
Good stuff to quickly identify relevant papers in the large amount of genomic researches!
Literome: PubMed-scale genomic knowledge base in the cloud
Hoifung Poon, Chris Quirk, Charlie DeZiel and David Heckerman
Motivation: Advances in sequencing technology have led to an exponential growth of genomics data, yet it remains a formidable challenge to interpret such data for identifying disease genes and drug targets. There has been increasing interest in adopting a systems approach that incorporates prior knowledge such as gene networks and genotype–phenotype associations. The majority of such knowledge resides in text such as journal publications, which has been undergoing its own exponential growth. It has thus become a significant bottleneck to identify relevant knowledge for genomic interpretation as well as to keep up with new genomics findings.
Results: In the Literome project, we have developed an automatic curation system to extract genomic knowledge from PubMed articles and made this knowledge available in the cloud with a Web site to facilitate browsing, searching and reasoning. Currently, Literome focuses on two types of knowledge most pertinent to genomic medicine: directed genic interactions such as pathways and genotype–phenotype associations. Users can search for interacting genes and the nature of the interactions, as well as diseases and drugs associated with a single nucleotide polymorphism or gene. Users can also search for indirect connections between two entities, e.g. a gene and a disease might be linked because an interacting gene is associated with a related disease.
Availability and implementation: Literome is freely available at literome.azurewebsites.net. Download for non-commercial use is available via Web services.