Wow, de novo genomic sequencing improved by greedy algorithms! I wonder how this could affect the sensing aspect of genomic sequencing.
Accurate Decoding of Pooled Sequenced Data Using Compressed Sensing by Denisa Duma, Mary Wootters, Anna C. Gilbert, Hung Q. Ngo, Atri Rudra,Matthew Alpert, Timothy J. Close, Gianfranco Ciardo, Stefano Lonardi
In order to overcome the limitations imposed by DNA barcoding when multiplexing a large number of samples in the current generation of high-throughput sequencing instruments, we have recently proposed a new protocol that leverages advances in combinatorial pooling design (group testing) doi:10.1371/journal.pcbi.1003010. We have also demonstrated how this new protocol would enable de novo selective sequencing and assembly of large, highly-repetitive genomes. Here we address the problem of decoding pooled sequenced data obtained from such a protocol. Our algorithm employs a synergistic combination of ideas from compressed sensing and the decoding of error-correcting codes. Experimental results on synthetic data for the rice genome and real data for the barley genome show that our novel decoding algorithm enables significantly higher quality assemblies than the previous approach.The initial paper that got the most attentiuon is this one (which uses a different decoding scheme):
Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space by Stefano Lonardi, Denisa Duma, Matthew Alpert, Francesca Cordero, Marco Beccuti, Prasanna R. Bhat, Yonghui Wu, Gianfranco Ciardo, Burair Alsaihati, Yaqin Ma, Steve Wanamaker, Josh Resnik, Serdar Bozdag, Ming-Cheng Luo, Timothy J. Close
For the vast majority of species – including many economically or ecologically important organisms, progress in biological research is hampered due to the lack of a reference genome sequence. Despite recent advances in sequencing technologies, several factors still limit the availability of such a critical resource. At the same time, many research groups and international consortia have already produced BAC libraries and physical maps and now are in a position to proceed with the development of whole-genome sequences organized around a physical map anchored to a genetic map. We propose a BAC-by-BAC sequencing protocol that combines combinatorial pooling design and second-generation sequencing technology to efficiently approach denovo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when preparing sequencing libraries for hundreds or thousands of DNA samples, such as in this case gene-bearing minimum-tiling-path BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundred millions of short reads and assign them to the correct BAC clones (deconvolution) so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is very accurate, and the resulting BAC assemblies have high quality. Results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate and the BAC assemblies have good quality. While our method cannot provide the level of completeness that one would achieve with a comprehensive whole-genome sequencing project, we show that it is quite successful in reconstructing the gene sequences within BACs. In the case of plants such as barley, this level of sequence knowledge is sufficient to support critical end-point objectives such as map-based cloning and marker-assisted breeding.
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