Read e-book online Bioinformatics Research and Applications: 12th International PDF

By Anu Bourgeois, Pavel Skums, Xiang Wan, Alex Zelikovsky

ISBN-10: 3319387812

ISBN-13: 9783319387819

ISBN-10: 3319387820

ISBN-13: 9783319387826

This ebook constitutes the complaints of the twelfth overseas Symposium on Bioinformatics learn and purposes, ISBRA 2016, held in Minsk, Belarus, in June 2016.
The 25 papers provided during this quantity have been conscientiously reviewed and chosen from seventy seven submissions. They have been prepared in topical sections named: subsequent iteration sequencing facts research; protein-protein interactions and networks; protein and RNA constitution; phylogenetics; series research; and statistical methods.

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Read Online or Download Bioinformatics Research and Applications: 12th International Symposium, ISBRA 2016, Minsk, Belarus, June 5-8, 2016, Proceedings PDF

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Additional info for Bioinformatics Research and Applications: 12th International Symposium, ISBRA 2016, Minsk, Belarus, June 5-8, 2016, Proceedings

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1 Method Poisson-Markov Model (PMM) We assume a set of n DNA sequencing reads are sampled from g bins with N sequencing reads from each bins. A DNA sequence read is defined as S with 18 L. Wang et al. discrete variables yi from {A, T, C, G}. We also assume reads abundance in j th bin follows a Poisson distribution with parameter λj and the reads base composition in the bin is calculated by a Markov model with parameter τ . Please refer to Table 1 for the list of mathematical symbols used in this paper.

Wang et al. discrete variables yi from {A, T, C, G}. We also assume reads abundance in j th bin follows a Poisson distribution with parameter λj and the reads base composition in the bin is calculated by a Markov model with parameter τ . Please refer to Table 1 for the list of mathematical symbols used in this paper. A joint probability model f (yi ) is shown as: f (yi ) = P (kj |λj )P (yi | τ ), (1) where i represents read index and j represents bin index. Assuming there are kj sequences in j th bin, so the abundance of j th bin can be shown in Poisson as: k P (kj |λj ) = λj j e−λj .

4. The temporal changes of the individual’s microbiome composition from Day 1 to Day 302. We also compared running time of the parallelized PMM algorithm with the non-parallelized version. As shown in Algorithm 1, we split the calculation of expected log-likelihood into different number of partitions so that we calculate all partitions in parallel. 6 GHz, 256 GB RAM). We compare the running time per iteration since different numbers of iterations are needed for different data sets. From Table 3, we observe a markedly faster running time of the parallelized PMM algorithm compared with the non-parallelized version without sacrificing the accuracy and precision.

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Bioinformatics Research and Applications: 12th International Symposium, ISBRA 2016, Minsk, Belarus, June 5-8, 2016, Proceedings by Anu Bourgeois, Pavel Skums, Xiang Wan, Alex Zelikovsky


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