| 11. |
- Koslicki, David, et al.
(author)
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ARK : Aggregation of Reads by K-Means for Estimation of Bacterial Community Composition
- 2015
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In: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 10:10
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Journal article (peer-reviewed)abstract
- Motivation Estimation of bacterial community composition from high-throughput sequenced 16S rRNA gene amplicons is a key task in microbial ecology. Since the sequence data from each sample typically consist of a large number of reads and are adversely impacted by different levels of biological and technical noise, accurate analysis of such large datasets is challenging. Results There has been a recent surge of interest in using compressed sensing inspired and convex-optimization based methods to solve the estimation problem for bacterial community composition. These methods typically rely on summarizing the sequence data by frequencies of low-order k-mers and matching this information statistically with a taxonomically structured database. Here we show that the accuracy of the resulting community composition estimates can be substantially improved by aggregating the reads from a sample with an unsupervised machine learning approach prior to the estimation phase. The aggregation of reads is a pre-processing approach where we use a standard K-means clustering algorithm that partitions a large set of reads into subsets with reasonable computational cost to provide several vectors of first order statistics instead of only single statistical summarization in terms of k-mer frequencies. The output of the clustering is then processed further to obtain the final estimate for each sample. The resulting method is called Aggregation of Reads by K-means (ARK), and it is based on a statistical argument via mixture density formulation. ARK is found to improve the fidelity and robustness of several recently introduced methods, with only a modest increase in computational complexity. Availability An open source, platform-independent implementation of the method in the Julia programming language is freely available at https://github.com/dkoslicki/ARK. A Matlab implementation is available at http://www.ee.kth.se/ctsoftware.
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| 12. |
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| 13. |
- Simola, U., et al.
(author)
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Machine learning accelerated likelihood-free event reconstruction in dark matter direct detection
- 2019
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In: Journal of Instrumentation. - 1748-0221. ; 14
-
Journal article (peer-reviewed)abstract
- Reconstructing the position of an interaction for any dual-phase time projection chamber (TPC) with the best precision is key to directly detecting Dark Matter. Using the likelihood-free framework, a newalgorithm to reconstruct the 2-D (x; y) position and the size of the charge signal (e) of an interaction is presented. The algorithm uses the secondary scintillation light distribution (S2) obtained by simulating events using a waveform generator. To deal with the computational effort required by the likelihood-free approach, we employ the Bayesian Optimization for LikelihoodFree Inference (BOLFI) algorithm. Together with BOLFI, prior distributions for the parameters of interest (x; y; e) and highly informative discrepancy measures to performthe analyses are introduced. We evaluate the quality of the proposed algorithm by a comparison against the currently existing alternative methods using a large-scale simulation study. BOLFI provides a natural probabilistic uncertainty measure for the reconstruction and it improved the accuracy of the reconstruction over the next best algorithm by up to 15% when focusing on events at large radii (R > 30 cm, the outer 37% of the detector). In addition, BOLFI provides the smallest uncertainties among all the tested methods.
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| 14. |
- von Mentzer, Astrid, 1983, et al.
(author)
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Identification of enterotoxigenic Escherichia coli (ETEC) clades with long-term global distribution
- 2014
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In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 46:12, s. 1321-1326
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Journal article (peer-reviewed)abstract
- Enterotoxigenic Escherichia coil (ETEC), a major cause of infectious diarrhea, produce heat-stable and/or heat-labile enterotoxins and at least 25 different colonization factors that target the intestinal mucosa. The genes encoding the enterotoxins and most of the colonization factors are located on plasmids found across diverse E. coli serogroups. Whole-genome sequencing of a representative collection of ETEC isolated between 1980 and 2011 identified globally distributed lineages characterized by distinct colonization factor and enterotoxin profiles. Contrary to current notions, these relatively recently emerged lineages might harbor chromosome and plasmid combinations that optimize fitness and transmissibility. These data have implications for understanding, tracking and possibly preventing ETEC disease.
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