Sökning: WFRF:(Innocenti Nicolas 1986 ) >
SEK: Sparsity explo...
SEK: Sparsity exploiting k-mer-based estimation of bacterial community composition
-
- Chatterjee, Saikat (författare)
- KTH,Kommunikationsteori
-
- Koslicki, David (författare)
- Dept of Mathematics, Oregon State University, Corvallis, USA
-
- Dong, Siyuan (författare)
- KTH,Beräkningsbiologi, CB
-
visa fler...
-
- Innocenti, Nicolas, 1986- (författare)
- KTH,Beräkningsbiologi, CB,Computational Biological Physics, CBP
-
- Cheng, Lu (författare)
- Dept of Mathematics and Statistics, University of Helsinki, Finland
-
- Lan, Yueheng (författare)
- Dept of Physics, Tsinghua University, Beijing, China
-
- Vehkaperä, Mikko (författare)
- KTH,Kommunikationsteori
-
- Skoglund, Mikael (författare)
- KTH,ACCESS Linnaeus Centre,Kommunikationsteori
-
- K. Rasmussen, Lars (författare)
- KTH,Kommunikationsteori
-
- Aurell, Erik (författare)
- KTH,Beräkningsbiologi, CB
-
- Corander, Jukka (författare)
- Dept of Signal Processing, Aalto University, Finland
-
visa färre...
-
(creator_code:org_t)
- 2014-05-07
- 2014
- Engelska.
-
Ingår i: Bioinformatics. - : Oxford University Press. - 1460-2059 .- 1367-4803 .- 1367-4811. ; 30:17, s. 2423-2431
- Relaterad länk:
-
http://bioinformatic...
-
visa fler...
-
https://kth.diva-por... (primary) (Raw object)
-
https://kth.diva-por...
-
https://academic.oup...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment.Results: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method.Availability and implementation: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Nyckelord
- bacterial community composition
- sparsity
- metagenomics
- Computer Science
- Datalogi
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas
- Av författaren/redakt...
-
Chatterjee, Saik ...
-
Koslicki, David
-
Dong, Siyuan
-
Innocenti, Nicol ...
-
Cheng, Lu
-
Lan, Yueheng
-
visa fler...
-
Vehkaperä, Mikko
-
Skoglund, Mikael
-
K. Rasmussen, La ...
-
Aurell, Erik
-
Corander, Jukka
-
visa färre...
- Om ämnet
-
- NATURVETENSKAP
-
NATURVETENSKAP
-
och Data och informa ...
-
och Bioinformatik
- Artiklar i publikationen
-
Bioinformatics
- Av lärosätet
-
Kungliga Tekniska Högskolan