RTK: efficient rarefaction analysis of large datasets
Abstract: Motivation: The rapidly expanding microbiomics field is generating increasingly larger datasets, characterizing the microbiota in diverse environments. Although classical numerical ecology methods provide a robust statistical framework for their analysis, software currently available is in...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
15 August 2017
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| In: |
Bioinformatics
Year: 2017, Volume: 33, Issue: 16, Pages: 2594-2595 |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btx206 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1093/bioinformatics/btx206 Verlag, Volltext: https://academic.oup.com/bioinformatics/article/33/16/2594/3111845 
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| Author Notes: | Paul Saary, Kristoffer Forslund, Peer Bork and Falk Hildebrand |
| Summary: | Abstract: Motivation: The rapidly expanding microbiomics field is generating increasingly larger datasets, characterizing the microbiota in diverse environments. Although classical numerical ecology methods provide a robust statistical framework for their analysis, software currently available is inadequate for large datasets and some computationally intensive tasks, like rarefaction and associated analysis. Results: Here we present a software package for rarefaction analysis of large count matrices, as well as estimation and visualization of diversity, richness and evenness. Our software is designed for ease of use, operating at least 7x faster than existing solutions, despite requiring 10x lessmemory. Availability and Implementation: Cþþand R source code (GPL v.2) as well as binaries are available from https://github.com/hildebra/Rarefaction and from CRAN (https://cran.r-project.org/). |
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| Item Description: | Advance Access Publication Date: 7 April 2017 Gesehen am 30.05.2018 |
| Physical Description: | Online Resource |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btx206 |