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Documents authored by Meinicke, Peter

Document
DOI: 10.4230/OASIcs.GCB.2013.1
On the estimation of metabolic profiles in metagenomics

Authors: Kathrin Petra Aßhauer and Peter Meinicke

Published in: OASIcs, Volume 34, German Conference on Bioinformatics 2013

Abstract
Metagenomics enables the characterization of the specific metabolic potential of a microbial community. The common approach towards a quantitative representation of this potential is to count the number of metagenomic sequence fragments that can be assigned to metabolic pathways by means of predicted gene functions. The resulting pathway abundances make up the metabolic profile of the metagenome and several different schemes for computing these profiles have been used. So far, none of the existing approaches actually estimates the proportion of sequences that can be assigned to a particular pathway. In most publications of metagenomic studies, the utilized abundance scores lack a clear statistical meaning and usually cannot be compared across different studies. Here, we introduce a mixture model-based approach to the estimation of pathway abundances that provides a basis for statistical interpretation and fast computation of metabolic profiles. Using the KEGG database our results on a large-scale analysis of data from the Human Microbiome Project show a good representation of metabolic differences between different body sites. Further, the results indicate that our mixture model even provides a better representation than the dedicated HUMAnN tool which has been developed for metabolic analysis of human microbiome data.
Cite as

Kathrin Petra Aßhauer and Peter Meinicke. On the estimation of metabolic profiles in metagenomics. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{ahauer_et_al:OASIcs.GCB.2013.1,
  author =	{A{\ss}hauer, Kathrin Petra and Meinicke, Peter},
  title =	{{On the estimation of metabolic profiles in metagenomics}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{1--13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2013.1},
  URN =		{urn:nbn:de:0030-drops-42380},
  doi =		{10.4230/OASIcs.GCB.2013.1},
  annote =	{Keywords: metagenomics, metabolic profiling, taxonomic profiling, abundance estimation, mixture modeling}
}
Document
DOI: 10.4230/OASIcs.GCB.2013.80
Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences

Authors: Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke

Published in: OASIcs, Volume 34, German Conference on Bioinformatics 2013

Abstract
With the advent of metatranscriptomics it has now become possible to study the dynamics of microbial communities. The analysis of environmental RNA-Seq data implies several challenges for the development of efficient tools in bioinformatics. One of the first steps in the computational analysis of metatranscriptomic sequencing reads requires the separation of rRNA and mRNA fragments to ensure that only protein coding sequences are actually used in a subsequent functional analysis. In the context of the rRNA filtering task it is desirable to have a broad spectrum of different methods in order to find a suitable trade-off between speed and accuracy for a particular dataset. We introduce a machine learning approach for the detection of rRNA in metatranscriptomic sequencing reads that is based on support vector machines in combination with dinucleotide distance histograms for feature representation. The results show that our SVM-based approach is at least one order of magnitude faster than any of the existing tools with only a slight degradation of the detection performance when compared to state-of-the-art alignment-based methods.
Cite as

Heiner Klingenberg, Robin Martinjak, Frank Oliver Glöckner, Rolf Daniel, Thomas Lingner, and Peter Meinicke. Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences. In German Conference on Bioinformatics 2013. Open Access Series in Informatics (OASIcs), Volume 34, pp. 80-89, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{klingenberg_et_al:OASIcs.GCB.2013.80,
  author =	{Klingenberg, Heiner and Martinjak, Robin and Gl\"{o}ckner, Frank Oliver and Daniel, Rolf and Lingner, Thomas and Meinicke, Peter},
  title =	{{Dinucleotide distance histograms for fast detection of rRNA in metatranscriptomic sequences}},
  booktitle =	{German Conference on Bioinformatics 2013},
  pages =	{80--89},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-59-0},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{34},
  editor =	{Bei{\ss}barth, Tim and Kollmar, Martin and Leha, Andreas and Morgenstern, Burkhard and Schultz, Anne-Kathrin and Waack, Stephan and Wingender, Edgar},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.GCB.2013.80},
  URN =		{urn:nbn:de:0030-drops-42324},
  doi =		{10.4230/OASIcs.GCB.2013.80},
  annote =	{Keywords: Metatranscriptomics, metagenomics, rRNA detection, distance histograms}
}
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