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See detailSystematic molecular measurements reveal key microbial populations driving community-wide phenotype
Muller, Emilie UL; Pinel, Nicolás; May, Patrick UL et al

Poster (2013)

Natural microbial communities are heterogeneous and dynamic. Therefore, a major consideration for multiple omic data studies is the sample-to-sample heterogeneity, which can lead to inconsistent results ... [more ▼]

Natural microbial communities are heterogeneous and dynamic. Therefore, a major consideration for multiple omic data studies is the sample-to-sample heterogeneity, which can lead to inconsistent results if the different biomolecular fractions are obtained from distinct sub-samples. Conversely, systematic omic measurements, i.e. the standardised, reproducible and simultaneous measurement of multiple features from a single undivided sample, result in fully integrable datasets. Objective In order to prove the feasibility and benefits of such systematic measurements in the study of the respective contributions of different populations to the community-wide phenotype, we purified and analysed all biomolecular fractions, i.e. DNA, RNA, proteins and metabolites, obtained from a unique undivided sample of lipid accumulating microbial community (LAMC) from wastewater treatment plant and integrate the resulting datasets. Methods One time point of particular interest was first selected out of 4 LAMC samples for its high diversity and strong lipid accumulation phenotype. Then, the systematic measurement strategy was applied to the selected undivided LAMC sample and the purified biomolecules were analysed by high-throughput techniques. DNA and RNA sequencing reads were assembled at the population-level using different binning strategies. A database, containing predicted proteins, was constructed to identify the detected peptides. Finally, all biomolecular information was mapped onto the assembled composite genomes to identify the precise roles of the different populations in the community-wide lipid accumulation phenotype. Results Metabolomics and 16S diversity analyses were used to select the sample of highest interest for detailed analysis. The systematic measurements of the selected sample followed by data integration have allowed us to probe the functional relevance of the population-level composite genomes, leading to the identification of the LAMC key players. Conclusion As community phenotype is not the sum of the different partner phenotypes, understanding a microbial community system requires more than the study of isolated organisms. Even if both approaches are complementary, top-down systematic approached only provides a holistic perspective of micro-ecological processes. [less ▲]

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See detailIsotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching.
Slawski, Martin; Hussong, René UL; Tholey, Andreas et al

in BMC Bioinformatics (2012), 13(291),

Background The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of ... [more ▼]

Background The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context. Results We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns. Conclusions We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html) [less ▲]

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