Biology subjects

Meinicke, P.

Publications and source records attributed to Meinicke, P..

6 recordsLinked to original sources

bioRxiv2024

CoCoPyE: feature engineering for learning and prediction of genome quality indices

The exploration of the microbial world has been greatly advanced by the reconstruction of genomes from metagenomic sequence data. However, the rapidly increasing number of metagenome-assembled genomes has also resulted in a wide variation in data quality. It is therefore essential to quantify the achieved completeness and possible contamination of a reconstructed genome before it is used in subsequent analyses. The classical approach for the estimation of quality indices solely relies on a relatively small number of universal single copy genes. Recent tools try to extend the genomic coverage of estimates for an increased accuracy. CoCoPyE is a fast tool based on a novel two-stage feature extraction and transformation scheme. First it identifies genomic markers and then refines the marker-based estimates with a machine learning approach. In our simulation studies, CoCoPyE showed a more accurate prediction of quality indices than the existing tools.

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bioRxiv2021

BinChecker: a new algorithm for quality assessment of microbial draft genomes

In the reconstruction of microbial genomes from metagenomic sequence data, the estimation of the final completeness and possible contamination is crucial for quality control. In metagenomics candidate genomes are usually obtained from a metagenome assembly and a subsequent binning of the assembled contigs. BinChecker provides a novel approach to quality assessment that is based on a fast protein domain search and a clustering approach for identification of marker domain ("feature") sets. The feature sets that are used for estimation are not pre-computed for a given database of reference genomes, but are individually found for each bin by adaptive clustering and feature selection. In particular, the adaptivity facilitates the creation and extension of the underlying database, which just requires to add protein feature profiles of reference genomes. Tests with simulated bins indicate that the prediction accuracy of BinChecker meets the current state of the art while providing significant advantages in terms of speed and flexibility.

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bioRxiv2021

Mining metagenomes for natural product biosynthetic gene clusters: unlocking new potential with ultrafast techniques

Microorganisms produce an immense variety of natural products through the expression of Biosynthetic Gene Clusters (BGCs): physically clustered genes that encode the enzymes of a specialized metabolic pathway. These natural products cover a wide range of chemical classes (e.g., aminoglycosides, lantibiotics, nonribosomal peptides, oligosaccharides, polyketides, terpenes) that are highly valuable for industrial and medical applications1. Metagenomics, as a culture-independent approach, has greatly enhanced our ability to survey the functional potential of microorganisms and is growing in popularity for the mining of BGCs. However, to effectively exploit metagenomic data to this end, it will be crucial to more efficiently identify these genomic elements in highly complex and ever-increasing volumes of data2. Here, we address this challenge by developing the ultrafast Biosynthetic Gene cluster MEtagenomic eXploration toolbox (BiG-MEx). BiG-MEx rapidly identifies a broad range of BGC protein domains, assess their diversity and novelty, and predicts the abundance profile of natural product BGC classes in metagenomic data. We show the advantages of BiG-MEx compared to standard BGC-mining approaches, and use it to explore the BGC domain and class composition of samples in the TARA Oceans3 and Human Microbiome Project datasets4. In these analyses, we demonstrate BiG-MExs applicability to study the distribution, diversity, and ecological roles of BGCs in metagenomic data, and guide the exploration of natural products with clinical applications.

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bioRxiv2018

Tax4Fun2: a R-based tool for the rapid prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene marker gene sequences

Main text Main text Methods Statistical data analysis Data availability Author Contributions Competing interests References Assessing the functional capability and redundancy of a microbial community is a major challenge in environmental microbiology. To address this challenge, we developed Tax4Fun2, a R-based tool for the rapid prediction of functional profiles and functional redundancy of prokaryotic communities from 16S rRNA gene sequences. By incorporating user-defined, habitat-specific genomic information, the accuracy and robustness of predicted functional profiles can be substantially enhanced.\n\nMicroorganisms play a key role in ecosystem functioning1. High-throughput sequencing of 16S rRNA genes is a powerful and widely used approach to study the composi ...

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bioRxiv2017

How To Normalize Metatranscriptomic Count Data For Differential Expression Analysis

BACKGROUNDDifferential expression analysis on the basis of RNA-Seq count data has become a standard tool in transcriptomics. Several studies have shown that prior normalization of the data is crucial for a reliable detection of transcriptional differences. Until now it is not clear whether and how the transcriptomic approach can be used for differential expression analysis in metatranscriptomics. The potential side effects that may result from direct application of transcriptomic tools to metatranscriptomic count data have not been studied so far.\n\nMETHODSWe propose a model for differential expression in metatranscriptomics that explicitly accounts for variations in the taxonomic composition of transcripts across different samples. As a main consequence the correct normalization of metatranscriptomic count data requires the taxonomic separation of the data into organism-specific bins. Then the taxon-specific scaling of organism profiles yields a valid normalization and allows to recombine the scaled profiles into a metatranscriptomic count matrix. This matrix can then be analyzed with statistical tools for transcriptomic count data. For taxon-specific scaling and recombination of scaled counts we provide a simple R script.\n\nRESULTSWhen applying transcriptomic tools for differential expression analysis directly to metatranscriptomic data the organism-independent (global) scaling of counts implies a high risk of falsely predicted functional differences. In simulation studies we show that incorrect normalization not only tends to loose significant differences but especially can produce a large number of false positives. In contrast, taxon-specific scaling can equalize the variation of relative library sizes from different organisms and therefore shows a reliable detection of significant differences in all simulations. On real metatranscriptomic data the results from taxon-specific and global scaling can largely differ. In our study, global scaling shows a high number of extra predictions which are not supported by single transcriptome analyses. Inspection of the scaling error suggests that these extra predictions may actually correspond to artifacts of an incorrect normalization.\n\nCONCLUSIONSAs in transcriptomics, a proper normalization of count data is also essential for differential expression analysis in metatranscriptomics. Our model implies a taxon-specific scaling of counts for normalization of the data. The application of taxon-specific scaling consequently removes taxonomic composition variations from functional profiles and therefore effectively prevents the risk of false predictions due to incorrect normalization.

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bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

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