Kay Schallert

Challenges and perspectives of metaproteomic data analysis

In nature microorganisms live in complex microbial communities. Comprehensive taxonomic and functional knowledge about microbial communities supports medical and technical application such as fecal diagnostics as well as operation of biogas plants or waste water treatment plants. Furthermore, microbial communities are crucial for the global carbon and nitrogen cycle in soil and in the ocean. Among the methods available for investigation of microbial communities, metaproteomics can approximate the activity of microorganisms by investigating the protein content of a sample. Although metaproteomics is a very powerful method, issues within the bioinformatic evaluation impede its success. In particular, construction of databases for protein identification, grouping of redundant proteins as well as taxonomic and functional annotation pose big challenges. Furthermore, growing amounts of data within a metaproteomics study require dedicated algorithms and software. This review summarizes recent metaproteomics software and addresses the introduced issues in detail.

Interactive Chord Visualization for Metaproteomics

Metaproteomics is an analytic approach to research microorganisms that live in complex microbial communities. A key aspect of understanding microbial communities is to link the functions of proteins identified by metaproteomics to their taxonomy. In this paper we demonstrate the interactive chord visualization as a powerful tool to explore such data. To evaluate the tools efficacy, we use the relation data between functions and taxonomies from a large metaproteomics experiment. We evaluated the work flow in comparison to previous methods of data analysis and showed that interactive exploration of data using the chord diagram is significantly faster in four of five tasks. Therefore, the chord visualization improves the users ability to discover complex biological relationships.

Protein Identification as a Suitable Application for Fast Data Architecture

Metaproteomics is a field of biology research that relies on mass spectrometry to characterize the protein complement of microbiological communities. Since only identified data can be analyzed, identification algorithms such as X!Tandem, OMSSA and Mascot are essential in the domain, to get insights into the biological experimental data. However, protein identification software has been developed for proteomics. Metaproteomics, in contrast, involves large biological communities, gigabytes of experimental data per sample, and greater amounts of comparisons, given the mixed culture of species in the protein database. Furthermore, the file-based nature of current protein identification tools makes them ill-suited for future metaproteomics research. In addition, possible medical use cases of metaproteomics require near real-time identification. From the technology perspective, Fast Data seems promising to increase throughput and performance of protein identification in a metaproteomics workflow. In this paper we analyze the core functions of the established protein identification engine X!Tandem and show that streaming Fast Data architectures are suitable for protein identification. Furthermore, we point out the bottlenecks of the current algorithms and how to remove them with our approach.

Streaming FDR Calculation for Protein Identification.

Identification of proteins is a key step of metaproteomics research. This protein identification task should be migrated to a fast data streaming architecture to increase horizontal scalability and performance. A protein database search involves two steps: the pairwise matching of experimental spectra against protein sequences creating peptide-spectrum-matches (PSM) and the statistical validation of PSMs. The peptide-spectrum-matching is inherently parallelizable since each match is independent. However, false positive matches are inherent to this method due to measurement errors and artifacts, thus requiring statistical validation. State of the art validation is achieved using the target-decoy method, which estimates the false discovery rate (FDR) by searching against a shuffled version of the original protein database. In contrast to the protein database search, validation by target-decoy is not parallelizable, because the FDR approximation requires all experimental data at once. In short, when using a fast data architecture for the workflow, the target-decoy approach is no longer feasible. Hence a novel approach is required to avoid false discovery of PSM on streaming single-pass experimental data. To this end, the recently proposed nokoi classifier seems promising to solve the aforementioned problems. In this paper, we present a general nokoi pipeline to create such a decoy-free classifier, that reach over 95% accuracy for general metaproteomics data.

SDS-PAGE fractionation to increase metaproteomic insight into the taxonomic and functional composition of microbial communities for biogas plant samples

Metaproteomics represent an important tool for the taxonomic and functional investigation of microbial communities in humans, environment, and technical applications. Due to the high complexity of the microbial communities, protein, and peptide fractionation is applied to improve the characterization of taxonomic and functional composition of microbial communities. In order to target scientific questions regarding taxonomic and functional composition adequately, a tradeoff between the number of fractions analyzed and the required depth of information has to be found. Two samples of a biogas plant were analyzed by either single LC‐MS/MS measurement (1D) or LC‐MS/MS measurements of fractions obtained after SDS‐PAGE (2D) separation. Fractionation with SDS‐PAGE increased the number of identified spectra by 273%, the number of peptides by 95%, and the number of metaproteins by 59%. Rarefaction plots of species and metaproteins against identified spectra showed that 2D separation was sufficient to identify most microbial families but not all metaproteins. More reliable quantitative comparison could be achieved with 2D. 1D separation enabled high‐throughput analysis of samples, however, depth in functional descriptions and reliability of quantification were lost. Nevertheless, the proteotyping of multiple samples was still possible. 2D separations provided more reliable quantitative data combined with a deeper insight into the taxonomic and functional composition of the microbial communities. Regarding taxonomic and functional composition, metaproteomics based on 2D is just the tip of an iceberg.