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See detailDevelopment of data integration tools within functional genomics
Teixeira Queiros, Pedro UL

Doctoral thesis (2022)

Due to technological advances across all scientific domains, data is generated at an extremely fast pace. This is especially true in biology, where advances in computational and sequencing technologies ... [more ▼]

Due to technological advances across all scientific domains, data is generated at an extremely fast pace. This is especially true in biology, where advances in computational and sequencing technologies led to the necessity to develop automated methods for data analysis; thus the field of bioinformatics was born. This thesis focuses on one specific field within bioinformatics - functional genomics. To be precise, in the development of techniques and software for the integration of data to generate novel insights. Indeed, as the amount of knowledge increases, so does the need to integrate it systematically. In this context, the work described herein relates to the integration of multiple resources to improve the functional annotation of proteins, which led to the development of two bioinformatic tools - Mantis and UniFunc. For the downstream integration and analysis of functional predictions, a network annotation tool was developed - UniFuncNet, which, together with the previous tools, enables the efficient functional characterisation of individual organisms or communities. [less ▲]

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See detailbinny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets 2021.12.22.473795
Hickl, Oskar UL; Teixeira Queiros, Pedro UL; Wilmes, Paul UL et al

E-print/Working paper (2021)

The reconstruction of genomes is a critical step in genome-resolved metagenomics as well as for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces ... [more ▼]

The reconstruction of genomes is a critical step in genome-resolved metagenomics as well as for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms existing state-of-the-art binning methods and finds unique genomes that could not be detected by other methods.binny uses k-mer-composition and coverage by metagenomic reads for iterative, non-linear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets.When compared to five widely used binning algorithms, binny recovers the most near-complete (\>95 pure, \>90 complete) and high-quality (\>90 pure, \>70 complete) genomes from simulated data sets from the Critical Assessment of Metagenome Interpretation (CAMI) initiative, as well as from a real-world benchmark comprised of metagenomes from various environments. binny is implemented as Snakemake workflow and available from https://github.com/a-h-b/binny.Competing Interest StatementThe authors have declared no competing interest. [less ▲]

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See detailMantis: flexible and consensus-driven genome annotation
Teixeira Queiros, Pedro UL; Delogu, Francesco UL; Hickl, Oskar UL et al

in GigaScience (2021), 10(6),

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously ... [more ▼]

The rapid development of the (meta-)omics fields has produced an unprecedented amount of high-resolution and high-fidelity data. Through the use of these datasets we can infer the role of previously functionally unannotated proteins from single organisms and consortia. In this context, protein function annotation can be described as the identification of regions of interest (i.e., domains) in protein sequences and the assignment of biological functions. Despite the existence of numerous tools, challenges remain in terms of speed, flexibility, and reproducibility. In the big data era, it is also increasingly important to cease limiting our findings to a single reference, coalescing knowledge from different data sources, and thus overcoming some limitations in overly relying on computationally generated data from single sources.We implemented a protein annotation tool, Mantis, which uses database identifiers intersection and text mining to integrate knowledge from multiple reference data sources into a single consensus-driven output. Mantis is flexible, allowing for the customization of reference data and execution parameters, and is reproducible across different research goals and user environments. We implemented a depth-first search algorithm for domain-specific annotation, which significantly improved annotation performance compared to sequence-wide annotation. The parallelized implementation of Mantis results in short runtimes while also outputting high coverage and high-quality protein function annotations.Mantis is a protein function annotation tool that produces high-quality consensus-driven protein annotations. It is easy to set up, customize, and use, scaling from single genomes to large metagenomes. Mantis is available under the MIT license at https://github.com/PedroMTQ/mantis. [less ▲]

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See detailUnification of functional annotation descriptions using text mining
Teixeira Queiros, Pedro UL; Novikova, Polina UL; Wilmes, Paul UL et al

in Biological Chemistry (2021)

A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference ... [more ▼]

A common approach to genome annotation involves the use of homology-based tools for the prediction of the functional role of proteins. The quality of functional annotations is dependent on the reference data used, as such, choosing the appropriate sources is crucial. Unfortunately, no single reference data source can be universally considered the gold standard, thus using multiple references could potentially increase annotation quality and coverage. However, this comes with challenges, particularly due to the introduction of redundant and exclusive annotations. Through text mining it is possible to identify highly similar functional descriptions, thus strengthening the confidence of the final protein functional annotation and providing a redundancy-free output. Here we present UniFunc, a text mining approach that is able to detect similar functional descriptions with high precision. UniFunc was built as a small module and can be independently used or integrated into protein function annotation pipelines. By removing the need to individually analyse and compare annotation results, UniFunc streamlines the complementary use of multiple reference datasets. [less ▲]

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See detailCritical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows
Van Den Bossche, Tim; Kunath, Benoît UL; Schallert, Kay et al

in Nature Communications (2021), 12(1), 7305

Abstract Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on ... [more ▼]

Abstract Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments. [less ▲]

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