References of "Hickl, Oskar 50034770"
     in
Bookmark and Share    
Full Text
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 ▲]

Detailed reference viewed: 60 (3 UL)
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 81 (11 UL)