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2. A primer on the analysis of high-throughput sequencing data for detection of plant virusesDenis Kutnjak, Lucie Tamisier, Ian Adams, Neil Boonham, Thierry Candresse, Michela Chiumenti, Kris De Jonghe, Jan F. Kreuze, Marie Lefebvre, Goncalo Silva, Martha Malapi-Wight, Paolo Margaria, Irena Mavrič Pleško, Sam McGreig, Laura Miozzi, Benoit Remenant, Jean-Sébastien Reynard, Johan Rollin, Mike Rott, Olivier Schumpp, Sébastien Massart, Annelies Haegeman, 2021, review article Abstract: High-throughput sequencing (HTS) technologies have become indispensable tools assisting plant virus diagnostics and research thanks to their ability to detect any plant virus in a sample without prior knowledge. As HTS technologies are heavily relying on bioinformatics analysis of the huge amount of generated sequences, it is of utmost importance that researchers can rely on efficient and reliable bioinformatic tools and can understand the principles, advantages, and disadvantages of the tools used. Here, we present a critical overview of the steps involved in HTS as employed for plant virus detection and virome characterization. We start from sample preparation and nucleic acid extraction as appropriate to the chosen HTS strategy, which is followed by basic data analysis requirements, an extensive overview of the in-depth data processing options, and taxonomic classification of viral sequences detected. By presenting the bioinformatic tools and a detailed overview of the consecutive steps that can be used to implement a well-structured HTS data analysis in an easy and accessible way, this paper is targeted at both beginners and expert scientists engaging in HTS plant virome projects.
Keywords: plant virus, high-throughput sequencing, bioinformatics, detection, discovery
Published in DiRROS: 05.08.2024; Views: 213; Downloads: 177
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3. Learning deep representations of enzyme thermal adaptationGang Li, Filip Buric, Jan Zrimec, Sandra Viknander, Jens Nielsen, Aleksej Zelezniak, Martin K. M. Engqvist, 2022, original scientific article Abstract: Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
Keywords: bioinformatics, deep neural networks, enzyme catalytic temperatures, optimal growth temperatures, protein thermostability, transfer learning
Published in DiRROS: 17.07.2024; Views: 245; Downloads: 169
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4. Evidence-based unification of potato gene models with the UniTato collaborative genome browserMaja Zagorščak, Jan Zrimec, Carissa Bleker, Nadja Francesca Nolte, Mojca Juteršek, Živa Ramšak, Kristina Gruden, Marko Petek, 2024, original scientific article Abstract: Potato (Solanum tuberosum) is the most popular tuber crop and a model organism. A variety of gene models for potato exist, and despite frequent updates, they are not unified. This hinders the comparison of gene models across versions, limits the ability to reuse experimental data without significant re-analysis, and leads to missing or wrongly annotated genes. Here, we unify the recent potato double monoploid v4 and v6 gene models by developing an automated merging protocol, resulting in a Unified poTato genome model (UniTato). We subsequently established an Apollo genome browser (unitato.nib.si) that enables public access to UniTato and further community-based curation. We demonstrate how the UniTato resource can help resolve problems with missing or misplaced genes and can be used to update or consolidate a wider set of gene models or genome information. The automated protocol, genome annotation files, and a comprehensive translation table are provided at github.com/NIB-SI/unitato.
Keywords: bioinformatics analysis, plant genome annotation, gene model annotations, Phureja group, GFF files, poTato genome model, UniTato
Published in DiRROS: 11.06.2024; Views: 252; Downloads: 260
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5. RNA-dependent cysteine biosynthesis in bacteria and archaeaTakahito Mukai, Ana Crnković, Takuya Umehara, Natalia N. Ivanova, Nikos C. Kyrpides, Dieter Söll, 2017, original scientific article Keywords: amino acyl-tRNA synthetases, archaea, archaeal proteins, bacteria, computational biology, crystallography, X-ray, genetic code, genome, archaeal, genome, bacterial, phosphoserine, protein binding, protein biosynthesis, RNA, archaeal, RNA, bacterial, RNA, transfer, amino acyl, RNA, transfer, cys, sulfur/metabolism, biochemistry, bioinformatics, cysteine biosynthesis, genetic code, translation
Published in DiRROS: 07.05.2020; Views: 2040; Downloads: 1453
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