SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB

Sequencing ribosomal RNA (rRNA) genes is currently the method of choice for phylogenetic reconstruction, nucleic acid based detection and quantification of microbial diversity. The ARB software suite with its corresponding rRNA datasets has been accepted by researchers worldwide as a standard tool for large scale rRNA analysis. However, the rapid increase of publicly available rRNA sequence data has recently hampered the maintenance of comprehensive and curated rRNA knowledge databases. A new system, SILVA (from Latin silva, forest), was implemented to provide a central comprehensive web resource for up to date, quality controlled databases of aligned rRNA sequences from the Bacteria, Archaea and Eukarya domains. All sequences are checked for anomalies, carry a rich set of sequence associated contextual information, have multiple taxonomic classifications, and the latest validly described nomenclature. Furthermore, two precompiled sequence datasets compatible with ARB are offered for download on the SILVA website: (i) the reference (Ref) datasets, comprising only high quality, nearly full length sequences suitable for in-depth phylogenetic analysis and probe design and (ii) the comprehensive Parc datasets with all publicly available rRNA sequences longer than 300 nucleotides suitable for biodiversity analyses. The latest publicly available database release 91 (August 2007) hosts 547 521 sequences split into 461 823 small subunit and 85 689 large subunit rRNAs

SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes

Motivation: In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements

asl/BandageNG: Continuous build

<p>Build log: https://github.com/asl/BandageNG/actions/runs/6948290151</p&gt

cokelaer/fitter: v1.7.0

<h2>What's Changed</h2> <ul> <li>Bump urllib3 from 2.0.4 to 2.0.7 by @dependabot in https://github.com/cokelaer/fitter/pull/88</li> <li>Bump pillow from 10.0.0 to 10.0.1 by @dependabot in https://github.com/cokelaer/fitter/pull/87</li> <li>Relaxed Pandas required version and better pinned some other ones by @sarusso in https://github.com/cokelaer/fitter/pull/92</li> <li>uses loguru and rich_click for better user interface and slight updates in the main standalone</li> </ul> <h2>New Contributors</h2> <ul> <li>@dependabot made their first contribution in https://github.com/cokelaer/fitter/pull/88</li> <li>@sarusso made their first contribution in https://github.com/cokelaer/fitter/pull/92</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/cokelaer/fitter/compare/v1.6.0...v1.7.0</p&gt

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium

ACE2 and TMPRSS2 have received recent attention as entry factors for SARS-CoV-2. Here the authors analyze nasal airway transcriptome data from 695 children determining ACE2 and TMPRSS2 expression is induced by viral and type2 inflammation, respectively, and both exhibit eQTLs that vary across world populations

Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences

Publicly available sequence databases of the small subunit ribosomal RNA gene, also known as 16S rRNA in bacteria and archaea, are growing rapidly, and the number of entries currently exceeds 4 million. However, a unified classification and nomenclature framework for all bacteria and archaea does not yet exist. In this Analysis article, we propose rational taxonomic boundaries for high taxa of bacteria and archaea on the basis of 16S rRNA gene sequence identities and suggest a rationale for the circumscription of uncultured taxa that is compatible with the taxonomy of cultured bacteria and archaea. Our analyses show that only nearly complete 16S rRNA sequences give accurate measures of taxonomic diversity. In addition, our analyses suggest that most of the 16S rRNA sequences of the high taxa will be discovered in environmental surveys by the end of the current decade. © 2014 Macmillan Publishers LimitedThis work has been co-funded by the Max Planck Society and the European Union (EU) project SYMBIOMICS (grant number 264774). R.R.M. acknowledges the scientific support given by the Spanish Ministry of Economy with the projects CE-CSD2007-0005 and CGL2012-39627-C03-03, which are both also supported with European Regional Development Fund (FEDER) funds, and the preparatory phase of Microbial Resource Research Infrastructure (MIRRI) funded by the EU (grant number 312251). W.B.W. acknowledges support of the Dimensions in Biodiversity program at the US National Science Foundation (NSF). P.Y. acknowledges support of the EU's Seventh Framework Program funds BioVeL, grant no. 283359Peer Reviewe