I initially hosted my blog on a lab server, and have since migrated to WordPress.com. As part of this migration, I am using figshare (as an alternative to my lab server) for hosting supplments to my blog posts, such as graphics and data files.

While multiple bioinformatics software are already available to generate and/or visualize pangenomes, interfaces do not necessary offer flexible analysis performances, limiting the user's capabilities interacting with their data. We recently have introduced a software platform, anvi'o, to bridge some of the gaps in our common bioinformatics toolkit. We are happy to announce that anvi'o can now process, visualize and manipulate pangenomic data in a user-friendly environment. Some modules are still under construction for a fully automatized workflow. Nevertheless, the current anvi'o interface already offers novel opportunities to combine pangenomes with a variety of contextual metadata and exports high-quality figures for publications. This blog describes original pangenomic investigations of publically available genomic collections. It is set to introduce the anvi'o pangenomic workflow to our enthusiastic users community.

Microbiome LCM (Library Consturction Method) model is a Named Entity Recognition (NER) model that identifies and annotates microbiome DNA library construction method or layout in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with LCM metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications

THIS RESOURCE IS NO LONGER IN SERVICE, documented September 6, 2016. The Allen Institute Neurowiki is a joint project between Vulcan Inc. and the Allen Institute to build a Semantic Wiki mapping genetic instances. It is a finished prototype testing the import pipelines and display componenets for combining 5 major RDF datasets from 4 different sources. Current planning includes mapping complete datasets, curating a better ontology, and creating multiple ontology management for a user class. Biological Linked Data Map: * Open, public online access * Data from multiple RDF data stores * Complete import pipeline using LDIF framework * Outlines of each imported instance embedding inline wiki properties and providing views of imported properties from original RDF datasets * Charting tools that ''''pivot'''' SPARQL queries providing several views of each query * Navigation and composition tools for accessing and mining the data Where did we get the data? * KEGG: Kyoto Encyclopedia of Genes and Genomes: KEGG GENES is a collection of gene catalogs for all complete genomes generated from publicly available resources, mostly NCBI RefSeq * Diseasome: The Diseasome website is a disease / disorder relationships explorer and a sample of an innovative map-oriented scientific work. Built by a team of researchers and engineers, it uses the Human Disease Network dataset. * DrugBank: The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug data with comprehensive drug target information. * Sider: Sider contains information on marketed medicines and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. Every piece of content on every instance page is generated by Semantic Result Formatters interpreting SPARQL results.

Database of known and predicted mammalian and eukaryotic protein-protein interactions, it is designed to be both a resource for the laboratory scientist to explore known and predicted protein-protein interactions, and to facilitate bioinformatics initiatives exploring protein interaction networks. It has been built by mapping high-throughput (HTP) data between species. Thus, until experimentally verified, these interactions should be considered predictions. It remains one of the most comprehensive sources of known and predicted eukaryotic PPI. It contains 490,600 Source Interactions, 370,002 Predicted Interactions, for a total of 846,116 interactions, and continues to expand as new protein-protein interaction data becomes available.

The genes listed in these files were generated by comparing the cross-references between the Ensembl and Entrez-gene databases. To arrive at a set of "consensus" genes, genes were only selected where Ensembl refers to an Entrez-gene with the same coordinates, and that Entrez-gene entry refers back to the same Ensembl gene. Nearly all cases of inconsistent cross-referencing are genes annotated based on electronic predictions, or genes with multiple or inconsistent mappings. For these genes, we then obtained the HUGO approved gene identifier. The coordinates for all genes are mapped using hg19/GRChB37. These were generated by Will Bush, and the procedure used is described in more detail at the blog post below.

Microbiome site model is a Named Entity Recognition (NER) model that identifies and annotates the site of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with site metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications

Trends in biotechnology Impact Factor 2025-2026 - ResearchHelpDesk - Trends in Biotechnology publishes reviews and perspectives on the applied biological sciences: useful science applied to, derived from, or inspired by living systems. The major themes that TIBTECH is interested in include Bioprocessing (biochemical engineering, applied enzymology, industrial biotechnology, biofuels, metabolic engineering) Omics (genome editing, single-cell technologies, bioinformatics, synthetic biology) Materials and devices (bionanotechnology, biomaterials, diagnostics/imaging/detection, soft robotics, biosensors/bioelectronics) Therapeutics (biofabrication, stem cells, tissue engineering and regenerative medicine, antibodies and other protein drugs, drug delivery) Agroenvironment (environmental engineering, bioremediation, genetically modified crops, sustainable development) We particularly seek articles that are relevant to more than one of these themes. Additionally, we welcome articles on law and intellectual property, policy and regulation, bioethics, scientific communication, and the economics of biotechnology. Reviews of mechanistic or phenomenological biology are generally not within TIBTECH's scope, although we do consider reviews of technologies developed from basic biology as long as there's an application in mind. TIBTECH has a diverse audience that reflects its intentionally broad scope. Our readers include not only biologists but also engineers, chemists, pharmacologists, computer scientists, and physicians, and they work in academic, clinical, industrial, NGO, and governmental settings. Therefore, we emphasize accessible articles that are easy to read, and we encourage authors to keep in mind that many readers may not be familiar with their field's specific terminology. For more of TIBTECH editor Matt Pavlovich's take on the journal's aims and scope, read his posts at CrossTalk, the Cell Press blog: "What I talk about when I talk about biotechnology" and "A data-driven map of biotechnology."

✅ Trends in biotechnology ISSN - ResearchHelpDesk - Trends in Biotechnology publishes reviews and perspectives on the applied biological sciences: useful science applied to, derived from, or inspired by living systems. The major themes that TIBTECH is interested in include Bioprocessing (biochemical engineering, applied enzymology, industrial biotechnology, biofuels, metabolic engineering) Omics (genome editing, single-cell technologies, bioinformatics, synthetic biology) Materials and devices (bionanotechnology, biomaterials, diagnostics/imaging/detection, soft robotics, biosensors/bioelectronics) Therapeutics (biofabrication, stem cells, tissue engineering and regenerative medicine, antibodies and other protein drugs, drug delivery) Agroenvironment (environmental engineering, bioremediation, genetically modified crops, sustainable development) We particularly seek articles that are relevant to more than one of these themes. Additionally, we welcome articles on law and intellectual property, policy and regulation, bioethics, scientific communication, and the economics of biotechnology. Reviews of mechanistic or phenomenological biology are generally not within TIBTECH's scope, although we do consider reviews of technologies developed from basic biology as long as there's an application in mind. TIBTECH has a diverse audience that reflects its intentionally broad scope. Our readers include not only biologists but also engineers, chemists, pharmacologists, computer scientists, and physicians, and they work in academic, clinical, industrial, NGO, and governmental settings. Therefore, we emphasize accessible articles that are easy to read, and we encourage authors to keep in mind that many readers may not be familiar with their field's specific terminology. For more of TIBTECH editor Matt Pavlovich's take on the journal's aims and scope, read his posts at CrossTalk, the Cell Press blog: "What I talk about when I talk about biotechnology" and "A data-driven map of biotechnology."

Blog post at http://microbe.net by Jonathan Eisen on "Studying – not wantonly killing – the microbes around us and the rise of the “microbiology of the built environment”

It is a structural classification of helix-cappings or caps compiled from protein structures. Caps extracted from protein structures have been structurally classified based on geometry and conformation and organized in a tree-like hierarchical classification where the different levels correspond to different properties of the caps. CASP-DB is fully browsable and searchable and is regularly updated. The regions of the polypeptide chain immediately preceding or following a helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. CapsDB v4.0 * PDB structures: 4591 * Number of clusters: 859 * Number of caps: 31452

See http://plindenbaum.blogspot.com/2011/04/404-not-found-database-of-non.html

Microbiome collection date model is a Named Entity Recognition (NER) model that identifies and annotates the collection date of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with collection date metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications

Trends in biotechnology CiteScore 2024-2025 - ResearchHelpDesk - Trends in Biotechnology publishes reviews and perspectives on the applied biological sciences: useful science applied to, derived from, or inspired by living systems. The major themes that TIBTECH is interested in include Bioprocessing (biochemical engineering, applied enzymology, industrial biotechnology, biofuels, metabolic engineering) Omics (genome editing, single-cell technologies, bioinformatics, synthetic biology) Materials and devices (bionanotechnology, biomaterials, diagnostics/imaging/detection, soft robotics, biosensors/bioelectronics) Therapeutics (biofabrication, stem cells, tissue engineering and regenerative medicine, antibodies and other protein drugs, drug delivery) Agroenvironment (environmental engineering, bioremediation, genetically modified crops, sustainable development) We particularly seek articles that are relevant to more than one of these themes. Additionally, we welcome articles on law and intellectual property, policy and regulation, bioethics, scientific communication, and the economics of biotechnology. Reviews of mechanistic or phenomenological biology are generally not within TIBTECH's scope, although we do consider reviews of technologies developed from basic biology as long as there's an application in mind. TIBTECH has a diverse audience that reflects its intentionally broad scope. Our readers include not only biologists but also engineers, chemists, pharmacologists, computer scientists, and physicians, and they work in academic, clinical, industrial, NGO, and governmental settings. Therefore, we emphasize accessible articles that are easy to read, and we encourage authors to keep in mind that many readers may not be familiar with their field's specific terminology. For more of TIBTECH editor Matt Pavlovich's take on the journal's aims and scope, read his posts at CrossTalk, the Cell Press blog: "What I talk about when I talk about biotechnology" and "A data-driven map of biotechnology."

Microbiome ecoregion model is a Named Entity Recognition (NER) model that identifies and annotates the ecoregion of microbiome samples in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with ecoregion metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications

Microbiome treatment model is a Named Entity Recognition (NER) model that identifies and annotates the treatment applied to microbiome environment or host in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with treatment metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications

Microbiome target gene model is a Named Entity Recognition (NER) model that identifies and annotates microbiome target genes, phylogenetic marker genes or hypervariable regions in texts. This is the final model version used to annotate metagenomics publications in Europe PMC and enrich metagenomics studies in MGnify with target genes metadata from literature. For more information, please refer to the following blogs: http://blog.europepmc.org/2020/11/europe-pmc-publications-metagenomics-annotations.html https://www.ebi.ac.uk/about/news/service-news/enriched-metadata-fields-mgnify-based-text-mining-associated-publications