Robust demarcation of the family Peptostreptococcaceae and its main genera based on phylogenomic studies and taxon-specific molecular markers.

The family Peptostreptococcaceae, which contains 15 genera including Clostridioides, presently lacks proper circumscription. Using 52 available genomes for Peptostreptococcaceae species, we report comprehensive phylogenomic and comparative analyses to reliably discern their evolutionary relationships. In phylogenetic trees based on core genome proteins and 16S rRNA gene sequences, the examined species formed a strongly supported clade designated as Peptostreptococcaceae sensu stricto. This clade encompassed the genera Peptostreptococcus (type genus), Asaccharospora, Clostridioides, Intestinibacter, Paeniclostridium, Paraclostridium, Peptacetobacter, Romboutsia and Terrisporobacter, and two misclassified species (viz. Eubacterium tenue and 'Clostridium dakarense'). The distinctness of this clade is strongly supported by eight identified conserved signature indels (CSIs), which are specific for the species from this clade. Based on the robust evidence provided by presented studies, we are proposing the emendment of family Peptostreptococcaceae to only the genera within the Peptostreptococcaceae sensu stricto clade. We also report 67 other novel CSIs, which reliably demarcate different Peptostreptococcaceae species clades and clarify the classification of some misclassified species. Based on the consistent evidence obtained from different presented studies, we are making the following proposals to clarify the classification of Peptostreptococcaceae species: (i) transfer of Eubacterium tenue, Paeniclostridium ghonii and Paeniclostridium sordellii as comb. nov. into the genus Paraclostridium; (ii) transfer of Clostridioides mangenotii as a comb. nov. into Metaclostridioides gen. nov.; (iii) classification of 'Clostridium dakarense' as a novel species Faecalimicrobium dakarense gen. nov., sp. nov. (type strain FF1T; genome and 16S rRNA accession numbers GCA_000499525.1 and KC517358, respectively); (iv) transfer of two misclassified species, Clostridium paradoxum and Clostridium thermoalcaliphilum, into Alkalithermobacter gen. nov.; and (v) proposals for two novel families, Peptoclostridiaceae fam. nov. and Tepidibacteraceae fam. nov., to accommodate remaining unclassified Peptostreptococcaceae genera. The described CSIs specific for different families and genera provide novel and reliable means for the identification, diagnostics and biochemical studies on these bacteria.

Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.

External content sources such as MEDLINE(®), National Institutes of Health (NIH) grants and conference websites provide access to the latest breaking biomedical information, which can inform pharmaceutical and biotechnology company pipeline decisions. The value of the sites for industry, however, is limited by the use of the public internet, the limited synonyms, the rarity of batch searching capability and the disconnected nature of the sites. Fortunately, many sites now offer their content for download and we have developed an automated internal workflow that uses text mining and tailored ontologies for programmatic search and knowledge extraction. We believe such an efficient and secure approach provides a competitive advantage to companies needing access to the latest information for a range of use cases and complements manually curated commercial sources.

Mining protein-protein interactions from published literature using Linguamatics I2E.

Natural language processing (NLP) technology can be used to rapidly extract protein-protein interactions from large collections of published literature. In this chapter we will work through a case study using MEDLINE biomedical abstracts (1) to find how a specific set of 50 genes interact with each other. We will show what steps are required to achieve this using the I2E software from Linguamatics ( www.linguamatics.com (2)).To extract protein networks from the literature, there are two typical strategies. The first is to find pairs of proteins which are mentioned together in the same context, for example, the same sentence, with the assumption that textual proximity implies biological association. The second approach is to use precise linguistic patterns based on NLP to find specific relationships between proteins. This can reveal the direction of the relationship and its nature such as "phosphorylation" or "upregulation". The I2E system uses a flexible text-mining approach, supporting both of these strategies, as well as hybrid strategies which fall between the two. In this chapter we show how multiple strategies can be combined to obtain high-quality results.