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Nucleic Acids Research Studies on Rat Transcriptome, Marine Metagenomic Tool, More

An international team led by investigators in China shares findings from an annotation effort focused on the rat transcriptome. Using deep RNA sequencing data spanning more than 300 samples from 11 rat tissues or organs from a project called BodyMap RNA-seq, the researchers put together the "Rat Transcriptome Re-annotation" (RTR) resource that contains 114,152 new or known transcripts produced from some 52,807 genes, including nearly 15,900 transcripts from 11,715 genes that were present across all of the tissues tested. "By carefully tuning the reconstruction pipeline, we obtained a relatively complete and reliable set of rat transcripts that was used to create a re-annotate transcriptome database and functional annotations comparable to that of the well-annotated mouse transcriptome," the authors write, noting that the RTR findings were validated through comparisons with several other available rat organ datasets.

Researchers at the University of Arizona, Lawrence Berkeley National Laboratory, and the University of Hawaii outline a web-based resource for characterizing marine microbes within a shared framework based on sequenced ocean samples. The online platform, known as Planet Microbe, makes it possible to bring together marine microbe metagenomic data collected from a given site or sampling event, the team reports, along with relevant sampling protocols and metadata. The resource currently contains omics datasets coinciding with samples collected over time in Hawaii, Bermuda, the Amazon, and other ocean sites around the world for prior projects and ocean expedition efforts, the authors write, noting that "these omics data have been reintegrated with their in situ environmental contextual data, including biological and physiochemical measurements, and information about sampling events, and sampling stations."

A McGill University-led team takes a look at RNA small molecule binding preferences with the help of an open-source, network-based binding site and molecular fingerprint prediction tool called RNAmigos, based on RNA crystal structure, base pairing information, domain data, and other features. "Our work is a first attempt at bringing the scalability and generalization abilities of machine learning methods to the problem of RNA drug discovery, as well as a step toward understanding the interactions which drive binding specificity," the researchers write, noting that the work points to the possibility of using "Augmented Base Pairing networks" (ABPNs) to explore RNA function, structure-function relationships, and RNA-based targets more broadly.