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Genome Research Studies on LncRNA Annotation, Vertebrate Transcriptomics, More

For the FANTOM6 project, an international team led by investigators in Japan shares findings from a molecular phenotyping study focused on tracking long non-coding RNAs (lncRNAs) functions in human fibroblast cells. Using antisense LNA-modified GapmeR antisense oligonucleotide knockdown, Cap Analysis Gene Expression (CAGE) sequencing, and other approaches, the researchers dialed down the expression of almost 300 lncRNAs in a human cell line, tracking the growth and molecular pathway expression consequences. "In contrast to cellular phenotyping," they note, "molecular phenotyping provides a detailed assessment of the response to a lncRNA knockdown at the molecular level, allowing biological pathways to be associated to lncRNAs even in the absence of an observable cellular phenotype."

A team from Qatar, Japan, and elsewhere takes a look at transcriptome patterns in primary cells from several vertebrate species. The researchers relied on CAGE and short RNA sequencing to assess gene expression, microRNA profiles, and more in at least three primary cell types from humans, mice, rats, dogs, and chickens, delving into up to a dozen different human and mouse cell types. Among their findings, for example, they note that conserved expression tended to turn up for genes contributing to processes such as transcription and RNA processing and for miRNAs going back further in evolutionary time. On the other hand, expression appeared more variable when it came to relatively new miRNAs or for genes with ties to communication between cells. "We conclude that key aspects of the regulatory network are conserved," the authors write, "while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species."

Finally, University of Geneva researchers present a platform meant for benchmarking microbiome metagenome sequence-related software and continuously integrating new methods, providing a look at the workflow behind it. The read classification-based platform, known as LEMMI, "enables the integration of newly published methods in an independent and centralized benchmark designed to be continuously open to new submissions," the team writes. "LEMMI encourages developers to consider biocontainers to disseminate their work and to standardize the results formats so users can obtain easy-to-use and stable implementations of up-to-date methods as they appear in the benchmark."