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This Week in Genome Biology: Dec 27, 2017

Researchers from the University of California, San Francisco, the University of Pittsburgh, and elsewhere explore tumor-associated macrophage (TAM) patterns in human glioma using single-cell RNA sequencing. The team used Fluidigm C1 or 10X Genomics methods to profile RNA in hundreds of individual TAMs associated with seven untreated primary glioma cases. When the authors compared TAM transcripts to one another and to published sequences from low-grade glioma, astrocytoma, oligodendroglioma, and other samples, they reportedly saw varying TAM infiltration into the gliomas depending on the type of TAM, glioma subtype, and tumor region considered. Such findings "argue against status quo therapeutic strategies that target TMAs indiscriminately," they say, "and in favor of strategies that specifically target immunosuppressive blood-derived TAMs."

A team from China and the UK describes a systems epigenomics algorithm it used to tease out regulatory and expression features contributing to lung cancer development. The algorithm — known as "Systems Epigenomics Inference of Regulatory Activity," or SEPIRA — incorporates information on tissue-specific enhancements in transcription factor expression with transcription factor target information to get a glimpse at transcription factor regulation in a given tissue. When the researchers applied SEPIRA to RNA sequence and DNA methylation data generated for lung squamous cell carcinomas by the Cancer Genome Atlas, alongside a lung-specific regulatory network data, they narrowed in on transcription factors with potential ties to smoking-associated lung cancer, including apparent inactivation of the aryl hydrocarbon receptor in lung cancer precursors.

A University of Lille- and Pasteur Institute of Lille-led team introduces an automatic pipeline called "Microbial Identification and Characterization through Reads Analysis," or MICRA, for identifying and characterizing microbial genomes using high-throughput sequence data. "The originality of MICRA lies in the way it exploits the increasing number of sequenced microbial genomes combined with efficient read mapping methods, rather than following the prevalent procedure of de novo assembly and sequence annotation," the researchers say. For their validation and proof-of-principle analyses, they used MICRA to assess simulated and authentic microbial reads from microbes such as Escherichia coli, Bordetella pertussis, Staphylococcus aureus, and Clostridium autoethanogenum, including E. coli associated with an outbreak involving contaminated bean sprouts that affected almost 4,100 individuals.