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Nature Studies Examine Native RNA Sequencing, Nanopore Sequencing Approach to Analyze STR Expansions

The native RNA sequencing and analysis of a human poly(A) transcriptome using the Oxford Nanopore Technologies platform is described in a new study in Nature Methods this week. An international research team reports generating 9.9 million aligned sequence reads for the human cell line GM12878 using 30 MinION flow cells at six institutions. These native RNA reads, the authors write, had a median length of 771 bases and a maximum aligned length of over 21,000 bases. Mitochondrial poly(A) reads provided an internal measure of read-length quality, and a combination of the long nanopore reads with higher-accuracy short reads and annotated GM12878 promoter regions enabled the identification of 33,984 plausible RNA isoforms. The scientists also offer strategies for assessing 3' poly(A) tail length, base modifications, and transcript haplotypes.

A nanopore sequencing-based method for analyzing short tandem repeat expansions, as well as their methylation state, is presented in Nature Biotechnology. Called STRique — for short tandem repeat identification, quantification, and evaluation — the approach integrates conventional sequence mapping of nanopore reads with raw signal alignment for the localization of repeat boundaries and a hidden Markov model-based repeat counting mechanism. STRique, its developers write, "enables the study of previously inaccessible genomic regions and their epigenetic marks."

By analyzing the genomes of European ash trees, a team of scientists has identified a genetic basis for the resistance some of the trees have to an invasive fungus that causes deadly ash dieback. Using sequencing and genome-wide association study data, the investigators uncover a number of SNPs linked to low ash dieback damage, including several in or around genes with putative homologs known to be involved in plant pathogen resistance. They also develop a genome prediction model, validated using sequence data from healthy and disease-damaged trees from the same seed source, that could predict ash tree health with over 90 percent accuracy. They conclude that ash dieback resistance is a highly polygenic trait and "may therefore respond well to artificial and natural selection, allowing the breeding or evolution of durable increased resistance." Genomic prediction with models like the one they developed may accelerate such efforts. The Scan has more on this, here.