Functional filter for whole-genome sequencing data identifies HHT and stress-associated non-coding SMAD4 polyadenylation site variants >5 kb from coding DNA.

Despite whole-genome sequencing (WGS), many cases of single-gene disorders remain unsolved, impeding diagnosis and preventative care for people whose disease-causing variants escape detection. Since early WGS data analytic steps prioritize protein-coding sequences, to simultaneously prioritize variants in non-coding regions rich in transcribed and critical regulatory sequences, we developed GROFFFY, an analytic tool that integrates coordinates for regions with experimental evidence of functionality. Applied to WGS data from solved and unsolved hereditary hemorrhagic telangiectasia (HHT) recruits to the 100,000 Genomes Project, GROFFFY-based filtration reduced the mean number of variants/DNA from 4,867,167 to 21,486, without deleting disease-causal variants.

Identification and validation of a novel pathogenic variant in GDF2 (BMP9) responsible for hereditary hemorrhagic telangiectasia and pulmonary arteriovenous malformations.

Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant multisystemic vascular dysplasia, characterized by arteriovenous malformations (AVMs), mucocutaneous telangiectasia and nosebleeds. HHT is caused by a heterozygous null allele in ACVRL1, ENG, or SMAD4, which encode proteins mediating bone morphogenetic protein (BMP) signaling. Several missense and stop-gain variants identified in GDF2 (encoding BMP9) have been reported to cause a vascular anomaly syndrome similar to HHT, however none of these patients met diagnostic criteria for HHT.

Meta-analysis of 16S rRNA Microbial Data Identified Distinctive and Predictive Microbiota Dysbiosis in Colorectal Carcinoma Adjacent Tissue.

As research focusing on the colorectal cancer fecal microbiome using shotgun sequencing continues, increasing evidence has supported correlations between colorectal carcinomas (CRCs) and fecal microbiome dysbiosis. However, large-scale on-site and off-site (surrounding adjacent) tissue microbiome characterization of CRC was underrepresented. Here, considering each taxon as a feature, we demonstrate a machine learning-based method to investigate tissue microbial differences among CRC, colorectal adenoma (CRA), and healthy control groups using 16S rRNA data sets retrieved from 15 studies.