Background Among the approximately 8000 Mendelian disorders, >1000 have cutaneous manifestations. In many of these conditions, the underlying mutated genes have been identified by DNA-based techniques which, however, can overlook certain types of mutations, such as exonic-synonymous and deep-intronic sequence variants. Whole-transcriptome sequencing by RNA sequencing (RNA-seq) can identify such mutations and provide information about their consequences . Methods We analyzed the whole transcriptome of 40 families with different types of Mendelian skin disorders with extensive genetic heterogeneity. The RNA-seq data were examined for variant detection and prioritization, pathogenicity confirmation, RNA expression profiling, and genome-wide homozygosity mapping in the case of consanguineous families. Among the families examined, RNA-seq was able to provide information complementary to DNA-based analyses for exonic and intronic sequence variants with aberrant splicing. In addition, we tested the possibility of using RNA-seq as the first-tier strategy for unbiased genome-wide mutation screening without information from DNA analysis. Results We found pathogenic mutations in 35 families (88%) with RNA-seq in combination with other next-generation sequencing methods, and we successfully prioritized variants and found the culprit genes. In addition, as a novel concept, we propose a pipeline that increases the yield of variant calling from RNA-seq by concurrent use of genome and transcriptome references in parallel. Conclusions Our results suggest that “clinical RNA-seq” could serve as a primary approach for mutation detection in inherited diseases, particularly in consanguineous families, provided that tissues and cells expressing the relevant genes are available for analysis.

Severe viral infections of the skin can occur in patients with inborn errors of immunity (IEI). We report an all-in-one whole-transcriptome sequencing-based method by RNA-Seq on a single skin biopsy for concomitantly identifying the cutaneous virome and the underlying IEI. Skin biopsies were obtained from healthy and lesional skin from patients with cutaneous infections suspected to be of viral origin. RNA-Seq was utilized as the first-tier strategy for unbiased human genome-wide rare variant detection. Reads unaligned to the human genome were utilized for the exploration of 926 viruses in a viral genome catalog. In 9 families studied, the patients carried pathogenic variants in 6 human IEI genes, including IL2RG, WAS, CIB1, STK4, GATA2, and DOCK8. Gene expression profiling also confirmed pathogenicity of the human variants and permitted genome-wide homozygosity mapping, which assisted in identification of candidate genes in consanguineous families. This automated, online, all-in-one computational pipeline, called VirPy, enables simultaneous detection of the viral triggers and the human genetic variants underlying skin lesions in patients with suspected IEI and viral dermatosis.

Rumen microbial environment hosts a variety of microorganisms that interact with each other to carry out the feed digestion and generation of several by-products especially methane, which plays an essential role in global warming as a greenhouse gas. However, due to its multi-factorial nature, the exact cause of methane production in the rumen has not yet been fully determined. the current study is an attempt to use system modeling to analyze the relationship between interacting components of rumen microbiome and its role in methane production. Metagenomic data of sheep rumen, with equal numbers of high methane yield (HMY) and low methane yield (LMY) samples, were used. As a well-known approach for the systematic comparative study of complex traits, the co-abundance networks were constructed in both operational taxonomic unit (otU) and gene levels. A gene-catalog of 1,444 different rumen microbial strains was developed as a reference to measure gene abundances. The results from both types of co-abundance networks showed that methanogens, which are the main ruminal source for methanogenesis, need other microbial species to accomplish the task of methane production through producing the main precursor molecules like H 2 and acetate for methanogenesis pathway as their byproducts. KeGG orthology(Ko) analysis of the current study shows that the metabolism and growth rate of methanogens will be increased due to the higher rate of the metabolism and carbohydrate/fiber digestion pathways in the hidden elements. This finding proposes that any ruminant methane yield alteration strategy should consider complex interactions of rumen microbiome components as one tightly integrated unit rather than several separate parts.

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Dying tumor cells shed DNA fragments into the circulation that are known as circulating tumor DNA (ctDNA). Liquid biopsy tests aim to detect cancer using known markers, including genetic alterations and epigenetic profiles of ctDNA. Despite various advantages, the major limitation remains the low fraction of tumor-originating DNA fragments in a high background of normal blood-cell originating fragments in the cell-free DNA (cfDNA) pool in plasma. Deep targeted sequencing of cfDNA allows for enrichment of fragments in known cancer marker-associated regions of the genome, thus increasing the chances of detecting the low fraction variant harboring fragments. Most targeted sequencing panels are designed to include known recurrent mutations or methylation markers of cancer. Here, we propose the integration of cancer-specific chromatin accessibility states into panel designs for liquid biopsy. Using machine learning approaches, we first identify accessible and inaccessible chromatin regions specific to each major human cancer type. We then introduce a score that quantifies local chromatin accessibility in tumor relative to blood cells and show that this metric can be useful for prioritizing marker regions with higher chances of being detected in cfDNA for inclusion in future panel designs.