Al-Ghalith, G. | bioRxiv Science

bioRxiv2023

Skin surface biomarkers are associated with future development of atopic dermatitis in children with family history of allergic disease

BackgroundAtopic dermatitis (AD) is a common childhood chronic inflammatory skin disorder that can significantly impact quality of life and has been linked to the subsequent development of food allergy, asthma, and allergic rhinitis, an association known as the "atopic march." ObjectiveThe aim of this study was to identify biomarkers collected non-invasively from the skin surface in order to predict AD before diagnosis across a broad age range of children. MethodsNon-invasive skin surface measures and biomarkers were collected from 160 children (3-48 months of age) of three groups: (A) healthy with no family history of allergic disease, (B) healthy with family history of allergic disease, and (C) diagnosed AD. ResultsEleven of 101 children in group B reported AD diagnosis in the subsequent 12 months following the measurements. The children who developed AD had increased skin immune markers before disease onset, compared to those who did not develop AD in the same group and to the control group. In those enrolled with AD, lesional skin was characterized by increased concentrations of certain immune markers and transepidermal water loss, and decreased skin surface hydration. ConclusionsDefining risk susceptibility before onset of AD through non-invasive methods may help identify children who may benefit from early preventative interventions.

immunology↗

bioRxiv2020

BURST enables mathematically optimal short-read alignment for big data

One of the fundamental tasks in analyzing next-generation sequencing data is genome database search, in which DNA sequences are compared to known reference genomes for identification or annotation. Although algorithms exist for optimal database search with perfect sensitivity and specificity, these have largely been abandoned for next-generation sequencing (NGS) data in favor of faster heuristic algorithms that sacrifice alignment quality. Virtually all DNA alignment tools that are commonly used in genomic and metagenomic database search use approximate methods that sometimes report the wrong match, and sometimes fail to find a valid match when present. Here we introduce BURST, a high-throughput DNA short-read aligner that uses several new synergistic optimizations to enable provably optimal alignment in NGS datasets. BURST finds all equally good matches in the database above a specified identity threshold and can either report all of them, pick the most likely among tied matches, or provide lowest-common-ancestor taxonomic annotation among tied matches. BURST can align, disambiguate, and assign taxonomy at a rate of 1,000,000 query sequences per minute against the RefSeq v82 representative prokaryotic genome database (5,500 microbial genomes, 19GB) at 98% identity on a 32-core computer, representing a speedup of up to 20,000-fold over current optimal gapped alignment techniques. This may have broader implications for clinical applications, strain tracking, and other situations where fast, exact, extremely sensitive alignment is desired.

bioinformatics↗