Aleksey Shatunov

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

This study investigates the role of Neanderthal introgression and natural selection in the heritability of neurodegenerative diseases and finds no significant contribution from either.

The paper identifies genetic variants influencing DNA methylation in blood and shows their associations with complex traits and diseases, but finds limited direct causality.

The paper identifies non-coding variants in the IL18RAP 3'UTR that reduce ALS risk by modulating neuro-inflammation through NF-κB signaling in microglia.

The paper identifies de-novo SPTLC1 mutations in juvenile ALS patients and suggests serine supplementation may alleviate symptoms.

The paper identifies an enrichment of rare protein-truncating variants in ALS patients, highlighting novel genes like DNAJC7 and known ALS genes such as SOD1, NEK1, and FUS.

DNAscan is a fast and memory-efficient pipeline for analyzing DNA NGS data, capable of detecting SNVs, indels, SVs, and other variants with minimal computational resources.

ALSgeneScanner is a tool that provides automated, detailed reports on ALS-related gene variants from whole genome or exome sequencing data, aiding non-specialists in interpretation.

Project MinE analyzes whole-genome sequencing data from ALS patients and controls to study genetic variation and its implications for disease susceptibility.

The paper introduces ExpansionHunter, a tool for detecting long repeat expansions from PCR-free WGS data, achieving high sensitivity and specificity in identifying pathogenic expansions.