Ahmad Al Khleifat

The paper identifies seven sex-dependent differentially methylated positions in ALS blood samples, suggesting sex-specific biological mechanisms in the disease.

This paper conducts a meta-analysis to determine the lower limit of ATXN2 repeat expansions associated with ALS risk, finding that ≥30 repeats define significant risk.

The paper identifies FGF14 GAA repeat expansions in ALS patients, showing enrichment compared to controls and suggesting a broader pathogenic role in motor neuron disease.

The paper quantifies exosomal HERV-K transcripts in ALS patients and controls, finding elevated pol expression that could serve as a biomarker for treatment monitoring.

The paper evaluates gene expression-based ALS subtypes across brain and blood tissues using machine learning, finding three subtypes with consistent classification in both tissues.

This study benchmarks TE detection tools on long-read data, showing significant variation in performance and highlighting tool-specific strengths and limitations.

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

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

The paper identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

This study uses GWAS and functional genomics to identify ALS risk genes, repurpose drugs, and improve risk prediction using polytranscriptomic scores.

The paper introduces predicTTE, a tool for time-to-event prediction in neurological diseases, demonstrating its utility in genetic discovery and personalized medicine.

The paper identifies sex-specific risk loci for ALS, including MEF2C, which is upregulated in female ALS motor neurons, and an X-chromosome locus at IL1RAPL2.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

The paper evaluates bioinformatics tools for detecting human endogenous retroviral insertions in short-read genome sequencing data, finding performance variability across datasets and recommending a consensus approach.

This study uses molecular dynamics and clinical data to show structural and dynamic differences between WTL and MBR SOD1 variants, linking these to distinct ALS clinical outcomes.

This study identifies NEFH tail domain mutations as risk factors for ALS through meta-analysis and variant burden analysis in case-control studies and whole-genome sequencing data.

The paper investigates how mitochondrial function affects ALS severity but not risk, identifying specific genetic and expression factors linked to disease progression.

DNAscan2 is a versatile, scalable, and user-friendly NGS analysis pipeline that supports variant detection and end-to-end data processing.

RetroSnake detects HERVs in NGS data using an end-to-end pipeline validated by wet-lab experiments, producing annotated insertions in an interactive HTML file.

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.