Deep learning modeling of rare noncoding genetic variants in human motor neurons definesCCDC146as a therapeutic target for ALS DOI Creative Commons
Sai Zhang, Tobias Moll,

Jasper Rubin-Sigler

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 1, 2024

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective progressive death of motor neurons (MNs). Understanding genetic molecular factors influencing ALS survival crucial for management therapeutics. In this study, we introduce deep learning-powered analysis framework to link rare noncoding variants survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, method prioritizes functional using learning, links cis-regulatory elements (CREs) target genes epigenomics data, integrates these through gene-level burden tests identify survival-modifying variants, CREs, genes. We apply approach analyze 6,715 genomes, pinpoint four novel associated with survival, including chr7:76,009,472:C>T linked CCDC146 . CRISPR-Cas9 editing variant increases expression in iPSC-derived MNs exacerbates ALS-specific phenotypes, TDP-43 mislocalization. Suppressing an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated defects derived sporadic patients carriers G4C2-repeat expansion within C9ORF72 ASO targeting may be broadly effective therapeutic ALS. Our provides generic powerful studying genetics complex diseases.

Language: Английский

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity DOI
Xuejian Cui, Xiaoyang Chen, Zhen Li

et al.

Nature Computational Science, Journal Year: 2024, Volume and Issue: 4(5), P. 346 - 359

Published: May 10, 2024

Language: Английский

Citations

6
A survey on algorithms to characterize transcription factor binding sites DOI Open Access
Manuel Tognon, Rosalba Giugno, Luca Pinello

et al.

Briefings in Bioinformatics, Journal Year: 2023, Volume and Issue: 24(3)

Published: April 25, 2023

Abstract Transcription factors (TFs) are key regulatory proteins that control the transcriptional rate of cells by binding short DNA sequences called transcription factor sites (TFBS) or motifs. Identifying and characterizing TFBS is fundamental to understanding mechanisms governing state cells. During last decades, several experimental methods have been developed recover containing TFBS. In parallel, computational proposed discover identify motifs based on these sequences. This one most widely investigated problems in bioinformatics referred as motif discovery problem. this manuscript, we review classical novel characterize sequences, highlighting their advantages drawbacks. We also discuss open challenges future perspectives could fill remaining gaps field.

Language: Английский

Citations

14
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology DOI Creative Commons
Tatsuhiko Naito, Yukinori Okada

Journal of Human Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 15, 2024

The imputation of unmeasured genotypes is essential in human genetic research, particularly enhancing the power genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype methods for variants with capability learning complex linkage disequilibrium patterns have been developed. Additionally, has applied to a distinct genomic region known as major histocompatibility complex, referred HLA imputation. Despite their various advantages, current do certain limitations not yet become standard. These include modest accuracy improvement over statistical conventional machine methods. However, benefits other aspects, such "reference-free" nature, which ensures complete privacy protection, higher computational efficiency. Furthermore, continuing evolution technologies expected contribute further improvements prediction usability future.

Language: Английский

Citations

5
Toward a comprehensive catalog of regulatory elements DOI
Kaili Fan, Edith L. Pfister, Zhiping Weng

et al.

Human Genetics, Journal Year: 2023, Volume and Issue: 142(8), P. 1091 - 1111

Published: March 19, 2023

Language: Английский

Citations

12
Deep learning modeling of rare noncoding genetic variants in human motor neurons definesCCDC146as a therapeutic target for ALS DOI Creative Commons
Sai Zhang, Tobias Moll,

Jasper Rubin-Sigler

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 1, 2024

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective progressive death of motor neurons (MNs). Understanding genetic molecular factors influencing ALS survival crucial for management therapeutics. In this study, we introduce deep learning-powered analysis framework to link rare noncoding variants survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, method prioritizes functional using learning, links cis-regulatory elements (CREs) target genes epigenomics data, integrates these through gene-level burden tests identify survival-modifying variants, CREs, genes. We apply approach analyze 6,715 genomes, pinpoint four novel associated with survival, including chr7:76,009,472:C>T linked CCDC146 . CRISPR-Cas9 editing variant increases expression in iPSC-derived MNs exacerbates ALS-specific phenotypes, TDP-43 mislocalization. Suppressing an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated defects derived sporadic patients carriers G4C2-repeat expansion within C9ORF72 ASO targeting may be broadly effective therapeutic ALS. Our provides generic powerful studying genetics complex diseases.

Language: Английский

Citations

4