AIMC Topic: Polymorphism, Single Nucleotide
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Predicting functional consequences of SNPs on mRNA translation via machine learning.
Nucleic acids research
Aug 25, 2023
The functional impact of single nucleotide polymorphisms (SNPs) on translation has yet to be considered when prioritizing disease-causing SNPs from genome-wide association studies (GWAS). Here we apply machine learning models to genome-wide ribosome ...
The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.
Nucleic acids research
Jan 6, 2023
The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to >200 000 users per year from academic research, healthcare and industr...
DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.
Bioinformatics (Oxford, England)
Dec 13, 2022
MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited...
E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.
Bioinformatics (Oxford, England)
Nov 30, 2022
MOTIVATION: The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations fro...
TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
Bioinformatics (Oxford, England)
Oct 14, 2022
MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of h...
Deciphering signatures of natural selection via deep learning.
Briefings in bioinformatics
Sep 20, 2022
Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning...
KLFDAPC: a supervised machine learning approach for spatial genetic structure analysis.
Briefings in bioinformatics
Jul 18, 2022
Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect and describe them is principal component analysis (PCA) or the supervised linear discriminant analysis of princ...
The COPILOT Raw Illumina Genotyping QC Protocol.
Current protocols
Apr 1, 2022
The Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses that rely on various software, different programming languag...
SVPath: an accurate pipeline for predicting the pathogenicity of human exon structural variants.
Briefings in bioinformatics
Mar 10, 2022
Although there are a large number of structural variations in the chromosomes of each individual, there is a lack of more accurate methods for identifying clinical pathogenic variants. Here, we proposed SVPath, a machine learning-based method to pred...
Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.
Briefings in bioinformatics
Jan 17, 2022
More than 6000 human diseases have been recorded to be caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Rapid and accurate prediction of pathogenic nsSNPs can improve our understanding of the principle and design of new drugs, which...
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