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Sequence Analysis, DNA

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Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification.

Nucleic acids research
Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associa...

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.

Nucleic acids research
ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragm...

Analysis of machine learning algorithms as integrative tools for validation of next generation sequencing data.

European review for medical and pharmacological sciences
OBJECTIVE: While next generation sequencing (NGS) has become the technology of choice for clinical diagnostics, most genetic laboratories still use Sanger sequencing for orthogonal confirmation of NGS results. Previous studies have shown that when th...

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches.

Briefings in bioinformatics
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems is how to computationally analyze their structures and functions. Machine learning techniques are playing key roles in this field. Typi...

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Nucleic acids research
ChIP-seq is a technique to determine binding locations of transcription factors, which remains a central challenge in molecular biology. Current practice is to use a 'control' dataset to remove background signals from a immunoprecipitation (IP) 'targ...

DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.

Bioinformatics (Oxford, England)
MOTIVATION: Recognition of different genomic signals and regions (GSRs) in DNA is crucial for understanding genome organization, gene regulation, and gene function, which in turn generate better genome and gene annotations. Although many methods have...

Deep repeat resolution-the assembly of the Drosophila Histone Complex.

Nucleic acids research
Though the advent of long-read sequencing technologies has led to a leap in contiguity of de novo genome assemblies, current reference genomes of higher organisms still do not provide unbroken sequences of complete chromosomes. Despite reads in exces...

DNA Steganalysis Using Deep Recurrent Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in convent...

Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and...