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Sequence Analysis, DNA

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Single-cell specific and interpretable machine learning models for sparse scChIP-seq data imputation.

PloS one
MOTIVATION: Single-cell Chromatin ImmunoPrecipitation DNA-Sequencing (scChIP-seq) analysis is challenging due to data sparsity. High degree of sparsity in biological high-throughput single-cell data is generally handled with imputation methods that c...

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks.

Scientific reports
This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used i...

Adaptive sequencing using nanopores and deep learning of mitochondrial DNA.

Briefings in bioinformatics
Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecting nucleic acid sequences and identifies the various chemical modifications they carry. Deep learning has increased in popularity as a useful techniqu...

Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Methods (San Diego, Calif.)
Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variati...

SVision: a deep learning approach to resolve complex structural variants.

Nature methods
Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequ...

LanceOtron: a deep learning peak caller for genome sequencing experiments.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing experiments have revolutionized molecular biology by allowing researchers to identify important DNA-encoded elements genome wide. Regions where these elements are found appear as peaks in the analog signal of an assay's ...

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data.

Nucleic acids research
De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and funct...

Deep learning-assisted genome-wide characterization of massively parallel reporter assays.

Nucleic acids research
Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small...

Deep-learning optimized DEOCSU suite provides an iterable pipeline for accurate ChIP-exo peak calling.

Briefings in bioinformatics
Recognizing binding sites of DNA-binding proteins is a key factor for elucidating transcriptional regulation in organisms. ChIP-exo enables researchers to delineate genome-wide binding landscapes of DNA-binding proteins with near single base-pair res...