AIMC Topic: Sequence Analysis, DNA

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CoCoNet: an efficient deep learning tool for viral metagenome binning.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It con...

Locating transcription factor binding sites by fully convolutional neural network.

Briefings in bioinformatics
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learnin...

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

Briefings in bioinformatics
MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues a...

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

Briefings in bioinformatics
The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as h...

Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks.

Briefings in bioinformatics
A promoter is a region in the DNA sequence that defines where the transcription of a gene by RNA polymerase initiates, which is typically located proximal to the transcription start site (TSS). How to correctly identify the gene TSS and the core prom...

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network.

Briefings in bioinformatics
It is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, the...

A survey on deep learning in DNA/RNA motif mining.

Briefings in bioinformatics
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and managemen...

DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences.

Briefings in bioinformatics
Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology...

Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning.

Briefings in bioinformatics
DNA N4-methylcytosine (4mC) modification represents a novel epigenetic regulation. It involves in various cellular processes, including DNA replication, cell cycle and gene expression, among others. In addition to experimental identification of 4mC s...

Identification of Gene Regulatory Networks from Single-Cell Expression Data.

Methods in molecular biology (Clifton, N.J.)
Single-cell RNAseq is an emerging technology that allows the quantification of gene expression in individual cells. In plants, single-cell sequencing technology has been applied to generate root cell expression maps under many experimental conditions...