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Sequence Analysis, DNA

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MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.

BMC bioinformatics
BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagen...

Feasibility of predicting allele specific expression from DNA sequencing using machine learning.

Scientific reports
Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. Howev...

An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods.

Scientific reports
Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtain...

Improving protein domain classification for third-generation sequencing reads using deep learning.

BMC genomics
BACKGROUND: With the development of third-generation sequencing (TGS) technologies, people are able to obtain DNA sequences with lengths from 10s to 100s of kb. These long reads allow protein domain annotation without assembly, thus can produce impor...

SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.

BMC research notes
OBJECTIVE: To address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale.

Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning.

Nature communications
Elucidating functionality in non-coding regions is a key challenge in human genomics. It has been shown that intolerance to variation of coding and proximal non-coding sequence is a strong predictor of human disease relevance. Here, we integrate into...

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Genomics, proteomics & bioinformatics
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported e...

Use of machine learning to identify a T cell response to SARS-CoV-2.

Cell reports. Medicine
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...

iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.

Computational and mathematical methods in medicine
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulati...

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

International journal of molecular sciences
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. ...