AIMC Topic: Sequence Analysis, DNA

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Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning.

Nature biomedical engineering
The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning...

Using deep learning to identify recent positive selection in malaria parasite sequence data.

Malaria journal
BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and ...

DNAscent v2: detecting replication forks in nanopore sequencing data with deep learning.

BMC genomics
BACKGROUND: Measuring DNA replication dynamics with high throughput and single-molecule resolution is critical for understanding both the basic biology behind how cells replicate their DNA and how DNA replication can be used as a therapeutic target f...

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...