AIMC Topic: Sequence Analysis, DNA

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A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data.

Nature genetics
Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consumin...

A neural network based model effectively predicts enhancers from clinical ATAC-seq samples.

Scientific reports
Enhancers are cis-acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction...

A universal SNP and small-indel variant caller using deep neural networks.

Nature biotechnology
Despite rapid advances in sequencing technologies, accurately calling genetic variants present in an individual genome from billions of short, errorful sequence reads remains challenging. Here we show that a deep convolutional neural network can call...

Machine Learning for detection of viral sequences in human metagenomic datasets.

BMC bioinformatics
BACKGROUND: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as "unknown", as conv...

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

Journal of computational biology : a journal of computational molecular cell biology
The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, bu...

HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.

Scientific reports
Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions...

Epigenetic machine learning: utilizing DNA methylation patterns to predict spastic cerebral palsy.

BMC bioinformatics
BACKGROUND: Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of...

Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review.

IEEE reviews in biomedical engineering
Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this co...

Prevalence and genotypes of Giardia lamblia from stray dogs and cats in Guangdong, China.

Veterinary parasitology, regional studies and reports
Giardia lamblia is a worldwide zoonotic intestinal parasite that infects humans and a wide range of mammals including dogs and cats, causing giardiasis with diarrhea. To investigate the infection and distribution of G. lamblia genotypes from stray do...