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High-Throughput Nucleotide Sequencing

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Annotation of gene product function from high-throughput studies using the Gene Ontology.

Database : the journal of biological databases and curation
High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The repres...

Data-driven supervised learning of a viral protease specificity landscape from deep sequencing and molecular simulations.

Proceedings of the National Academy of Sciences of the United States of America
Biophysical interactions between proteins and peptides are key determinants of molecular recognition specificity landscapes. However, an understanding of how molecular structure and residue-level energetics at protein-peptide interfaces shape these l...

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data.

BMC bioinformatics
BACKGROUND: Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of ...

Knowledge base toward understanding actionable alterations and realizing precision oncology.

International journal of clinical oncology
In Japan, the National Cancer Center and university hospitals have initiated next-generation sequencing-based in vitro diagnostic testing for cancer patients as a method of clinical sequencing. Based on the molecular alterations detected, physicians ...

Porcine single nucleotide polymorphisms and their functional effect: an update.

BMC research notes
OBJECTIVE: To aid in the development of a comprehensive list of functional variants in the swine genome, single nucleotide polymorphisms (SNP) were identified from whole genome sequence of 240 pigs. Interim data from 72 animals in this study was publ...

Prediction of plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models.

BMC genomics
BACKGROUND: An increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenom...

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

Deepbinner: Demultiplexing barcoded Oxford Nanopore reads with deep convolutional neural networks.

PLoS computational biology
Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barco...

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...