AIMC Topic: High-Throughput Nucleotide Sequencing

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Jointly Integrating VCF-Based Variants and OWL-Based Biomedical Ontologies in MongoDB.

IEEE/ACM transactions on computational biology and bioinformatics Nov 4, 2019
The development of the next-generation sequencing (NGS) technologies has led to massive amounts of VCF (Variant Call Format) files, which have been the standard formats developed with 1000 Genomes Project. At the same time, with the widespread use of...
Humans Programming Languages High-Throughput Nucleotide Sequencing Semantics Biological Ontologies Algorithms Genomics Databases, Genetic
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Forensic STR allele extraction using a machine learning paradigm.

Forensic science international. Genetics Oct 28, 2019
We present a machine learning approach to short tandem repeat (STR) sequence detection and extraction from massively parallel sequencing data called Fragsifier. Using this approach, STRs are detected on each read by first locating the longest repeat ...
Humans Alleles Genetic Loci Electrophoresis, Capillary High-Throughput Nucleotide Sequencing Machine Learning Algorithms Genotype Microsatellite Repeats Forensic Genetics DNA Fingerprinting
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A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1.

Blood cancer journal Oct 1, 2019
Neoplasm, Residual Genes, Neoplasm Nucleophosmin Nuclear Proteins Aged, 80 and over Humans Mutation Young Adult Machine Learning High-Throughput Nucleotide Sequencing Adult Algorithms Middle Aged Survival Rate Adolescent Aged Leukemia, Myeloid, Acute
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A Machine Learning Classifier for Assigning Individual Patients With Systemic Sclerosis to Intrinsic Molecular Subsets.

Arthritis & rheumatology (Hoboken, N.J.) Sep 2, 2019
OBJECTIVE: High-throughput gene expression profiling of tissue samples from patients with systemic sclerosis (SSc) has identified 4 "intrinsic" gene expression subsets: inflammatory, fibroproliferative, normal-like, and limited. Prior methods require...
High-Throughput Nucleotide Sequencing Datasets as Topic Scleroderma, Systemic Transcriptome Gene Expression Supervised Machine Learning Female Aged Young Adult Middle Aged Gene Expression Profiling Adult Male Humans Machine Learning Algorithms
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An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.

Marine pollution bulletin Aug 24, 2019
Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial c...
Random Allocation Glyphosate Water Microbiology Algorithms Neural Networks, Computer Water Pollutants, Chemical Glycine Environmental Monitoring Microbiota Machine Learning RNA, Ribosomal, 16S High-Throughput Nucleotide Sequencing
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Hierarchical Hidden Markov models enable accurate and diverse detection of antimicrobial resistance sequences.

Communications biology Aug 6, 2019
The characterization of antimicrobial resistance genes from high-throughput sequencing data has become foundational in public health research and regulation. This requires mapping sequence reads to databases of known antimicrobial resistance genes to...
High-Throughput Nucleotide Sequencing Sequence Analysis, DNA Markov Chains Drug Resistance, Microbial Machine Learning Metagenomics Data Mining Reproducibility of Results Databases, Genetic
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Identification of city specific important bacterial signature for the MetaSUB CAMDA challenge microbiome data.

Biology direct Jul 24, 2019
BACKGROUND: Metagenomic data of whole genome sequences (WGS) from samples across several cities around the globe may unravel city specific signatures of microbes. Illumina MiSeq sequencing data was provided from 12 cities in 7 different countries as ...
Metagenomics Cities Bacteria Microbiota High-Throughput Nucleotide Sequencing Machine Learning
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Wx: a neural network-based feature selection algorithm for transcriptomic data.

Scientific reports Jul 19, 2019
Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. ...
Humans Neural Networks, Computer Neoplasms Gene Expression Profiling Algorithms Transcriptome Biomarkers, Tumor RNA, Neoplasm High-Throughput Nucleotide Sequencing Genes, Neoplasm Datasets as Topic
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Robotic assisted generation of 2'-deoxy-2'-fluoro-modifed RNA aptamers - High performance enabling strategies in aptamer selection.

Methods (San Diego, Calif.) May 30, 2019
Aptamer selection is a laborious procedure, requiring expertise and significant resources. These characteristics limit the accessibility of researchers to these molecular tools. We describe a selection procedure, making use of a robotic system that a...
High-Throughput Nucleotide Sequencing Pyrimidines Robotics Humans RNA Aptamers, Nucleotide
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Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BMC bioinformatics May 28, 2019
BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the...
Algorithms Complementarity Determining Regions Tumor Microenvironment Receptors, Antigen, B-Cell ROC Curve Immunity, Humoral Humans High-Throughput Nucleotide Sequencing Amino Acid Sequence Immunoglobulins Amino Acid Motifs Databases, Genetic Area Under Curve Supervised Machine Learning
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