Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.

Journal of applied genetics - 1 Dec 2020

Kotlarz Krzysztof, Mielczarek Magda, Suchocki Tomasz, Czech Bartosz, Guldbrandtsen Bernt, Szyda Joanna

Abstract excerpt

A downside of next-generation sequencing technology is the high technical error rate. We built a tool, which uses array-based genotype information to classify next-generation sequencing-based SNPs into the correct and the incorrect calls. The deep learning algorithms were implemented via Keras. Several algorithms were tested: (i) the basic, naïve algorithm, (ii) the naïve algorithm modified by pre-imposing...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Cattle

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Deep Learning

Share to Genotyping Techniques

Share to Polymorphism, Single Nucleotide

Share to Whole Genome Sequencing

Source

Open in PubMed Open full text

Identifiers

PMID: 32996082
DOI: 10.1007/s13353-020-00586-0
PMCID: PMC7652806

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines.PMID 32996082Read ↗

A SNP discovery method to assess variant allele probability from next-generation resequencing data.Semantic proximity Read ↗

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype.Semantic proximity Read ↗

Creating a universal SNP and small indel variant caller with deep neural networksSemantic proximity Read ↗

A machine learning model to determine the accuracy of variant calls in capture-based next generation sequencing.Semantic proximity Read ↗

Genotype and SNP calling from next-generation sequencing data.Semantic proximity Read ↗

A comprehensive review of deep learning-based variant calling methods.Semantic proximity Read ↗

Comprehensive Evaluation of Genotype Imputation Tools for Ultra-low-depth Whole-Genome Sequencing Data.Semantic proximity Read ↗

SNP allele calling of Illumina Infinium Omni5-4 data using the butterfly method.Semantic proximity Read ↗

Evaluating the accuracy of variant calling methods using the frequency of parent-offspring genotype mismatch.Semantic proximity Read ↗

A deep learning model for predicting next-generation sequencing depth from DNA sequence.Semantic proximity Read ↗

Software update: Interpreting killer-cell immunoglobulin-like receptors from whole genome sequence data with PING.Semantic proximity Read ↗

An empirical Bayes method for genotyping and SNP detection using multi-sample next-generation sequencing data.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.