ArticlePloS one2016
Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Effect of Methodological Considerations on the Construction of Gene-Based Plant Pan-genomes.Genome biology and evolution · 2023Pooled it
- Short-Read NGS of a Long-RangeInternational journal of molecular sciences · 2026Article
- Evidence for strong purifying selection of humanProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Best practices for improving alignment and variant calling on human sex chromosomes.American journal of human genetics · 2026Article
- Eduomics: a Nextflow pipeline to simulate -omics data for education.NAR genomics and bioinformatics · 2026Article
- A novel simulation tool for low-coverage whole-genome sequencing using multivariate Gaussian mixture models.BMC bioinformatics · 2026Article
- AmpliFuse: an amplicon simulation tool with enhanced chimera generation for Illumina platforms.Microbiology resource announcements · 2026Article
- Synth4bench: generating synthetic data for benchmarking tumor-only somatic variant calling algorithms.Frontiers in bioinformatics · 2026Article
- rDNAcaller: a fast and robust pipeline to call ribosomal DNA variants.NAR genomics and bioinformatics · 2025Article
- Systematic evaluation of de novo mutation calling tools using whole genome sequencing data.Briefings in bioinformatics · 2025Article
- Comprehensive assessment of whole-genome sequencing-based variant calling pipelines in monozygotic twins.Briefings in bioinformatics · 2025Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- Sequencing airborne DNA to monitor crop pathogens and pests.iScience · 2025Article
- GENOMICON-Seq enables realistic simulation of amplicon and exome sequencing for low-frequency mutation detection.Scientific reports · 2025Article
- Flexible, production-scale, human whole genome sequencing on a benchtop sequencer.BMC genomics · 2025Article
- MeSS and assembly_finder: a toolkit for in silico metagenomic sample generation.Bioinformatics (Oxford, England) · 2024Article
- What can we infer about mutation calling by using time-series mutation accumulation data and a Bayesian Mutation Finder?Ecology and evolution · 2024Article
- A rapid phylogeny-based method for accurate community profiling of large-scale metabarcoding datasets.eLife · 2024Article
- Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.BMC bioinformatics · 2024Article
- Overcoming Limitations to Deep Learning in Domesticated Animals with TrioTrain.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the "ground truth" about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as well as sequencing artifacts. Read simulators can fulfill this requirement, but are often criticized for limited resemblance to true data and overall inflexibility. We present NEAT (NExt-generation sequencing Analysis Toolkit), a set of tools that not only includes an easy-to-use read simulator, but also scripts to facilitate variant comparison and tool evaluation. NEAT has a wide variety of tunable parameters which can be set manually on the default model or parameterized using real datasets. The software is freely available at github.com/zstephens/neat-genreads.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.