ArticleBMC genomics2009
The effect of sequencing errors on metagenomic gene prediction.
Article in BMC genomics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
- Evaluation of the correctable decoding sequencing as a new powerful strategy for DNA sequencing.Life science alliance · 2022Article
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- DiNAMO: highly sensitive DNA motif discovery in high-throughput sequencing data.BMC bioinformatics · 2018Article
- Elevated temperature drives kelp microbiome dysbiosis, while elevated carbon dioxide induces water microbiome disruption.PloS one · 2018Article
- Genetic repertoires of anaerobic microbiomes driving generation of biogas.Biotechnology for biofuels · 2018Article
- Tackling critical parameters in metazoan meta-barcoding experiments: a preliminary study based onPeerJ · 2018Article
- Biocontrol activity of a cold-adapted yeast from Tibet against gray mold in cherry tomato and its action mechanism.Extremophiles : life under extreme conditions · 2017Article
- MetaGaAP: A Novel Pipeline to Estimate Community Composition and Abundance from Non-Model Sequence Data.Biology · 2017Article
- Evaluating techniques for metagenome annotation using simulated sequence data.FEMS microbiology ecology · 2016Article
- RAS testing in metastatic colorectal cancer: advances in Europe.Virchows Archiv : an international journal of pathology · 2016Review
- MetLab: An In Silico Experimental Design, Simulation and Analysis Tool for Viral Metagenomics Studies.PloS one · 2016Article
- REPURPOSING GERMLINE EXOMES OF THE CANCER GENOME ATLAS DEMANDS A CAUTIOUS APPROACH AND SAMPLE-SPECIFIC VARIANT FILTERING.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGene prediction is an essential step in the annotation of metagenomic sequencing reads. Since most metagenomic reads cannot be assembled into long contigs, specialized statistical gene prediction tools have been developed for short and anonymous DNA fragments, e.g. MetaGeneAnnotator and Orphelia. While conventional gene prediction methods have been subject to a benchmark study on real sequencing reads with typical errors, such a comparison has not been conducted for specialized tools, yet. Their gene prediction accuracy was mostly measured on error free DNA fragments.
resultsIn this study, Sanger and pyrosequencing reads were simulated on the basis of models that take all types of sequencing errors into account. All metagenomic gene prediction tools showed decreasing accuracy with increasing sequencing error rates. Performance results on an established metagenomic benchmark dataset are also reported. In addition, we demonstrate that ESTScan, a tool for sequencing error compensation in eukaryotic expressed sequence tags, outperforms some metagenomic gene prediction tools on reads with high error rates although it was not designed for the task at hand.
conclusionThis study fills an important gap in metagenomic gene prediction research. Specialized methods are evaluated and compared with respect to sequencing error robustness. Results indicate that the integration of error-compensating methods into metagenomic gene prediction tools would be beneficial to improve metagenome annotation quality.
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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.