ArticleBMC bioinformatics2017
Decontaminating eukaryotic genome assemblies with machine learning.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 29 citations in OpenAlex.
- ParaRef: a decontaminated reference database for parasite detection in ancient and modern metagenomic datasets.Genome biology · 2025Article
- Missing microbial eukaryotes and misleading meta-omic conclusions.Nature communications · 2024Article
- Identifying transgene insertions inPeerJ · 2024Article
- Genome Size Changes by Duplication, Divergence, and Insertion in Caenorhabditis Worms.Molecular biology and evolution · 2023Article
- Metagenomic clustering reveals microbial contamination as an essential consideration in ultraconserved element design for phylogenomics with insect museum specimens.Ecology and evolution · 2022Article
- Music of metagenomics-a review of its applications, analysis pipeline, and associated tools.Functional & integrative genomics · 2022Review
- Comparing whole-genome shotgun sequencing and DNA metabarcoding approaches for species identification and quantification of pollen species mixtures.Ecology and evolution · 2021Article
- ZWA: Viral genome assembly and characterization hindrances from virus-host chimeric reads; a refining approach.PLoS computational biology · 2021Article
- Macrosynteny analysis between Lentinula edodes and Lentinula novae-zelandiae reveals signals of domestication in Lentinula edodes.Scientific reports · 2021Article
- Optimizing experimental design for genome sequencing and assembly with Oxford Nanopore Technologies.GigaByte (Hong Kong, China) · 2021Article
- Autometa: automated extraction of microbial genomes from individual shotgun metagenomes.Nucleic acids research · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundHigh-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism.
resultsWe introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols.
conclusionsA decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.
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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.