ArticleJournal of computational biology : a journal of computational molecular cell biology2013
Alignment-free sequence comparison based on next-generation sequencing reads.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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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.
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Who cites it
48 citing papers in PubMed.
- Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks.PLoS computational biology · 2024Article
- AC-PCoA: Adjustment for confounding factors using principal coordinate analysis.PLoS computational biology · 2022Article
- Phage-bacterial contig association prediction with a convolutional neural network.Bioinformatics (Oxford, England) · 2022Article
- Reads Binning Improves the Assembly of Viral Genome Sequences From Metagenomic Samples.Frontiers in microbiology · 2021Article
- Sequence Comparison Without Alignment: The SpaM Approaches.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data.Quantitative biology (Beijing, China) · 2020Article
- Beyond DNA barcoding: The unrealized potential of genome skim data in sample identification.Molecular ecology · 2020Article
- A network-based integrated framework for predicting virus-prokaryote interactions.NAR genomics and bioinformatics · 2020Article
- 'Multi-SpaM': a maximum-likelihood approach to phylogeny reconstruction using multiple spaced-word matches and quartet trees.NAR genomics and bioinformatics · 2020Article
- The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances.PloS one · 2020Article
- Classifying the Lifestyle of Metagenomically-Derived Phages Sequences Using Alignment-Free Methods.Frontiers in microbiology · 2020Article
- Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.Biomolecules · 2019Article
- Afann: bias adjustment for alignment-free sequence comparison based on sequencing data using neural network regression.Genome biology · 2019Article
- The statistical power ofSynthetic and systems biotechnology · 2019Article
- Prot-SpaM: fast alignment-free phylogeny reconstruction based on whole-proteome sequences.GigaScience · 2019Article
- Reads Binning Improves Alignment-Free Metagenome Comparison.Frontiers in genetics · 2019Article
- Alignment-free genome comparison enables accurate geographic sourcing of white oak DNA.BMC genomics · 2018Article
- Alignment-Free Sequence Analysis and Applications.Annual review of biomedical data science · 2018Article
- GePMI: A statistical model for personal intestinal microbiome identification.NPJ biofilms and microbiomes · 2018Article
- Optimal choice of word length when comparing two Markov sequences using a χBMC genomics · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Next-generation sequencing (NGS) technologies have generated enormous amounts of shotgun read data, and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, D(2), D(*)(2) and D(s)(2), both theoretically and by simulations. Theoretical formulas for the power of detecting the relationship between two sequences related through a common motif model are derived. It is shown that both D(*)(2) and D(s)(2), outperform D(2) for detecting the relationship between two sequences based on NGS data. We then study the effects of length of the tuple, read length, coverage, and sequencing error on the power of D(*)(2) and D(s)(2). Finally, variations of these statistics, d(2), d(*)(2) and d(s)(2), respectively, are used to first cluster five mammalian species with known phylogenetic relationships, and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d(s)(2) are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d(s)(2) provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly.
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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.