ArticleBMC bioinformatics2015
Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Genome Survey Analysis of Female and MaleGenes · 2025Article
- K-mer-based Approaches to Bridging Pangenomics and Population Genetics.Molecular biology and evolution · 2025Review
- RNA-clique: a method for computing genetic distances from RNA-seq data.BMC bioinformatics · 2024Article
- FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis.BMC bioinformatics · 2022Article
- The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances.PloS one · 2020Article
- IDP-denovo: de novo transcriptome assembly and isoform annotation by hybrid sequencing.Bioinformatics (Oxford, England) · 2018Article
- GePMI: A statistical model for personal intestinal microbiome identification.NPJ biofilms and microbiomes · 2018Article
- kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity.PLoS computational biology · 2017Article
- Analysis of Plant Pan-Genomes and Transcriptomes with GET_HOMOLOGUES-EST, a Clustering Solution for Sequences of the Same Species.Frontiers in plant science · 2017Article
- Moderate Genetic Diversity and Genetic Differentiation in the Relict TreeFrontiers in plant science · 2016Article
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6 authors.
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Abstract
backgroundIdentification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.
resultsWe present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100% identification accuracy at supra-species level and 78% accuracy at the species level.
conclusionCNIDARIA allows for fast, resource-efficient comparison and identification of both raw and assembled genome and transcriptome data. This can help answer both fundamental (e.g. in phylogeny, ecological diversity analysis) and practical questions (e.g. sequencing quality control, primer design).
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