Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data

S.A. Aflitos*, E.I. Severing, G.F. Sanchez Perez, S.A. Peters, H. de Jong, D. de Ridder

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)


Background Identification 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).
Original languageEnglish
Article number352
Number of pages10
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 2015


  • Clustering
  • K-mer
  • NGS
  • Phylogeny
  • RNA-seq
  • Species identification


Dive into the research topics of 'Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data'. Together they form a unique fingerprint.

Cite this