Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDeep learning models for bacteria taxonomic classification of metagenomic data
Anno di pubblicazione2018
FormatoElettronico
Autore/iA. Fiannaca; L. La Paglia; M. La Rosa; G. Lo Bosco; G. Renda, R. Rizzo; S. Gaglio; A. Urso
Affiliazioni autoriCNR-ICAR; CNR-ICAR; CNR-ICAR; University of Palermo; CNR-ICAR; CNR-ICAR; CNR-ICAR and University of Palermo; CNR-ICAR
Autori CNR e affiliazioni
  • SALVATORE GAGLIO
  • LAURA LA PAGLIA
  • GIOVANNI RENDA
  • RICCARDO RIZZO
  • ALFONSO URSO
  • ANTONINO FIANNACA
  • MASSIMO LA ROSA
Lingua/e
  • inglese
AbstractBackground: An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them. Results: To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data. Conclusions: In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da61
Pagine a76
Pagine totali16
RivistaBMC bioinformatics
Attiva dal 2000
Editore: BioMed Central, - [London]
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1471-2105
Titolo chiave: BMC bioinformatics
Titolo proprio: BMC bioinformatics
Titolo abbreviato: BMC bioinformatics
Titoli alternativi:
  • BioMed Central bioinformatics
  • Bioinformatics
Numero volume della rivista19
Fascicolo della rivistaS7
DOI10.1186/s12859-018-2182-6
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveMetagenomic, Classification, CNN, DBN, k-mer representation, Amplicon, Shotgun
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
Moduli/Attività/Sottoprogetti CNR
  • INT.P02.007.001 : Analisi intelligente dei dati per la bioinformatica
  • DIT.AD010.010.001 : BIOINFORMATICA TRASLAZIONALE (TBLAB)
Progetti Europei-
Allegati
Deep learning models for bacteria taxonomic classification of metagenomic data (documento privato )
Descrizione: papaer in formato pdf
Tipo documento: application/pdf