Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 1109303

Benchmarking metagenomic classification tools for long read sequencing data


(A*STAR Genome Institute of Singapore, Singapore) Josip Marić; Sylvain Riondet; Krešimir Križanović; Niranjan Nagarajan; Mile Šikić
Benchmarking metagenomic classification tools for long read sequencing data // 28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020
Virtual, 2020. doi:10.7490/f1000research.1118120.1 (poster, međunarodna recenzija, pp prezentacija, znanstveni)


CROSBI ID: 1109303 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Benchmarking metagenomic classification tools for long read sequencing data

Autori
Josip Marić ; Sylvain Riondet ; Krešimir Križanović ; Niranjan Nagarajan ; Mile Šikić

Kolaboracija
A*STAR Genome Institute of Singapore, Singapore

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, pp prezentacija, znanstveni

Skup
28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020

Mjesto i datum
Virtual, 13-16.07.2020

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Metagenomics ; long read sequencing ; taxonomic classification

Sažetak
In recent years, both long-read sequencing and metagenomic analysis have been significantly advanced. Although long-read sequencing technologies have been primarily used for de novo genome assembly, they are rapidly maturing for widespread use in other applications. In particular, long reads could potentially lead to more precise taxonomic identification which has sparked an interest in using them for metagenomic analysis. Here we present a benchmark of several tools for metagenomic taxonomic classification, tested on in- silico datasets that were constructed using real long reads from isolate sequencing. We compared tools that were either newly developed for or modified to work with long reads, including Kraken, Centrifuge, CLARK, MetaMaps and MEGAN-LR. The test datasets were constructed with varying numbers of bacterial and eukaryotic genomes, to simulate different metagenomic applications. The tools were tested on their ability to accurately detect species and precisely estimate species abundances in the samples. Our analysis showed that all tested classifiers provide useful results, and that accuracy was strongly influenced by the comprehensiveness of the default database used. Using the same database for all tools provided comparable results across methods except for MetaMaps which had slightly better performance, but was slower than k-mer based tools.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Krešimir Križanović (autor)

Avatar Url Mile Šikić (autor)

Avatar Url Josip Marić (autor)

doi

Citiraj ovu publikaciju

(A*STAR Genome Institute of Singapore, Singapore) Josip Marić; Sylvain Riondet; Krešimir Križanović; Niranjan Nagarajan; Mile Šikić
Benchmarking metagenomic classification tools for long read sequencing data // 28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020
Virtual, 2020. doi:10.7490/f1000research.1118120.1 (poster, međunarodna recenzija, pp prezentacija, znanstveni)
(A*STAR Genome Institute of Singapore, Singapore) (A*STAR Genome Institute of Singapore, S., Sylvain Riondet, Krešimir Križanović, Niranjan Nagarajan & Mile Šikić (2020) Benchmarking metagenomic classification tools for long read sequencing data. U: 28th International Conference on Intelligent Systems for Molecular Biology (ISMB) 2020 doi:10.7490/f1000research.1118120.1.
@article{article, year = {2020}, DOI = {10.7490/f1000research.1118120.1}, keywords = {Metagenomics, long read sequencing, taxonomic classification}, doi = {10.7490/f1000research.1118120.1}, title = {Benchmarking metagenomic classification tools for long read sequencing data}, keyword = {Metagenomics, long read sequencing, taxonomic classification}, publisherplace = {Virtual} }
@article{article, year = {2020}, DOI = {10.7490/f1000research.1118120.1}, keywords = {Metagenomics, long read sequencing, taxonomic classification}, doi = {10.7490/f1000research.1118120.1}, title = {Benchmarking metagenomic classification tools for long read sequencing data}, keyword = {Metagenomics, long read sequencing, taxonomic classification}, publisherplace = {Virtual} }

Citati





    Contrast
    Increase Font
    Decrease Font
    Dyslexic Font