Analyzing the Differences Between Reads and Contigs When Performing a Taxonomic Assignment Comparison in Metagenomics

Abstract

Abstract. Metagenomics is an inherently complex field in which one of the primary goals is to determine the compositional organisms present in an environmental sample. Thereby, diverse tools have been developed that are based on the similarity search results obtained from comparing a set of sequences against a database. However, to achieve this goal there still are affairs to solve such as dealing with genomic variants and detecting repeated sequences that could belong to different species in a mixture of uneven and unknown representation of organisms in a sample. Hence, the question of whether analyzing a sample with reads provides further understanding of the metagenome than with contigs arises. The assembly yields larger genomic fragments but bears the risk of producing chimeric contigs. On the other hand, reads are shorter and therefore their statistical significance is harder to asses, but there is a larger number of them. Consequently, we have developed a workflow to assess and compare the quality of each of these alternatives. Synthetic read datasets beloging to previously identified organisms are generated in order to validate the results. Afterwards, we assemble these into a set of contigs and perform a taxonomic analysis on both datasets. The tools we have developed demonstrate that analyzing with reads provide a more trustworthy representation of the species in a sample than contigs especially in cases that present a high genomic variability.

Shotgun-metagenomics based prediction of antibiotic resistance and virulence determinants in Staphylococcus aureus from periprosthetic tissue on blood culture bottles

“…Our results suggest that analyzing reads provides a more trustworthy representation of the species in a clinical sample than analyzing contigs. Moreover, it is considered that the reads will describe more accurately the proper distribution of species in the sample and provide a more accurate and specific assignment of taxa than using the contigs 29 . This statement applies especially to quantitative community profiling and identification of organisms with close relatives in the database as in the case of clinical microbiology, where the focus is mainly the presence or absence of infectious pathogens, which are well studied and have many completed genomes in the reference databases 30 .…”

Shotgun-Metagenomics Based Prediction Of Antibiotic Resistance And Virulence Determinants In Staphylococcus Aureus From Prosthetic Joint Tissue On Blood Culture Bottles

“…In addition, it could also be errors in joining contigs from two closely related species. Speci city is lost when working with contigs as the quality of the assembly will depend strongly on the length and quality of the reads, sometimes misrepresenting the original sample 26 .…”

Metagenomic Approaches to Analyze Antimicrobial Resistance: An Overview

“…Taxonomic classification without the assembly of contigs is a faster approach, with a lower computational cost and no assembly problems ( Rodríguez-Brazzarola et al, 2018 ). However, the quality and length of sequences are important during taxonomic assignment analysis, and poor-quality or short sequences, which are common in the non-assembly based approach, tend to generate matches with low statistical significance ( Breitwieser et al, 2019 ; Ye et al, 2019 ).…”

“…Contrarily, the length of contigs is an advantage for taxonomic classification using contig assembly. Thus, this approach predominantly makes use of databases ( Rodríguez-Brazzarola et al, 2018 ). Moreover, in some cases, contig assembly may enable partial genome reconstruction of a previously unknown organism.…”

“…However, it is important to emphasize that researchers must be aware of the type of sample being worked with, if the sample is too heterogeneous and if there is sufficient computational power to analyze the amount of data collected. Even for good-quality, long sequences, taxonomic classification without assembly could be a more appropriate approach from a computational point of view, depending on the dataset and the computational power available ( Rodríguez-Brazzarola et al, 2018 ).…”