MetaGenomic analysis of short and long reads

Abstract

Identifying single organisms in environmental samples is one of the key tasks of metagenomics.During the last few years, third generation sequencing technologies have enabled researchers to sequence much longer molecules, but at the expense of sequencing accuracy. Thus, new algorithms needed to be developed to cope with this new type of data. With this in mind, we developed a tool called MetaG. An intuitive web interface makes the software accessible to a vast range of users, including those without extensive bioinformatic expertise. Evaluation of MetaG's performance showed that it makes nearly perfect classifications of viral isolates using simulated short and long reads.MetaG also outperformed current state-of-the-art algorithms on data from targeted sequencing of the 16S and 28S rRNA genes. Since MetaG's output is also supplemented with information about hosts and antibiotic resistances of pathogens, we expect it to be especially useful to the healthcare sector.Moreover, the outstanding accuracy of the taxonomic assignments will make MetaG a serious alternative for anyone working with metagenomic sequences. MetaG can be accessed at

CuReSim-LoRM: A Tool to Simulate Metabarcoding Long Reads

“…Simulated reads were obtained for the newLot dataset with NanoSim-H, DeepSimulator, and CuReSim-LoRM (see Section 4 for more details). Three datasets were simulated with NanoSim-H: dataset1 corresponds to the use of option as in [ 10 ], dataset2 was obtained without the option, and dataset3 was generated by training the error model with the newLot data and the option . DeepSimulator was used with default parameters as done in [ 8 ].…”

“…For NanoSim-H (version 1.1.0.4), three datasets were generated. Dataset1 was obtained using the option, as in [ 10 ], dataset2 was generated without the option, and dataset3 was obtained by training the error model with the real data of the newLot dataset and using the option . We cannot test without this option because of an execution error.…”

“…The authors used simulated data obtained with DeepSimulator [ 9 ] and mock communities to show that Emu is capable of accurate microbial community profiling. In Manske et al [ 10 ], the authors introduced a tool called MetaG with an intuitive web interface that makes the software accessible to a vast range of users. Evaluation of MetaG’s performances was performed on real and simulated data produced with NanoSim [ 11 ].…”

Application of a new data management system to the study of the gut microbiome of children who are small for their gestational age

“…This enhances use for collaborative projects with medical practitioners, lab scientists, and bioinformaticians, who typically prefer different input formats. Sequencing results in the FASTQ format and taxonomic classifications from MetaG (Manske, Grundmann, and Makałowski 2020) software are retrieved from a common directory and automatically connected to the respective samples in the database using unique identifiers from the spreadsheet. With the graphical interface, investigators can export data of interest from the relational database to MicrobiomeAnalyst (Lu et al 2023) for statistical analyses.…”

Global research alliance in infectious disease: a collaborative effort to combat infectious diseases through dissemination of portable sequencing

“…So far, we have published research papers within the GRAID framework: a genetic polymorphisms analysis of malaria parasite’s chloroquine resistance genes [ 8 ], a comprehensive transcriptome analysis of malaria–host interaction [ 9 ], serotyping of dengue virus using MinION [ 10 ], and a comprehensive survey of drug resistance in malaria parasites [ 11 ]. Additionally, specialized software for easy analysis and interpretation of MinION-generated data has been developed within the consortium [ 12 , 13 ].…”