%0 Journal Article
%@ 2563-3570
%I JMIR Publications
%V 3
%N 1
%P e31536
%T The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study
%A Rueca,Martina
%A Giombini,Emanuela
%A Messina,Francesco
%A Bartolini,Barbara
%A Di Caro,Antonino
%A Capobianchi,Maria Rosaria
%A Gruber,Cesare EM
%+ Laboratory of Microbiology and Biological Bank, National Institute for Infectious Diseases “Lazzaro Spallanzani”, Istituto di Ricovero e Cura a Carattere Scientifico, Via Portuense 292, Rome, 00149, Italy, 39 0655170668, francesco.messina@inmi.it
%K SARS-CoV-2 genome
%K bioinformatics tool
%K NGS data analysis
%K COVID-19
%K genome
%K health informatics
%K bioinformatic
%K digital tools
%K algorithms
%D 2022
%7 14.3.2022
%9 Original Paper
%J JMIR Bioinform Biotech
%G English
%X Background: Early sequencing and quick analysis of the SARS-CoV-2 genome have contributed to the understanding of the dynamics of COVID-19 epidemics and in designing countermeasures at a global level. Objective: Amplicon-based next-generation sequencing (NGS) methods are widely used to sequence the SARS-CoV-2 genome and to identify novel variants that are emerging in rapid succession as well as harboring multiple deletions and amino acid–changing mutations. Methods: To facilitate the analysis of NGS sequencing data obtained from amplicon-based sequencing methods, here, we propose an easy-to-use SARS-CoV-2 genome assembler: the Easy-to-use SARS-CoV-2 Assembler (ESCA) pipeline. Results: Our results have shown that ESCA could perform high-quality genome assembly from Ion Torrent and Illumina raw data and help the user in easily correct low-coverage regions. Moreover, ESCA includes the possibility of comparing assembled genomes of multisample runs through an easy table format. Conclusions: In conclusion, ESCA automatically furnished a variant table output file, fundamental to rapidly recognizing variants of interest. Our pipeline could be a useful method for obtaining a complete, rapid, and accurate analysis even with minimal knowledge in bioinformatics. 
%M 35309411
%R 10.2196/31536
%U https://bioinform.jmir.org/2022/1/e31536
%U https://doi.org/10.2196/31536
%U http://www.ncbi.nlm.nih.gov/pubmed/35309411