Dataset Information

Optimizing the Illumina COVIDSeq laboratorial and bioinformatics pipeline on thousands of samples for SARS-CoV-2 Variants of Concern tracking.

ABSTRACT: The SARS-CoV-2 Variants of Concern tracking via Whole Genome Sequencing represents a pillar of public health measures for the containment of the pandemic. The ability to track down the lineage distribution on a local and global scale leads to a better understanding of immune escape and to adopting interventions to contain novel outbreaks. This scenario poses a challenge for NGS laboratories worldwide that are pressed to have both a faster turnaround time and a high-throughput processing of swabs for sequencing and analysis. In this study, we present an optimization of the Illumina COVID-seq protocol carried out on thousands of SARS-CoV-2 samples at the wet and dry level. We discuss the unique challenges related to processing hundreds of swabs per week such as the tradeoff between ultra-high sensitivity and negative contamination levels, cost efficiency and bioinformatics quality metrics.

SUBMITTER: Donzelli S

PROVIDER: S-EPMC9119164 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Optimizing the Illumina COVIDSeq laboratorial and bioinformatics pipeline on thousands of samples for SARS-CoV-2 Variants of Concern tracking.

Donzelli Sara S Ciuffreda Ludovica L Pontone Martina M Betti Martina M Massacci Alice A Mottini Carla C De Nicola Francesca F Orlandi Giulia G Goeman Frauke F Giuliani Eugenia E Sperandio Eleonora E Piaggio Giulia G Morrone Aldo A Ciliberto Gennaro G Fanciulli Maurizio M Blandino Giovanni G Pimpinelli Fulvia F Pallocca Matteo M

Computational and structural biotechnology journal 20220519

The SARS-CoV-2 Variants of Concern tracking via Whole Genome Sequencing represents a pillar of public health measures for the containment of the pandemic. The ability to track down the lineage distribution on a local and global scale leads to a better understanding of immune escape and to adopting interventions to contain novel outbreaks. This scenario poses a challenge for NGS laboratories worldwide that are pressed to have both a faster turnaround time and a high-throughput processing of swabs ...[more]

PMID: 35611117

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Project description:Next-Generation Sequencing (NGS) is widely used to investigate genomic variation. In several studies, the genetic variation of Mycobacterium tuberculosis has been analyzed in sputum samples without previous culture, using target enrichment methodologies for NGS. Alignments obtained by different programs generally map the sequences under default parameters, and from these results, it is assumed that only Mycobacterium reads will be obtained. However, variants of interest microorganism in clinical samples can be confused with a vast collection of reads from other bacteria, viruses, and human DNA. Currently, there are no standardized pipelines, and the cleaning success is never verified since there is a lack of rigorous controls to identify and remove reads from other sputum-microorganisms genetically similar to M. tuberculosis. Therefore, we designed a bioinformatic pipeline to process NGS data from sputum samples, including several filters and quality control points to identify and eliminate non-M. tuberculosis reads to obtain a reliable genetic variant report. Our proposal uses the SURPI software as a taxonomic classifier to filter input sequences and perform a mapping that provides the highest percentage of Mycobacterium reads, minimizing the reads from other microorganisms. We then use the filtered sequences to perform variant calling with the GATK software, ensuring the mapping quality, realignment, recalibration, hard-filtering, and post-filter to increase the reliability of the reported variants. Using default mapping parameters, we identified reads of contaminant bacteria, such as Streptococcus, Rhotia, Actinomyces, and Veillonella. Our final mapping strategy allowed a sequence identity of 97.8% between the input reads and the whole M. tuberculosis reference genome H37Rv using a genomic edit distance of three, thus removing 98.8% of the off-target sequences with a Mycobacterium reads loss of 1.7%. Finally, more than 200 unreliable genetic variants were removed during the variant calling, increasing the report's reliability.

Dataset Information

Optimizing the Illumina COVIDSeq laboratorial and bioinformatics pipeline on thousands of samples for SARS-CoV-2 Variants of Concern tracking.

Publications

Optimizing the Illumina COVIDSeq laboratorial and bioinformatics pipeline on thousands of samples for SARS-CoV-2 Variants of Concern tracking.

Similar Datasets

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