Sequence-matching adapter trimmers generate consistent quality and assembly metrics for Illumina sequencing of RNA viruses.

ABSTRACT: Trimming adapters and low-quality bases from next-generation sequencing (NGS) data is crucial for optimal analysis. We evaluated six trimming programs, implementing five different algorithms, for their effectiveness in trimming adapters and improving quality, contig assembly, and single-nucleotide polymorphism (SNP) quality and concordance for poliovirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and norovirus paired data sequenced on Illumina iSeq and MiSeq platforms. Trimmomatic and BBDuk effectively removed adapters from all datasets, unlike FastP, AdapterRemoval, SeqPurge, and Skewer. All trimmers improved read quality (Q ≥ 30, 87.8 - 96.1%) compared to raw reads (83.6 - 93.2%). Trimmers implementing traditional sequence-matching (Trimmomatic and AdapterRemoval) and

SUBMITTER: Nabakooza G 

PROVIDER: S-EPMC11476207 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature