Evaluating Nanopore read trimming and quality filtering in bacterial genomics
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Oxford Nanopore Technologies (ONT) sequencing is widely used in bacterial genomics, particularly for reference-based variant calling and de novo assembly. Both analyses routinely start with quality control (QC) preprocessing, but as ONT basecalling accuracy has improved, its necessity and downstream trade-offs remain poorly defined. We benchmarked 17 preprocessing combinations, built from three adapter trimmers (Dorado, Porechop ABI, Barbell) and three quality-based processing tools (Filtlong, Chopper, SeqKit) with untrimmed and unprocessed controls, plus Fastplong, which performs both. Each combination was evaluated across 13 bacterial isolates at 100× and 20× depth under the HAC and SUP basecalling models. Variant calling was assessed by F1 score. Assemblies were scored on sequence accuracy, contiguity, replicon recovery and residual adapter contamination, then aggregated into a weighted arithmetic mean using weights from a survey of the microbial genomics community. With SUP basecalling, raw reads with no trimming or quality-based processing produced accurate SNP calls and outperformed several pipelines that applied both operations; adapter trimming alone left SNP accuracy unchanged. Subsetting reads by combined length and quality with Filtlong gave the highest SNP F1 scores at both depths, except with Barbell at 20×, where it ranked second lowest. As is often the case in benchmarking studies, assembly results involved trade-offs, and the best tool depends on which aspects of performance matter most for a given use. Filtlong produced the lowest error rates, but every Filtlong pipeline lost three small plasmids at both depths. Barbell trimming raised error rates and reduced contiguity at 20×, while skipping adapter trimming left residual adapter and barcode sequences in assemblies. Under the community-derived weights, Porechop ABI followed by Chopper had the highest overall score at both depths, without winning any single metric. QC tool choice should therefore be guided by the downstream analysis goal rather than applied indiscriminately across all workflows.
Author summary
Oxford Nanopore reads are routinely processed to remove adapter and barcode sequences introduced during library preparation and discard low-quality reads or read segments. As sequencing accuracy improves, the benefits and costs of these steps need reassessment. We compared 17 preprocessing combinations across 13 bacterial isolates for detecting genetic differences (variant calling) and reconstructing genomes (genome assembly). Most combinations produced similar results to unprocessed reads, but some affected the genetic information recovered. Filtlong, which selects reads by length and quality, improved detection of single-nucleotide variants in several combinations and reduced assembly errors. However, it removed reads supporting three small plasmids, preventing their assembly. Missing small plasmids in bacterial assemblies can have a large impact, as they are known to carry important genes (e.g., antibiotic resistance). Adapter trimming offered little benefit for single-nucleotide variant detection, but omitting it left adapter and barcode sequences in some assemblies. These findings support choosing preprocessing tools according to the intended analysis. We surveyed researchers to weight four aspects of assembly quality: sequence accuracy, contiguity, chromosome and plasmid recovery, and residual adapter contamination. Our interactive dashboard lets users adjust these weights to compare pipelines according to their own priorities.
