ntEdit+Sealer: Efficient Targeted Error Resolution and Automated Finishing of Long‐Read Genome Assemblies

Abstract

High-quality genome assemblies are crucial to many biological studies, and utilizing long sequencing reads can help achieve higher assembly contiguity. While long reads can resolve complex and repetitive regions of a genome, their relatively high associated error rates are still a major limitation. Long reads generally produce draft genome assemblies with lower base quality, which must be corrected with a genome polishing step. Hybrid genome polishing solutions can greatly improve the quality of long-read genome assemblies by utilizing more accurate short reads to validate bases and correct errors. Currently available hybrid polishing methods rely on read alignments, and are therefore memory-intensive and do not scale well to large genomes. Here we describe ntEdit+Sealer, an alignment-free, k-mer-based genome finishing protocol that employs memory-efficient Bloom filters. The protocol includes ntEdit for correcting base errors and small indels, and for marking potentially problematic regions, then Sealer for filling both assembly gaps and problematic regions flagged by ntEdit. ntEdit+Sealer produces highly accurate, error-corrected genome assemblies, and is available as a Makefile pipeline from https:// github.com/ bcgsc/ ntedit_sealer_protocol.

GoldRush: Ade novolong read genome assembler with linear time complexity

“…After the golden path is built, the goldtigs are polished to correct mismatches and indels. The polishing protocol, GR-Edit, closely follows the ntEdit+Sealer protocol (J. X. Li et al, 2022), which has been shown to perform well using short sequencing reads.…”

“…The polishing protocol, GR-Edit, closely follows the ntEdit+Sealer protocol (J. X. Li et al, 2022; Paulino et al, 2015; Warren et al, 2019), which has been shown to perform well using short sequencing reads. Unlike the currently published paradigm, which stores all the short read k-mers in a single Bloom filter used for correction, GR-Edit uses a targeted approach where each goldtig has a dedicated Bloom filter, each containing a hash representation of k-mers derived from long read subsets.…”

ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads

“…Following this gap-filling step, the scaffolds are output in FASTA format. Because the gaps are filled with raw long-read sequence, we recommend polishing the output assembly using long-read polishing tools such as ntEdit+Sealer (Li et al, 2022;Wong et al, 2022), Racon (Vaser et al, 2017) For more information about installing these dependencies, see Support Protocol and Basic Protocol 1, steps 1-2. Instructions for creating a conda environment that can be used for installing protocol-specific dependencies, as described below, are available in Support Protocol.…”

“…If soft_mask=True is specified in the ntLink command when gap‐filling is enabled, the gaps will be filled with lowercase bases instead of uppercase bases. This soft masking could be useful for downstream analyses such as targeted polishing, for example (Li et al, 2022).…”

“…Although ntLink was developed as a scaffolding tool, the initial minimizer‐guided step of mapping long reads to the draft assembly can also benefit other bioinformatics utilities that require approximate mapping information, including the ntEdit+Sealer polishing component of the GoldRush assembler (Current Protocols article: Li et al., 2022; Paulino et al., 2015; Warren et al., 2019; Wong et al., 2022) and Tigmint‐long (Coombe et al., 2021; Jackman et al., 2018), an assembly correction tool also integrated in the LongStitch pipeline. In Alternate Protocols 1 and 2, we highlight the usage of ntLink as a mapping tool, and, as an example of using ntLink mappings in a different downstream application, showcase how these mappings can be utilized in Tigmint‐long.…”