MultiNanopolish: refined grouping method for reducing redundant calculations in Nanopolish

Hu, Kang; Huang, Ning; Zou, You; Liao, Xingyu

doi:10.1093/bioinformatics/btab078

Cited by 10 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We found ST, ARG, and gene annotation data were greatly improved when medaka was applied emphasizing the importance of including multiple polishing rounds. Nanopolish is another option, but as a polisher reliant on raw FAST5 data, this remains extremely computationally bottlenecked ( Hu et al, 2021 ). There are many recent publications promoting alternative LR polishers, and early data from Homopolish (March 2021) indicate that combining it with preliminary correction of random sequencing errors by Racon or Marginpolish yielded results superior to both Nanopolish and medaka ( Huang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Comparing Long-Read Assemblers to Explore the Potential of a Sustainable Low-Cost, Low-Infrastructure Approach to Sequence Antimicrobial Resistant Bacteria With Oxford Nanopore Sequencing

et al. 2022

View full text Add to dashboard Cite

Long-read sequencing (LRS) can resolve repetitive regions, a limitation of short read (SR) data. Reduced cost and instrument size has led to a steady increase in LRS across diagnostics and research. Here, we re-basecalled FAST5 data sequenced between 2018 and 2021 and analyzed the data in relation to gDNA across a large dataset (n = 200) spanning a wide GC content (25–67%). We examined whether re-basecalled data would improve the hybrid assembly, and, for a smaller cohort, compared long read (LR) assemblies in the context of antimicrobial resistance (AMR) genes and mobile genetic elements. We included a cost analysis when comparing SR and LR instruments. We compared the R9 and R10 chemistries and reported not only a larger yield but increased read quality with R9 flow cells. There were often discrepancies with ARG presence/absence and/or variant detection in LR assemblies. Flye-based assemblies were generally efficient at detecting the presence of ARG on both the chromosome and plasmids. Raven performed more quickly but inconsistently recovered small plasmids, notably a ∼15-kb Col-like plasmid harboring blaKPC. Canu assemblies were the most fragmented, with genome sizes larger than expected. LR assemblies failed to consistently determine multiple copies of the same ARG as identified by the Unicycler reference. Even with improvements to ONT chemistry and basecalling, long-read assemblies can lead to misinterpretation of data. If LR data are currently being relied upon, it is necessary to perform multiple assemblies, although this is resource (computing) intensive and not yet readily available/useable.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparing Long-Read Assemblers to Explore the Potential of a Sustainable Low-Cost, Low-Infrastructure Approach to Sequence Antimicrobial Resistant Bacteria With Oxford Nanopore Sequencing

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The highest accuracy ever reported after bioinformatics polishing, which might be computation-intensive and may take weeks, stands at >99% ( Ashikawa et al, 2018 ; Morrison et al, 2020 ). In addition, new bioinformatic tools to improve the accuracy of nanopore reads are now readily available ( Loman et al, 2015 ; Koren et al, 2017 ; Salmela et al, 2017 ; Xiao et al, 2017 ; Hu et al, 2021 ). As sequencing quality and length depends on the quality of the applied DNA/RNA samples a major drawback is that impurities within the loaded library may block pores and render them unable to sequence.…”

Section: Long-read Sequencing Technologiesmentioning

confidence: 99%

Long-Reads-Based Metagenomics in Clinical Diagnosis With a Special Focus on Fungal Infections

Hoang

Irinyi

et al. 2022

Front. Microbiol.

View full text Add to dashboard Cite

Identification of the causative infectious agent is essential in the management of infectious diseases, with the ideal diagnostic method being rapid, accurate, and informative, while remaining cost-effective. Traditional diagnostic techniques rely on culturing and cell propagation to isolate and identify the causative pathogen. These techniques are limited by the ability and the time required to grow or propagate an agent in vitro and the facts that identification based on morphological traits are non-specific, insensitive, and reliant on technical expertise. The evolution of next-generation sequencing has revolutionized genomic studies to generate more data at a cheaper cost. These are divided into short- and long-read sequencing technologies, depending on the length of reads generated during sequencing runs. Long-read sequencing also called third-generation sequencing emerged commercially through the instruments released by Pacific Biosciences and Oxford Nanopore Technologies, although relying on different sequencing chemistries, with the first one being more accurate both platforms can generate ultra-long sequence reads. Long-read sequencing is capable of entirely spanning previously established genomic identification regions or potentially small whole genomes, drastically improving the accuracy of the identification of pathogens directly from clinical samples. Long-read sequencing may also provide additional important clinical information, such as antimicrobial resistance profiles and epidemiological data from a single sequencing run. While initial applications of long-read sequencing in clinical diagnosis showed that it could be a promising diagnostic technique, it also has highlighted the need for further optimization. In this review, we show the potential long-read sequencing has in clinical diagnosis of fungal infections and discuss the pros and cons of its implementation.

show abstract

“…We identify substantial parallelism opportunities in the methylation score calculation step of methylation calling, which was not addressed by prior works ( Gamaarachchi et al , 2020 ; Hu et al , 2021 ; Simpson et al , 2017 ). Specifically, this step employs a Hidden Markov Model (HMM) algorithm to calculate methylation scores for a matrix, thereby presenting significant parallelization opportunities thanks to the lack of data dependencies.…”

Section: Introductionmentioning

confidence: 95%

“…Several prior works such as Nanopolish call-methylation ( Simpson et al , 2017 ) and f5c ( Gamaarachchi et al , 2020 ) have parallelized the different steps of the methylation calling. MultiNanopolish ( Hu et al , 2021 ) uses multi-threading to decompose the iterative calculation in Nanopolish into small independent calculation tasks to run in parallel mode. However, Nanopolish and MultiNanopolish primarily employ CPU-based parallelization and f5c only uses GPU to parallelize a certain task within methylation calling.…”

Section: Introductionmentioning

confidence: 99%

“…However, Nanopolish and MultiNanopolish primarily employ CPU-based parallelization and f5c only uses GPU to parallelize a certain task within methylation calling. Among the different implementations of methylation calling ( Gamaarachchi et al , 2020 ; Hu et al , 2021 ; Simpson et al , 2017 ), Nanopolish is a widely used software tool for genomic sequencing. f5c is the modified version of the popular methylation detection tool Nanopolish .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GPU-accelerated and pipelined methylation calling

Feng

Akbulut

Tang

et al. 2022

Bioinformatics Advances

View full text Add to dashboard Cite

The third-generation DNA sequencing technologies, such as Nanopore Sequencing, can operate at very high speeds and produce longer reads, which in turn results in a challenge for the computational analysis of such massive data. Nanopolish is a software package for signal-level analysis of Oxford Nanopore sequencing data. Call-methylation module of Nanopolish can detect methylation based on Hidden Markov Model (HMM). However, Nanopolish is limited by the long running time of some serial and computationally-expensive processes. Among these, Adaptive Banded Event Alignment (ABEA) is the most time-consuming step, and the prior work, f5c, has already parallelized and optimized ABEA on GPU. As a result, the remaining methylation score calculation part, which uses HMM to identify if a given base is methylated or not, has become the new performance bottleneck. This paper focuses on the call-methylation module that resides in the Nanopolish package. We propose Galaxy-methyl, which parallelizes and optimizes the methylation score calculation step on GPU and then pipelines the four steps of the call-methylation module. Galaxy-methyl increases the execution concurrency across CPUs and GPUs as well as hardware resource utilization for both. The experimental results collected indicate that Galaxy-methyl can achieve 3x-5x speedup compared to Nanopolish, and reduce the total execution time by 35% compared to f5c, on average. The source code of Galaxy-methyl is available at https://github.com/fengyilin118/.

show abstract

MultiNanopolish: refined grouping method for reducing redundant calculations in Nanopolish

Cited by 10 publications

References 7 publications

Comparing Long-Read Assemblers to Explore the Potential of a Sustainable Low-Cost, Low-Infrastructure Approach to Sequence Antimicrobial Resistant Bacteria With Oxford Nanopore Sequencing

Comparing Long-Read Assemblers to Explore the Potential of a Sustainable Low-Cost, Low-Infrastructure Approach to Sequence Antimicrobial Resistant Bacteria With Oxford Nanopore Sequencing

Long-Reads-Based Metagenomics in Clinical Diagnosis With a Special Focus on Fungal Infections

GPU-accelerated and pipelined methylation calling

Contact Info

Product

Resources

About