Learning based Methods for Code Runtime Complexity Prediction

Sikka, Jagriti; Satya, Kushal; Kumar, Yaman; Uppal, Shagun; Shah, Rajiv Ratn; Zimmermann, Roger

doi:10.48550/arxiv.1911.01155

Cited by 1 publication

(1 citation statement)

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“…predicting computing time of dataset with different depth of coverage, read length and assembly complexity. 79 , 86 , 87 While acknowledging this consideration, it is important to note that conducting a benchmark on real machines provides practical insights and actionable data on a specific use case, despite potential confounders introduced by the machine itself or from software virtualization.…”

Section: Discussionmentioning

confidence: 99%

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline

Wattanasombat,

Tongjai

2024

F1000Res

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Background Determining the appropriate computational requirements and software performance is essential for efficient genomic surveillance. The lack of standardized benchmarking complicates software selection, especially with limited resources. Methods We developed a containerized benchmarking pipeline to evaluate seven long-read assemblers—Canu, GoldRush, MetaFlye, Strainline, HaploDMF, iGDA, and RVHaplo—for viral haplotype reconstruction, using both simulated and experimental Oxford Nanopore sequencing data of HIV-1 and other viruses. Benchmarking was conducted on three computational systems to assess each assembler’s performance, utilizing QUAST and BLASTN for quality assessment. Results Our findings show that assembler choice significantly impacts assembly time, with CPU and memory usage having minimal effect. Assembler selection also influences the size of the contigs, with a minimum read length of 2,000 nucleotides required for quality assembly. A 4,000-nucleotide read length improves quality further. Canu was efficient among de novo assemblers but not suitable for multi-strain mixtures, while GoldRush produced only consensus assemblies. Strainline and MetaFlye were suitable for metagenomic sequencing data, with Strainline requiring high memory and MetaFlye operable on low-specification machines. Among reference-based assemblers, iGDA had high error rates, RVHaplo showed the best runtime and accuracy but became ineffective with similar sequences, and HaploDMF, utilizing machine learning, had fewer errors with a slightly longer runtime. Conclusions The HIV-64148 pipeline, containerized using Docker, facilitates easy deployment and offers flexibility to select from a range of assemblers to match computational systems or study requirements. This tool aids in genome assembly and provides valuable information on HIV-1 sequences, enhancing viral evolution monitoring and understanding.

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Section: Discussionmentioning

confidence: 99%