• Title/Summary/Keyword: reads-based and assembly-based method

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An Optimized Strategy for Genome Assembly of Sanger/pyrosequencing Hybrid Data using Available Software

  • Jeong, Hae-Young;Kim, Ji-Hyun F.
    • Genomics & Informatics
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    • v.6 no.2
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    • pp.87-90
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    • 2008
  • During the last four years, the pyrosequencing-based 454 platform has rapidly displaced the traditional Sanger sequencing method due to its high throughput and cost effectiveness. Meanwhile, the Sanger sequencing methodology still provides the longest reads, and paired-end sequencing that is based on that chemistry offers an opportunity to ensure accurate assembly results. In this report, we describe an optimized approach for hybrid de novo genome assembly using pyrosequencing data and varying amounts of Sanger-type reads. 454 platform-derived contigs can be used as single non-breakable virtual reads or converted to simpler contigs that consist of editable, overlapping pseudoreads. These modified contigs maintain their integrity at the first jumpstarting assembly stage and are edited by fragmenting and rejoining. Pre-existing assembly software then can be applied for mixed assembly with 454-derived data and Sanger reads. An effective method for identifying genomic differences between reference and sample sequences in whole-genome resequencing procedures also is suggested.

Sequencing Methods to Study the Microbiome with Antibiotic Resistance Genes in Patients with Pulmonary Infections

  • Tingyan Dong;Yongsi Wang;Chunxia Qi;Wentao Fan;Junting Xie;Haitao Chen;Hao Zhou;Xiaodong Han
    • Journal of Microbiology and Biotechnology
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    • v.34 no.8
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    • pp.1617-1626
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    • 2024
  • Various antibiotic-resistant bacteria (ARB) are known to induce repeated pulmonary infections and increase morbidity and mortality. A thorough knowledge of antibiotic resistance is imperative for clinical practice to treat resistant pulmonary infections. In this study, we used a reads-based method and an assembly-based method according to the metagenomic next-generation sequencing (mNGS) data to reveal the spectra of ARB and corresponding antibiotic resistance genes (ARGs) in samples from patients with pulmonary infections. A total of 151 clinical samples from 144 patients with pulmonary infections were collected for retrospective analysis. The ARB and ARGs detection performance was compared by the reads-based method and assembly-based method with the culture method and antibiotic susceptibility testing (AST), respectively. In addition, ARGs and the attribution relationship of common ARB were analyzed by the two methods. The comparison results showed that the assembly-based method could assist in determining pathogens detected by the reads-based method as true ARB and improve the predictive capabilities (46% > 13%). ARG-ARB network analysis revealed that assembly-based method could promote determining clear ARG-bacteria attribution and 101 ARGs were detected both in two methods. 25 ARB were obtained by both methods, of which the most predominant ARB and its ARGs in the samples of pulmonary infections were Acinetobacter baumannii (ade), Pseudomonas aeruginosa (mex), Klebsiella pneumoniae (emr), and Stenotrophomonas maltophilia (sme). Collectively, our findings demonstrated that the assembly-based method could be a supplement to the reads-based method and uncovered pulmonary infection-associated ARB and ARGs as potential antibiotic treatment targets.

K-mer Based RNA-seq Read Distribution Method For Accelerating De Novo Transcriptome Assembly

  • Kwon, Hwijun;Jung, Inuk
    • Journal of the Korea Society of Computer and Information
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    • v.25 no.8
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    • pp.1-8
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    • 2020
  • In this paper, we propose a gene family based RNA-seq read distribution method in means to accelerate the overal transcriptome assembly computation time. To measure the performance of our transcriptome sequence data distribution method, we evaluated the performance by testing four types of data sets of the Arabidopsis thaliana genome (Whole Unclassified Reads, Family-Classified Reads, Model-Classified Reads, and Randomly Classified Reads). As a result of de novo transcript assembly in distributed nodes using model classification data, the generated gene contigs matched 95% compared to the contig generated by WUR, and the execution time was reduced by 4.2 times compared to a single node environment using the same resources.