[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121508-en":3,"doc-seo-121508-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121508,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics","Genome assembly tools reconstruct genomic sequences from raw sequencing reads, enabling identification of organisms present in metagenomic samples. Recent machine learning methods have expanded bioinformatics workflows, and this study evaluates their use for organism identification. Several metagenomic assembly tools (PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, PathRacer) are compared with deep learning classifiers (DNABERT, DeLUCS) using two synthetic mock community datasets. Results test whether ensembling assembly and machine learning improves performance versus using individual tools, and assess effectiveness by organism repetitiveness, genome size, and GC content.","San Jose State University  \nSJSU ScholarWorks  \n\n| Faculty Research, Scholarly, and Creative Activity |\n| --- |\n| 7-25-2024\u003Cbr>Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics\u003Cbr>Stephanie Chau\u003Cbr>San Jose State University\u003Cbr>Carlos Rojas\u003Cbr>San Jose State University, [carlos.rojas@sjsu.edu](carlos.rojas@sjsu.edu)\u003Cbr>Jorjeta G. Jetcheva\u003Cbr>San Jose State University, [jorjeta.jetcheva@sjsu.edu](jorjeta.jetcheva@sjsu.edu)\u003Cbr>Mary Markart\u003Cbr>San Jose State University\u003Cbr>Sudha Vijayakumar\u003Cbr>San Jose State University\u003Cbr>See next page for additional authors\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/faculty_rsca](https://scholarworks.sjsu.edu/faculty_rsca) |\n\nRecommended Citation  \nStephanie Chau, Carlos Rojas, Jorjeta G. Jetcheva, Mary Markart, Sudha Vijayakumar, Sophia Yuan, Vincent Stowbunenko, Amanda N. Shelton, and William B. Andreopoulos. \"Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics\" Current Bioinformatics (2024): 595-619 . [https://doi.org/10.2174/](https://doi.org/10.2174/)  \n0115748936299440240709070105  \nThis Article is brought to you for free and open access by SJSU ScholarWorks. It has been accepted for inclusion in Faculty Research, Scholarly, and Creative Activity by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nAuthors  \nStephanie Chau, Carlos Rojas, Jorjeta G. Jetcheva, Mary Markart, Sudha Vijayakumar, Sophia Yuan, Vincent Stowbunenko, Amanda N. Shelton, and William B. Andreopoulos  \nThis article is available at SJSU ScholarWorks: [https://scholarworks.sjsu.edu/faculty_rsca/6233](https://scholarworks.sjsu.edu/faculty_rsca/6233)  \nSend Orders for Reprints [to reprints@benthamscience.net](to reprints@benthamscience.net)  \nCurrent Bioinformatics, xxxx, xx, x-x 1  \nRESEARCH ARTICLE  \nComparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics  \nStephanie Chau 1,\\#, Carlos Rojas 1,\\#, Jorjeta G. Jetcheva 1,\\#, Mary Markart 1, Sudha Vijayakumar 1, Sophia Yuan 1, Vincent Stowbunenko2, Amanda N. Shelton3 and William B. Andreopoulos2,\\#,*  \n1Department of Computer Engineering, San José State University, San José, CA, USA; 2Department of Computer Science, San José State University, San José, CA, USA; 3Department of Plant Biology, Carnegie Institution for Science,  \nStanford, CA, USA  \nA R T I C L E H I S T O R Y  \nReceived: February 12, 2024  \nRevised: April 25, 2024  \nAccepted: May 03, 2024  \nDOI:  \n10. 2174/0115748936299440240709070105  \nAbstract: Background: Genome assembly tools are used to reconstruct genomic sequences from raw sequencing data, which are then used for identifying the organisms present in a metagenomic sample.  \nMethodology: More recently, machine learning approaches have been applied to a variety of bioinformatics problems, and in this paper, we explore their use for organism identification. We start by evaluating several commonly used metagenomic assembly tools, including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, and PathRacer, and compare them against state-of-theart deep learning-based machine learning classification approaches represented by DNABERT and DeLUCS, in the context of two synthetic mock community datasets.  \nResult: Our analysis focuses on determining whether ensembling metagenome assembly tools with machine learning tools have the potential to improve identification performance relative to using the tools individually.  \nConclusion: We find that this is indeed the case, and analyze the level of effectiveness of potential tool ensembling for organisms with different characteristics (based on factors such as repetitiveness, genome size, and GC content) .  \nKeywords: PhyloFlash, MEGAHIT, metaSPAdes, ","cbCaimDYlOpJlU4X","https://ap.wps.com/l/cbCaimDYlOpJlU4X","pdf",9642789,1,27,"English","en",105,"# Abstract\n## Background and problem scope\n## Methodology and tool comparisons\n## Ensembling evaluation\n## Key findings by organism characteristics","[{\"question\":\"What is the main goal of this study on metagenomic analysis?\",\"answer\":\"The study aims to compare ribosomal/metagenomic assembly tools with machine learning tools for identifying organisms in metagenomic samples, and to test whether combining them improves identification accuracy.\"},{\"question\":\"Which tools and approaches are evaluated in the comparison?\",\"answer\":\"The work evaluates multiple metagenomic assembly tools (including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, PathRacer) and compares them with deep learning classification methods represented by DNABERT and DeLUCS.\"},{\"question\":\"How does the study assess performance improvements across organism types?\",\"answer\":\"It analyzes whether ensembling metagenome assembly with machine learning improves identification relative to using tools individually, and evaluates effectiveness based on organism characteristics such as repetitiveness, genome size, and GC content.\"}]","Comparison between Ribosomal Assembly and Machine Learning Tools for Microbial Identification of Organisms with Different Characteristics | 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is the main goal of this study on metagenomic analysis?","Question",{"text":75,"@type":76},"The study aims to compare ribosomal/metagenomic assembly tools with machine learning tools for identifying organisms in metagenomic samples, and to test whether combining them improves identification accuracy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which tools and approaches are evaluated in the comparison?",{"text":80,"@type":76},"The work evaluates multiple metagenomic assembly tools (including PhyloFlash, MEGAHIT, MetaSPAdes, Kraken2, Mothur, UniCycler, PathRacer) and compares them with deep learning classification methods represented by DNABERT and DeLUCS.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study assess performance improvements across organism types?",{"text":84,"@type":76},"It analyzes whether ensembling metagenome assembly with machine learning improves identification relative to using tools individually, and evaluates effectiveness based on organism 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