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(PDF) Development of a cost-effective high-throughput process of microsatellite analysis involving miniaturized multiplexed PCR amplification and automated allele identification
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lineage and clonality, and the measurement of genome instability or the emergence of drug resistance reflective of mismatch repair deficiency. Such analyses may involve the parallel evaluation of many microsatellite loci, which are often limited by sample DNA, are labor intensive, and require large data processing. Results: To overcome these challenges, we developed a cost-effective high-throughput approach of microsatellite analysis, in which the amplifications of microsatellites are performed in miniaturized, multiplexed polymerase chain reaction (PCR) adaptable to 96 or 384 well plates, and accurate automated allele identification has been optimized with a collective reference dataset of 5,508 alleles using the GeneMapper software. Conclusions: In this investigation, we have documented our experience with the optimization of multiplex PCR conditions and automated allele identification, and have generated a unique body of data that provide a starting point for a cost-effective, high-throughput process of microsatellite analysis using the studied markers.","publication_date":"2013,,","grobid_abstract_attachment_id":"113360293"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Development of a cost-effective high-throughput process of microsatellite analysis involving miniaturized multiplexed PCR amplification and automated allele identification","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [114271958]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":113360293,"attachmentType":"pdf"}"><img alt="First page of “Development of a cost-effective high-throughput process of microsatellite analysis involving miniaturized multiplexed PCR amplification and automated allele identification”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/113360293/mini_magick20240802-1-achxgn.png?1722625735" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Development of a cost-effective high-throughput process of microsatellite analysis involving miniaturized multiplexed PCR amplification and automated allele identification</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="114271958" href="https://independent.academia.edu/trungnh%C3%A2nnguy%E1%BB%85n2"><img alt="Profile image of trung nhân nguyễn" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/114271958/44844752/35167132/s65_trung_nh_n.nguy_n.jpg" />trung nhân nguyễn</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2013</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">12 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 117525870; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=117525870"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">Background: Microsatellites are nucleotide sequences of tandem repeats occurring throughout the genome, which have been widely used in genetic linkage analysis, studies of loss of heterozygosity, determination of lineage and clonality, and the measurement of genome instability or the emergence of drug resistance reflective of mismatch repair deficiency. Such analyses may involve the parallel evaluation of many microsatellite loci, which are often limited by sample DNA, are labor intensive, and require large data processing. Results: To overcome these challenges, we developed a cost-effective high-throughput approach of microsatellite analysis, in which the amplifications of microsatellites are performed in miniaturized, multiplexed polymerase chain reaction (PCR) adaptable to 96 or 384 well plates, and accurate automated allele identification has been optimized with a collective reference dataset of 5,508 alleles using the GeneMapper software. Conclusions: In this investigation, we have documented our experience with the optimization of multiplex PCR conditions and automated allele identification, and have generated a unique body of data that provide a starting point for a cost-effective, high-throughput process of microsatellite analysis using the studied markers.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":113360293,"attachmentType":"pdf","workUrl":"https://www.academia.edu/117525870/Development_of_a_cost_effective_high_throughput_process_of_microsatellite_analysis_involving_miniaturized_multiplexed_PCR_amplification_and_automated_allele_identification"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":113360293,"attachmentType":"pdf","workUrl":"https://www.academia.edu/117525870/Development_of_a_cost_effective_high_throughput_process_of_microsatellite_analysis_involving_miniaturized_multiplexed_PCR_amplification_and_automated_allele_identification"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas" data-impression-entity-id="117525870" data-impression-entity-type="2" data-impression-source="signup-banner"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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Léger</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34618703" href="https://independent.academia.edu/ErwanGuichoux">Erwan Guichoux</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="38243521" href="https://independent.academia.edu/FSalin">F. Salin</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Molecular Ecology Resources, 2011</p><p class="ds-related-work--abstract ds2-5-body-sm">Microsatellites have been popular molecular markers ever since their advent in the late eighties. Despite growing competition from new genotyping and sequencing techniques, the use of these versatile and cost-effective markers continues to increase, boosted by successive technical advances. First, methods for multiplexing PCR have considerably improved over the last years, thereby decreasing genotyping costs and increasing throughput. Second, next-generation sequencing technologies allow the identification of large numbers of microsatellite loci at reduced cost in non-model species. As a consequence, more stringent selection of loci is possible, thereby further enhancing multiplex quality and efficiency. However, current practices are lagging behind. By surveying recently published population genetic studies relying on simple sequence repeats, we show that more than half of the studies lack appropriate quality controls and do not make use of multiplex PCR. To make the most of the latest technical developments, we outline the need for a well-established strategy including standardized high-throughput bench protocols and specific bioinformatic tools, from primer design to allele calling.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Current trends in microsatellite genotyping","attachmentId":38693610,"attachmentType":"pdf","work_url":"https://www.academia.edu/13532309/Current_trends_in_microsatellite_genotyping","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13532309/Current_trends_in_microsatellite_genotyping"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="62670408" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/62670408/PolyMorphPredict_A_Universal_Web_Tool_for_Rapid_Polymorphic_Microsatellite_Marker_Discovery_From_Whole_Genome_and_Transcriptome_Data">PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="49342460" href="https://independent.academia.edu/SarikaJaiswal">Sarika Jaiswal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Frontiers in Plant Science</p><p class="ds-related-work--abstract ds2-5-body-sm">Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PolyMorphPredict: A Universal Web-Tool for Rapid Polymorphic Microsatellite Marker Discovery From Whole Genome and Transcriptome Data","attachmentId":75356438,"attachmentType":"pdf","work_url":"https://www.academia.edu/62670408/PolyMorphPredict_A_Universal_Web_Tool_for_Rapid_Polymorphic_Microsatellite_Marker_Discovery_From_Whole_Genome_and_Transcriptome_Data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/62670408/PolyMorphPredict_A_Universal_Web_Tool_for_Rapid_Polymorphic_Microsatellite_Marker_Discovery_From_Whole_Genome_and_Transcriptome_Data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="60364939" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/60364939/Fast_cost_effective_development_of_species_specific_microsatellite_markers_by_genomic_sequencing">Fast, cost-effective development of species-specific microsatellite markers by genomic sequencing</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="52845617" href="https://independent.academia.edu/JoAnnStanton">Jo-Ann Stanton</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BioTechniques, 2009</p><p class="ds-related-work--abstract ds2-5-body-sm">Microsatellites are the genetic markers of choice for many population genetic studies, but must be isolated de novo using recombinant approaches where prior genetic data are lacking. Here we utilized high-throughput genomic sequencing technology to produce millions of base pairs of short fragment reads, which were screened with bioinformatics toolsets to identify primers that amplify polymorphic microsatellite loci. Using this approach we isolated 13 polymorphic microsatellites for the blue duck (Hymenolaimus malacorhynchos), a species for which limited genetic data were available. Our genomic approach eliminates recombinant genetic steps, significantly reducing the time and cost requirements of marker development compared with traditional approaches. While this application of genomic sequencing may seem obvious to many, this study is, to the best of our knowledge, the first attempt to describe the use of genomic sequencing for the development of microsatellite markers in a non-mode...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fast, cost-effective development of species-specific microsatellite markers by genomic sequencing","attachmentId":73843966,"attachmentType":"pdf","work_url":"https://www.academia.edu/60364939/Fast_cost_effective_development_of_species_specific_microsatellite_markers_by_genomic_sequencing","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/60364939/Fast_cost_effective_development_of_species_specific_microsatellite_markers_by_genomic_sequencing"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="14062914" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14062914/Accurate_determination_of_microsatellite_allele_frequencies_in_pooled_DNA_samples">Accurate determination of microsatellite allele frequencies in pooled DNA samples</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33080553" href="https://umcu.academia.edu/HugoSchnack">Hugo Schnack</a></div><p class="ds-related-work--metadata ds2-5-body-xs">European Journal of Human Genetics, 2004</p><p class="ds-related-work--abstract ds2-5-body-sm">Pooling of DNA samples instead of individual genotyping can speed up genetic association studies. However, for microsatellite markers, the electrophoretic pattern of DNA pools can be complex, and procedures for deriving allele frequencies are often confounded by PCR-induced stutter artefacts. We have developed a mathematical procedure to remove stutter noise and accurately determine allele frequencies in pools. A stutter correction model can be reliably derived from one standard 'training set' of the same 10 individual DNA samples for each marker, which can also include heterozygous patterns with partially overlapping peaks. Compared with earlier methods, this reduces the number of genotypes needed in the training set considerably, and allows standardization of analyses for different markers. Moreover, the use of a procedure that fits all data simultaneously makes the method less sensitive to aberrant data. The model was tested with 34 markers, 18 of which were newly defined from human sequence data. Allele frequencies derived from stutter-corrected DNA pool patterns were compared with the summed individual genotyping results of all the individuals in the pools (n ¼ 109 and n ¼ 64). We show that the model is robust and accurately extracts allele frequencies from pooled DNA samples for 32 of the 34 microsatellite markers tested. Finally, we performed a case -control study in celiac disease and found that weakly associated disease alleles, identified by individual genotyping, were only detectable in pools after stutter correction. This efficient method for correcting stutter artefacts in microsatellite markers enables large-scale genetic association studies using DNA pools to be performed.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Accurate determination of microsatellite allele frequencies in pooled DNA samples","attachmentId":44645741,"attachmentType":"pdf","work_url":"https://www.academia.edu/14062914/Accurate_determination_of_microsatellite_allele_frequencies_in_pooled_DNA_samples","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14062914/Accurate_determination_of_microsatellite_allele_frequencies_in_pooled_DNA_samples"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="61115782" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/61115782/MEGASAT_automated_inference_of_microsatellite_genotypes_from_sequence_data">MEGASAT: automated inference of microsatellite genotypes from sequence data</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32805539" href="https://ucriverside.academia.edu/DavidReznick">David Reznick</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Molecular Ecology Resources, 2016</p><p class="ds-related-work--abstract ds2-5-body-sm">MEGASAT is software that enables genotyping of microsatellite loci using next-generation sequencing data. Microsatellites are amplified in large multiplexes, and then sequenced in pooled amplicons. MEGASAT reads sequence files and automatically scores microsatellite genotypes. It uses fuzzy matches to allow for sequencing errors and applies decision rules to account for amplification artefacts, including nontarget amplification products, replication slippage during PCR (amplification stutter) and differential amplification of alleles. An important feature of MEGASAT is the generation of histograms of the length-frequency distributions of amplification products for each locus and each individual. These histograms, analogous to electropherograms traditionally used to score microsatellite genotypes, enable rapid evaluation and editing of automatically scored genotypes. MEGASAT is written in Perl, runs on Windows, Mac OS X and Linux systems, and includes a simple graphical user interface. We demonstrate MEGASAT using data from guppy, Poecilia reticulata. We genotype 1024 guppies at 43 microsatellites per run on an Illumina MiSeq sequencer. We evaluated the accuracy of automatically called genotypes using two methods, based on pedigree and repeat genotyping data, and obtained estimates of mean genotyping error rates of 0.021 and 0.012. In both estimates, three loci accounted for a disproportionate fraction of genotyping errors; conversely, 26 loci were scored with 0-1 detected error (error rate ≤0.007). Our results show that with appropriate selection of loci, automated genotyping of microsatellite loci can be achieved with very high throughput, low genotyping error and very low genotyping costs.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"MEGASAT: automated inference of microsatellite genotypes from sequence data","attachmentId":74269291,"attachmentType":"pdf","work_url":"https://www.academia.edu/61115782/MEGASAT_automated_inference_of_microsatellite_genotypes_from_sequence_data","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/61115782/MEGASAT_automated_inference_of_microsatellite_genotypes_from_sequence_data"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="43704051" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43704051/Fast_sequence_based_microsatellite_genotyping_development_workflow">Fast sequence-based microsatellite genotyping development workflow</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="55869490" href="https://univbatna.academia.edu/AissiAbdeldjalil">Aissi Abdeldjalil</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Application of high-throughput sequencing technologies to microsatellite genotyping (SSRseq) has been shown to remove many of the limitations of electrophoresis-based methods and to refine inference of population genetic diversity and structure. We present here a streamlined SSRseq development workflow that includes microsatellite development, multiplexed marker amplification and sequencing, and automated bioinformatics data analysis. We illustrate its application to five groups of species across phyla (fungi, plant, insect and fish) with different levels of genomic resource availability. We found that relying on previously developed microsatellite assay is not optimal and leads to a resulting low number of reliable locus being genotyped. In contrast, de novo ad hoc primer designs gives highly multiplexed microsatellite assays that can be sequenced to produce high quality genotypes for 20-40 loci. We highlight critical upfront development factors to consider for effective SSRseq setup in a wide range of situations. Sequence analysis accounting for all linked polymorphisms along the sequence quickly generates a powerful multi-allelic haplotype-based genotypic dataset, calling to new theoretical and analytical frameworks to extract more information from multi-nucleotide polymorphism marker systems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Fast sequence-based microsatellite genotyping development workflow","attachmentId":64009343,"attachmentType":"pdf","work_url":"https://www.academia.edu/43704051/Fast_sequence_based_microsatellite_genotyping_development_workflow","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43704051/Fast_sequence_based_microsatellite_genotyping_development_workflow"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="14696788" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/14696788/Efficient_isolation_of_polymorphic_microsatellites_from_high_throughput_sequence_data_based_on_number_of_repeats">Efficient isolation of polymorphic microsatellites from high-throughput sequence data based on number of repeats</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4060826" href="https://independent.academia.edu/DavidGoncalves">David Goncalves</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33647119" href="https://gulbenkian.academia.edu/SaraCardoso">Sara D. Cardoso</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="697906" href="https://ualg.academia.edu/AdelinoCanario">Adelino V M Canario</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Marine Genomics, 2013</p><p class="ds-related-work--abstract ds2-5-body-sm">Transcriptome data are a good resource to develop microsatellites due to their potential in targeting candidate genes. However, developing microsatellites can be a time-consuming enterprise due to the numerous primer pairs to be tested. Therefore, the use of methodologies that make it efficient to identify polymorphic microsatellites is desirable. Here we used a 62,038 contigs transcriptome assembly, obtained from pyrosequencing a peacock blenny (Salaria pavo) multi-tissue cDNA library, to mine for microsatellites and in silico evaluation of their polymorphism. A total of 4190 microsatellites were identified in 3670 unique unigenes, and from these microsatellites, in silico polymorphism was detected in 733. We selected microsatellites based either on their in silico polymorphism and annotation results or based only on their number of repeats. Using these two approaches, 28 microsatellites were successfully amplified in twenty-six individuals, and all but 2 were found to be polymorphic, being the first genetic markers for this species. Our results showed that the strategy of selection based on number of repeats is more efficient in obtaining polymorphic microsatellites than the strategy of in silico polymorphism (allelic richness was 8.2 ± 3.85 and 4.56 ± 2.45 respectively). This study demonstrates that combining the knowledge of number of repeats with other predictors of variability, for example in silico microsatellite polymorphism, improves the rates of polymorphism, yielding microsatellites with higher allelic richness, and decreases the number of monomorphic microsatellites obtained.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Efficient isolation of polymorphic microsatellites from high-throughput sequence data based on number of repeats","attachmentId":43968671,"attachmentType":"pdf","work_url":"https://www.academia.edu/14696788/Efficient_isolation_of_polymorphic_microsatellites_from_high_throughput_sequence_data_based_on_number_of_repeats","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14696788/Efficient_isolation_of_polymorphic_microsatellites_from_high_throughput_sequence_data_based_on_number_of_repeats"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="81774927" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/81774927/PERF_An_Exhaustive_Algorithm_for_Ultra_Fast_and_Efficient_Identification_of_Microsatellites_from_Large_DNA_Sequences">PERF: An Exhaustive Algorithm for Ultra-Fast and Efficient Identification of Microsatellites from Large DNA Sequences</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="33007277" href="https://ccmb.academia.edu/RakeshMishra">Rakesh Mishra</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Bioinformatics (Oxford, England), 2017</p><p class="ds-related-work--abstract ds2-5-body-sm">Microsatellites or Simple Sequence Repeats (SSRs) are short tandem repeats of DNA motifs present in all genomes. They have long been used for a variety of purposes in the areas of population genetics, genotyping, marker-assisted selection, and forensics. Numerous studies have highlighted their functional roles in genome organization and gene regulation. Though several tools are currently available to identify SSRs from genomic sequences, they have significant limitations. We present a novel algorithm called PERF for extremely fast and comprehensive identification of microsatellites from DNA sequences of any size. PERF is several fold faster than existing algorithms and uses up to 5-fold lesser memory. It provides a clean and flexible command-line interface to change the default settings, and produces output in an easily-parseable tab-separated format. In addition, PERF generates an interactive and stand-alone HTML report with charts and tables for easy downstream analysis. PERF is i...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"PERF: An Exhaustive Algorithm for Ultra-Fast and Efficient Identification of Microsatellites from Large DNA Sequences","attachmentId":87702592,"attachmentType":"pdf","work_url":"https://www.academia.edu/81774927/PERF_An_Exhaustive_Algorithm_for_Ultra_Fast_and_Efficient_Identification_of_Microsatellites_from_Large_DNA_Sequences","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/81774927/PERF_An_Exhaustive_Algorithm_for_Ultra_Fast_and_Efficient_Identification_of_Microsatellites_from_Large_DNA_Sequences"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="4532544" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/4532544/Microsatellite_discovery_by_deep_sequencing_of_enriched_genomic_libraries">Microsatellite discovery by deep sequencing of enriched genomic libraries</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="5646481" href="https://up-za.academia.edu/EmmaSteenkamp">Emma Steenkamp</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Biotechniques, 2009</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Microsatellite discovery by deep sequencing of enriched genomic libraries","attachmentId":49801967,"attachmentType":"pdf","work_url":"https://www.academia.edu/4532544/Microsatellite_discovery_by_deep_sequencing_of_enriched_genomic_libraries","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/4532544/Microsatellite_discovery_by_deep_sequencing_of_enriched_genomic_libraries"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="3336163" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/3336163/Optimizing_Selection_of_Microsatellite_Loci_from_454_Pyrosequencing_via_Post_sequencing_Bioinformatic_Analyses">Optimizing Selection of Microsatellite Loci from 454 Pyrosequencing via Post-sequencing Bioinformatic Analyses</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="3862515" href="https://www2-hawaii.academia.edu/IriaFernandezSilva">Iria Fernandez-Silva</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Methods in Molecular Biology, 2013</p><p class="ds-related-work--abstract ds2-5-body-sm">The comparatively low cost of massive parallel sequencing technology, also known as next-generation sequencing (NGS), has transformed the isolation of microsatellite loci. The most common NGS approach consists of obtaining large amounts of sequence data from genomic DNA or enriched microsatellite libraries, which is then mined for the discovery of microsatellite repeats using bioinformatics analyses. Here, we describe a bioinformatics approach to isolate microsatellite loci, starting from the raw sequence data through a subset of microsatellite primer pairs. The primary difference to previously published approaches includes analyses to select the most accurate sequence data and to eliminate repetitive elements prior to the design of primers. These analyses aim to minimize the testing of primer pairs by identifying the most promising microsatellite loci.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Optimizing Selection of Microsatellite Loci from 454 Pyrosequencing via Post-sequencing Bioinformatic Analyses","attachmentId":31151069,"attachmentType":"pdf","work_url":"https://www.academia.edu/3336163/Optimizing_Selection_of_Microsatellite_Loci_from_454_Pyrosequencing_via_Post_sequencing_Bioinformatic_Analyses","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/3336163/Optimizing_Selection_of_Microsatellite_Loci_from_454_Pyrosequencing_via_Post_sequencing_Bioinformatic_Analyses"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":113360293,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":113360293,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_113360293" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/23693606/M13_tailed_primers_improve_the_readability_and_usability_of_microsatellite_analyses_performed_with_two_different_allele_sizing_methods">M13-tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="45742105" href="https://umontreal.academia.edu/ChristianDeschepper">Christian Deschepper</a></div><p class="ds-related-work--metadata ds2-5-body-xs">BioTechniques, 2001</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"M13-tailed primers improve the readability and usability of 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data-collection-position="3" data-entity-id="111554932" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/111554932/Multi_individual_microsatellite_identification_A_multiple_genome_approach_to_microsatellite_design_MiMi_">Multi‐individual microsatellite identification: A multiple genome approach to microsatellite design (MiMi)</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="77241777" href="https://independent.academia.edu/RPreziosi">Richard Preziosi</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Molecular Ecology Resources, 2019</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Multi‐individual microsatellite identification: A multiple genome 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