基于改进遗传算法的NGSPooling分组优化

打开文本图片集

中图分类号：0141.4 文献标识码：A文章编号：1006-8228（2025）09-12-04

Abstract：Next-GenerationSequencing（NGS），ahigh-throughputtechnologywidelyusedinbiomedicalresearchandapplications， oferstheadvantagesofrapidandcost-effectivesequencingofgeneticmaterial.Leveragingthehigh-throughputcharacteristicsof NGS，barcode-basedmultiplexsequencingenablespoledsequencingof multplesamples，significantlyimprovingsequencing eficiencyandreducingcosts.However，inlarge-scalesamplepolingexperiments，maximizingchiputilization，minimizingbarcode conflictrates，andensuringhigh-qualitysequencingdataremainkeychalenges.Inthisstudywedevelopedasoftwaresystem basedonoperationsresearchoptimizationmodelsandintllgentoptimizationalgorithmstorapidlyperformautomatedsample grouping.Thesystemsubstantiallenhanceschiputilization，shortenswaitingtimeforgrouping，andavoidssampleindexconflicts. Experimental results demonstrate the effectiveness of the proposed algorithm and model.

Keywords：Next-GenerationSequencing（NGS）；SamplePooling;AutomatedGrouping;OperationsResearch OptimizationModel; Intelligent Optimization Algorithm

0引言

高通量测序（Next-Generation Sequencing，NGS）作为一种前沿的测序技术，已被众多生物医学研究机构广泛应用于遗传性疾病研究、突变识别及个性化治疗方案开发等领域，对于深入理解并有效治疗罕见病和部分遗传性癌症具有重要意义。（剩余5070字）