A Survey of Metagenomic Binning Algorithms as Applied to the Analysis of Next-Generation Datasets

• Main Authors: Sara Roland, 羅玳琳
• Other Authors: Kun-mao Chao
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/16528721139579803327

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === This thesis presents a survey of classification, or binning, algorithms for the purpose of the evaluation of the accuracy of datasets generated with next-generation sequencing technologies in metagenomic studies. In the past few years, great advances have taken place in the field of next-generation sequencing technologies, and many cutting edge algorithms have been developed to process the data generated by studies utilizing these technologies. However, the development of technologies able to generate vast amounts of data has sometimes outpaced the ability of scientists and researchers to develop ways to properly evaluate the data. The purpose of this survey is to access the applicability of algorithms developed over the last decade to the most popular sequencing technologies today, which often have much shorter read lengths than and different error profiles from earlier sequencing technologies.