Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler
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- 作者：Samuel S. Shepard ; Sarah Meno ; Justin Bahl ; Malania M. Wilson ; John Barnes…
- 关键词：Deep sequencing ; NGS ; Influenza ; Ebola ; Surveillance ; High throughput ; Public health
- 刊名：BMC Genomics
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：17
- 期：1
- 全文大小：2059KB
- 刊物主题：Life Sciences, general; Microarrays; Proteomics; Animal Genetics and Genomics; Microbial Genetics and Genomics; Plant Genetics & Genomics;
- 出版者：BioMed Central
- ISSN：1471-2164
- 卷排序：17
文摘
BackgroundDeep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes.