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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-06-01
摘要
Background: Advances in 基因組 sequencing over the last years have lead to a fundamental paradigm shift in the field. With steadily decreasing sequencing costs, 基因組 projects are no longer limited by the cost of raw sequencing data, but rather by 計算 problems associated with 基因組 assembly. There is an urgent demand for more efficient and and more accurate methods is particular with regard to the highly complex and often very large 基因組s of animals and plants. Most recently, "hybrid" methods that integrate short and long read data have been devised to address this need. Results: LazyB is such a hybrid 基因組 assembler. It has been designed specificially with an emphasis on utilizing low-coverage short and long reads. LazyB starts from a bipartite overlap graph between long reads and restrictively fil
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