Adrián Goga, Lore Depuydt, Nathaniel K Brown, Jan Fostier, Travis Gagie, Gonzalo Navarro
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引用次数: 0
摘要
MONI（Rossi 等人，JCB 2022）是一种基于 BWT 的压缩索引，用于计算模式（通常是 DNA 读取）与高度重复文本（通常是基因组数据库）的匹配统计和最大精确匹配 (MEM)，使用了两种操作：对文本的语法压缩表示进行 LF 步骤和最长公共扩展（LCE）查询。在实践中，大部分操作都是恒时 LF 步，但大部分时间都花在评估 LCE 查询上。在本文中，我们展示了如何对后者（的一种变体）进行懒散评估，从而在保持对数延迟的情况下，用模式和文本之间的 MEM 数量来约束 MONI 处理模式所需的总时间。
Faster Maximal Exact Matches with Lazy LCP Evaluation.
MONI (Rossi et al., JCB 2022) is a BWT-based compressed index for computing the matching statistics and maximal exact matches (MEMs) of a pattern (usually a DNA read) with respect to a highly repetitive text (usually a database of genomes) using two operations: LF-steps and longest common extension (LCE) queries on a grammar-compressed representation of the text. In practice, most of the operations are constant-time LF-steps but most of the time is spent evaluating LCE queries. In this paper we show how (a variant of) the latter can be evaluated lazily, so as to bound the total time MONI needs to process the pattern in terms of the number of MEMs between the pattern and the text, while maintaining logarithmic latency.
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