Kohdista: an efficient method to index and query possible Rmap alignments.

IF 1.5 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Algorithms for Molecular Biology Pub Date : 2019-12-12 eCollection Date: 2019-01-01 DOI:10.1186/s13015-019-0160-9
Martin D Muggli, Simon J Puglisi, Christina Boucher
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引用次数: 6

Abstract

Background: Genome-wide optical maps are ordered high-resolution restriction maps that give the position of occurrence of restriction cut sites corresponding to one or more restriction enzymes. These genome-wide optical maps are assembled using an overlap-layout-consensus approach using raw optical map data, which are referred to as Rmaps. Due to the high error-rate of Rmap data, finding the overlap between Rmaps remains challenging.

Results: We present Kohdista, which is an index-based algorithm for finding pairwise alignments between single molecule maps (Rmaps). The novelty of our approach is the formulation of the alignment problem as automaton path matching, and the application of modern index-based data structures. In particular, we combine the use of the Generalized Compressed Suffix Array (GCSA) index with the wavelet tree in order to build Kohdista. We validate Kohdista on simulated E. coli data, showing the approach successfully finds alignments between Rmaps simulated from overlapping genomic regions.

Conclusion: we demonstrate Kohdista is the only method that is capable of finding a significant number of high quality pairwise Rmap alignments for large eukaryote organisms in reasonable time.

Abstract Image

Abstract Image

Abstract Image

Kohdista:一个有效的方法来索引和查询可能的Rmap对齐。
背景:全基因组光学图谱是有序的高分辨率限制性内切位点图谱,它给出了一个或多个限制性内切酶对应的限制性内切位点的发生位置。这些全基因组光学图是使用重叠布局一致的方法,使用原始光学图数据,这被称为rmap。由于Rmap数据的高错误率,寻找Rmap之间的重叠仍然是一个挑战。结果:我们提出了Kohdista,这是一种基于索引的算法,用于查找单分子图(rmap)之间的成对比对。我们的方法的新颖之处在于将对齐问题表述为自动路径匹配,以及现代基于索引的数据结构的应用。特别地,我们结合使用广义压缩后缀数组(GCSA)索引和小波树来构建Kohdista。我们在模拟的大肠杆菌数据上验证了Kohdista,表明该方法成功地发现了重叠基因组区域模拟的rmap之间的比对。结论:我们证明Kohdista是唯一能够在合理时间内为大型真核生物找到大量高质量成对Rmap比对的方法。
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来源期刊
Algorithms for Molecular Biology
Algorithms for Molecular Biology 生物-生化研究方法
CiteScore
2.40
自引率
10.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: Algorithms for Molecular Biology publishes articles on novel algorithms for biological sequence and structure analysis, phylogeny reconstruction, and combinatorial algorithms and machine learning. Areas of interest include but are not limited to: algorithms for RNA and protein structure analysis, gene prediction and genome analysis, comparative sequence analysis and alignment, phylogeny, gene expression, machine learning, and combinatorial algorithms. Where appropriate, manuscripts should describe applications to real-world data. However, pure algorithm papers are also welcome if future applications to biological data are to be expected, or if they address complexity or approximation issues of novel computational problems in molecular biology. Articles about novel software tools will be considered for publication if they contain some algorithmically interesting aspects.
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