III: Small: Collaborative Research: Supporting Efficient Discrete Box Queries for Sequence Analysis on Large Scale Genome Databases

这项合作研究项目由密歇根州立大学(MSU)和密歇根大学迪尔伯恩分校(UM-D)的研究人员联合开展，旨在研究生物信息学中用于序列分析的大规模k-mer数据集(即从基因组序列获得的重叠k长子序列)的存储和搜索/查询的问题和技术。随着测序数据集大小的大幅增加，高效的k-mer索引、存储和检索对于诸如纠错之类的序列分析任务至关重要。大多数现有的存储和搜索k-MERS的方法都针对精确或范围查询进行了优化。然而，这种依赖限制了可以有效完成的序列分析的类型。此外，大多数现有的存储k-MERS的方法不支持在多个字长的k-MERS的有效存储。对于许多序列分析问题，包括纠错、变异检测和组装，具有多个词长的搜索可以实现更高的灵敏度和特异度。在这个项目中，研究了在大规模k-mer数据集上有效支持所谓的(离散)框查询和其他相关查询(例如，混合查询)以进行序列分析的各种技术。研究了在解决纠错等序列分析问题时优化框查询的方法。探讨了用于支持在k-mer数据集上进行多个词长的搜索的存储结构和框查询的采用。这项研究的结果将促进基因组序列数据库存储、索引和检索技术的知识状况。预计它们将通过为序列分析提供新的高效磁盘解决方案，对当前的生物信息学实践产生重大影响。它们还将影响其他一些流行的应用领域，包括生物识别、图像处理、社交网络和电子商务，在这些领域，处理无序离散多维数据至关重要。这项由密歇根州立大学(MSU)和密歇根大学迪尔伯恩分校(UM-D)的研究人员联合开展的合作研究项目，调查了生物信息学中用于序列分析的大规模k-mer数据集的存储和搜索/查询的问题和技术。随着测序数据集大小的大幅增加，高效的k-mer索引、存储和检索对于诸如纠错之类的序列分析任务至关重要。大多数现有的存储和搜索k-MERS的方法都针对精确或范围查询进行了优化。然而，这种依赖限制了可以有效完成的序列分析的类型。此外，大多数现有的存储k-MERS的方法不支持在多个字长的k-MERS的有效存储。对于许多序列分析问题，具有多个单词长度的搜索可以实现更好的灵敏度和特异度。在这个项目中，研究了在大规模k-mer数据集上有效支持所谓的(离散)框查询和其他相关查询(例如，混合查询)以进行序列分析的各种技术。特别地，针对以k-mer数据集为特征的无序离散数据空间，提出了一种新的索引树--键合树。利用空间的独特性质，提出了一种新的索引树节点分裂启发式算法，并进行了理论分析，证明了该启发式算法的最优性。除了基于数据划分的键合树外，还提出了基于空间划分的盒式查询索引方案。为了支持更灵活的查询类型(即盒子和范围的混合查询)，研究了结合盒子查询索引和范围查询索引的优点的混合索引方案。为了便于大规模k-mer数据集的高效索引构建，还为所提出的索引树开发了批量加载技术。此外，还研究了在解决纠错等序列分析问题时优化框查询的方法。还探讨了用于支持在k-mer数据集上进行多词长搜索的存储结构和框查询的采用。该项目的研究将导致发现生物信息学中序列数据的数据空间的基本属性，开发一些利用这种数据空间的属性的新的存储、索引和检索技术，并应用所提出的技术来解决序列分析中的重要问题。这些结果将促进基因组序列数据库存储、索引和检索技术的知识状态。预计它们将通过为序列分析提供新的高效磁盘解决方案，对当前的生物信息学实践产生重大影响。它们还将影响其他一些流行的应用领域，包括生物识别、图像处理、社交网络和电子商务，在这些领域，处理无序离散多维数据至关重要。

This collaborative research project, conducted jointly by the investigators from the Michigan State University (MSU) and the University of Michigan at Dearborn (UM-D), investigates the issues and techniques for storing and searching/querying large scale k-mer data sets (i.e., overlapping k-length subsequences obtained from genome sequences) for sequence analysis in bioinformatics. Efficient k-mer indexing, storage and retrieval are vital to sequence analysis tasks like error correction as sequencing data set sizes increase vastly. Most existing methods for storing and searching k-mers are optimized for exact or range queries. However, this reliance limits the types of sequence analysis that can be done efficiently. Moreover, most existing methods for storing k-mers do not support efficient storage of k-mers at multiple word lengths. For many sequence analysis problems, including error correction, variant detection, and assembly, searches with multiple word lengths enable better sensitivity and specificity. In this project, various techniques for efficiently supporting so-called (discrete) box queries and other related queries (e.g., hybrid queries) on large scale k-mer data sets for sequence analysis are investigated. The approaches to optimizing box queries in solving sequence analysis problems like the error correction are examined. The storage structure and adoption of box queries for supporting searches with multiple word lengths on k-mer data sets are explored. The results from this research will advance the state of knowledge for storage, indexing and retrieval techniques for genome sequence databases. They are expected to significantly impact current practice in bioinformatics by making available new efficient on-disk solutions for sequence analysis. They will also impact a number of other popular application areas including biometrics, image processing, social network, and E-commerce, where processing non-ordered discrete multidimentional data is crucial. This collaborative research project, conducted jointly by the investigators from the Michigan State University (MSU) and the University of Michigan at Dearborn (UM-D), investigates the issues and techniques for storing and searching/querying large scale k-mer data sets for sequence analysis in bioinformatics. Efficient k-mer indexing, storage and retrieval are vital to sequence analysis tasks like error correction as sequencing data set sizes increase vastly. Most existing methods for storing and searching k-mers are optimized for exact or range queries. However, this reliance limits the types of sequence analysis that can be done efficiently. Moreover, most existing methods for storing k-mers do not support efficient storage of k-mers at multiple word lengths. For many sequence analysis problems, searches with multiple word lengths enable better sensitivity and specificity. In this project, various techniques for efficiently supporting so-called (discrete) box queries and other related queries (e.g., hybrid queries) on large scale k-mer data sets for sequence analysis are investigated. In particular, a new index tree, named the BoND-tree, specially designed for a non-ordered discrete data space characterized by k-mer data sets is developed. The unique properties of the space are exploited to develop new node splitting heuristics for the index tree, and theoretical analysis is performed to show the optimality of the proposed heuristics. Besides the BoND-tree, which is based on data partitioning, space-partitioning based index schemes for box quieres in such a space are also developed. To support a more flexible type of query (i.e., hybrid box and range queries), hybrid index schemes integrating strengths of both box query indexes and range query indexes are studied. To facilitate an efficient index construction for large scale k-mer data sets, bulk loading techniques are also developed for the proposed index trees. In addition, the approaches to optimizing box queries in solving sequence analysis problems like the error correction are examined. The storage structure and adoption of box queries for supporting searches with multiple word lengths on k-mer data sets are also explored. The research in the project will result in the discovery of fundamental properties of the data space for sequence data in bioinformatics, the development of a number of novel storage, indexing and retrieval techniques exploiting the properties of such a data space, and the applications of the proposed techniques for solving important problems in sequence analysis. These results will advance the state of knowledge for storage, indexing and retrieval techniques for genome sequence databases. They are expected to significantly impact current practice in bioinformatics by making available new efficient on-disk solutions for sequence analysis. They will also impact a number of other popular application areas including biometrics, image processing, social network, and E-commerce, where processing non-ordered discrete multidimentional data is crucial.