Prefix Tree Indexing for Similarity Search and Similarity Joins on Genomic Data

Abstract

Abstract. Similarity search and similarity join on strings are important for applications such as duplicate detection, error detection, data cleansing, or comparison of biological sequences. Especially DNA sequencing produces large collections of erroneous strings which need to be searched, compared, and merged. However, current RDBMS offer similarity operations only in a very limited and inefficient form that does not scale to the amount of data produced in Life Science projects. We present PETER, a prefix tree based indexing algorithm supporting approximate search and approimate joins. Our tool supports Hamming and edit distance as similarity measure and is available as C++ library, as Unix command line tool, and as cartridge for a commercial database. It combines an efficient implementation of compressed prefix trees with advanced pre-filtering techniques that exclude many candidate strings early. The achieved speed-ups are dramatic, especially for DNA with its small alphabet. We evaluate our tool on several collections of long strings containing up to 5,000,000 entries of length up to 3,500. We compare its performance to agrep, nrgrep, and user-defined functions inside a relational database. Our experiments reveal that PETER is faster by orders of magnitudes compared to the command-line tools. Compared to RDBMS, it computes similarity joins in minutes for which UDFs did not finish within a day and outperforms the built-in join methods even in the exact case.

Scalable Sequence Similarity Search and Join in Main Memory on Multi-cores

“…In PeARL, we support Hamming and edit distance. We focus on edit distance based operations in this paper, but see [14] for the key ideas on Hamming distancebased queries. In general, the edit distance of r and s is computed in O(|r| * |s|) using dynamic programming.…”

“…These filters, namely prefix and edit distance pruning [16], character frequency pruning [1], and q−gram filtering [7], have been introduced in slightly different contexts before. Their concrete usage and efficiency for trie-based search and join queries is shown in [14]. Therefore, we only briefly summarize our search and join strategies in the following and concentrate on our novel parallelization scheme later.…”

“…Whenever a new child of the current node is reached, we first check whether we can prune this node (see [14] for details on filtering). If all filters have been passed successfully, we compute the edit distance between the query and the prefix of the node.…”

“…In order to retain these advantages for similarity-based queries, we store additional information at each node that enable early pruning of whole subtries. Previously, we demonstrated that these strategies effectively speed up similarity-based queries in PETER [14], a disk-based index structure and predecessor of PeARL.…”

Toward efficient and reliable genome analysis using main-memory database systems

“…Beside command-line tools, approaches exist that use relational database management systems (DBMSs) to facilitate downstream analyses as DBMSs provide excellent data integration capabilities for heterogeneous data sources (e.g., Atlas [22]). Furthermore, approaches exist that already integrate some of the genome analysis steps into relational DBMSs [18,19]. However, an approach that efficiently integrates all genome analysis steps into a DBMS does not yet exist.…”