Homology search is a key tool for understanding the role, structure, and biochemical function of genomic sequences. The most popular technique for rapid homology search is BLAST, which has been in widespread use within universities, research centers, and commercial enterprises since the early 1990s. In this paper, we propose a new step in the BLAST algorithm to reduce the computational cost of searching with negligible effect on accuracy. This new step-semigapped alignment-compromises between the efficiency of ungapped alignment and the accuracy of gapped alignment, allowing BLAST to accurately filter sequences with lower computational cost. In addition, we propose a heuristic-restricted insertion alignment-that avoids unlikely evolutionary paths with the aim of reducing gapped alignment cost with negligible effect on accuracy. Together, after including an optimization of the local alignment recursion, our two techniques more than double the speed of the gapped alignment stages in BLAST. We conclude that our techniques are an important improvement to the BLAST algorithm. Source code for the alignment algorithms is available for download at http://www.bsg.rmit.edu.au/iga/.

Compression of large collections can lead to improvements in retrieval times by offsetting the CPU decompression costs with the cost of seeking and retrieving data from disk. We propose a semistatic phrase-based approach called xray that builds a model offline using sample training data extracted from a collection, and then compresses the entire collection online in a single pass. The particular benefits of xray are that it can be used in applications where individual records or documents must be decompressed, and that decompression is fast. The xray scheme also allows new data to be added to a collection without modifying the semistatic model. Moreover, xray can be used to compress general-purpose data such as genomic, scientific, image, and geographic collections without prior knowledge of the structure of the data. We show that xray is effective on both text and general-purpose collections. In general, xray is more effective than the popular gzip and compress schemes, while being marginally less effective than bzip2. We also show that xray is efficient: of the popular schemes we tested, it is typically only slower than gzip in decompression. Moreover, the query evaluation costs of retrieval of documents from a large collection with our search engine is improved by more than 30% when xray is incorporated compared to an uncompressed approach. We use simple techniques for obtaining the training data from the collection to be compressed and show that with just over 4% of data the entire collection can be effectively compressed. We also propose four schemes for phrase-match selection during the single pass compression of the collection. We conclude that with these novel approaches xray is a fast and effective scheme for compression and decompression of large general-purpose collections.

Compression of databases not only reduces space requirements but can also reduce overall retrieval times. In text databases, compression of documents based on semistatic modeling with words has been shown to be both practical and fast. Similarly, for specific applications—such as databases of integers or scientific databases—specially designed semistatic compression schemes work well. We propose a scheme for general-purpose compression that can be applied to all types of data stored in large collections. We describe our approach—which we call RAY—in detail, and show experimentally the compression available, compression and decompression costs, and performance as a stream and random-access technique. We show that, in many cases, RAY achieves better compression than an efficient Huffman scheme and popular adaptive compression techniques, and that it can be used as an efficient general-purpose compression scheme.

Summary form only given. Current adaptive compression schemes such as GZIP and COMPRESS are impractical for database compression as they do not allow random access to individual records. A compression algorithm for general-purpose database systems must address the problem of randomly accessing and individually decompressing records, while maintaining compact storage of data. The SEQUITUR algorithm of Nevill-Manning et al., (1994, 1996, 1997) also adaptively compresses data, achieving excellent compression but with significant main-memory requirements. A preliminary version of SEQUITUR used a semi-static modelling approach to achieve slightly worse compression than the adaptive approach. We describe a new variant of the semi-static SEQUITUR algorithm, RAY, that reduces main-memory use and allows random-access to databases. RAY models repetition in sequences by progressively constructing a hierarchical grammar with multiple passes through the data. The multiple pass approach of RAY uses statistics on character pair repetition, or digram frequency, to create rules in the grammar. While our preliminary implementation is not especially fast, the multi-pass approach permits reductions in compression time, at the cost of affecting compression performance, by limiting the number of passes. We have found that RAY has practicable main-memory requirements and achieves better compression than an efficient Huffmann scheme and popular adaptive compression techniques. Moreover, our scheme allows random access to data and is not restricted to databases of text.