An Algorithm for Browsing the Referentially-compressed Genomes

Abstract

Genome resequencing produces enormous amount of data daily. Biologists need to frequently mine this data with the provided processing and storage resources. Therefore, it becomes very critical to professionally store this data in order to efficiently browse it in a frequent manner. Reference-based Compression algorithms (RbCs) showed significant genome compression results compared to the traditional text compression algorithms. By avoiding the complete decompression of the compressed genomes, they can be browsed by performing partial decompressions at specific regions, taking lower runtime and storage resources. This paper introduces the inCompressi algorithm that is designed and implemented to efficiently pick sequences from genomes, that are compressed by an existing Reference-based Compression algorithm (RbC), through partial decompressions. Moreover, inCompressi performs a more efficient complete genome decompression compared to the original decompression algorithm. The experimental results showed a significant reduction in both runtime and memory consumption compared to the original algorithm.

Searching the Referentially-compressed Genomes by Incomplete Patterns

“…For more details about the exact functionality of the Stanford's compression and decompression, please refer to [6], or alternatively refer to subsection 1.1 to 1.3 in [10] for more illustrative examples.…”

“…The reader can find more details about the inCompressi algorithm in subsection 2.1 in [10]. Also, (Figure 1) contains an illustrative diagram about how inCompressi operates.…”

“…The experiments of this article are performed using the same two datasets used in [10]. The first dataset contains the Arabidopsis thaliana TAIR8 (TAIR8 Website) and TAIR9 (TAIR9 Website) genomes with approximate size of 120 MB for each.…”

“…The experimental results in [10] showed that selecting a specific block length depends on how fast the architecture that inCompressi is running on, and in turn, how well inCompressi will perform. For example, the best block length for the "2.10 GHz" machine was 100,000 characters, whereas, the best block length for the "2.50 GHz" machine was 1,000,000 characters.…”

“…Overcoming this problem can effectively reduce one or all of the disk storage, memory consumption, and runtime overheads. In [10], an algorithm, namely inCompressi, is applied to efficiently eliminate all these overheads by performing partial decompressions of genomes that are referentially compressed using the Stanford algorithm. In addition, inCompressi is exploited to perform a complete genome decompression that is more efficient than the original Stanford's decomresssion.…”