Compression of Multiple DNA Sequences Using Intra-Sequence and Inter-Sequence Similarities

Abstract

Traditionally, intra-sequence similarity is exploited for compressing a single DNA sequence. Recently, remarkable compression performance of individual DNA sequence from the same population is achieved by encoding its difference with a nearly identical reference sequence. Nevertheless, there is lack of general algorithms that also allow less similar reference sequences. In this work, we extend the intra-sequence to the inter-sequence similarity in that approximate matches of subsequences are found between the DNA sequence and a set of reference sequences. Hence, a set of nearly identical DNA sequences from the same population or a set of partially similar DNA sequences like chromosome sequences and DNA sequences of related species can be compressed together. For practical compressors, the compressed size is usually influenced by the compression order of sequences. Fast search algorithms for the optimal compression order are thus developed for multiple sequences compression. Experimental results on artificial and real datasets demonstrate that our proposed multiple sequences compression methods with fast compression order search are able to achieve good compression performance under different levels of similarity in the multiple DNA sequences.

Sketch distance-based clustering of chromosomes for large genome database compression

“…GDC2 [10] applies a two-level Ziv Lempel factorization [27] to compress large set of genome sequences. MSC [16] utilizes both intra-sequence and inter-sequence similarities for compression via searching subsequence matches in reference sequence and other parts of the target sequence itself, the compression order is determined by a recursive full search algorithm.…”

“…Reference-based genome compression for compressing a single genome sequence has been intensively studied and achieved much higher compression ratio than reference free compression [8]. Existing reference-based genome compression algorithms include GDC [9], GDC2 [10], iDoComp [11], ERGC [12], HiRGC [13], CoGI [14], RlZAP [15], MSC [16], RCC [17], NRGC [18], SCCG [19] and FRESCO [20]. A straightforward application of these reference-based compression algorithms to solve the challenging problem of compressing a database containing n number of genome sequences is to conduct a one-by-one sequential reference-based compression for every genome in the database using one fixed reference genome.…”

“…The key differences between ECC and other compression schemes for sequence databases such as MSC [16] and RCC [17] include: (i) Our estimation on pairwise sequence distances is based on the sketch distance of the reduced k -mer sets [21] instead of the Euclidean distance between vectors of k -mer frequencies [17]; (ii) Our initial setting of the centroid in the clustering is not randomly as by RCC, but determined by the analysis on the whole database;(iii) The reference selection within the clusters is also decided by the clustering method instead of the reconstruction of the original target genome set by RCC.…”

Application of signal processing for DNA sequence compression

“…Seed-based methods are used frequently in DNA compression methods. Examples include DNACompress [30], MSC [56] and RCC [60].…”

“…In other words, similar subsequences are encoded once only which act as references to their occurrence at other locations of the same sequence or other sequences. Examples of methods in this group include DNAzip [12], RLCSA [46], RLZ [47,48], GRS [49], GReEn [50], iDoComp [51], COMRAD [52,53], ERGC [54], CoGI [55], MSC [56], GDC [57,58], FRESCO [59] and RCC [60]. With the use of appropriate reference sequences, the storage size reduction in some cases can be over 90%.…”