Efficient Extraction of Structured Motifs Using Box-Links

Abstract

In this paper, we propose a new algorithm for the extraction of repeated motifs that may represent binding-site consensi in genomic sequences. In particular, the algorithm extracts structured motifs, which we define as a collection of highly conserved motifs with pre-specified sizes and spacings between them. This type of motifs is highly relevant in the search for gene regulatory mechanisms since promoter models can be effectively represented by structured motifs.The algorithm uses factor trees, a variation of suffix trees, and a new data structure, called box-links, to store the information about conserved regions that repeat often in the dataset sequences. The complexity analysis shows a gain over previous algorithms that is exponential on the spacings between boxes.The application of a prototype implementation of this algorithm to biologically relevant datasets shows the ability of the method to extract relevant consensi. The experimental results also show that this algorithm is much faster than existing ones, sometimes by more than two orders of magnitude.

Genome-scale disk-based suffix tree indexing

“…The idea is to replace each edge with the start and end indexes (positions) in the input string S for the label (substring) on the edge. For example, the edge labeled ACG from the root node, will be encoded as [0, 2], whereas edge labeled ACG leading to leaf (suffix) 0, will be encoded as [3,5]. Using edge-encoding, each edge requires only a constant amount of space, so the total space required for the whole tree is O(n).…”

“…Some of the approaches [22,23,26,27,28] completely abandon the use of suffix links and sacrifice the theoretically superior linear construction time in exchange for a quadratic time algorithm with better locality of reference. However, many fast string-processing algorithms, such as tandem repeats [19], structural motifs [5], approximate string matching [6], and genome alignment [10,3,21], rely heavily on suffix links for efficiency, and thus cannot be used directly with these disk-based suffix trees. Some approaches [22,23,26,4] also suffer from the skewed partitions problem.…”

Indexing genomic sequences on the IBM Blue Gene

“…Many important string processing applications require suffix links [5,6,12]. For such applications, we invoke the opInput: Input string S, Set of suffix sub-trees T , Block Size B Output: Set of suffix sub-trees T with suffix links /* Phase 1 */ ;…”

EXMOTIF: efficient structured motif extraction

“…RISO [15-17] improves SMILE in two aspects. First, instead of building the whole suffix tree for the input sequences, RISO builds a suffix tree only up to a certain level l , called a factor tree , which leads to a large space saving.…”