An Improved Minimum Description Length Learning Algorithm for Nucleotide Sequence Analysis

Abstract

We present an improved Minimum Description Length (MDL) Learning Algorithm -MDLCompress -for nucleotide sequence analysis that outperforms the compression of other Grammar Based Coding methods such as DNA Sequitur while retaining a two-part code that highlights biologically significant phrases. Phrases are recursively added to the MDLCompress model that are not necessarily the longest matches, or the most often repeated phrase of a certain length, but a combination of length and repetition such that inclusion of the phrase in the model maximizes compression. The deep recursion of MDLCompress combined with its two-part coding nature makes it uniquely able to identify biologically meaningful sequence without limiting assumptions. The ability to quantify cost in bits for phrases in the MDL model promotes prediction of fragile regions where single nucleotide polymorphisms (SNPs) may have the most impact on biological activity. MDLCompress improves our previous algorithm in runtime performance through use of an innovative data structure and in specificity of motif detection (compression) through use of improved heuristics. We also discuss recent results from MDLCompress analysis of 144 known overexpressed genes from a breast cancer cell line, BT474. Novel motifs, including potential microRNA (miRNA) binding sites, have been identified within certain genes and are being considered for in vitro validation studies.

The minimum description length principle for pattern mining: a survey

“…The algorithm devised by Evans et al (2006) (also Markham et al 2009) searches for the best set of substrings to encode an input string according to the proposed Optimal Symbol Compression Ratio (OSCR) (Evans et al 2003). The algorithm, which has been applied primarily to analyse genetic sequences (Evans et al 2007), is iterative, at each step picking the substring that compresses most and replacing it by a temporary code.…”

MicroRNA Target Detection and Analysis for Genes Related to Breast Cancer Using MDLcompress

“…(2) MDLcompress was designed with the express intent of estimating the algorithmic minimum sufficient statis-tic, and thus has more stringent separation of model and data costs and more specific model cost calculations resulting in greater specificity. (3) As described in [21] and will be discussed in later sections, the computational architecture of MDLcompress differs from the suffix tree with counts architecture of GREEDY. Specifically, MDLcompress gathers statistics in a single pass and then updates the data structure and statistics after selecting each phrase as opposed GREEDY's practice of reforming the suffix tree with counts data structure at each iteration.…”

“…In this paper, we describe initial results of miRNA analysis using OSCR and introduce improvements to OSCR that reduce execution time and enhance its capacity to identify biologically meaningful sequence. These modifications, some of which were first introduced in [21], retain the deep recursion of the original algorithm but exploit novel data structures that make more efficient use of time and memory by gathering phrase statistics in a single pass and subsequently selecting multiple codebook phrases. Our data structure incorporates candidate phrase frequency information and pointers identifying location of candidate phrases in the sequence, enabling efficient computation.…”

MicroRNA Target Detection and Analysis for Genes Related to Breast Cancer Using MDLcompress

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