Discovering patterns in Plasmodium falciparum genomic DNA

Abstract

A method has been developed for discovering patterns in DNA sequences. Loosely based on the well-known Lempel Ziv model for text compression, the model detects repeated sequences in DNA. The repeats can be forward or inverted, and they need not be exact. The method is particularly useful for detecting distantly related sequences, and for finding patterns in sequences of biased nucleotide composition, where spurious patterns are often observed because the bias leads to coincidental nucleotide matches. We show here the utility of the method by applying it to genomic sequences of Plasmodium falciparum. A single scan of chromosomes 2 and 3 of P. falciparum, using our method and no other a priori information about the sequences, reveals regions of low complexity in both telomeric and central regions, long repeats in the subtelomeric regions, and shorter repeat areas in dense coding regions. Application of the method to a recently sequenced contig of chromosome 10 that has a particularly biased base composition detects a long internal repeat more readily than does the conventional dot matrix plot. Space requirements are linear, so the method can be used on large sequences. The observed repeat patterns may be related to large-scale chromosomal organization and control of gene expression. The method has general application in detecting patterns of potential interest in newly sequenced genomic material.

A Simple Statistical Algorithm for Biological Sequence Compression

“…As a statistical compressor, the expert model is able to produce the information content sequence from DNA or protein. This is important when we want to analyze areas of interest [21,9,10]. For example, figure 1 shows a graph of information content along the HUMHBB sequence.…”

“…Compression of biological sequences is useful, not primarily for managing the genome database, but for modelling and learning about sequences. Work by Stern et al [21] recognizes the importance of mutual compressibility for discovering patterns of interest from genomes. Chen et al [6] and Powell et al [19] show that compressibility is a good measurement of relatedness between sequences and can be effectively used in sequence alignment and evolutionary tree construction.…”

A top-down linguistic approach to the analysis of genomic sequences: The metabotropic glutamate receptors 1 and 5 in human and in mouse as a case study

“…Since this pioneeristic work, several other papers have been produced where linguistic approaches have been applied to understand a wide variety of characteristics in genomes, from the identification of active genes to the large scale comparison [18], [22], [10].…”

“…Dictionary-based compression algorithms, like those of the Lempel-Ziv family have been already used in the past to have an automatic selector of over-represented words, in order to select repeats along a genomic sequence [22] [18] [16], or to classify coding/non-coding sequences on the basis of the compression factor or similar indexes [19].…”

Prediction of Disease in Compressed DNA Sequences: An Open Problem

“…The importance of common compressibility for identifying patterns of interest from genomes is recognized [2] and it is also established that compressibility is a well dimension of relatedness among sequences [3]. Many compression algorithms use the characteristics of DNA like point mutation [4] or reverse complement to achieve a god compression rate.…”