A binary search approach to whole-genome data analysis

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 107; no. 39; pp. 16893 - 16898
• Main Authors: Brodsky, Leonid, Kogan, Simon, BenJacob, Eshel, Nevo, Eviatar
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 28.09.2010; National Acad Sciences
• Subjects: Accuracy; Algorithms; Arabidopsis; Arabidopsis - genetics; Binding sites; Biological Sciences; Biology; chromatin; Chromatin remodeling; Chromosome Mapping - statistics & numerical data; Chromosomes; Comparative analysis; data collection; Data processing; Evolution; Exons; Gene Expression Profiling - statistics & numerical data; gene expression regulation; Genome-Wide Association Study - statistics & numerical data; Genomes; Genomics; Histones; Introns; Landscape; Lysine; Methylation; Oligonucleotide Array Sequence Analysis - statistics & numerical data; RNA Splicing; Saccharomyces cerevisiae; Saccharomyces cerevisiae - genetics; Sequence Analysis, DNA - methods; Signal detection; Signal noise; Tiling; transcription (genetics); Transcription factors
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1011134107

A sequence analysis-oriented binary search-like algorithm was transformed to a sensitive and accurate analysis tool for processing whole-genome data. The advantage of the algorithm over previous methods is its ability to detect the margins of both short and long genome fragments, enriched by up-regulated signals, at equal accuracy. The score of an enriched genome fragment reflects the difference between the actual concentration of up-regulated signals in the fragment and the chromosome signal baseline. The "divide-and-conquer"-type algorithm detects a series of nonintersecting fragments of various lengths with locally optimal scores. The procedure is applied to detected fragments in a nested manner by recalculating the lower-than-baseline signals in the chromosome. The algorithm was applied to simulated whole-genome data, and its sensitivity/specificity were compared with those of several alternative algorithms. The algorithm was also tested with four biological tiling array datasets comprising Arabidopsis (i) expression and (ii) histone 3 lysine 27 trimethylation CHIP-on-chip datasets; Saccharomyces cerevisiae (iii) spliced intron data and (iv) chromatin remodeling factor binding sites. The analyses' results demonstrate the power of the algorithm in identifying both the short up-regulated fragments (such as exons and transcription factor binding sites) and the long—even moderately up-regulated zones—at their precise genome margins. The algorithm generates an accurate whole-genome landscape that could be used for cross-comparison of signals across the same genome in evolutionary and general genomic studies.