RAP: a new computer program for de novo identification of repeated sequences in whole genomes

Campagna, Davide; Romualdi, Chiara; Vitulo, Nicola; Favero, M.; Lexa, Matej; Cannata, Nicola; Valle, Giorgio

doi:10.1093/bioinformatics/bti039

Cited by 42 publications

(24 citation statements)

References 16 publications

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“…The counting methods were applied to human chromosome 1 (245.5 Mbp), and the speed and memory requirements were compared with published results for other counting methods [8,15]. The direct count method was the fastest (as expected) but limited to words of length 13 or less under the 1Gb memory constraint ( Table 1).…”

Section: Computational Efficiency Of the P-cloud Methodsmentioning

confidence: 87%

“…The direct count method was the fastest (as expected) but limited to words of length 13 or less under the 1Gb memory constraint ( Table 1). The RAP method uses a similar algorithm as our direct count method, and as expected achieves similar speeds (after compensating for different computer speeds and their use of dual processors) for words up to length 16, but requires 8 Gb of memory [15].…”

Section: Computational Efficiency Of the P-cloud Methodsmentioning

confidence: 91%

“…To determine a reasonable oligo length (W) for analyses of different length genomes (n), we used W = log 4 (n) + 1, which has reasonable sensitivity and specificity, assuming that oligo sequences are sampled approximately randomly [8,15]. This rough approximation predicts that individual oligo words of length W are expected to occur less than 1 time in the genome by random chance.…”

Section: Counting the Observance Of Oligo Wordsmentioning

confidence: 99%

“…Repeat structure in large genomes has been analyzed without first constructing consensus repeat family sequences [11,12], including the use of oligonucleotide (hereafter "oligo") or lmer similarity, rather than sequence similarity [13,14], and analytical counting methods, such as RAP [15] and the method of Healy and colleagues [16]. There has been some statistical evaluation of oligo-based repeat region identification using these methods [15,16], but no comprehensive genomic annotation approaches have been developed for oligo-based repeat analysis.…”

Section: Introductionmentioning

confidence: 99%

“…There has been some statistical evaluation of oligo-based repeat region identification using these methods [15,16], but no comprehensive genomic annotation approaches have been developed for oligo-based repeat analysis.…”

Section: Introductionmentioning

confidence: 99%

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Identification of repeat structure in large genomes using repeat probability clouds

Castoe

Hedges

et al. 2008

Analytical Biochemistry

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The identification of repeat structure in eukaryotic genomes can be time-consuming and difficult because of the large amount of information (~3×10 9 bp) that needs to be processed and compared. We introduce a new approach based on exact word counts to evaluate, de novo, the repeat structure present within large eukaryotic genomes. This approach avoids sequence alignment and similarity search, two of the most time-consuming components of traditional methods for repeat identification. Algorithms were implemented to efficiently calculate exact counts for any length oligonucleotide in large genomes. Based on these oligonucleotide counts, oligonucleotide excess probability clouds, or "P-clouds", were constructed. P-clouds are composed of clusters of related oligonucleotides that occur, as a group, more often than expected by chance. After construction, P-clouds were mapped back onto the genome, and regions of high P-cloud density were identified as repetitive regions based on a sliding window approach. This efficient method is capable of analyzing the repeat content of the entire human genome on a single desktop computer in less than half a day, at least 10-fold faster than current approaches. The predicted repetitive regions strongly overlap with known repeat elements, as well as other repetitive regions such as gene families, pseudogenes and segmental duplicons. This method should be extremely useful as a tool for use in de novo identification of repeat structure in large newly sequenced genomes.

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Section: Computational Efficiency Of the P-cloud Methodsmentioning

confidence: 87%

Section: Computational Efficiency Of the P-cloud Methodsmentioning

confidence: 91%

Section: Counting the Observance Of Oligo Wordsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of repeat structure in large genomes using repeat probability clouds

Castoe

Hedges

et al. 2008

Analytical Biochemistry

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Repeatfinding

Sucgang¹

2005

Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics

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Finding repeats in biological sequences is a necessary first step in recognizing patterns and biologically relevant motifs. Given the inexact nature of biological repeats, the software required to detect, identify, classify, and curate them have grown not only in variety but in sophistication as well, accommodating increases both in amount of data and available computing power. Open source and free‐for‐academic‐use programs implementing repeat finding algorithms, as well as databases that curate known biological repeat motifs are surveyed in this section.

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Current Awareness on Comparative and Functional Genomics

2005

Comparative and Functional Genomics

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RAP: a new computer program for de novo identification of repeated sequences in whole genomes

Abstract: The program has been tested on the Caenorhabditis elegans genome using word lengths of 12, 14 and 16 bases. The full analysis has been implemented in the UCSC Genome Browser and is accessible at http://genome.cribi.unipd.it.

Cited by 42 publications

References 16 publications

Identification of repeat structure in large genomes using repeat probability clouds

Identification of repeat structure in large genomes using repeat probability clouds

Repeatfinding

Current Awareness on Comparative and Functional Genomics

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