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Cross chromosomal similarity for DNA sequence compression.

ABSTRACT: Current DNA compression algorithms work by finding similar repeated regions within the DNA sequence and then encoding these regions together to achieve compression. Our study on chromosome sequence similarity reveals that the length of similar repeated regions within one chromosome is about 4.5% of the total sequence length. The compression gain is often not high because of these short lengths. It is well known that similarity exist among different regions of chromosome sequences. This implies that similar repeated sequences are found among different regions of chromosome sequences. Here, we study cross-chromosomal similarity for DNA sequence compression. The length and location of similar repeated regions among the sixteen chromosomes of S. cerevisiae are studied. It is found that the average percentage of similar subsequences found between two chromosome sequences is about 10% in which 8% comes from cross-chromosomal prediction and 2% from self-chromosomal prediction. The percentage of similar subsquences is about 18% in which only 1.2% comes from self-chromosomal prediction while the rest is from cross-chromosomal prediction among the 16 chromosomes studied. This suggests the importance of cross-chromosomal similarities in addition to self-chromosomal similarities in DNA sequence compression. An additional 23% of storage space could be reduced on average using self-chromosomal and cross-chromosomal predictions in compressing the 16 chromosomes of S. cerevisiae.

SUBMITTER: Wu CP

PROVIDER: S-EPMC2533061 | biostudies-literature | 2008 Jul

REPOSITORIES: biostudies-literature

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Cross chromosomal similarity for DNA sequence compression.

Wu Choi-Ping Paula CP Law Ngai-Fong NF Siu Wan-Chi WC

Bioinformation 20080714 9

Current DNA compression algorithms work by finding similar repeated regions within the DNA sequence and then encoding these regions together to achieve compression. Our study on chromosome sequence similarity reveals that the length of similar repeated regions within one chromosome is about 4.5% of the total sequence length. The compression gain is often not high because of these short lengths. It is well known that similarity exist among different regions of chromosome sequences. This implies t ...[more]

PMID: 18795115

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Project description:The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution-tolerant context models. We benchmark GeCo3 as a reference-free DNA compressor in 5 datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, 2 compilations of archaeal and virus genomes, 4 whole genomes, and 2 collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of $2.4\%$, $7.1\%$, $6.1\%$, $5.8\%$, and $6.0\%$, respectively. To test its performance as a reference-based DNA compressor, we benchmark GeCo3 in 4 datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in $12.4\%$, $11.7\%$, $10.8\%$, and $10.1\%$ over the state of the art. The cost of this compression improvement is some additional computational time (1.7-3 times slower than GeCo2). The RAM use is constant, and the tool scales efficiently, independently of the sequence size. Overall, these values outperform the state of the art. GeCo3 is a genomic sequence compressor with a neural network mixing approach that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3.

Project description:BackgroundPopulation genetic studies of dogs have so far mainly been based on analysis of mitochondrial DNA, describing only the history of female dogs. To get a picture of the male history, as well as a second independent marker, there is a need for studies of biallelic Y-chromosome polymorphisms. However, there are no biallelic polymorphisms reported, and only 3200 bp of non-repetitive dog Y-chromosome sequence deposited in GenBank, necessitating the identification of dog Y chromosome sequence and the search for polymorphisms therein. The genome has been only partially sequenced for one male dog, disallowing mapping of the sequence into specific chromosomes. However, by comparing the male genome sequence to the complete female dog genome sequence, candidate Y-chromosome sequence may be identified by exclusion.ResultsThe male dog genome sequence was analysed by Blast search against the human genome to identify sequences with a best match to the human Y chromosome and to the female dog genome to identify those absent in the female genome. Candidate sequences were then tested for male specificity by PCR of five male and five female dogs. 32 sequences from the male genome, with a total length of 24 kbp, were identified as male specific, based on a match to the human Y chromosome, absence in the female dog genome and male specific PCR results. 14437 bp were then sequenced for 10 male dogs originating from Europe, Southwest Asia, Siberia, East Asia, Africa and America. Nine haplotypes were found, which were defined by 14 substitutions. The genetic distance between the haplotypes indicates that they originate from at least five wolf haplotypes. There was no obvious trend in the geographic distribution of the haplotypes.ConclusionWe have identified 24159 bp of dog Y-chromosome sequence to be used for population genetic studies. We sequenced 14437 bp in a worldwide collection of dogs, identifying 14 SNPs for future SNP analyses, and giving a first description of the dog Y-chromosome phylogeny.

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Cross chromosomal similarity for DNA sequence compression.

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Cross chromosomal similarity for DNA sequence compression.

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