Dataset Information

Reprever: resolving low-copy duplicated sequences using template driven assembly.

ABSTRACT: Genomic sequence duplication is an important mechanism for genome evolution, often resulting in large sequence variations with implications for disease progression. Although paired-end sequencing technologies are commonly used for structural variation discovery, the discovery of novel duplicated sequences remains an unmet challenge. We analyze duplicons starting from identified high-copy number variants. Given paired-end mapped reads, and a candidate high-copy region, our tool, Reprever, identifies (a) the insertion breakpoints where the extra duplicons inserted into the donor genome and (b) the actual sequence of the duplicon. Reprever resolves ambiguous mapping signatures from existing homologs, repetitive elements and sequencing errors to identify breakpoint. At each breakpoint, Reprever reconstructs the inserted sequence using profile hidden Markov model (PHMM)-based guided assembly. In a test on 1000 artificial genomes with simulated duplication, Reprever could identify novel duplicates up to 97% of genomes within 3 bp positional and 1% sequence errors. Validation on 680 fosmid sequences identified and reconstructed eight duplicated sequences with high accuracy. We applied Reprever to reanalyzing a re-sequenced data set from the African individual NA18507 to identify >800 novel duplicates, including insertions in genes and insertions with additional variation. polymerase chain reaction followed by capillary sequencing validated both the insertion locations of the strongest predictions and their predicted sequence.

SUBMITTER: Kim S

PROVIDER: S-EPMC3695505 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Reprever: resolving low-copy duplicated sequences using template driven assembly.

Kim Sangwoo S Medvedev Paul P Paton Tara A TA Bafna Vineet V

Nucleic acids research 20130508 12

Genomic sequence duplication is an important mechanism for genome evolution, often resulting in large sequence variations with implications for disease progression. Although paired-end sequencing technologies are commonly used for structural variation discovery, the discovery of novel duplicated sequences remains an unmet challenge. We analyze duplicons starting from identified high-copy number variants. Given paired-end mapped reads, and a candidate high-copy region, our tool, Reprever, identif ...[more]

PMID: 23658221

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Project description:BACKGROUND: Large scale gene analysis of most organisms is hampered by incomplete genomic sequences. In many organisms, such as soybean, the best source of sequence information is the existence of expressed sequence tag (EST) libraries. Soybean has a large (1115 Mbp) genome that has yet to be fully sequenced. However it does have the 6th largest EST collection comprised of ESTs from a variety of soybean genotypes. Many EST libraries were constructed from RNA extracted from various genetic backgrounds, thus gene identification from these sources is complicated by the existence of both gene and allele sequence differences. We used the ESTminer suite of programs to identify potential soybean gene transcripts from a single genetic background allowing us to observe functional classifications between gene families as well as structural differences between genes and gene paralogs within families. The identification of potential gene sequences (pHaps) from soybean allows us to begin to get a picture of the genomic history of the organism as well as begin to observe the evolutionary fates of gene copies in this highly duplicated genome. RESULTS: We identified approximately 45,000 potential gene sequences (pHaps) from EST sequences of Williams/Williams82, an inbred genotype of soybean (Glycine max L. Merr.) using a redundancy criterion to identify reproducible sequence differences between related genes within gene families. Analysis of these sequences revealed single base substitutions and single base indels are the most frequently observed form of sequence variation between genes within families in the dataset. Genomic sequencing of selected loci indicate that intron-like intervening sequences are numerous and are approximately 220 bp in length. Functional annotation of gene sequences indicate functional classifications are not randomly distributed among gene families containing few or many genes. CONCLUSION: The predominance of single nucleotide insertion/deletions and substitution events between genes within families (individual genes and gene paralogs) is consistent with a model of gene amplification followed by single base random mutational events expected under the classical model of duplicated gene evolution. Molecular functions of small and large gene families appear to be non-randomly distributed possibly indicating a difference in retention of duplicates or local expansion.

Project description:The hierarchical clustering and statistical techniques usually used to analyse microarray data do not inherently represent the underlying biology. Herein, a hybrid approach involving characteristics of both supervised and unsupervised learning is presented. This approach is based on template matching in which the interaction of the variables of inherent malignancy and the ability to express the malignant phenotype are modelled. Immortalised normal urothelial cells and bladder cancer cells of different malignancy were grown in conventional two-dimensional tissue culture and in three dimensions on extracellular matrices (ECMs) that were either permissive or restrictive for expression of the malignant phenotype. The transcriptome represents the effects of two variables--inherent malignancy and the modulatory effect of ECM. By assigning values to each of the biological variables of inherent malignancy and the ability to express the malignant phenotype, a template was constructed, which encapsulated the interaction between them. Gene expression correlating both positively and negatively with the template was observed, but when iterative correlations were carried out, the different models for the template converged on the same actual template. A subset of 21 genes was identified, which correlated with two a priori models or an optimised model above the 95% confidence limits identified in a bootstrap resampling with 5000 permutations of the data set. The correlation coefficients of expression of several genes were > 0.8. Analysis of upstream transcriptional regulatory elements (TREs) confirmed that these genes were not a randomly selected set of genes. Several TREs were identified as significantly over-expressed in the sample of 20 genes for which TREs were identified, and the high correlations of several genes were consistent with transcriptional co-regulation. The authors suggest that the template method can be used to identify a unique set of genes for further investigation.

Dataset Information

Reprever: resolving low-copy duplicated sequences using template driven assembly.

Publications

Reprever: resolving low-copy duplicated sequences using template driven assembly.

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