Dataset Information

Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

ABSTRACT: MOTIVATION: Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic approaches. RESULTS: Here we present a novel method, the Genomic Fusion Detection algorithm, to predict fusion genes on a genomic level based on SNP-array data. This algorithm detects genes at the transition region of segments with copy number variation. With the application of defined constraints, certain properties of the detected genes are evaluated to predict whether they may be fused. We evaluated our prediction by calculating the observed frequency of known fusions in both primary cancers and cell lines. We tested a set of cell lines positive for the BCR-ABL1 fusion and prostate cancers positive for the TMPRSS2-ERG fusion. We could detect the fusions in all positive cell lines, but not in the negative controls.

SUBMITTER: Thieme S

PROVIDER: S-EPMC3597144 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

Thieme Sebastian S Groth Philip P

Bioinformatics (Oxford, England) 20130122 6

<h4>Motivation</h4>Fusion genes result from genomic rearrangements, such as deletions, amplifications and translocations. Such rearrangements can also frequently be observed in cancer and have been postulated as driving event in cancer development. to detect them, one needs to analyze the transition region of two segments with different copy number, the location where fusions are known to occur. Finding fusion genes is essential to understanding cancer development and may lead to new therapeutic ...[more]

PMID: 23341502

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Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.

Dataset Information

Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

Publications

Genome Fusion Detection: a novel method to detect fusion genes from SNP-array data.

Similar Datasets

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