Dataset Information

A new method for discovering EMAST sequences in animal models of cancer.

ABSTRACT: Elevated Microsatellite Alterations at Selected Tetranucleotide repeats (EMAST) occur in up to 60% of colorectal cancers and may associate with aggressive and advanced disease in patients. Although EMAST occurs in many cancer types, current understanding is limited due to the lack of an animal model. Reported here is the design and implementation of an algorithm for detecting EMAST repeats in mice. This algorithm incorporates properties of known human EMAST sequences to identify repeat sequences in animal genomes and was able to identify EMAST-like sequences in the mouse. Seven of the identified repeats were analyzed further in a colon cancer mouse model and six of the seven displayed EMAST instability characteristic of that seen in human colorectal cancers. In conclusion, the algorithm developed successfully identified EMAST repeats in an animal genome and, for the first time, EMAST has been shown to occur in a mouse model of colon cancer.

SUBMITTER: Bhaskaran N

PROVIDER: S-EPMC6137214 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A new method for discovering EMAST sequences in animal models of cancer.

Bhaskaran Nitya N Luu Jennifer J Kelley Scott T ST Khan Mohammad W MW Mamindla Priyadarshini P McGuire Kathleen L KL

Scientific reports 20180913 1

Elevated Microsatellite Alterations at Selected Tetranucleotide repeats (EMAST) occur in up to 60% of colorectal cancers and may associate with aggressive and advanced disease in patients. Although EMAST occurs in many cancer types, current understanding is limited due to the lack of an animal model. Reported here is the design and implementation of an algorithm for detecting EMAST repeats in mice. This algorithm incorporates properties of known human EMAST sequences to identify repeat sequences ...[more]

PMID: 30214002

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Project description:Computational methods for discovery of sequence elements that are enriched in a target set compared with a background set are fundamental in molecular biology research. One example is the discovery of transcription factor binding motifs that are inferred from ChIP-chip (chromatin immuno-precipitation on a microarray) measurements. Several major challenges in sequence motif discovery still require consideration: (i) the need for a principled approach to partitioning the data into target and background sets; (ii) the lack of rigorous models and of an exact p-value for measuring motif enrichment; (iii) the need for an appropriate framework for accounting for motif multiplicity; (iv) the tendency, in many of the existing methods, to report presumably significant motifs even when applied to randomly generated data. In this paper we present a statistical framework for discovering enriched sequence elements in ranked lists that resolves these four issues. We demonstrate the implementation of this framework in a software application, termed DRIM (discovery of rank imbalanced motifs), which identifies sequence motifs in lists of ranked DNA sequences. We applied DRIM to ChIP-chip and CpG methylation data and obtained the following results. (i) Identification of 50 novel putative transcription factor (TF) binding sites in yeast ChIP-chip data. The biological function of some of them was further investigated to gain new insights on transcription regulation networks in yeast. For example, our discoveries enable the elucidation of the network of the TF ARO80. Another finding concerns a systematic TF binding enhancement to sequences containing CA repeats. (ii) Discovery of novel motifs in human cancer CpG methylation data. Remarkably, most of these motifs are similar to DNA sequence elements bound by the Polycomb complex that promotes histone methylation. Our findings thus support a model in which histone methylation and CpG methylation are mechanistically linked. Overall, we demonstrate that the statistical framework embodied in the DRIM software tool is highly effective for identifying regulatory sequence elements in a variety of applications ranging from expression and ChIP-chip to CpG methylation data. DRIM is publicly available at http://bioinfo.cs.technion.ac.il/drim.

Dataset Information

A new method for discovering EMAST sequences in animal models of cancer.

Publications

A new method for discovering EMAST sequences in animal models of cancer.

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