In plants and animals, gene expression can be altered by changes that do not alter the sequence of nucleotides in DNA but rather modify the chemical structure of either the DNA or the histones that interact with the D...In plants and animals, gene expression can be altered by changes that do not alter the sequence of nucleotides in DNA but rather modify the chemical structure of either the DNA or the histones that interact with the DNA. These so-called epigenetic modifications are not transient, but persist through cell divisions. Rapidly advancing technologies, such as next-generation DNA sequencing, have dramatically increased our ability to survey epigenetic markers throughout an entire genome. These techniques are revealing in great detail that the many forms and stages of cancer are characterized by a massive number of epigenetic changes. Interpreting such epigenetic marks in cell differentiation and in carcinogenesis is computationally challenging. We review several examples of epigenetic data analysis and discuss the need for computational methods that will enable us to learn from the data the relationships between different kinds of histone modifications and DNA methylation.展开更多
In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites （TFBSs） in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, h...In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites （TFBSs） in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding DNA sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs.展开更多
基金supported by US NIH/NCI under Grant No. 5 K25CA123344-02
文摘In plants and animals, gene expression can be altered by changes that do not alter the sequence of nucleotides in DNA but rather modify the chemical structure of either the DNA or the histones that interact with the DNA. These so-called epigenetic modifications are not transient, but persist through cell divisions. Rapidly advancing technologies, such as next-generation DNA sequencing, have dramatically increased our ability to survey epigenetic markers throughout an entire genome. These techniques are revealing in great detail that the many forms and stages of cancer are characterized by a massive number of epigenetic changes. Interpreting such epigenetic marks in cell differentiation and in carcinogenesis is computationally challenging. We review several examples of epigenetic data analysis and discuss the need for computational methods that will enable us to learn from the data the relationships between different kinds of histone modifications and DNA methylation.
文摘In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites （TFBSs） in non-coding DNA sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding DNA sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs.
