Published in RNA
Adenosine to inosine (A-to-I) pre-mRNA editing by the ADAR enzyme family has the potential to increase the variety of the proteome. This editing by adenosine deamination is essential in mammals for a functional brain. To detect novel substrates for A-to-I editing we have used an experimental method to find selectively edited sites and combined it w...
Published in RNA
Adenosine to inosine (A-to-I) pre-mRNA editing by the ADAR enzyme family has the potential to increase the variety of the proteome. This editing by adenosine deamination is essential in mammals for a functional brain. To detect novel substrates for A-to-I editing we have used an experimental method to find selectively edited sites and combined it w...
Published in RNA
Adenosine to inosine (A-to-I) pre-mRNA editing by the ADAR enzyme family has the potential to increase the variety of the proteome. This editing by adenosine deamination is essential in mammals for a functional brain. To detect novel substrates for A-to-I editing we have used an experimental method to find selectively edited sites and combined it w...
Published in RNA
Adenosine to inosine (A-to-I) pre-mRNA editing by the ADAR enzyme family has the potential to increase the variety of the proteome. This editing by adenosine deamination is essential in mammals for a functional brain. To detect novel substrates for A-to-I editing we have used an experimental method to find selectively edited sites and combined it w...
Published in RNA
Adenosine to inosine (A-to-I) pre-mRNA editing by the ADAR enzyme family has the potential to increase the variety of the proteome. This editing by adenosine deamination is essential in mammals for a functional brain. To detect novel substrates for A-to-I editing we have used an experimental method to find selectively edited sites and combined it w...
Published in Nature
The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a compleme...
Published in Nature
The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a compleme...
Published in Nature
The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a compleme...
Published in Nature
The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a compleme...
Published in Nature
The systematic comparison of genomic sequences from different organisms represents a central focus of contemporary genome analysis. Comparative analyses of vertebrate sequences can identify coding and conserved non-coding regions, including regulatory elements, and provide insight into the forces that have rendered modern-day genomes. As a compleme...
Published in Bioinformatics
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are fo...
Published in Bioinformatics
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are fo...
Published in Bioinformatics
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are fo...
Published in Bioinformatics
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are fo...
Published in Bioinformatics
Motivation: The enormous amount of protein sequence data uncovered by genome research has increased the demand for computer software that can automate the recognition of new proteins. We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins. GPCRs are fo...
Published in Nature
The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be...
Published in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
In this paper we consider the problem of extracting information from the upstream untranslated regions of genes to make predictions about their transcriptional regulation. We present a method for classifying genes based on motif-based hidden Markov models (HMMs) of their promoter regions. Sequence motifs discovered in yeast promoters are used to co...
Published in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
In this paper we consider the problem of extracting information from the upstream untranslated regions of genes to make predictions about their transcriptional regulation. We present a method for classifying genes based on motif-based hidden Markov models (HMMs) of their promoter regions. Sequence motifs discovered in yeast promoters are used to co...
Published in Bioinformatics
Motivation: DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. We have developed a new method to analyse this kind of data using support vector machines (SVMs). This analysi...
Published in International Conference on Intelligent Systems for Molecular Biology
We consider the problem of parsing a sequence into different classes of subsequences. Two common examples are finding the exons and introns in genomic sequences and identifying the secondary structure domains of protein sequences. In each case there are various types of evidence that are relevant to the classification, but none are completely relia...
