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2006

Genetics and Genomics

Genetics, Development and Cell Biology Publications

Articles 1 - 4 of 4

Full-Text Articles in Life Sciences

Transposition Of Reversed Ac Element Ends Generates Novel Chimeric Genes In Maize, Jianbo Zhang, Feng Zhang, Thomas Peterson Oct 2006

Transposition Of Reversed Ac Element Ends Generates Novel Chimeric Genes In Maize, Jianbo Zhang, Feng Zhang, Thomas Peterson

Genetics, Development and Cell Biology Publications

The maize Activator/Dissociation (Ac/Ds) elements are members of the hAT (hobo, Ac, and Tam3) superfamily of type II (DNA) transposons that transpose through a “cut-and-paste” mechanism. Previously, we reported that a pair of Ac ends in reversed orientation is capable of undergoing alternative transposition reactions that can generate large-scale chromosomal rearrangements, including deletions and inversions. We show here that rearrangements induced by reversed Ac ends transposition can join the coding and regulatory sequences of two linked paralogous genes to generate a series of chimeric genes, some of which are functional. To our knowledge, this is the first report ...


Detection And Quantification Of Protein Biomarkers From Fewer Than 10 Cells, Saju Nettikadan, Korinna Radke, James Johnson, Juntao Xu, Michael Lynch, Curtis Mosher, Eric Henderson Feb 2006

Detection And Quantification Of Protein Biomarkers From Fewer Than 10 Cells, Saju Nettikadan, Korinna Radke, James Johnson, Juntao Xu, Michael Lynch, Curtis Mosher, Eric Henderson

Genetics, Development and Cell Biology Publications

The use of antibody microarrays continues to grow rapidly due to the recent advances in proteomics and automation and the opportunity this combination creates for high throughput multiplexed analysis of protein biomarkers. However, a primary limitation of this technology is the lack of PCR-like amplification methods for proteins. Therefore, to realize the full potential of array-based protein biomarker screening it is necessary to construct assays that can detect and quantify protein biomarkers with very high sensitivity, in the femtomolar range, and from limited sample quantities. We describe here the construction of ultramicroarrays, combining the advantages of microarraying including multiplexing capabilities ...


Predicting Dna-Binding Sites Of Proteins From Amino Acid Sequence, Changhui Yan, Michael Terribilini, Feihong Wu, Robert L. Jernigan, Drena Dobbs, Vasant Honavar Jan 2006

Predicting Dna-Binding Sites Of Proteins From Amino Acid Sequence, Changhui Yan, Michael Terribilini, Feihong Wu, Robert L. Jernigan, Drena Dobbs, Vasant Honavar

Genetics, Development and Cell Biology Publications

Background

Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.

Results

We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on ...


Prediction Of Rna Binding Sites In Proteins From Amino Acid Sequence, Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, Vasant Honavar, Drena Dobbs Jan 2006

Prediction Of Rna Binding Sites In Proteins From Amino Acid Sequence, Michael Terribilini, Jae-Hyung Lee, Changhui Yan, Robert L. Jernigan, Vasant Honavar, Drena Dobbs

Genetics, Development and Cell Biology Publications

RNA–protein interactions are vitally important in a wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses. We have developed a computational tool for predicting which amino acids of an RNA binding protein participate in RNA–protein interactions, using only the protein sequence as input. RNABindR was developed using machine learning on a validated nonredundant data set of interfaces from known RNA–protein complexes in the Protein Data Bank. It generates a classifier that captures primary sequence signals sufficient for predicting which amino acids in a given protein are located ...