A novel method for prokaryotic promoter prediction based on DNA stability

Background  

In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability.     

Improved residue contact prediction using support vector machines and a large feature set

Background  

Predicting protein residue-residue contacts is an important 2D prediction task. It is useful for ab initio structure prediction and understanding protein folding. In spite of steady progress over the past decade, contact prediction remains still largely unsolved.     

CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks

Background  

One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes.     

Impact of RNA structure on the prediction of donor and acceptor splice sites

Background  

gene identification in genomic DNA sequences by computational methods has become an important task in bioinformatics and computational gene prediction tools are now essential components of every genome sequencing project. Prediction of splice sites is a key step of all gene structural prediction algorithms.     

A comparison study on feature selection of DNA structural properties for promoter prediction

Background  

Promoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task.     

A comprehensive comparison of comparative RNA structure prediction approaches

Background  

An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms.     

Prediction of backbone dihedral angles and protein secondary structure using support vector machines

Background  

The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.     

Dual energy x-ray absorptiometry analysis contributes to the prediction of hip osteoarthritis progression

Introduction  

To determine if structural bone parameters obtained from dual energy X-ray absorptiometry (DXA) contribute to the prediction of progression of hip osteoarthritis (OA) and to test if the difference between the most affected (OA) hip and the contralateral hip adds to this prediction.     

svmPRAT: SVM-based Protein Residue Annotation Toolkit

Background  

Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequence-derived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models.     

Prediction of antigenic epitopes on protein surfaces by consensus scoring

Background  

Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance.     

Gene finding in novel genomes

Background  

Computational gene prediction continues to be an important problem, especially for genomes with little experimental data.     

EMD: an ensemble algorithm for discovering regulatory motifs in DNA sequences

Background  

Understanding gene regulatory networks has become one of the central research problems in bioinformatics. More than thirty algorithms have been proposed to identify DNA regulatory sites during the past thirty years. However, the prediction accuracy of these algorithms is still quite low. Ensemble algorithms have emerged as an effective strategy in bioinformatics for improving the prediction accuracy by exploiting the synergetic prediction capability of multiple algorithms.     

SuperLigands – a database of ligand structures derived from the Protein Data Bank

Background  

Currently, the PDB contains approximately 29,000 protein structures comprising over 70,000 experimentally determined three-dimensional structures of over 5,000 different low molecular weight compounds. Information about these PDB ligands can be very helpful in the field of molecular modelling and prediction, particularly for the prediction of protein binding sites and function.     

Selective prediction of interaction sites in protein structures with THEMATICS

Background  

Methods are now available for the prediction of interaction sites in protein 3D structures. While many of these methods report high success rates for site prediction, often these predictions are not very selective and have low precision. Precision in site prediction is addressed using Theoretical Microscopic Titration Curves (THEMATICS), a simple computational method for the identification of active sites in enzymes. Recall and precision are measured and compared with other methods for the prediction of catalytic sites.     

PreDisorder: ab initio sequence-based prediction of protein disordered regions

Background  

Disordered regions are segments of the protein chain which do not adopt stable structures. Such segments are often of interest because they have a close relationship with protein expression and functionality. As such, protein disorder prediction is important for protein structure prediction, structure determination and function annotation.     

PEPOP: Computational design of immunogenic peptides

Background  

Most methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.     

Filtering high-throughput protein-protein interaction data using a combination of genomic features

Background  

Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.     

MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction

Background  

Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy.