BioDArt - Catalogue of biological data artifact examples

Information in biological data repositories continues to grow exponentially due to the increasing genomic and proteomic sequencing projects. As with any database, these data repositories are subjected to data quality issues related to correctness, uniformity, completeness, redundancy, among others. Data cleaning is a prerequisite to prevent the interference of low quality data with the accuracy of data mining and analysis. This in turn involves the detection and resolution of data artifacts (errors, discrepancies, redundancies, ambiguities, and incompleteness). Understanding the causes of data artifacts and systematically classifying them are critical towards their elimination in molecular sequence databases. This paper highlights eight data artifacts found among public molecular databases. Examples of major molecular sequence database records containing these artifacts are collected into the BioDArt catalogue (http://antigen.i2r.a-star.edu.sg/BioDArt)

Methods for prediction of peptide binding to MHC molecules: a comparative study.

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design

Dragon Promoter Finder: Recognition of vertebrate RNA polymerase II promoters

Dragon Promoter Finder (DPF) locates RNA polymerase II promoters in DNA sequences of vertebrates by predicting Transcription Start Site (TSS) positions. DPF’s algorithm uses sensors for three functional regions (promoters, exons and introns) and an Artificial Neural Network (ANN). Results on a large and diverse evaluation set indicate that DPF exhibits a superior predicting ability for TSS location compared to three other promoter-finding programs