Abstract Motivation: Next-generation sequencing affords an efficient analysis of transposon insertion libraries, which can be used to identify essential genes in bacteria. To analyse this high-resolution data, we present a formal Bayesian framework for estimating the posterior probability of essentiality for each gene, using the extreme-value distribution to characterize the statistical significance of the longest region lacking insertions within a gene. We describe a sampling procedure based on the Metropolis–Hastings algorithm to calculate posterior probabilities of essentiality while simultaneously integrating over unknown internal parameters. Results: Using a sequence dataset from a transposon library for Mycobacterium tuberculosis, we show that this Bayesian approach predicts essential genes that correspond well with genes shown to be essential in previous studies. Furthermore, we show that by using the extreme-value distribution to characterize genomic regions lacking transposon insertions, this method is capable of identifying essential domains within genes. This approach can be used for analysing transposon libraries in other organisms and augmenting essentiality predictions with statistical confidence scores. Availability: A python script implementing the method described is available for download from http://saclab.tamu.edu/essentiality/. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Database approaches are widely used in structural bioinformatics, since ab initio techniques are often computationally prohibitive, and the structure of biological macromolecules are typically derived from a limited set of motifs. There are several issues and challenges that arise when developing methods to enable efficient database retrieval. For example, how can complex data be represented efficiently, and what should be the size and composition of the database? In this work, we discuss some of these challenges, based on a crystallographic protein model-building program called TEXTAL. In particular, we discuss how structural information on amino acids is represented (as numeric features), how difficult it is to recognize amino acids (based on 3D electron density patterns), and what types of examples (and how many of them) need to be stored in the database. These insights are potentially useful in many other related applications, such as structure-based drug design, protein-protein interaction, discriminating nucleic acids and proteins in hybrid complexes, etc.

UNLABELLED: X-ray crystallography is the most widely used method to determine the 3D structure of protein molecules. One of the most difficult steps in protein crystallography is model-building, which consists of constructing a backbone and then amino acid side chains into an electron density map. Interpretation of electron density maps represents a major bottleneck in protein structure determination pipelines, and thus, automated techniques to interpret maps can greatly improve the throughput. We have developed WebTex, a simple and yet powerful web interface to TEXTAL, a program that automates this process of fitting atoms into electron density maps. TEXTAL can also be downloaded for local installation. AVAILABILITY: Web interface, downloadable binaries and documentation at http://textal.tamu.edu

Inefficient case retrieval is a major problem in many case-based reasoning systems, especially when case matching is expensive and the case-base is large. In this paper, we present a two-phase approach where an inexpensive feature-based method is used to jind a set of potential matches and a more expensive and accurate case matching method is used to make the jinal selection. This approach has been successfully employed in TEXTALTM, a system that retrieves previously solved 3D patterns of electron density from a database to determine the structure of proteins. Electron density patterns are characterized by numeric features and an appropriate distance measure is used to efficiently jilter good matches through an exhaustive search of the database. These matches are then examined using a computationally expensive density correlation procedure based on jinding an optimal superposition between 3D patterns. We provide an empirical and theoretical analysis of some of the keys issues related to this method. In particular, we dejine a model for estimating how approximate various featurebased similarity measures are (relative to an objective matching metric), and determine its relation to the number of cases that should be jiltered from a given database to make the approach effective.