Global, Highly Specific and Fast Filtering of Alignment Seeds

An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only.We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity.An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks.

Binding-Site Match Maker (BSMM): A Computational Method for the Design of Multi-Target Ligands

Multi-target ligand strategies provide a valuable method of drug design. However, to develop a multi-target drug with the desired profile remains a challenge. Herein, we developed a computational method binding-site match maker (BSMM) for the design of multi-target ligands based on binding site matching. BSMM was built based on geometric hashing algorithms and the representation of a binding-site with physicochemical (PC) points. The BSMM software was used to detect proteins with similar binding sites or subsites. In particular, BSMM is independent of protein global folds and sequences and is therefore applicable to the matching of any binding sites. The similar sites between protein pairs with low homology and/or different folds are generally not obvious to the visual inspection. The detection of such similar binding sites by BSMM could be of great value for the design of multi-target ligands.

MATCHING REAL AND SYNTHETIC PANORAMIC IMAGES USING A VARIANT OF GEOMETRIC HASHING

This work demonstrates an approach to automatically initialize a visual model-based tracker, and recover from lost tracking, without prior camera pose information. These approaches are commonly referred to as <i>tracking-by-detection</i>. Previous tracking-by-detection techniques used either fiducials (i.e. landmarks or markers) or the object’s texture. The main contribution of this work is the development of a tracking-by-detection algorithm that is based solely on natural geometric features. A variant of geometric hashing, a model-to-image registration algorithm, is proposed that searches for a matching panoramic image from a database of synthetic panoramic images captured in a 3D virtual environment. The approach identifies corresponding features between the matched panoramic images. The corresponding features are to be used in a photogrammetric space resection to estimate the camera pose. The experiments apply this algorithm to initialize a model-based tracker in an indoor environment using the 3D CAD model of the building.