PathCase: pathways database system.

MOTIVATION: As the blueprints of cellular actions, biological pathways characterize the roles of genomic entities in various cellular mechanisms, and as such, their availability, manipulation and queriability over the web is important to facilitate ongoing biological research. RESULTS: In this article, we present the new features of PathCase, a system to store, query, visualize and analyze metabolic pathways at different levels of genetic, molecular, biochemical and organismal detail. The new features include: (i) a web-based system with a new architecture, containing a server-side and a client-side, and promoting scalability, and flexible and easy adaptation of different pathway databases, (ii) an interactive client-side visualization tool for metabolic pathways, with powerful visualization capabilities, and with integrated gene and organism viewers, (iii) two distinct querying capabilities: an advanced querying interface for computer savvy users, and built-in queries for ease of use, that can be issued directly from pathway visualizations and (iv) a pathway functionality analysis tool. PathCase is now available for three different datasets, namely, KEGG pathways data, sample pathways from the literature and BioCyc pathways for humans. AVAILABILITY: Available online at http://nashua.case.edu/pathways

Efficient computation of kinship and identity coefficients on large pedigrees.

With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wright's path-counting method for computing inbreeding coefficient. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. Furthermore, we propose an improved scheme using Family NodeCodes for the computation of generalized kinship coefficients, which is motivated by the significant improvement of using Family NodeCodes for inbreeding coefficient over the use of NodeCodes. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.

Scalable computation of kinship and identity coefficients on large pedigrees.

With the rapidly expanding field of medical genetics and genetic counseling, genealogy information is becoming increasingly abundant. An important computation on pedigree data is the calculation of identity coefficients, which provide a complete description of the degree of relatedness of a pair of individuals. The areas of application of identity coefficients are numerous and diverse, from genetic counseling to disease tracking, and thus, the computation of identity coefficients merits special attention. However, the computation of identity coefficients is not done directly, but rather as the final step after computing a set of generalized kinship coefficients. In this paper, we first propose a novel Path-Counting Formula for calculating generalized kinship coefficients, which is motivated by Wright's path-counting method for computing the inbreeding coefficient for an individual. We then present an efficient and scalable scheme for calculating generalized kinship coefficients on large pedigrees using NodeCodes, a special encoding scheme for expediting the evaluation of queries on pedigree graph structures. We also perform experiments for evaluating the efficiency of our method, and compare it with the performance of the traditional recursive algorithm for three individuals. Experimental results demonstrate that the resulting scheme is more scalable and efficient than the traditional recursive methods for computing generalized kinship coefficients.