UNLABELLED: The Madeline 2.0 Pedigree Drawing Engine (PDE) is a pedigree drawing program for use in linkage and family-based association studies. The program is designed to handle large and complex pedigrees with an emphasis on readability and aesthetics. For complex pedigrees, we use a hybrid algorithm in which consanguinous loops are drawn as cyclic graphs whenever possible, but we resort to acyclic graphs when matings can no longer be connected without line crossings. A similar hybrid approach is used to avoid line crossings for matings between distant descendants of different founding groups. Written in object-oriented C++ and released under the GNU General Public License (GPL), Madeline 2.0 PDE reads input files specified on the command line and generates pedigree drawings without user interaction. Pedigree output in scalable vector graphics (SVG) format can be viewed in browsers with native SVG rendering support or in vector graphics editors. We provide an easy-to-use public web service, which is experimental and still under development. AVAILABILITY: http://kellogg.umich.edu/madeline.

The ongoing revolution in life sciences research is producing vast amounts of genetic and proteomic sequence data. Scientists want to pose increasingly complex queries on this data, but current methods for querying biological sequences are primitive and largely procedural. This limits the ease with which complex queries can be posed, and often results in very inefficient query plans. There is a growing and urgent need for declarative and efficient methods for querying biological sequence data. In this paper, we introduce a system called Periscope/SQ which addresses this need. Queries in our system are based on a well-defined extension of relational algebra. We introduce new physical operators and support for novel indexes in the database. As part of the optimization framework, we describe a new technique for selectivity estimation of string pattern matching predicates that is more accurate than previous methods. We also describe a simple, yet highly effective algorithm to optimize sequence queries. Finally, using a real-world application in eye genetics, we show how Periscope/SQ can be used to achieve a speedup of two orders of magnitude over existing procedural methods!

UNLABELLED: Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway. AVAILABILITY: The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.

We introduce a robust method for detecting evolutionary trends of gene expression from a temporal sequence of microarray data. In this method we perform gene clustering via multi-objective optimization to reveal genes with interesting and statistically significant temporal patterns. We illustrate this gene filtering methodology in the context of exploring the time trajectories of mouse retinal genes acquired at different points over the lifetimes of a population of mice. For 6 time points sampled over 24 mouse subjects, our method can reliably reveal genes whose expression level increases or decreases monotonically, hits a peak or valley at birth, or exhibits other trends.