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R Druker

Publications and source records attributed to R Druker.

2 recordsLinked to original sources

Retrotransposon-derived elements in the mammalian genome: a potential source of disease.

The plethora of genomic information gathered by the sequencing of the human and mouse genomes has paved the way for a new era of genetics. While in the past we focused mainly on the small percentage of DNA that codes for proteins, we can now concentrate on the remainder, i.e. the noncoding sequences that interrupt and separate genes. This portion of the genome is made up, in most part, of repetitive DNA sequences including DNA transposons, long terminal repeat (LTR) retrotransposons, LINEs (long interspersed nuclear elements) and SINEs (short interspersed nuclear elements). Some of these elements are transcriptionally active and can transpose or retrotranspose around the genome, resulting in insertional mutagenesis that can cause disease. In these cases, insertions have occurred in the coding sequence. However, recent evidence suggests that the main effect of these elements is their ability to influence transcription of neighbouring genes. The elements themselves contain promoters that can initiate transcription of flanking genomic DNA. Furthermore, they are susceptible to epigenetic silencing, which is often stochastic and incomplete, resulting in complex patterns of transcription. This review discusses some diseases in both human and mouse that are caused by these repetitive elements.

Animals↗

Diversity and coverage of structural sublibraries selected using the SAGE and SCA algorithms.

It is often impractical to synthesize and test all compounds in a large exhaustive chemical library. Herein, we discuss rational approaches to selecting representative subsets of virtual libraries that help direct experimental synthetic efforts for diverse library design. We compare the performance of two stochastic sampling algorithms, Simulating Annealing Guided Evaluation (SAGE; Zheng, W.; Cho, S. J.; Waller, C. L.; Tropsha, A. J. Chem. Inf. Comput. Sci. 1999, 39, 738-746.) and Stochastic Cluster Analysis (SCA; Reynolds, C. H.; Druker, R.; Pfahler, L. B. Lead Discovery Using Stochastic Cluster Analysis (SCA): A New Method for Clustering Structurally Similar Compounds J. Chem. Inf. Comput. Sci. 1998, 38, 305-312.) for their ability to select both diverse and representative subsets of the entire chemical library space. The SAGE and SCA algorithms were compared using u- and s-optimal metrics as an independent assessment of diversity and coverage. This comparison showed that both algorithms were capable of generating sublibraries in descriptor space that are diverse and give reasonable coverage (i.e. are representative) of the original full library. Tests were carried out using simulated two-dimensional data sets and a 27 000 compound proprietary structural library as represented by computed Molconn-Z descriptors. One of the key observations from this work is that the algorithmically simple SCA method is capable of selecting subsets that are comparable to the more computationally intensive SAGE method.

Journal Article↗