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David L Adelson

Publications and source records attributed to David L Adelson.

3 recordsLinked to original sources

LINES between species: Evolutionary dynamics of LINE-1 retrotransposons across the eukaryotic tree of life

LINE-1 (L1) retrotransposons are dynamic elements. They have the potential to cause great genomic change due to their ability to jump around the genome and amplify themselves, resulting in the duplication and rearrangement of regulatory DNA. Active L1, in particular, are often thought of as tightly constrained, homologous and ubiquitous elements with well-characterised domain organisation. For the past 30 years, model organisms have been used to define L1s as 6-8kb sequences containing a 5-UTR, two open reading frames working harmoniously in cis, and a 3-UTR with a polyA tail.\n\nIn this study, we demonstrate the remarkable and overlooked diversity of L1s via a comprehensive phylogenetic analysis of over 500 species from widely divergent branches of the tree of life. The rapid and recent growth of L1 elements in mammalian species is juxtaposed against their decline in plant species and complete extinction in most reptiles and insects. In fact, some of these previously unexplored mammalian species (e.g. snub-nosed monkey, minke whale) exhibit L1 retrotranspositional hyperactivity far surpassing that of human or mouse. In contrast, non-mammalian L1s have become so varied that the current classification system seems to inadequately capture their structural characteristics. Our findings illustrate how both long-term inherited evolutionary patterns and random bursts of activity in individual species can significantly alter genomes, highlighting the importance of L1 dynamics in eukaryotes.

Genomics

bíogo: a simple high-performance bioinformatics toolkit for the Go language

biogo is a framework designed to ease development and maintenance of computationally intensive bioinformatics applications. The library is written in the Go programming language, a garbage-collected, strictly typed compiled language with built in support for concurrent processing, and performance comparable to C and Java. It provides a variety of data types and utility functions to facilitate manipulation and analysis of large scale genomic and other biological data. biogo uses a concise and expressive syntax, lowering the barriers to entry for researchers needing to process large data sets with custom analyses while retaining computational safety and ease of code review. We believe biogo provides an excellent environment for training and research in computational biology because of its combination of strict typing, simple and expressive syntax, and high performance.

Bioinformatics

Application of Global Transcriptome Data in Gene Ontology Classification and Construction of a Gene Ontology Interaction Network

Gene Ontology (GO) classification of statistically significant over/under expressed genes is a commonly used to interpret transcriptomics data in functional genomic analysis. In this approach, all significant genes contribute equally to the final GO classification regardless of their actual expression levels. However, the original level of gene expression can significantly affect protein production and consequently GO term enrichment, and genes with low expression levels can participate in the final GO enrichment through cumulative effects. In addition, GO terms have regulatory relationships that allow the construction of a regulatory network that incorporates gene expression levels to better study biological mechanisms. In this report, we have used gene expression levels in bacteria to determine GO term enrichments. This approach provided the opportunity to enrich GO terms across the entire transcriptome (instead of a subset of differentially expressed genes). In the second part, we show a dynamically developed enriched interaction network between Biological Process GO terms for any gene samples. This type of network presents regulatory relationships between GO terms and their genes. We then demonstrate the efficiency of these methods using public data from two important bacterial pathogens as models. We also explain how these methods help us understand potential pathogenesis mechanisms employed by these bacteria.

Bioinformatics