Genome-Wide Comparative Gene Family Classification

Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species

DendroBlast: approximate phylogenetic trees in the absence of multiple sequence alignments

The rapidly growing availability of genome information has created considerable demand for both fast and accurate phylogenetic inference algorithms. We present a novel method called DendroBLAST for reconstructing phylogenetic dendrograms/trees from protein sequences using BLAST. This method differs from other methods by incorporating a simple model of sequence evolution to test the effect of introducing sequence changes on the reliability of the bipartitions in the inferred tree. Using realistic simulated sequence data we demonstrate that this method produces phylogenetic trees that are more accurate than other commonly-used distance based methods though not as accurate as maximum likelihood methods from good quality multiple sequence alignments. In addition to tests on simulated data, we use DendroBLAST to generate input trees for a supertree reconstruction of the phylogeny of the Archaea. This independent analysis produces an approximate phylogeny of the Archaea that has both high precision and recall when compared to previously published analysis of the same dataset using conventional methods. Taken together these results demonstrate that approximate phylogenetic trees can be produced in the absence of multiple sequence alignments, and we propose that these trees will provide a platform for improving and informing downstream bioinformatic analysis. A web implementation of the DendroBLAST method is freely available for use at http://www.dendroblast.com/

A dual-port THz Time Domain Spectroscopy System optimized for recovery of a sample’s Jones matrix

We describe the design, build and characterization of a novel two-output port configuration for a THz-Time Domain Spectroscopy (TDS) system. By introducing a tilted THz ultra-broadband polarizer, we split the THz beam in two orthogonal polarization detector branches. The probe laser is similarly split (with an optical polarizer) replicating the detection chain to obtain two independent orthogonal polarization detection units. We describe the system’s performance highlighting some of the advantages of this system in one of its two modes of operation: optimized polarimetry for Jones matrix measurements. A bi-refringent sapphire standard was measured to confirm its capabilities and assess the performance of the system showing good agreement with existing literature data

Understanding the functional properties of tools: chimpanzees (Pan troglodytes) and capuchin monkeys (Cebus apella) attend to tool features differently

We examined whether eight capuchins and eight chimpanzees were able to retrieve a reward placed inside a tube, of varying length, by selecting the correct stick from different sets of three sticks differing in length (functional feature) and handle (non-functional feature). Moreover, to investigate whether seeing the stick inside the tube (visual feedback) improves performance, half of the subjects were tested with a transparent apparatus and the other half with an opaque apparatus. Phase 1 included (a) Training 1 in which each stick had a different handle and (b) Transfer 1 in which the handles were switched among sticks, so that the functional tool had the same length but a different handle than before. The seven chimpanzees and one capuchin that passed Transfer 1 received Transfer 2. The other subjects received (a) Training 2, which used the same sticks from Phase 1 with handles switched in every trial, and (b) Transfer 2 in which the tube was longer, all sticks had the same new handle, and the formerly longest tool became intermediate in length. Eight chimpanzees and three capuchins passed Transfer 2. Results showed that (1) chimpanzees applied relational structures in tool using tasks more quickly than capuchins and (2) capuchins required more varied experience to attend to the functional feature of the tool. Interestingly, visual feedback did not improve performance in either species