Seminars

The availability of phylogenetic trees based on molecular sequence data has revolutionized evolutionary biology by providing a map from which we can understand divergence and diversification across the tree of life. Numerous phylogenetic comparative approaches have opened up new avenues for testing macroevolutionary hypotheses regarding the drivers of the tempo and mode of trait evolution and lineage diversification. However, recent crises in the field have suggested that many of the methods we commonly use don't tell us what we would like them to. I discuss how these crises are driven by a common underlying cause---the frequent occurrence of singular, unreplicated evolutionary events in large phylogenies. I discuss statistical and computational approaches to detecting and accounting for these unreplicated shifts while simultaneously testing our process-based hypotheses of evolutionary change.

Dr. Uyeda's lab site:   http://www.uyedalab.com/

Advances in sequencing over the last decade have made it possible to generate genomic, metagenomic, and/or transcriptomic data from virtually any sample of interest.  Analysis and understanding remain limiting factors. The speaker will talk about some of the lightweight computational approaches his group is building to help connect sequence data to biological discovery and hypothesis testing.

Here are some links that you can read about him and his research:

http://ivory.idyll.org/blog/category/science.html
http://ivory.idyll.org/lab/
https://scholar.google.com/citations?user=O4rYanMAAAAJ
https://twitter.com/ctitusbrown

Alternaria is considered one of the most common saprophytic fungal genera on the planet. It is comprised of many species that exhibit a neurotrophic phytopathogenic lifestyle. Several species are clinically associated with allergic respiratory disorders and have also been found to cause invasive infections in humans. Finally, Alternaria spp. are among the most well-known producers of diverse fungal secondary metabolites, especially toxins. Inour laboratory we have sequenced and annotated over 25 isolates of Alternaria spp. exhibiting diverse lifestyles and adapted a modified version of Ensembl for housing and display of sequence and annotated data. From a functional perspective, we have been exploring fungal cell wall associate polysaccharides in Alternaria, mainly chitin, secreted allergenic proteins, and secondary metabolites in the context of their effects on innate immunity and allergic inflammation in vitro and in murine models. During these studies we have discovered several novel components of the innate immune system. Results of these studies will be presented and discussed.

https://www.biol.vt.edu/faculty/lawrence/

Defining the molecules and processes that regulate tumor cell survival is an essential prerequisite for the development of targeted approaches to cancer treatment.  Whereas many studies aimed at identifying such targets use human tumor cells grown as tissue culture monolayers or as subcutaneous xenografts, it is unclear whether such experimental models replicate the phenotype of the in situ tumor cell.  To begin addressing this issue, we have used microarray analysis to define the gene expression profile of two human glioma cell lines (U251 and U87) when grown in vitro as monolayer cultures and in vivo as subcutaneous or as orthotopic intracerebral xenografts.  For each cell line, the gene expression profile generated from tissue culture was significantly different from that generated from the subcutaneous tumor, which in turn was significantly different from those grown intracerebrally.  The disparity between the intracerebral gene expression profiles and those generated from subcutaneous xenografts suggests that whereas an in vivo growth environment modulates gene expression, orthotopic growth conditions induce a different set of modifications.  An additional aspect of this study was to compare the U251 and U87 gene expression profiles generated under the three growth conditions.  As expected, the profiles of the two glioma cell lines were significantly different when grown as monolayer cultures.  However, the glioma cell lines had similar gene expression profiles when grown intracerebrally.  Many of the genes over-represented in the intracerebral tumors were those related to CNS function and development; the genes under-represented were primarily related to cell proliferation.  These results suggest that tumor cell gene expression, and thus phenotype, as defined in vitro is affected by not only in vivo growth but also by orthotopic growth, which may have implications regarding the identification of relevant targets for cancer therapy.

Links to Dr. Camphausen's work:
https://ccr.cancer.gov/Radiation-Oncology-Branch/kevin-a-camphausen

https://irp.nih.gov/pi/kevin-camphausen

Short reads RNA-seq followed by de novo transcriptome assembly has been widely used to investigate the transcriptome of species without reference genome. How to remove false positive transcripts from an assembly is still an open challenge for computational biology. Recently, single molecule sequencing (PacBio sequencing) has emerged as a promising method, as it directly sequences individual RNA molecules, with read length much longer than short reads. However, long reads from PacBio sequencing are known to have higher error rates than short reads and often cover only a small portion of all expressed genes. In this study, we propose to combine both short reads and long reads sequencing data to produce high-quality transcriptome assembly. We used published short read and long read data generated from the model organism Arabidopsis as gold standard to train several machine learning methods including K-Nearest Neighbors, Random Forest, Support Vector Machine, Logistic Regression and Naïve Bayes classifiers to identify high-quality contigs. This model was then used to predict the quality of the contigs that are of intermediate quality. We further compared the performance of our method with two other transcriptome assembly evaluation tools, RSEM-eval and TransRate. The results showed that our evaluation score has better correlation with contig F-scores than RSEM-eval and TransRate. The whole framework can serve as an automatic evaluation tool for transcriptome assembly without reference genome. We will apply this tool to the long reads and short reads sequencing data from a parasitic species, O. cernua, to generate a high quality transcriptome for this species.