Genevieve, Alex, and Daniel present posters at ISMB in Prague

A good chunk of the lab had the pleasure of attending the ISMB 2017 in Prague, ending just a couple days ago. Before and during the conference, we had a chance to meet up with several lab collaborators, and to attend an endless stream of informative talks. We also managed to get a little touring in, with trips to the castle, the Charles bridge, a unique marionette performance of Don Giovanni, and exposure to the singularly Czech style of table service.

Continue reading

Introducing Dfam_consensus – Dfam’s consensus sequence twin

Since its inception in 2012, Dfam has demonstrated the promise of using profile hidden Markov Models (HMMs) to improve the detection sensitivity and annotation quality of Transposable Element (TEs) families in human[1] and subsequently for four additional reference organisms[2].  Despite these advances, the tools used to discover new families ( de-novo repeat finders ), improve families ( extend, defragment, subfamily clustering ), and classify TE families continue to depend on consensus sequence models.  This discordance between methodologies is a direct impediment to Dfam’s expansion.

Read more: Introducing Dfam_consensus – Dfam’s consensus sequence twin

Summer Research Fellowship for Daniel Olson

Daniel Olson has been named as a 2017 CBSD Summer Graduate Fellow. Each year, the University of Montana’s Center for Biomolecular and Structural Dynamics supports a small number of graduate students for a summer of research. Daniel, will enter the grad program here in the fall but has already begun research on models for repetitive biological sequences. That he was granted a fellowship for the summer prior to his first year in a grad program is a testimony to the excellent contributions he has already made to research on this campus, beginning with Art Woods and continuing in the Wheeler lab. Congratulations, Daniel!

NIH R15 grant for improved sequence database search

The Wheeler lab has been awarded an NIH R15 grant from the National Institute of General Medical Sciences to develop “Improved protein-DNA models for translated sequence search with profile Hidden Markov models”. The grant is for $426K over three years, beginning April 1, 2017.

Fast and sensitive sequence database search is fundamental to modern molecular biology. The funded research will improve the accuracy of annotation of protein-coding content in sequenced genomes and metagenomic datasets. The research builds on established sequence database search software that employs probabilistic models to increase sensitivity through greater statistical power and ability to better model family complexity. The probabilistic models are called profile hidden Markov models (profile HMMs), and the software is HMMER.

Dr. Wheeler’s group will develop new models that account for frameshifting mutations or errors that obscure the protein-coding nature of sequence, and for splice sites that break genes or domains into distant fragments on the genome. Through a combination of new algorithms and application of existing approaches, these models will be fast enough to use for large-scale annotation, such as in the EMBL European Bioinformatics Institute’s Metagenomics Portal.

(See the press release: here)

UM stands out for educational excellence

I’m proud to be a signatory on a guest column in today’s Missoulian, which highlights the terrific educational atmosphere and excellent research found on the University of Montana campus. Despite trying financial times resulting from an enrollment drop caused by multiple complex factors, this University is home to a great many tremendous educators and researchers.