Research Methods in Life Sciences Commons^™

Sma-Causing Missense Mutations In Survival Motor Neuron (Smn) Display A Wide Range Of Phenotypes When Modeled In Drosophila, Kavita Praveen, Ying Wen, Kelsey M. Gray, John J. Noto, Akash R. Patlolla, Gregory D. Van Duyne, A. Gregory Matera

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Mutations in the human survival motor neuron 1 (SMN) gene are the primary cause of spinal muscular atrophy (SMA), a devastating neuromuscular disorder. SMN protein has a well-characterized role in the biogenesis of small nuclear ribonucleoproteins (snRNPs), core components of the spliceosome. Additional tissue-specific and global functions have been ascribed to SMN; however, their relevance to SMA pathology is poorly understood and controversial. Using Drosophila as a model system, we created an allelic series of twelve Smn missense mutations, originally identified in human SMA patients. We show that animals expressing these SMA-causing mutations display a broad range of …

Pathoscope 2.0: A Complete Computational Framework For Strain Identification In Environmental Or Clinical Sequencing Samples., Changjin Hong, Solaiappan Manimaran, Ying Shen, Joseph F Perez-Rogers, Allyson L Byrd, Eduardo Castro-Nallar, Keith A Crandall, William Evan Johnson

Computational Biology Institute

BACKGROUND: Recent innovations in sequencing technologies have provided researchers with the ability to rapidly characterize the microbial content of an environmental or clinical sample with unprecedented resolution. These approaches are producing a wealth of information that is providing novel insights into the microbial ecology of the environment and human health. However, these sequencing-based approaches produce large and complex datasets that require efficient and sensitive computational analysis workflows. Many recent tools for analyzing metagenomic-sequencing data have emerged, however, these approaches often suffer from issues of specificity, efficiency, and typically do not include a complete metagenomic analysis framework.

RESULTS: We present PathoScope …

Characteristics And Prediction Of Rna Structure., Hengwu Li, Daming Zhu, Caiming Zhang, Huijian Han, Keith A Crandall

Computational Biology Institute

RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA …