A
Alain Laederach
Researcher at University of North Carolina at Chapel Hill
Publications - 95
Citations - 4724
Alain Laederach is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: RNA & Gene. The author has an hindex of 34, co-authored 88 publications receiving 4078 citations. Previous affiliations of Alain Laederach include New York State Department of Health & University of Neuchâtel.
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Increased Transcript Complexity in Genes Associated with Chronic Obstructive Pulmonary Disease
TL;DR: It is found that COPD-associated genes have a statistically significant enrichment in transcript complexity stemming from a disproportionately high level of alternative splicing, however, Type II Diabetes, Alzheimer's and Parkinson’s disease genes were not significantly enriched.
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Classification of RNA structure change by ‘gazing’ at experimental data
TL;DR: The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8, which is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine.
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RNA molecules with conserved catalytic cores but variable peripheries fold along unique energetically optimized pathways
TL;DR: A fundamental general principle of RNA folding emerges: the dominant folding flux always proceeds through an optimally structured kinetic intermediate that has sufficient stability to act as a nucleating scaffold while retaining enough conformational freedom to avoid kinetic trapping.
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Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure.
TL;DR: Overall, these data reveal a robust mRNA structural landscape where differences in environmental conditions and most sequence variants do not significantly alter RNA structural ensembles, and will provide the community with benchmarks for further algorithmic development.
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A novel application of pattern recognition for accurate SNP and indel discovery from high-throughput data: Targeted resequencing of the glucocorticoid receptor co-chaperone FKBP5 in a Caucasian population
Linda L. Pelleymounter,Irene Moon,Julie A. Johnson,Alain Laederach,Matt Halvorsen,Bruce W. Eckloff,Ryan Abo,Sandro Rossetti +7 more
TL;DR: The methodology presents applications of both pattern recognition and sensitivity analysis to eliminate false positives and aid in the detection of SNP/indel loci and genotypes from high-throughput data and shows how the discovery of rare variants may change current conceptions of evolution at this locus.