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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Journal ArticleDOI
Exhaustive Enumeration of Kinetic Model Topologies for the Analysis of Time-Resolved RNA Folding
TL;DR: This work establishes clear criteria for determining if experimental ·OH data is sufficient to determine the underlying kinetic model, or if other experimental modalities are required to resolve any degeneracy, as well as studying potential degeneracy in kinetic model selection.
Posted ContentDOI
Characterization of a COPD-Associated NPNT Functional Splicing Genetic Variant in Human Lung Tissue via Long-Read Sequencing
Aabida Saferali,Zhonghui Xu,Gloria M. Sheynkman,Craig P. Hersh,Michael H. Cho,Edwin K. Silverman,Alain Laederach,Christopher Vollmers,Peter J. Castaldi +8 more
TL;DR: The data indicate that rs34712979 modulates COPD risk and lung function by creating a novel splice acceptor site which results in the inclusion of a 3 nucleotide exon extension, coding for a serine residue near the N-terminus of the protein.
Posted ContentDOI
A unified analytic framework for prioritization of non-coding variants of uncertain significance in heritable breast and ovarian cancer
Eliseos J. Mucaki,Natasha G. Caminsky,Ami M. Perri,Ruipeng Lu,Alain Laederach,Matthew Halvorsen,Joan H.M. Knoll,Peter K. Rogan +7 more
TL;DR: This approach distills large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes and presents a strategy for complete gene sequence analysis followed by a unified framework for interpreting non-coding variants that may affect gene expression.
Journal ArticleDOI
Mapping the kinetic barriers of a Large RNA molecule's folding landscape.
TL;DR: This segregation of kinetic control reveals distinctly different molecular mechanisms during the two stages of RNA folding and documents the importance of entropic barriers to defining rugged RNA folding landscapes.
Journal ArticleDOI
Computational functional genomics
TL;DR: The author presents some specific examples regarding the possibility of representing biological data in a machine-learning framework as well as the contributions these representations impart to both the prediction and discovery of the biological function.