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Alain Laederach

Bio: Alain Laederach is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: RNA & Untranslated region. 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.


Papers
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Journal ArticleDOI
TL;DR: The genome-wide association study of 2,000 individuals with Parkinson's disease and unaffected controls confirmed associations with SNCA and MAPT and detected a new association with the HLA region, which was uniform across all genetic and environmental risk strata and was strong in sporadic and late-onset disease.
Abstract: Haydeh Payami and colleagues report results of a genome-wide association study for Parkinson's disease. They identify common variants in the HLA region associated with the late-onset sporadic form of the disease and replicate published associations with SNCA, MAPT and GAK.

733 citations

Journal ArticleDOI
TL;DR: The prerequisites for data commoning are described and an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision are presented.
Abstract: To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

387 citations

Journal ArticleDOI
01 Feb 2009-RNA
TL;DR: A protocol for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures, and is used to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes.
Abstract: Understanding the function of complex RNA molecules depends critically on understanding their structure However, creating three-dimensional (3D) structural models of RNA remains a significant challenge We present a protocol (the nucleic acid simulation tool [NAST]) for RNA modeling that uses an RNA-specific knowledge-based potential in a coarse-grained molecular dynamics engine to generate plausible 3D structures We demonstrate NAST’s capabilities by using only secondary structure and tertiary contact predictions to generate, cluster, and rank structures Representative structures in the best ranking clusters averaged 80 6 03 A u and 163 6 10 A u RMSD for the yeast phenylalanine tRNA and the P4-P6 domain of the Tetrahymena thermophila group I intron, respectively The coarse-grained resolution allows us to model large molecules such as the 158residue P4-P6 or the 388-residue T thermophila group I intron One advantage of NAST is the ability to rank clusters of structurally similar decoys based on their compatibility with experimental data We successfully used ideal small-angle X-ray scattering data and both ideal and experimental solvent accessibility data to select the best cluster of structures for both tRNA and P4-P6 Finally, we used NAST to build in missing loops in the crystal structures of the Azoarcus and Twort ribozymes, and to incorporate crystallographic data into the Michel–Westhof model of the T thermophila group I intron, creating an integrated model of the entire molecule Our software package is freely available at https://simtkorg/home/nast

321 citations

Journal ArticleDOI
01 Mar 2005-RNA
TL;DR: SAFA, a semi-automated footprinting analysis software package that achieves accurate gel quantification while reducing the time to analyze a gel from several hours to 15 min or less, and may allow a more comprehensive understanding of molecular interactions.
Abstract: Footprinting is a powerful and widely used tool for characterizing the structure, thermodynamics, and kinetics of nucleic acid folding and ligand binding reactions. However, quantitative analysis of the gel images produced by footprinting experiments is tedious and time-consuming, due to the absence of informatics tools specifically designed for footprinting analysis. We have developed SAFA, a semi-automated footprinting analysis software package that achieves accurate gel quantification while reducing the time to analyze a gel from several hours to 15 min or less. The increase in analysis speed is achieved through a graphical user interface that implements a novel methodology for lane and band assignment, called “gel rectification,” and an optimized band deconvolution algorithm. The SAFA software yields results that are consistent with published methodologies and reduces the investigator-dependent variability compared to less automated methods. These software developments simplify the analysis procedure for a footprinting gel and can therefore facilitate the use of quantitative footprinting techniques in nucleic acid laboratories that otherwise might not have considered their use. Further, the increased throughput provided by SAFA may allow a more comprehensive understanding of molecular interactions. The software and documentation are freely available for download at http://safa.stanford.edu.

321 citations

Journal ArticleDOI
TL;DR: The SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble, and it is proposed that the UTR and SNP combinations the authors identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype.
Abstract: Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

289 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal Article
Fumio Tajima1
30 Oct 1989-Genomics
TL;DR: It is suggested that the natural selection against large insertion/deletion is so weak that a large amount of variation is maintained in a population.

11,521 citations

Journal ArticleDOI
TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Abstract: There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

7,602 citations

01 Aug 2000
TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

4,833 citations

01 Jan 2011
TL;DR: The sheer volume and scope of data posed by this flood of data pose a significant challenge to the development of efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data.
Abstract: Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

2,187 citations