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http://www.stats.ox.ac.uk/%7Emarchini/software.html
An R package that specifically focuses on statistical and population genetics methods. The motivation behind the package is to produce an easy to use interface to many of the commonly used methods and models used in statistical and population genetics and an alternative interface for some of the methodology produced by our group. (entry from Genetic Analysis Software)
Proper citation: POPGEN (RRID:SCR_007315) Copy
A genomics data analysis platform which generates decision models for healthcare organizations and medical research. This service is meant to utilize data through machine learning methods.
Proper citation: iOMICS (RRID:SCR_000239) Copy
http://courses.jax.org/2012/addiction.html
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. This course emphasizes genetic applications and approaches to drug addiction research through methodological instruction based on literature, data sets and informatics resources drawn from studies of addiction related phenotypes. The course includes plenary sessions on major progress in addiction genetics, and discussion sessions in which students present their work for discussion on applications of genetic methods. Students will leave the course able to design and interpret genetic and genomic studies of addiction as they relate to their specific research question, and will be able to make use of current bioinformatics resources to identify research resources and make use of public data sources in their own research.
Proper citation: Short Course on the Genetics of Addiction (RRID:SCR_005560) Copy
http://www.stats.ox.ac.uk/~marchini/software.html
A R package for assessing the power of genome-wide association studies using commercially available genotyping chips. The package encapsulates extensive simulation results generated by our program HAPGEN. (entry from Genetic Analysis Software)
Proper citation: GWAPOWER (RRID:SCR_009216) Copy
http://www.cs.cmu.edu/~genome/FAST-MAP.html
Fluorescent allele-calling software toolkit: a computer software for fully automated microsatellite genotyping. (entry from Genetic Analysis Software)
Proper citation: FASTMAP (1) (RRID:SCR_008346) Copy
https://unclineberger.org/tgl/
Core facility within Lineberger Comprehensive Cancer Center that performs sample processing for the molecular, pathologic, and genomic characterization of patient-derived specimens using high-end instrumentation and state-of-the-art methods. Service offerings include nucleic acid extraction, gene expression profiling, spatial genomics, next-generation sequencing library preparation, and high-throughput sequencing. Protocols leverage the reproducibility and reliability of automated instrumentation to minimize batch effects and processing errors (e.g. sample swaps). These workflows have been continuously optimized over the last decade, with a sample-to-answer historic success rate of ~90% for the >15,000 FFPE samples TGL has processed.
Proper citation: University of North Carolina at Chapel Hill Translational Genomics Lab Core Facility (RRID:SCR_025231) Copy
Web application to automate germline genomic variant curation from clinical sequencing based on ACMG guidelines. Aggregates multiple tracks of genomic, protein and disease specific information from public sources.
Proper citation: PathoMAN (RRID:SCR_026552) Copy
https://aimrc.uark.edu/data-science-core/
Core specializes in artificial intelligence-based approaches to elucidate relationships between large imaging, bioenergetics, genomic, and proteomic data sets. Provided services include: 1) foundational training for those getting started with high-performance computing and Arkansas Research Platform (ARP), 2) training and support for the collaborative use of a 508 TB data storage server exclusively maintained for and catering to AIMRC researchers, 3) training for Python programming, basic data mining, and machine learning, 4) training and support for using open-source deep learning based biomedical imaging resources (e.g., ZeroCostDL4Mic and Bioimage Model Zoo), and 5) customized solutions for deep learning based and large foundational models based biomedical imaging analysis, multi-omics data integration and analysis, and quantitative analysis pipelines for large data sets.
Proper citation: University of Arkansas AIMRC Data Science Core Facility (RRID:SCR_028681) Copy
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