High-throughput SNP scoring with GAMMArrays: genomic analysis using multiplexed microsphere arrays

2002 ◽  
Author(s):  
Lance D. Green ◽  
Hong Cai ◽  
David C. Torney ◽  
Diane J. Wood ◽  
Francisco J. Uribe-Romeo ◽  
...  
Keyword(s):  
High Throughput ◽  
Genomic Analysis

scholarly journals Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1221 ◽  
Author(s):  
Phillip A. Richmond ◽  
Wyeth W. Wasserman
Keyword(s):  
High Throughput ◽  
High Performance ◽  
Life Science ◽  
Genomic Analysis ◽  
High Throughput Analysis ◽  
Time Period ◽  
Research Organizations ◽  
Efficacy Measures ◽  
Training Resources ◽  
Short Time

Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use.  In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.

scholarly journals Plastic Microfluidic Systems for High-Throughput Genomic Analysis and Drug Screening

2001 ◽  
Vol 6 (1) ◽  
pp. 71-75
Author(s):  
Maureen T. Cronin ◽  
Travis Boone ◽  
Alexander P. Sassi ◽  
Hongdong Tan ◽  
Qifeng Xue ◽  
...  
Keyword(s):  
High Throughput ◽  
Drug Screening ◽  
Genomic Analysis ◽  
Microfluidic Systems

scholarly journals How Do We Define the Genomic Landscape?

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. SCI-13-SCI-13
Author(s):  
Sandeep S. Dave
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Conflicts Of Interest ◽  
Genomic Analysis ◽  
Future Directions ◽  
Tissue Samples ◽  
Genomic Landscape ◽  
Fresh Frozen ◽  
Definition Of ◽  
Formalin Fixed

High throughput sequencing is a revolutionary technology for the definition of the genomic features of tumors. This talk will provide a review of the relevant methodologies for non-experts in the field. The presentation will include a discussion of how high throughput sequencing is performed, its relative strengths and weaknesses, and how it is applicable to formalin-fixed and fresh/frozen tissue samples. The talk will also describe future directions in the genomic analysis of tumors. Disclosures No relevant conflicts of interest to declare.

High-Throughput Genomic Analysis in Waldenström's Macroglobulinemia

2011 ◽  
Vol 11 (1) ◽  
pp. 106-108 ◽  
Author(s):  
Stéphanie Poulain ◽  
Esteban Braggio ◽  
Christophe Roumier ◽  
Rachid Aijjou ◽  
Natacha Broucqsault ◽  
...  
Keyword(s):  
High Throughput ◽  
Genomic Analysis ◽  
Waldenström’S Macroglobulinemia ◽  
Waldenstrom's Macroglobulinemia ◽  
Waldenstrom’S Macroglobulinemia ◽  
Waldenström's Macroglobulinemia

scholarly journals Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis

2018 ◽  
Author(s):  
Phillip Andrew Richmond ◽  
Wyeth W Wasserman
Keyword(s):  
High Throughput ◽  
High Performance ◽  
Life Science ◽  
Genomic Analysis ◽  
High Throughput Analysis ◽  
Time Period ◽  
Research Organizations ◽  
Efficacy Measures ◽  
Training Resources ◽  
Short Time

Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use. In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.

scholarly journals Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study

2021 ◽  
Author(s):  
Mustafa Umit Oner ◽  
Jianbin Chen ◽  
Egor Revkov ◽  
Anne James ◽  
Seow Ye Heng ◽  
...  
Keyword(s):  
High Throughput ◽  
Sample Selection ◽  
Genomic Analysis ◽  
Multiple Instance Learning ◽  
Tumor Formation ◽  
Spatially Resolved ◽  
Pathologic Evaluation ◽  
Tumor Purity ◽  
Golden Standard ◽  
Fresh Frozen

Tumor purity is the proportion of cancer cells in the tumor tissue. An accurate tumor purity estimation is crucial for accurate pathologic evaluation and for sample selection to minimize normal cell contamination in high throughput genomic analysis. We developed a novel deep multiple instance learning model predicting tumor purity from H&E stained digital histopathology slides. Our model successfully predicted tumor purity from slides of fresh-frozen sections in eight different TCGA cohorts and formalin-fixed paraffin-embedded sections in a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values, which were inferred from genomic data and accepted as the golden standard. Besides, we obtained spatially resolved tumor purity maps and showed that tumor purity varies spatially within a sample. Our analyses on tumor purity maps also suggested that pathologists might have chosen high tumor content regions inside the slides during tumor purity estimation in the TCGA cohorts, which resulted in higher values than genomic tumor purity values. In short, our model can be utilized for high throughput sample selection for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Moreover, spatial tumor purity maps can help better understand the tumor microenvironment as a key determinant in tumor formation and therapeutic response.

scholarly journals Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

2015 ◽  
Author(s):  
Ben Busby ◽  
Allissa Dillman ◽  
Claire L. Simpson ◽  
Ian Fingerman ◽  
Sijung Yun ◽  
...  
Keyword(s):  
Web Service ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Genomic Analysis ◽  
National Institutes Of Health ◽  
Rna Seq ◽  
Sequencing Data ◽  
Collaborative Software ◽  
High Throughput Sequencing Data ◽  
Collaborative Software Development

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

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