Name
Abstract | ||
|---|---|---|
Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. Here, we present a scalable, flexible workflow for MethylCap-seq Quality Control, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/GENSiPS.2011.6169426 | Genomic Signal Processing and Statistics |
Keywords | Field | DocType |
DNA,bioinformatics,data analysis,data visualisation,genomics,quality control,statistical analysis,MethylCap-Seq data analysis,MethylCap-seq quality control,NGS application,bioinformatics challenges,biological interpretation,cancer research field,computational costs,data storage,data visualization,flexible workflow,methylation profiling projects,next generation sequencing,parallel sequencing,sequence processing,subsequent analysis,DNA methylation,cancer,data analysis,data visualization,epigenetics,next generation sequencing | Massive parallel sequencing,Data science,Genome,Data visualization,Computer science,Profiling (computer programming),Genomics,DNA sequencing,Bioinformatics,Workflow,Scalability | Conference |
ISSN | ISBN | Citations |
2150-3001 E-ISBN : 978-1-4673-0489-4 | 978-1-4673-0489-4 | 1 |
PageRank | References | Authors |
0.44 | 1 | 14 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Rodriguez | 1 | 1 | 0.44 |
| Hok-Hei Tam | 2 | 1 | 0.44 |
| David Frankhouser | 3 | 3 | 1.24 |
| Michael Trimarchi | 4 | 38 | 2.18 |
| Mark Murphy | 5 | 5 | 1.27 |
| Chris Kuo | 6 | 1 | 0.44 |
| Deval Parikh | 7 | 1 | 0.44 |
| Bryan Ball | 8 | 1 | 0.44 |
| Sebastian Schwind | 9 | 1 | 0.44 |
| John Curfman | 10 | 2 | 0.88 |
| William Blum | 11 | 5 | 1.18 |
| Guido Marcucci | 12 | 3 | 1.58 |
| Pearlly Yan | 13 | 82 | 4.28 |
| Ralf Bundschuh | 14 | 77 | 13.85 |