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Journal article
iSeqQC: a tool for expression-based quality control in RNA sequencing
BMC bioinformatics, v 21(1), pp 56-56
13 Feb 2020
PMID: 32054449
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases during sequencing or alignment, yet not many tools exist to interpret biases due to outliers. Results Hence, we developed iSeqQC, an expression-based QC tool that detects outliers either produced due to variable laboratory conditions or due to dissimilarity within a phenotypic group. iSeqQC implements various statistical approaches including unsupervised clustering, agglomerative hierarchical clustering and correlation coefficients to provide insight into outliers. It can be utilized through command-line (Github: ) or web-interface (). A local shiny installation can also be obtained from github (). Conclusion iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.
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Details
- Title
- iSeqQC: a tool for expression-based quality control in RNA sequencing
- Creators
- Gaurav Kumar - Kimmel Cancer CenterAdam Ertel - Sidney Kimmel Cancer CenterGeorge Feldman - Thomas Jefferson UniversityJoan Kupper - Kimmel Cancer CenterPaolo Fortina - Kimmel Cancer Center
- Publication Details
- BMC bioinformatics, v 21(1), pp 56-56
- Publisher
- Springer Nature
- Number of pages
- 10
- Grant note
- NIH-NCI 2 P30 CA056036-23 / Sidney Kimmel Cancer Center of Thomas Jefferson University
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000515902100001
- Scopus ID
- 2-s2.0-85079336198
- Other Identifier
- 991019176794304721
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InCites Highlights
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- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology