iSeqQC: a tool for expression-based quality control in RNA sequencing.
BMC Bioinformatics
; 21(1): 56, 2020 Feb 13.
Article
in En
| MEDLINE
| ID: mdl-32054449
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 https//github.com/gkumar09/iSeqQC) or web-interface (http//cancerwebpa.jefferson.edu/iSeqQC). A local shiny installation can also be obtained from github (https//github.com/gkumar09/iSeqQC).CONCLUSION:
iSeqQC is a fast, light-weight, expression-based QC tool that detects outliers by implementing various statistical approaches.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Software
/
Sequence Analysis, RNA
/
Gene Expression Profiling
/
High-Throughput Nucleotide Sequencing
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
BMC Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2020
Type:
Article
Affiliation country:
United States