Authors:
Mark Onyango
1
;
Carsten Ade
2
;
Franz Cemič
1
and
Jürgen Hemberger
1
Affiliations:
1
University of Applied Sciences Giessen, Germany
;
2
University of Würzburg, Germany
Keyword(s):
DGE, RNA-Seq, Pipeline, Differential Expression, Quality Assessment, Tag-Seq, shRNA.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Next Generation Sequencing
;
Sequence Analysis
Abstract:
With the development of next generation high-throughput sequencing solutions to expression profiling, the efficient and effortless handling of such profiling data became a key challenge for bioinformaticians and biologists alike. We therefore present a "fire and forget" style pipeline implemented in C and R, named QUASI. It is capable of quality assessments, sequence alignments, shRNA quantification and statistically inferring significant differential sequence abundance from datasets presented to it. Through blackboxing the often complex and laborious steps, QUASI presents itself as a user-friendly and time-efficient solution to handle pooled shRNA library screening data.