TY - GEN
T1 - Nucleotide composition based measurement bias in high throughput gene expression studies
AU - Jaksik, Roman
AU - Bensz, Wojciech
AU - Smieja, Jaroslaw
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - High throughput gene expression profiling methods suffer from various sources of measurement bias inherent to the experimental procedures used. Most of the commonly used data standardization methods, designed to reduce the sample-tosample variability of technical origin, do not account for probe-or transcript-specific effects. However, the efficiency of RNA isolation, cDNA synthesis and amplification does depend on the percentage of GC nucleotides in the transcript sequences and therefore constitutes a strong bias for the analysis of gene expression data. This work is focused on analysis of how and to what extent GC-content bias of oligonucleotide microarray probes affects the measurement data. We propose amechanism explaining this phenomenon, the implications of GC-content bias for differentially expressed genes (DEGs) detection, and propose a new data standardization method, which by using sample-specific background intensity estimation and LOESS regression, allows to counteract the described effects.
AB - High throughput gene expression profiling methods suffer from various sources of measurement bias inherent to the experimental procedures used. Most of the commonly used data standardization methods, designed to reduce the sample-tosample variability of technical origin, do not account for probe-or transcript-specific effects. However, the efficiency of RNA isolation, cDNA synthesis and amplification does depend on the percentage of GC nucleotides in the transcript sequences and therefore constitutes a strong bias for the analysis of gene expression data. This work is focused on analysis of how and to what extent GC-content bias of oligonucleotide microarray probes affects the measurement data. We propose amechanism explaining this phenomenon, the implications of GC-content bias for differentially expressed genes (DEGs) detection, and propose a new data standardization method, which by using sample-specific background intensity estimation and LOESS regression, allows to counteract the described effects.
KW - High throughput gene expression studies
KW - Microarray probes sequences
UR - https://www.scopus.com/pages/publications/84983184629
U2 - 10.1007/978-3-319-23437-3_17
DO - 10.1007/978-3-319-23437-3_17
M3 - Conference contribution
AN - SCOPUS:84983184629
SN - 9783319234366
T3 - Advances in Intelligent Systems and Computing
SP - 205
EP - 214
BT - Man–Machine Interactions - 4th International Conference on Man–Machine Interactions, ICMMI 2015
A2 - Czachórski, Tadeusz
A2 - Gruca, Aleksandra
A2 - Brachman, Agnieszka
A2 - Kozielski, Stanisław
A2 - Czachórski, Tadeusz
PB - Springer Verlag
T2 - 4th International Conference on Man–Machine Interactions, ICMMI 2015
Y2 - 6 October 2015 through 9 October 2015
ER -