TY - JOUR
T1 - RiboDiff
T2 - Detecting changes of mRNA translation efficiency from ribosome footprints
AU - Zhong, Yi
AU - Karaletsos, Theofanis
AU - Drewe, Philipp
AU - Sreedharan, Vipin T.
AU - Kuo, David
AU - Singh, Kamini
AU - Wendel, Hans Guido
AU - Rätsch, Gunnar
N1 - Funding Information:
We thank M. Kloft and A. Burcul for help. Funding from the EU Marie Curie ITN framework (Grant # PITN-GA-2012-316861 to GR), National Cancer Institute (R01-CA142798-01 to HGW), Memorial Sloan Kettering Cancer Center (to GR), and ETH Zurich (to GR).
Publisher Copyright:
© The Author 2016. Published by Oxford University Press.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Motivation: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. Results: We present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy.
AB - Motivation: Deep sequencing based ribosome footprint profiling can provide novel insights into the regulatory mechanisms of protein translation. However, the observed ribosome profile is fundamentally confounded by transcriptional activity. In order to decipher principles of translation regulation, tools that can reliably detect changes in translation efficiency in case-control studies are needed. Results: We present a statistical framework and an analysis tool, RiboDiff, to detect genes with changes in translation efficiency across experimental treatments. RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq and ribosome profiling measurements separately, and performs a statistical test for differential translation efficiency using both mRNA abundance and ribosome occupancy.
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U2 - 10.1093/bioinformatics/btw585
DO - 10.1093/bioinformatics/btw585
M3 - Article
C2 - 27634950
AN - SCOPUS:85014807736
SN - 1367-4803
VL - 33
SP - 139
EP - 141
JO - Bioinformatics
JF - Bioinformatics
IS - 1
ER -