@Article{Love2014,
author={Love, Michael I.
and Huber, Wolfgang
and Anders, Simon},
title={Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2},
journal={Genome Biology},
year={2014},
month={Dec},
day={05},
volume={15},
number={12},
pages={550},
abstract={In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html.},
issn={1474-760X},
doi={10.1186/s13059-014-0550-8},
url={https://doi.org/10.1186/s13059-014-0550-8}
}

@article {Reddy026062,
	author = {Reddy, Rahul},
	title = {A Comparison of Methods: Normalizing High-Throughput RNA Sequencing Data},
	elocation-id = {026062},
	year = {2015},
	doi = {10.1101/026062},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {As RNA-Seq and other high-throughput sequencing grow in use and remain critical for gene expression studies, technical variability in counts data impedes studies of differential expression studies, data across samples and experiments, or reproducing results. Studies like Dillies et al. (2013) compare several between-lane normalization methods involving scaling factors, while Hansen et al. (2012) and Risso et al. (2014) propose methods that correct for sample-specific bias or use sets of control genes to isolate and remove technical variability. This paper evaluates four normalization methods in terms of reducing intra-group, technical variability and facilitating differential expression analysis or other research where the biological, inter-group variability is of interest. To this end, the four methods were evaluated in differential expression analysis between data from Pickrell et al. (2010) and Montgomery et al. (2010) and between simulated data modeled on these two datasets. Though the between-lane scaling factor methods perform worse on real data sets, they are much stronger for simulated data. We cannot reject the recommendation of Dillies et al. to use TMM and DESeq normalization, but further study of power to detect effects of different size under each normalization method is merited.},
	URL = {https://www.biorxiv.org/content/early/2015/09/03/026062},
	eprint = {https://www.biorxiv.org/content/early/2015/09/03/026062.full.pdf},
	journal = {bioRxiv}
}

@Article{Hsieh2012,
author={Hsieh, Andrew C.
and Liu, Yi
and Edlind, Merritt P.
and Ingolia, Nicholas T.
and Janes, Matthew R.
and Sher, Annie
and Shi, Evan Y.
and Stumpf, Craig R.
and Christensen, Carly
and Bonham, Michael J.
and Wang, Shunyou
and Ren, Pingda
and Martin, Michael
and Jessen, Katti
and Feldman, Morris E.
and Weissman, Jonathan S.
and Shokat, Kevan M.
and Rommel, Christian
and Ruggero, Davide},
title={The translational landscape of mTOR signalling steers cancer initiation and metastasis},
journal={Nature},
year={2012},
month={May},
day={01},
volume={485},
number={7396},
pages={55-61},
abstract={The mammalian target of rapamycin (mTOR) kinase is a master regulator of protein synthesis that couples nutrient sensing to cell growth and cancer. However, the downstream translationally regulated nodes of gene expression that may direct cancer development are poorly characterized. Using ribosome profiling, we uncover specialized translation of the prostate cancer genome by oncogenic mTOR signalling, revealing a remarkably specific repertoire of genes involved in cell proliferation, metabolism and invasion. We extend these findings by functionally characterizing a class of translationally controlled pro-invasion messenger RNAs that we show direct prostate cancer invasion and metastasis downstream of oncogenic mTOR signalling. Furthermore, we develop a clinically relevant ATP site inhibitor of mTOR, INK128, which reprograms this gene expression signature with therapeutic benefit for prostate cancer metastasis, for which there is presently no cure. Together, these findings extend our understanding of how the `cancerous' translation machinery steers specific cancer cell behaviours, including metastasis, and may be therapeutically targeted.},
issn={1476-4687},
doi={10.1038/nature10912},
url={https://doi.org/10.1038/nature10912}
}


