Thesis/code-refs.bib

%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

%% Created for Ryan C. Thompson at 2019-10-08 22:10:03 -0700 


%% Saved with string encoding Unicode (UTF-8) 



@misc{sra-toolkit,
	Author = {{Sequence Read Archive Submissions Staff}},
	Date-Added = {2019-10-01 18:04:23 -0700},
	Date-Modified = {2019-10-01 18:06:20 -0700},
	Howpublished = {\url{https://www.ncbi.nlm.nih.gov/books/NBK158900/}},
	Title = {Using the SRA Toolkit to convert .sra files into other formats.},
	Year = {2011}}

@book{chambers:1992,
	Added-At = {2014-01-27T23:46:56.000+0100},
	Author = {Chambers, J.M. and Hastie, T.},
	Biburl = {https://www.bibsonomy.org/bibtex/24109d2f7212a5005fc76a37d54796b34/vivion},
	Date-Added = {2019-10-01 17:52:55 -0700},
	Date-Modified = {2019-10-01 17:52:55 -0700},
	Description = {Statistical models in S - John M. Chambers, Trevor Hastie - Google Livres},
	Interhash = {aa1194ca3e26fedfcc7a6d95fb6edfec},
	Intrahash = {4109d2f7212a5005fc76a37d54796b34},
	Isbn = {9780534167646},
	Keywords = {S models statistical statistics},
	Lccn = {91017646},
	Publisher = {Wadsworth \& Brooks/Cole Advanced Books \& Software},
	Series = {Wadsworth \& Brooks/Cole computer science series},
	Timestamp = {2014-01-27T23:46:56.000+0100},
	Title = {Statistical models in S},
	Url = {http://books.google.fr/books?id=uyfvAAAAMAAJ},
	Year = 1992,
	Bdsk-Url-1 = {http://books.google.fr/books?id=uyfvAAAAMAAJ}}

@manual{R-lang,
	Address = {Vienna, Austria},
	Author = {{R Core Team}},
	Date-Added = {2019-10-01 17:51:36 -0700},
	Date-Modified = {2019-10-01 17:52:10 -0700},
	Organization = {R Foundation for Statistical Computing},
	Title = {R: A Language and Environment for Statistical Computing},
	Url = {https://www.R-project.org/},
	Year = {2019},
	Bdsk-Url-1 = {https://www.R-project.org/}}

@misc{gh-idr,
	Author = {Nathan Boley},
	Date-Added = {2019-09-12 00:06:36 -0700},
	Date-Modified = {2019-09-12 00:43:32 -0700},
	Howpublished = {\url{https://github.com/nboley/idr}},
	Month = {jun},
	Title = {Irreproducible Discovery Rate (IDR)},
	Year = {2017}}

@misc{gh-shoal,
	Abstract = {shoal is a tool which jointly quantify transcript abundances across multiple samples. Specifically, shoal learns an empirical prior on transcript-level abundances across all of the samples in an experiment, and subsequently applies a variant of the variational Bayesian expectation maximization algorithm to apply this prior adaptively across multi-mapping groups of reads.

shoal can increase quantification accuracy, inter-sample consistency, and reduce false positives in downstream differential analysis when applied to multi-condition RNA-seq experiments. Moreover, shoal, runs downstream of Salmon and requires less than a minute per-sample to re-estimate transcript abundances while accounting for the learned empirical prior.},
	Author = {Avi Srivastava, Michael Love, Rob Patro},
	Date-Added = {2019-09-11 22:55:19 -0700},
	Date-Modified = {2019-09-11 22:58:18 -0700},
	Howpublished = {\url{https://github.com/COMBINE-lab/shoal/}},
	Keywords = {rnaseq},
	Month = {jul},
	Title = {Shoal: Improved multi-sample transcript abundance estimates using adaptive priors},
	Year = {2017}}

@misc{gh-cd4-csaw,
	Author = {Ryan C. Thompson},
	Date-Added = {2019-08-01 02:15:39 -0700},
	Date-Modified = {2019-08-28 09:49:36 -0700},
	Howpublished = {\url{https://github.com/DarwinAwardWinner/CD4-csaw}},
	Keywords = {chipseq, rnaseq},
	Month = {nov},
	Publisher = {GitHub, Inc.},
	Title = {Reproducible reanalysis of a combined ChIP-Seq \& RNA-Seq data set},
	Year = {2018}}

@manual{bioc-greylistchip,
	Author = {Gord Brown},
	Date-Added = {2019-08-01 02:00:09 -0700},
	Date-Modified = {2019-10-08 22:09:47 -0700},
	Edition = {R package version 1.16.0.},
	Organization = {Bioconductor},
	Title = {GreyListChIP: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs},
	Url = {https://bioconductor.org/packages/release/bioc/html/GreyListChIP.html},
	Year = {2019}}

@misc{gh-epic,
	Abstract = {epic is a software package for finding medium to diffusely enriched domains in chip-seq data. It is a fast, parallel and memory-efficient implementation of the incredibly popular SICER algorithm. By running epic on a set of data ("ChIP") files and control ("Input") files, epic is able to quickly differentially enriched regions.

epic is an improvement over the original SICER by being faster, more memory efficient, multicore, and significantly much easier to install and use.},
	Author = {Endre Bakken Stovner},
	Date-Added = {2019-08-01 01:47:19 -0700},
	Date-Modified = {2019-08-01 01:47:19 -0700},
	Howpublished = {\url{https://github.com/biocore-ntnu/epic}},
	Keywords = {chipseq},
	Month = {nov},
	Publisher = {GitHub, Inc.},
	Title = {epic: diffuse domain ChIP-Seq caller based on SICER},
	Year = {2018}}

@misc{gh-hg38-ref,
	Author = {Ryan C. Thompson},
	Date-Added = {2019-08-01 01:44:09 -0700},
	Date-Modified = {2019-08-28 09:49:47 -0700},
	Howpublished = {\url{https://github.com/DarwinAwardWinner/hg38-ref}},
	Month = {dec},
	Publisher = {GitHub, Inc.},
	Title = {Workflow to download/generate various mapping indices for the human hg38 genome},
	Year = {2016}}