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-%% This BibTeX bibliography file was created using BibDesk.
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-%% http://bibdesk.sourceforge.net/
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-%% Created for Ryan C. Thompson at 2019-10-08 22:10:03 -0700
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-%% Saved with string encoding Unicode (UTF-8)
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-@misc{sra-toolkit,
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- Author = {{Sequence Read Archive Submissions Staff}},
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- Date-Added = {2019-10-01 18:04:23 -0700},
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- Date-Modified = {2019-10-01 18:06:20 -0700},
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- Howpublished = {\url{https://www.ncbi.nlm.nih.gov/books/NBK158900/}},
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- Title = {Using the SRA Toolkit to convert .sra files into other formats.},
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- Year = {2011}}
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-@book{chambers:1992,
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- Added-At = {2014-01-27T23:46:56.000+0100},
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- Author = {Chambers, J.M. and Hastie, T.},
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- Biburl = {https://www.bibsonomy.org/bibtex/24109d2f7212a5005fc76a37d54796b34/vivion},
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- Date-Added = {2019-10-01 17:52:55 -0700},
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- Date-Modified = {2019-10-01 17:52:55 -0700},
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- Description = {Statistical models in S - John M. Chambers, Trevor Hastie - Google Livres},
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- Interhash = {aa1194ca3e26fedfcc7a6d95fb6edfec},
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- Intrahash = {4109d2f7212a5005fc76a37d54796b34},
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- Isbn = {9780534167646},
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- Keywords = {S models statistical statistics},
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- Lccn = {91017646},
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- Publisher = {Wadsworth \& Brooks/Cole Advanced Books \& Software},
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- Series = {Wadsworth \& Brooks/Cole computer science series},
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- Timestamp = {2014-01-27T23:46:56.000+0100},
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- Title = {Statistical models in S},
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- Url = {http://books.google.fr/books?id=uyfvAAAAMAAJ},
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- Year = 1992,
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- Bdsk-Url-1 = {http://books.google.fr/books?id=uyfvAAAAMAAJ}}
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-
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-@manual{R-lang,
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- Address = {Vienna, Austria},
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- Author = {{R Core Team}},
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- Date-Added = {2019-10-01 17:51:36 -0700},
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- Date-Modified = {2019-10-01 17:52:10 -0700},
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- Organization = {R Foundation for Statistical Computing},
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- Title = {R: A Language and Environment for Statistical Computing},
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- Url = {https://www.R-project.org/},
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- Year = {2019},
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- Bdsk-Url-1 = {https://www.R-project.org/}}
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-
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-@misc{gh-idr,
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- Author = {Nathan Boley},
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- Date-Added = {2019-09-12 00:06:36 -0700},
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- Date-Modified = {2019-09-12 00:43:32 -0700},
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- Howpublished = {\url{https://github.com/nboley/idr}},
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- Month = {jun},
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- Title = {Irreproducible Discovery Rate (IDR)},
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- Year = {2017}}
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-
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-@misc{gh-shoal,
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- 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.
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-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.},
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- Author = {Avi Srivastava, Michael Love, Rob Patro},
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- Date-Added = {2019-09-11 22:55:19 -0700},
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- Date-Modified = {2019-09-11 22:58:18 -0700},
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- Howpublished = {\url{https://github.com/COMBINE-lab/shoal/}},
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- Keywords = {rnaseq},
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- Month = {jul},
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- Title = {Shoal: Improved multi-sample transcript abundance estimates using adaptive priors},
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- Year = {2017}}
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-
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-@misc{gh-cd4-csaw,
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- Author = {Ryan C. Thompson},
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- Date-Added = {2019-08-01 02:15:39 -0700},
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- Date-Modified = {2019-08-28 09:49:36 -0700},
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- Howpublished = {\url{https://github.com/DarwinAwardWinner/CD4-csaw}},
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- Keywords = {chipseq, rnaseq},
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- Month = {nov},
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- Publisher = {GitHub, Inc.},
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- Title = {Reproducible reanalysis of a combined ChIP-Seq \& RNA-Seq data set},
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- Year = {2018}}
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-
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-@manual{bioc-greylistchip,
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- Author = {Gord Brown},
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- Date-Added = {2019-08-01 02:00:09 -0700},
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- Date-Modified = {2019-10-08 22:09:47 -0700},
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- Edition = {R package version 1.16.0.},
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- Organization = {Bioconductor},
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- Title = {GreyListChIP: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs},
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- Url = {https://bioconductor.org/packages/release/bioc/html/GreyListChIP.html},
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- Year = {2019}}
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-
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-@misc{gh-epic,
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- 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.
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-
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-epic is an improvement over the original SICER by being faster, more memory efficient, multicore, and significantly much easier to install and use.},
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- Author = {Endre Bakken Stovner},
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- Date-Added = {2019-08-01 01:47:19 -0700},
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- Date-Modified = {2019-08-01 01:47:19 -0700},
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- Howpublished = {\url{https://github.com/biocore-ntnu/epic}},
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- Keywords = {chipseq},
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- Month = {nov},
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- Publisher = {GitHub, Inc.},
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- Title = {epic: diffuse domain ChIP-Seq caller based on SICER},
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- Year = {2018}}
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-
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-@misc{gh-hg38-ref,
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- Author = {Ryan C. Thompson},
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- Date-Added = {2019-08-01 01:44:09 -0700},
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- Date-Modified = {2019-08-28 09:49:47 -0700},
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- Howpublished = {\url{https://github.com/DarwinAwardWinner/hg38-ref}},
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- Month = {dec},
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- Publisher = {GitHub, Inc.},
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- Title = {Workflow to download/generate various mapping indices for the human hg38 genome},
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- Year = {2016}}
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