Bioconductor contribution tracker

	A	B	C	D	E	F	G	H	I	J	K	L	M	N
1	Corresponding author	email address	Title	Description	Potential Reviewer 1	Potential Reviewer 2	Potential Reviewer 3	Potential Reviewer 4	Potential Reviewer 5	link to draft	checked by Bioconductor editors	submitted to F1000Research by author	ready for typesetting	ready for publishing
2	John Smith	johnsmith@f1000.com	title of tool/recipe	xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx	John Doe	Jane Doe	Bill Doe	Weir Doe	Amy Doe	(google doc)	YES/NO	YES/NO	YES/NO	YES/NO
3	Aaron Lun	alun@wehi.edu.au	Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments	The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.	Jonathan Cairns	Aleksandra Pekowska	Csilla Varnai	Nicholas Servant	Douglas Phanstiel	https://github.com/LTLA/BiocISet2016	NO	NO
4	Gordon Smyth	smyth@wehi.edu.au	From reads to results: detecting differentially expressed genes in RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline	This article describes a computational workflow for the detection of DE genes and pathways from RNA-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of analysis results. In particular, the read alignment and count quantification will be conducted using the Rsubread package and the statistical analyses will be performed using the edgeR package. Analyses will be demonstrated on real mouse mammary gland RNA-Seq data. This will provide readers with practical usage examples that can be applied in their own studies.	Steve Lianoglou <lianoglou.steve@gene.com>	Ryan Thompson <rct@thompsonclan.org>				https://www.overleaf.com/5327268zgbttr	NO	YES
5	Charity Law	law@wehi.edu.au	RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR	A workflow of an RNA-seq analysis using three Bioconductor packages - limma, Glimma and edgeR.						https://www.overleaf.com/4764408yqfzfm#/16959350/	NO	NO	NO	NO
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