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library.bib
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@article{Sheffield2015,
abstract = {UNLABELLED: Genomic datasets are often interpreted in the context of large-scale reference databases. One approach is to identify significantly overlapping gene sets, which works well for gene-centric data. However, many types of high-throughput data are based on genomic regions. Locus Overlap Analysis (LOLA) provides easy and automatable enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data.$\backslash$n$\backslash$nAVAILABILITY AND IMPLEMENTATION: R package available in Bioconductor and on the following website: http://lola.computational-epigenetics.org.$\backslash$n$\backslash$nCONTACT: [email protected] or [email protected].},
author = {Sheffield, Nathan C. and Bock, Christoph},
doi = {10.1093/bioinformatics/btv612},
issn = {14602059},
journal = {Bioinformatics},
number = {4},
pages = {587--589},
pmid = {26508757},
title = {{LOLA: Enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor}},
volume = {32},
year = {2015}
}
@article{Angermueller2016a,
abstract = {Recent technological advances have enabled assaying DNA methylation in single cells. Current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We here report DeepCpG, a computational approach based on deep neural networks to predict DNA methylation states from DNA sequence and incomplete methylation profiles in single cells. We validate DeepCpG on mouse embryonic stem cells, where we report substantially more accurate predictions than previous methods. Additionally, we show that DeepCpG provides new insights for interpreting the sources of epigenetic diversity. Our model can be used to estimate the effect of single nucleotide changes and we uncover sequence motifs that are associated with DNA methylation level and epigenetic heterogeneity.},
author = {Angermueller, Christof and Lee, Heather and Reik, Wolf and Stegle, Oliver},
doi = {10.1101/055715},
file = {:home/koneill/Library/Application Support/Mendeley Desktop/Downloaded/ZG4G7HSM/Angermueller et al. - 2016 - Accurate prediction of single-cell DNA methylation.pdf:pdf;:home/koneill/Library/Application Support/Mendeley Desktop/Downloaded/UG4C2IVE/055715.html:html},
journal = {bioRxiv},
language = {en},
month = {may},
pages = {055715},
title = {{Accurate prediction of single-cell DNA methylation states using deep learning}},
url = {http://biorxiv.org/content/early/2016/05/27/055715 http://biorxiv.org/content/biorxiv/early/2016/05/27/055715.full.pdf},
year = {2016}
}
@article{OConnor2017,
author = {O'Connor, Brian D. and Yuen, Denis and Chung, Vincent and Duncan, Andrew G. and Liu, Xiang Kun and Patricia, Janice and Paten, Benedict and Stein, Lincoln and Ferretti, Vincent},
doi = {10.12688/f1000research.10137.1},
file = {:home/koneill/Library/Application Support/Mendeley Desktop/Downloaded/CJZR32D9/v1.html:html},
issn = {2046-1402},
journal = {F1000Research},
language = {en},
month = {jan},
pages = {52},
shorttitle = {The Dockstore},
title = {{The Dockstore: enabling modular, community-focused sharing of Docker-based genomics tools and workflows}},
url = {https://f1000research.com/articles/6-52/v1},
volume = {6},
year = {2017}
}
@article{Gentleman2005,
abstract = {While scientific research and the methodologies involved have gone through substantial technological evolution the technology involved in the publication of the results of these endeavors has remained relatively stagnant. Publication is largely done in the same manner today as it was fifty years ago. Many journals have adopted electronic formats, however, their orientation and style is little different from a printed document. The documents tend to be static and take little advantage of computational resources that might be available. Recent work, Gentleman and Temple Lang (2003), suggests a methodology and basic infrastructure that can be used to publish documents in a substantially different way. Their approach is suitable for the publication of papers whose message relies on computation. Stated quite simply, Gentleman and Temple Lang (2003) propose a paradigm where documents are mixtures of code and text. Such documents may be self-contained or they may be a component of a compendium which provides the infrastructure needed to provide access to data and supporting software. These documents, or compendiums, can be processed in a number of different ways. One transformation will be to replace the code with its output -- thereby providing the familiar, but limited, static document. {\textless}p /{\textgreater} In this paper we apply these concepts to a seminal paper in bioinformatics, namely The Molecular Classification of Cancer, Golub et al (1999). The authors of that paper have generously provided data and other information that have allowed us to largely reproduce their results. Rather than reproduce this paper exactly we demonstrate that such a reproduction is possible and instead concentrate on demonstrating the usefulness of the compendium concept itself.},
author = {Gentleman, Robert},
doi = {10.2202/1544-6115.1034},
isbn = {1544-6115 (Electronic)$\backslash$r1544-6115 (Linking)},
issn = {1544-6115},
journal = {Statistical applications in genetics and molecular biology},
number = {2003},
pages = {Article2},
pmid = {16646837},
title = {{Reproducible research: a bioinformatics case study.}},
volume = {4},
year = {2005}
}
@article{Schwartzman2015,
abstract = {Epigenomics is the study of the physical modifications, associations and conformations of genomic DNA sequences, with the aim of linking these with epigenetic memory, cellular identity and tissue-specific functions. While current techniques in the field are characterizing the average epigenomic features across large cell ensembles, the increasing interest in the epigenetics within complex and heterogeneous tissues is driving the development of single-cell epigenomics. We review emerging single-cell methods for capturing DNA methylation, chromatin accessibility, histone modifications, chromosome conformation and replication dynamics. Together, these techniques are rapidly becoming a powerful tool in studies of cellular plasticity and diversity, as seen in stem cells and cancer.},
author = {Schwartzman, Omer and Tanay, Amos},
doi = {10.1038/nrg3980},
file = {:home/koneill/Library/Application Support/Mendeley Desktop/Downloaded/Schwartzman, Tanay - 2015 - Single-cell epigenomics techniques and emerging applications.pdf:pdf;:home/koneill/Library/Application Support/Mendeley Desktop/Downloaded/Schwartzman, Tanay - 2015 - Single-cell epigenomics techniques and emerging applications.html:html},
issn = {1471-0056},
journal = {Nature Reviews Genetics},
language = {en},
month = {oct},
number = {12},
pages = {716--26},
shorttitle = {Single-cell epigenomics},
title = {{Single-cell epigenomics: techniques and emerging applications}},
url = {http://www.nature.com/nrg/journal/vaop/ncurrent/full/nrg3980.html http://www.nature.com/nrg/journal/vaop/ncurrent/pdf/nrg3980.pdf},
volume = {16},
year = {2015}
}