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==Abstract== | |||
'''Background''': Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data, demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated [[Workflow|workflows]] capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner. | |||
'''Results''': To address this need, we have developed the R/Bioconductor package systemPipeR. It is an extensible environment for both building and running end-to-end analysis workflows with automated report generation for a wide range of NGS applications. Its unique features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software on local computers and computer clusters. A flexible sample annotation infrastructure efficiently handles complex sample sets and experimental designs. To simplify the analysis of widely used NGS applications, the package provides pre-configured workflows and reporting templates for RNA-Seq, ChIP-Seq, VAR-Seq, and Ribo-Seq. Additional workflow templates will be provided in the future. | |||
'''Conclusions''': systemPipeR accelerates the extraction of reproducible analysis results from NGS experiments. By combining the capabilities of many R/Bioconductor and command-line tools, it makes efficient use of existing software resources without limiting the user to a set of predefined methods or environments. systemPipeR is freely available for all common operating systems from Bioconductor (http://bioconductor.org/packages/devel/systemPipeR). | |||
'''Keywords''': analysis workflow, next generation sequencing (NGS), Ribo-Seq, ChIP-Seq, RNA-Seq, VAR-Seq | |||
==Background== | |||
By allowing scientists to rapidly sequence and quantify DNA and RNA molecules, next-generation sequencing (NGS) technology has transformed biology into one of the most data intensive research disciplines. In the past, experiments have been performed on a gene-by-gene basis, while NGS has introduced an age where it is has become a routine to sequence entire transcriptomes, genomes, or epigenomes rather than their isolated parts of interest. It will soon be possible to conduct these experiments on large numbers of single cell samples<ref name="KaliskySingle11">{{cite journal |title=Single-cell genomics |journal=Nature Methods |author=Kalisky, T.; Quake, S.R. |volume=8 |issue=4 |pages=311–4 |year=2011 |doi=10.1038/nmeth0411-311 |pmid=21451520}}</ref><ref name="TrapnellTheDynamics14">{{cite journal |title=The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells |journal=Nature Biotechnology |author=Trapnell, C.; Cacchiarelli, D.; Grimsby, J. et al. |volume=32 |issue=4 |pages=381–86 |year=2014 |doi=10.1038/nbt.2859 |pmid=24658644 |pmc=PMC4122333}}</ref> for a wide range of time points, treatments, and genetic backgrounds to study biological systems with greater resolution and precision. Sequencing the genetic material of each individual within entire populations of organisms of the same species or genus will enable the study of adaptation processes<ref name="Lindblad-TohAHigh11">{{cite journal |title=A high-resolution map of human evolutionary constraint using 29 mammals |journal=Nature |author=Lindblad-Toh, K.; Garber, M.; Zuk, O. et al. |volume=478 |issue=7370 |pages=476–82 |year=2011 |doi=10.1038/nature10530 |pmid=21993624 |pmc=PMC3207357}}</ref>, disease progression, and micro-evolution in real time.<ref name="Kato-MaedaUseOf13">{{cite journal |title=Use of whole genome sequencing to determine the microevolution of Mycobacterium tuberculosis during an outbreak |journal=PLoS One |author=Kato-Maeda, M.; Ho, C.; Passarelli, B. et al. |volume=8 |issue=3 |pages=e58235 |year=2013 |doi=10.1371/journal.pone.0058235 |pmid=23472164 |pmc=PMC3589338}}</ref> This technological shift empowers researchers to address questions at a [[Genomics|genome-wide]] scale, for example by profiling the mRNA, miRNA, and DNA methylation states of a large set of biological samples in parallel.<ref name="HoltTheNew08">{{cite journal |title=The new paradigm of flow cell sequencing |journal=Genome Research |author=Holt, R.A.; Jones, S.J. |volume=18 |issue=6 |pages=839-46 |year=2008 |doi=10.1101/gr.073262.107 |pmid=18519653}}</ref> | |||
==References== | ==References== | ||
Revision as of 19:52, 20 August 2018
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| Full article title | systemPipeR: NGS workflow and report generation environment |
|---|---|
| Journal | BMC Bioinformatics |
| Author(s) | Backman, Tyler W.H.; Girke, Thomas |
| Author affiliation(s) | University of California, Riverside |
| Primary contact | Email: thomas dot girke at ucr dot edu |
| Year published | 2016 |
| Volume and issue | 17 |
| Page(s) | 388 |
| DOI | 10.1186/s12859-016-1241-0 |
| ISSN | 1471-2105 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1241-0 |
| Download | https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1241-0 (PDF) |
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This article should not be considered complete until this message box has been removed. This is a work in progress. |
Abstract
Background: Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data, demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated workflows capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner.
Results: To address this need, we have developed the R/Bioconductor package systemPipeR. It is an extensible environment for both building and running end-to-end analysis workflows with automated report generation for a wide range of NGS applications. Its unique features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software on local computers and computer clusters. A flexible sample annotation infrastructure efficiently handles complex sample sets and experimental designs. To simplify the analysis of widely used NGS applications, the package provides pre-configured workflows and reporting templates for RNA-Seq, ChIP-Seq, VAR-Seq, and Ribo-Seq. Additional workflow templates will be provided in the future.
Conclusions: systemPipeR accelerates the extraction of reproducible analysis results from NGS experiments. By combining the capabilities of many R/Bioconductor and command-line tools, it makes efficient use of existing software resources without limiting the user to a set of predefined methods or environments. systemPipeR is freely available for all common operating systems from Bioconductor (http://bioconductor.org/packages/devel/systemPipeR).
Keywords: analysis workflow, next generation sequencing (NGS), Ribo-Seq, ChIP-Seq, RNA-Seq, VAR-Seq
Background
By allowing scientists to rapidly sequence and quantify DNA and RNA molecules, next-generation sequencing (NGS) technology has transformed biology into one of the most data intensive research disciplines. In the past, experiments have been performed on a gene-by-gene basis, while NGS has introduced an age where it is has become a routine to sequence entire transcriptomes, genomes, or epigenomes rather than their isolated parts of interest. It will soon be possible to conduct these experiments on large numbers of single cell samples[1][2] for a wide range of time points, treatments, and genetic backgrounds to study biological systems with greater resolution and precision. Sequencing the genetic material of each individual within entire populations of organisms of the same species or genus will enable the study of adaptation processes[3], disease progression, and micro-evolution in real time.[4] This technological shift empowers researchers to address questions at a genome-wide scale, for example by profiling the mRNA, miRNA, and DNA methylation states of a large set of biological samples in parallel.[5]
References
- ↑ Kalisky, T.; Quake, S.R. (2011). "Single-cell genomics". Nature Methods 8 (4): 311–4. doi:10.1038/nmeth0411-311. PMID 21451520.
- ↑ Trapnell, C.; Cacchiarelli, D.; Grimsby, J. et al. (2014). "The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells". Nature Biotechnology 32 (4): 381–86. doi:10.1038/nbt.2859. PMC PMC4122333. PMID 24658644. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4122333.
- ↑ Lindblad-Toh, K.; Garber, M.; Zuk, O. et al. (2011). "A high-resolution map of human evolutionary constraint using 29 mammals". Nature 478 (7370): 476–82. doi:10.1038/nature10530. PMC PMC3207357. PMID 21993624. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3207357.
- ↑ Kato-Maeda, M.; Ho, C.; Passarelli, B. et al. (2013). "Use of whole genome sequencing to determine the microevolution of Mycobacterium tuberculosis during an outbreak". PLoS One 8 (3): e58235. doi:10.1371/journal.pone.0058235. PMC PMC3589338. PMID 23472164. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3589338.
- ↑ Holt, R.A.; Jones, S.J. (2008). "The new paradigm of flow cell sequencing". Genome Research 18 (6): 839-46. doi:10.1101/gr.073262.107. PMID 18519653.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.
