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Mark J. Dunning

dblp:02/4661 · DBLP profile ↗
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6ranked-venue papers
2as first author
1since 2021 · last 2021
0000-0002-8853-9435ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 6 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
microarray data analysis
0.222008
BASH: a tool for managing BeadArray spatial artefacts · Bioinform. 2008
beadarray: R classes and methods for Illumina bead-based data · Bioinform. 2007
Bioinformatics and computational biology
gene expression analysis
0.112007
beadarray: R classes and methods for Illumina bead-based data · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

spatial artefact detection · 0.1image processing · 0.1background correction · 0.1
YearPublicationVenuePosition
2021 Fostering accessible online education using Galaxy as an e-learning platform
abstract
The COVID-19 pandemic is shifting teaching to an online setting all over the world. The Galaxy framework facilitates the online learning process and makes it accessible by providing a library of high-quality community-curated training materials, enabling easy access to data and tools, and facilitates sharing achievements and progress between students and instructors. By combining Galaxy with robust communication channels, effective instruction can be designed inclusively, regardless of the students' environments.
Beatriz Serrano-Solano, Melanie Christine Föll, Cristóbal Gallardo-Alba, Anika Erxleben-Eggenhofer, Helena Rasche, Saskia D. Hiltemann, Matthias Fahrner, Mark J. Dunning, Marcel H. Schulz, Beáta Scholtz, Dave Clements, Anton Nekrutenko, Bérénice Batut, Björn A. Grüning
PLoS Comput. Biol.8
2011 BeadArray Expression Analysis Using Bioconductor
abstract
Illumina whole-genome expression BeadArrays are a popular choice in gene profiling studies. Aside from the vendor-provided software tools for analyzing BeadArray expression data (GenomeStudio/BeadStudio), there exists a comprehensive set of open-source analysis tools in the Bioconductor project, many of which have been tailored to exploit the unique properties of this platform. In this article, we explore a number of these software packages and demonstrate how to perform a complete analysis of BeadArray data in various formats. The key steps of importing data, performing quality assessments, preprocessing, and annotation in the common setting of assessing differential expression in designed experiments will be covered.
Matthew E. Ritchie, Mark J. Dunning, Mike L. Smith, Andy G. Lynch
PLoS Comput. Biol.2
2010 Identification and correction of previously unreported spatial phenomena using raw Illumina BeadArray data
abstract
BACKGROUND: A key stage for all microarray analyses is the extraction of feature-intensities from an image. If this step goes wrong, then subsequent preprocessing and processing stages will stand little chance of rectifying the matter. Illumina employ random construction of their BeadArrays, making feature-intensity extraction even more important for the Illumina platform than for other technologies. In this paper we show that using raw Illumina data it is possible to identify, control, and perhaps correct for a range of spatial-related phenomena that affect feature-intensity extraction. RESULTS: We note that feature intensities can be unnaturally high when in the proximity of a number of phenomena relating either to the images themselves or to the layout of the beads on an array. Additionally we note that beads neighbour beads of the same type more often than one might expect, which may cause concern in some models of hybridization. We highlight issues in the identification of a bead's location, and in particular how this both affects and is affected by its intensity. Finally we show that beads can be wrongly identified in the image on either a local or array-wide scale, with obvious implications for data quality. CONCLUSIONS: The image processing issues identified will often pass unnoticed by an analysis of the standard data returned from an experiment. We detail some simple diagnostics that can be implemented to identify problems of this nature, and outline approaches to correcting for such problems. These approaches require access to the raw data from the arrays, not just the summarized data usually returned, making the acquisition of such raw data highly desirable.
Mike L. Smith, Mark J. Dunning, Simon Tavaré, Andy G. Lynch
BMC Bioinform.2
2008 BASH: a tool for managing BeadArray spatial artefacts
abstract
Abstract Summary: With their many replicates and their random layouts, Illumina BeadArrays provide greater scope fordetecting spatial artefacts than do other microarray technologies. They are also robust to artefact exclusion, yet there is a lack of tools that can perform these tasks for Illumina. We present BASH, a tool for this purpose. BASH adopts the concepts of Harshlight, but implements them in a manner that utilizes the unique characteristics of the Illumina technology. Using bead-level data, spatial artefacts of various kinds can thus be identified and excluded from further analyses. Availability: The beadarray Bioconductor package (version 1.10 onwards), www.bioconductor.org Contact: [email protected] Supplementary information: Additional information and a vignette are included in the beadarray package.
Jonathan M. Cairns, Mark J. Dunning, Matthew E. Ritchie, Roslin Russell, Andy G. Lynch
Bioinform.2
2008 Statistical issues in the analysis of Illumina data
abstract
BACKGROUND: Illumina bead-based arrays are becoming increasingly popular due to their high degree of replication and reported high data quality. However, little attention has been paid to the pre-processing of Illumina data. In this paper, we present our experience of analysing the raw data from an Illumina spike-in experiment and offer guidelines for those wishing to analyse expression data or develop new methodologies for this technology. RESULTS: We find that the local background estimated by Illumina is consistently low, and subtracting this background is beneficial for detecting differential expression (DE). Illumina's summary method performs well at removing outliers, producing estimates which are less biased and are less variable than other robust summary methods. However, quality assessment on summarised data may miss spatial artefacts present in the raw data. Also, we find that the background normalisation method used in Illumina's proprietary software (BeadStudio) can cause problems with a standard DE analysis. We demonstrate that variances calculated from the raw data can be used as inverse weights in the DE analysis to improve power. Finally, variability in both expression levels and DE statistics can be attributed to differences in probe composition. These differences are not accounted for by current analysis methods and require further investigation. CONCLUSION: Analysing Illumina expression data using BeadStudio is reasonable because of the conservative estimates of summary values produced by the software. Improvements can however be made by not using background normalisation. Access to the raw data allows for a more detailed quality assessment and flexible analyses. In the case of a gene expression study, data can be analysed on an appropriate scale using established tools. Similar improvements can be expected for other Illumina assays.
Mark J. Dunning, Nuno L. Barbosa-Morais, Andy G. Lynch, Simon Tavaré, Matthew E. Ritchie
BMC Bioinform.1
2007 beadarray: R classes and methods for Illumina bead-based data
abstract
UNLABELLED: The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software. The package also allows quality assessment to be carried out on the raw data. The data can then be summarized and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarized data processed by Illumina's; BeadStudio software can also be read and analysed in the same manner. AVAILABILITY: The beadarray package is available from the Bioconductor web page at www.bioconductor.org. A user's guide and example data sets are provided with the package.
Mark J. Dunning, Mike L. Smith, Matthew E. Ritchie, Simon Tavaré
Bioinform.1