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Elizabeth A. McClellan

dblp:123/8492 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none

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

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

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 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
cancer genomics
0.212013
iFUSE: integrated fusion gene explorer · Bioinform. 2013
Bioinformatics and computational biology › cancer genomics
gene fusion detection
0.212013
iFUSE: integrated fusion gene explorer · Bioinform. 2013
Bioinformatics and computational biology › proteomics
mass spectrometry data analysis
0.212013
NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data · Bioinform. 2013
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction network analysis
0.212013
NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data · Bioinform. 2013
Bioinformatics and computational biology
proteomics
0.212013
NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data · Bioinform. 2013
Bioinformatics and computational biology › genomics
structural variation
0.212013
iFUSE: integrated fusion gene explorer · Bioinform. 2013
Bioinformatics and computational biology › network bioinformatics › biological network analysis
biological network clustering
0.012013
NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data · Bioinform. 2013

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

permutation testing · 0.2dense cluster detection · 0.2breakpoint analysis · 0.2
YearPublicationVenuePosition
2013 iFUSE: integrated fusion gene explorer
abstract
UNLABELLED: We present iFUSE (integrated fusion gene explorer), an online visualization tool that provides a fast and informative view of structural variation data and prioritizes those breaks likely representing fusion genes. This application uses calculated break points to determine fusion genes based on the latest annotation for genomic sequence information, and where relevant the structural variation (SV) events are annotated with predicted RNA and protein sequences. iFUSE takes as input a Complete Genomics (CG) junction file, a FusionMap fusion detection report file or a file already analysed and annotated by the iFUSE application on a previous occasion. RESULTS: We demonstrate the use of iFUSE with case studies from tumour-normal SV detection derived from Complete Genomics whole-genome sequencing results. AVAILABILITY: iFUSE is available as a web service at http://ifuse.erasmusmc.nl.
Saskia D. Hiltemann, Elizabeth A. McClellan, Jos van Nijnatten, Sebastiaan Horsman, Ivo Palli, Ines Teles Alves, Thomas Hartjes, Jan Trapman, Peter J. van der Spek, Guido Jenster, Andrew Stubbs
Bioinform.2
2013 NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data
abstract
SUMMARY: The discovery of functionally related groups in a set of significantly abundant proteins from a mass spectrometry experiment is an important step in a proteomics analysis pipeline. Here we describe NetWeAvers (Network Weighted Averages) for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers is an R package that provides a novel method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks. AVAILABILITY AND IMPLEMENTATION: The NetWeAvers package is written in R, is open source and is freely available on CRAN and from netweavers.erasmusmc.nl under the GPL-v2 license. CONTACT: [email protected]
Elizabeth A. McClellan, Perry D. Moerland, Peter J. van der Spek, Andrew Stubbs
Bioinform.1