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Dave T. P. Tang

dblp:56/7392 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1

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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis
read mapping
0.112009
RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data · Bioinform. 2009
Bioinformatics and computational biology › transcriptomics › transcriptome sequencing
RNA sequencing analysis
0.112009
RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data · Bioinform. 2009
Bioinformatics and computational biology › sequence analysis › read mapping
spliced alignment
0.112009
RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data · Bioinform. 2009

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

recursive mapping · 0.1
YearPublicationVenuePosition
2009 RNA-MATE: a recursive mapping strategy for high-throughput RNA-sequencing data
abstract
UNLABELLED: Mapping of next-generation sequencing data derived from RNA samples (RNAseq) presents different genome mapping challenges than data derived from DNA. For example, tags that cross exon-junction boundaries will often not map to a reference genome, and the strand specificity of the data needs to be retained. Here we present RNA-MATE, a computational pipeline based on a recursive mapping strategy for placing strand specific RNAseq data onto a reference genome. Maximizing the mappable tags can provide significant savings in the cost of sequencing experiments. This pipeline provides an automatic and integrated way to align color-space sequencing data, collate this information and generate files for examining gene-expression data in a genomic context. AVAILABILITY: Executables, source code, and exon-junction libraries are available from http://grimmond.imb.uq.edu.au/RNA-MATE/
Nicole Cloonan, Qinying Xu, Geoffrey J. Faulkner, Darrin F. Taylor, Dave T. P. Tang, Gabriel Kolle, Sean M. Grimmond
Bioinform.5