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Daniel R. Caffrey

dblp:88/4688 · DBLP profile ↗
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4ranked-venue papers
2as first author
0since 2021 · last 2008
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 4 · 2 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%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Parallel and multicore computing · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › comparative genomics › orthology analysis
ortholog identification
0.012002
Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002
Bioinformatics and computational biology › sequence analysis › sequence similarity search
sequence database search
0.012002
Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002
Parallel and multicore computing
parallel programming models
0.012003
Wrapping up BLAST and other applications for use on Unix clusters · Bioinform. 2003
Parallel and multicore computing › parallel programming models
task parallelism
0.012003
Wrapping up BLAST and other applications for use on Unix clusters · Bioinform. 2003
Bioinformatics and computational biology
phylogenetics
0.012002
Improved database searches for orthologous sequences by conditioning on outgroup sequences · Bioinform. 2002

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

perl scripting · 0.1job distribution · 0.1outgroup conditioning · 0.0log-odds scoring · 0.0
YearPublicationVenuePosition
2008 Prediction of specificity-determining residues for small-molecule kinase inhibitors
abstract
BACKGROUND: Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors. RESULTS: Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison. CONCLUSION: S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.
Daniel R. Caffrey, Elizabeth A. Lunney, Deborah J. Moshinsky
BMC Bioinform.1
2007 PFAAT version 2.0: A tool for editing, annotating, and analyzing multiple sequence alignments
abstract
BACKGROUND: By virtue of their shared ancestry, homologous sequences are similar in their structure and function. Consequently, multiple sequence alignments are routinely used to identify trends that relate to function. This type of analysis is particularly productive when it is combined with structural and phylogenetic analysis. RESULTS: Here we describe the release of PFAAT version 2.0, a tool for editing, analyzing, and annotating multiple sequence alignments. Support for multiple annotations is a key component of this release as it provides a framework for most of the new functionalities. The sequence annotations are accessible from the alignment and tree, where they are typically used to label sequences or hyperlink them to related databases. Sequence annotations can be created manually or extracted automatically from UniProt entries. Once a multiple sequence alignment is populated with sequence annotations, sequences can be easily selected and sorted through a sophisticated search dialog. The selected sequences can be further analyzed using statistical methods that explicitly model relationships between the sequence annotations and residue properties. Residue annotations are accessible from the alignment viewer and are typically used to designate binding sites or properties for a particular residue. Residue annotations are also searchable, and allow one to quickly select alignment columns for further sequence analysis, e.g. computing percent identities. Other features include: novel algorithms to compute sequence conservation, mapping conservation scores to a 3D structure in Jmol, displaying secondary structure elements, and sorting sequences by residue composition. CONCLUSION: PFAAT provides a framework whereby end-users can specify knowledge for a protein family in the form of annotation. The annotations can be combined with sophisticated analysis to test hypothesis that relate to sequence, structure and function.
Daniel R. Caffrey, Paul H. Dana, Vidhya Mathur, Marco Ocano, Eun-Jong Hong, Yaoyu E. Wang, Shyamal Somaroo, Brian E. Caffrey, Shobha Potluri, Enoch S. Huang
BMC Bioinform.1
2003 Wrapping up BLAST and other applications for use on Unix clusters
abstract
Abstract Summary: We have developed two programs that speed up common bioinformatic applications by spreading them across a UNIX cluster.(1) BLAST.pm, a new module for the ‘MOLLUSC’ package. (2) WRAPID, a simple tool for parallelizing large numbers of small instances of programs such as BLAST, FASTA and CLUSTALW. Availability: The packages were developed in Perl on a 20-node Linux cluster and are provided together with a configuration script and documentation. They can be freely downloaded from http://wolfe.gen.tcd.ie/wrapper Contact: [email protected]@oscar.gen.tcd.ie * These authors contributed equally to this work. † Current Address: Pfizer Discovery Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA.
Karsten Hokamp, Denis C. Shields, Kenneth H. Wolfe, Daniel R. Caffrey
Bioinform.4
2002 Improved database searches for orthologous sequences by conditioning on outgroup sequences
abstract
Abstract Motivation: Searches of biological sequence databases are usually focussed on distinguishing significant from random matches. However, the increasing abundance of related sequences on databases present a second challenge: to distinguish the evolutionarily most closely related sequences (often orthologues) from more distantly related homologues. This is particularly important when searching a database of partial sequences, where short orthologous sequences from a non-conserved region will score much more poorly than non-orthologous (outgroup) sequences from a conserved region. Results: Such inferences are shown to be improved by conditioning the search results on the scores of an outgroup sequence. The log-odds score for each target sequence identified on the database has the log-odds score of the outgroup sequence subtracted from it. A test group of Caenorhabditis elegans kinase sequences and their identified C.elegans outgroups were searched against a test database of human Expressed Sequence Tag (EST) sequences, where the sets of true target sequences were known in advance. The outgroup conditioned method was shown to identify 58% more true positives ahead of the first false positive, compared to the straightforward search without an outgroup. A test dataset of 151 proteins drawn from the C.elegans genome, where the putative ‘outgroup’ was assigned automatically, similarly found 50% more true positives using outgroup conditioning. Thus, outgroup conditioning provides a means to improve the results of database searching with little increase in the search computation time. Availability: Perl scripts for the Outgroup Conditioned Score (OCS) method are available without charge for non-profit academic use from http://www.bioinf.org/vibe/software/OCS/. Scripts have been optimized for Linux or OSF with a Perl v5 interpreter. Contact: [email protected]. * Non-standard abbreviations: OCS: Outgroup Conditioned Score, HOCS Heuristic Outgroup Conditioned Score, HOCP: Heuristic Outgroup Conditioned P-value ratio, EST: Expressed Sequence Tag. 1 To whom correspondence should be addressed.
Philip J. Cotter, Daniel R. Caffrey, Denis C. Shields
Bioinform.2