Demo Data from VLDB 2026 / reviewers in the wild / expert
Nigel P. Brown
dblp:60/3738
· DBLP profile ↗
5ranked-venue papers
2as first author
0since 2021 · last 2007
0000-0002-5164-4186ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 5 · 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
5 papers |
Bioinformatics and computational biology · 100% |
Topics — the 10 heaviest of 10, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
sequence analysis |
0.1 | 2 | 2007 | Clustal W and Clustal X version 2.0 · Bioinform. 2007 MView: a web-compatible database search or multiple alignment viewer · Bioinform. 1998 |
Bioinformatics and computational biology
multiple sequence alignment |
0.1 | 1 | 2007 | Clustal W and Clustal X version 2.0 · Bioinform. 2007 |
Bioinformatics and computational biology › genome annotation
automatic annotation |
0.0 | 1 | 1999 | Automated genome sequence analysis and annotation · Bioinform. 1999 |
Bioinformatics and computational biology
genome annotation |
0.0 | 1 | 1999 | Automated genome sequence analysis and annotation · Bioinform. 1999 |
Bioinformatics and computational biology
protein function prediction |
0.0 | 1 | 1999 | Automated genome sequence analysis and annotation · Bioinform. 1999 |
Bioinformatics and computational biology
genomics |
0.0 | 1 | 1998 | Frame: detection of genomic sequencing errors · Bioinform. 1998 |
Bioinformatics and computational biology › multiple sequence alignment
multiple sequence alignment visualization |
0.0 | 1 | 1998 | MView: a web-compatible database search or multiple alignment viewer · Bioinform. 1998 |
Bioinformatics and computational biology › sequence analysis › sequence similarity search
sequence database search |
0.0 | 1 | 1999 | Automated genome sequence analysis and annotation · Bioinform. 1999 |
Bioinformatics and computational biology › genome annotation › coding region prediction
open reading frame prediction |
0.0 | 1 | 1998 | Frame: detection of genomic sequencing errors · Bioinform. 1998 |
Bioinformatics and computational biology › structural bioinformatics
protein structure classification |
0.0 | 1 | 1994 | Growing a classification tree using the apparent misclassification rate · Comput. Appl. Biosci. 1994 |
Methods — techniques the papers use, named apart from their topics
progressive alignment · 0.1sequence analysis · 0.0lexical analysis · 0.0homology matching · 0.0HTML rendering · 0.0BLASTX · 0.0linear discriminant analysis · 0.0dynamic programming · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Clustal W and Clustal X version 2.0abstractSUMMARY: The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems. AVAILABILITY: The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/ Mark A. Larkin, Gordon Blackshields, Nigel P. Brown, R. Chenna, Paul A. McGettigan, Hamish McWilliam, Franck Valentin, Iain M. Wallace, Andreas Wilm, Rodrigo Lopez, Julie Dawn Thompson, Toby J. Gibson, Desmond G. Higgins |
Bioinform. | 3 |
| 1999 | Automated genome sequence analysis and annotationabstractMOTIVATION: Large-scale genome projects generate a rapidly increasing number of sequences, most of them biochemically uncharacterized. Research in bioinformatics contributes to the development of methods for the computational characterization of these sequences. However, the installation and application of these methods require experience and are time consuming. RESULTS: We present here an automatic system for preliminary functional annotation of protein sequences that has been applied to the analysis of sets of sequences from complete genomes, both to refine overall performance and to make new discoveries comparable to those made by human experts. The GeneQuiz system includes a Web-based browser that allows examination of the evidence leading to an automatic annotation and offers additional information, views of the results, and links to biological databases that complement the automatic analysis. System structure and operating principles concerning the use of multiple sequence databases, underlying sequence analysis tools, lexical analyses of database annotations and decision criteria for functional assignments are detailed. The system makes automatic quality assessments of results based on prior experience with the underlying sequence analysis tools; overall error rates in functional assignment are estimated at 2.5-5% for cases annotated with highest reliability ('clear' cases). Sources of over-interpretation of results are discussed with proposals for improvement. A conservative definition for reporting 'new findings' that takes account of database maturity is presented along with examples of possible kinds of discoveries (new function, family and superfamily) made by the system. System performance in relation to sequence database coverage, database dynamics and database search methods is analysed, demonstrating the inherent advantages of an integrated automatic approach using multiple databases and search methods applied in an objective and repeatable manner. AVAILABILITY: The GeneQuiz system is publicly available for analysis of protein sequences through a Web server at http://www.sander.ebi.ac. uk/gqsrv/submit Miguel A. Andrade-Navarro, Nigel P. Brown, Christophe Leroy, S. Hörsch, Antoine de Daruvar, C. Reich, Angelo Franchini, Javier Tamames, Alfonso Valencia, Christos A. Ouzounis, Chris Sander |
Bioinform. | 2 |
| 1998 | MView: a web-compatible database search or multiple alignment viewerabstractUNLABELLED: MView is a tool for converting the results of a sequence database search into the form of a coloured multiple alignment of hits stacked against the query. Alternatively, an existing multiple alignment can be processed. In either case, the output is simply HTML, so the result is platform independent and does not require a separate application or applet to be loaded. AVAILABILITY: Free from http://www.sander.ebi.ac.uk/mview/ subject to copyright restrictions. CONTACT: [email protected] Nigel P. Brown, Christophe Leroy, Chris Sander |
Bioinform. | 1 |
| 1998 | Frame: detection of genomic sequencing errorsabstractMOTIVATION: The underlying error rate for genomic sequencing sometimes results in the introduction of artificial frameshifts and in-frame stop codons into putative protein encoding genes. Severe errors are then introduced into the inferred transcripts through mis-translation or premature termination. RESULTS: We describe a system for screening segments of DNA for frameshift and in-frame stop errors in coding regions. The method is based on homology matching using blastx to compare all six reading frames of the query nucleotide sequence against selected protein sequence databases. Fragments of protein matching neighbouring regions of the query DNA are united and extended laterally to define candidate open reading frames, within which, frameshifts and stops are identified. Suitable targets include prokaryotic or other intron-free genomic sequence and complementary DNAs. As an example of its use, we report here two frameshifted ORFs that deviate from the original TIGR sequence annotations for the recently released Helicobacter pylori genome. AVAILABILITY: The tool is accessible via the URL http://www.sander.ebi.ac.uk/frame/. CONTACT: [email protected]. Nigel P. Brown, Chris Sander, Peer Bork |
Bioinform. | 1 |
| 1994 | Growing a classification tree using the apparent misclassification rateabstractA method to determine the size of a classification tree is proposed. This method is based on the change of the apparent misclassification rate (AMR) of the tree at each growing stage. The method is simple and fast compared to the other classification tree methods, which are based on minimizing a cost complexity function. To test the method, it was used to classify species of fungi, and the results are in good agreement with those obtained by linear discriminant analysis. Also, 21 proteins with known structures and functions were classified using the proposed method. For this purpose the coefficient of variation for several properties of the secondary structures of these proteins has been used. Again, the results were in good agreement with the classification obtained previously using dynamic programming. Elias Zintzaras, Nigel P. Brown, Axel Kowald |
Comput. Appl. Biosci. | 2 |