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Jessica Ebert

dblp:16/2559 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 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
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › multiple sequence alignment
consistency-based alignment
0.112006
Development and validation of a consistency based multiple structure alignment algorithm · Bioinform. 2006
Bioinformatics and computational biology › protein structure analysis › protein structure alignment
multiple structure alignment
0.112006
Development and validation of a consistency based multiple structure alignment algorithm · Bioinform. 2006
Bioinformatics and computational biology
protein structure analysis
0.112006
Development and validation of a consistency based multiple structure alignment algorithm · Bioinform. 2006
Bioinformatics and computational biology
structural bioinformatics
0.112006
Development and validation of a consistency based multiple structure alignment algorithm · Bioinform. 2006

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

pairwise superposition · 0.1global and local refinement · 0.1
YearPublicationVenuePosition
2006 Development and validation of a consistency based multiple structure alignment algorithm
abstract
Abstract Summary: We introduce an algorithm that uses the information gained from simultaneous consideration of an entire group of related proteins to create multiple structure alignments (MSTAs). Consistency-based alignment (CBA) first harnesses the information contained within regions that are consistently aligned among a set of pairwise superpositions in order to realign pairs of proteins through both global and local refinement methods. It then constructs a multiple alignment that is maximally consistent with the improved pairwise alignments. We validate CBA's alignments by assessing their accuracy in regions where at least two of the aligned structures contain the same conserved sequence motif. Results: CBA correctly aligns well over 90% of motif residues in superpositions of proteins belonging to the same family or superfamily, and it outperforms a number of previously reported MSTA algorithms. Availability: CBA is available at and the source code is freely available at Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
Jessica Ebert, Douglas L. Brutlag
Bioinform.1