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A New Voronoi-Based Surface Reconstruction Algorithm
, 2002
"... We describe our experience with a new algorithm for the reconstruction of surfaces from unorganized sample points in R³. The algorithm is the first for this problem with provable guarantees. Given a “good sample” from a smooth surface, the output is guaranteed to be topologically correct and converg ..."
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Cited by 414 (9 self)
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We describe our experience with a new algorithm for the reconstruction of surfaces from unorganized sample points in R³. The algorithm is the first for this problem with provable guarantees. Given a “good sample” from a smooth surface, the output is guaranteed to be topologically correct and convergent to the original surface as the sampling density increases. The definition of a good sample is itself interesting: the required sampling density varies locally, rigorously capturing the intuitive notion that featureless areas can be reconstructed from fewer samples. The output mesh interpolates, rather than approximates, the input points. Our algorithm is based on the three-dimensional Voronoi diagram. Given a good program for this fundamental subroutine, the algorithm is quite easy to implement.
Pfam: clans, web tools and services
- Nucleic Acids Res
, 2006
"... Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tool ..."
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Cited by 296 (13 self)
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Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK
PROBCONS: Probabilistic consistency-based multiple sequence alignment
- Genome Res
, 2005
"... To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objec ..."
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Cited by 256 (10 self)
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To study gene evolution across a wide range of organisms, biologists need accurate tools for multiple sequence alignment of protein families. Obtaining accurate alignments, however, is a difficult computational problem because of not only the high computational cost but also the lack of proper objective functions for measuring alignment quality. In this paper, we introduce prob-abilistic consistency, a novel scoring function for multiple sequence comparisons. We present PROBCONS, a practical tool for progressive protein multiple sequence alignment based on prob-abilistic consistency, and evaluate its performance on several standard alignment benchmark datasets. On the BAliBASE, SABmark, and PREFAB benchmark alignment databases, PROB-CONS achieves statistically significant improvement over other leading methods while maintain-ing practical speed. PROBCONS is publicly available as a web resource. Source code and execu-tables are available under the GNU Public License at
Pfam: clans, web tools and services. Nucleic Acids Res
, 2006
"... doi:10.1093/nar/gkj149 ..."
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Predicting the functional impact of protein mutations: application to cancer genomics
- Nucleic Acids Res. 39, e118. ACS Chemical Biology Letters dx.doi.org/10.1021/cb500347p | ACS Chem. Biol
, 2011
"... As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conserva ..."
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Cited by 102 (3 self)
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As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (19200), assumed to be strongly functional, from common polymorphisms (35600), assumed to be weakly functional (area under the receiver operating characteristic curve of 0.86). In cancer, using recurrence, multiplicity and annotation for 10000 mutations in the COSMIC database, the method does well in assign-ing higher scores to more likely functional mutations (‘drivers’). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5 % of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.
OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups
- Nucleic Acids Res
, 2006
"... of ortholog groups ..."
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M-Coffee: combining multiple sequence alignment methods with T-Coffee
- Nucleic Acids Res
, 2006
"... We introduce M-Coffee, a meta-method for assembling multiple sequence alignments (MSA) by combining the output of several individual methods into one single MSA. M-Coffee is an extension of T-Coffee and uses consistency to estimate a consensus alignment. We show that the procedure is robust to varia ..."
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Cited by 60 (13 self)
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We introduce M-Coffee, a meta-method for assembling multiple sequence alignments (MSA) by combining the output of several individual methods into one single MSA. M-Coffee is an extension of T-Coffee and uses consistency to estimate a consensus alignment. We show that the procedure is robust to variations in the choice of constituent methods and reasonably tolerant to duplicate MSAs. We also show that performances can be improved by carefully selecting the constituent methods. M-Coffee outperforms all the individual methods on three major reference datasets: HOMSTRAD, Prefab and Balibase. We also show that on a case-by-case basis, M-Coffee is twice as likely to deliver the best alignment than any individual method. Given a collection of pre-computed MSAs, M-Coffee has similar CPU requirements to the original T-Coffee. M-Coffee is a freeware open-source package available from
Probalign: Multiple sequence alignment using partition function posterior probabilities
- Bioinformatics
, 2006
"... Motivation: The maximum expected accuracy optimization criterion for multiple sequence alignment uses pairwise posterior probabilities of residues to align sequences. The partition function methodology is one way of estimating these probabilities. Here, we combine these two ideas for the first time ..."
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Cited by 59 (7 self)
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Motivation: The maximum expected accuracy optimization criterion for multiple sequence alignment uses pairwise posterior probabilities of residues to align sequences. The partition function methodology is one way of estimating these probabilities. Here, we combine these two ideas for the first time to construct maximal expected accuracy sequence alignments. Results: We bridge the two techniques within the program Probalign. Our results indicate that Probalign alignments are generally more accurate than other leading multiple sequence alignment methods (i.e., Probcons, MAFFT, and MUSCLE) on the BAliBASE 3.0 protein alignment benchmark. Similarly, Probalign also outperforms these methods on the HOMSTRAD and OXBENCH benchmarks. Probalign ranks statistically significantly highest (P-value < 0.005) on all three benchmarks. Deeper scrutiny of the technique indicates that the improvements are largest on datasets containing N/C terminal extensions and on datasets containing long and heterogeneous length proteins. These points are demonstrated on both real and simulated data. Finally, our method also produces accurate alignments on long and heterogeneous length datasets containing protein repeats. There, alignment accuracy scores are at least 10% and 15 % higher than the other three methods when standard deviation of length is at least 300 and 400 respectively. Availability: Open source code implementing Probalign as well as for producing the simulated data, and all real and simulated data are freely available from
