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Tiago José P. Sobreira

dblp:78/5687 · DBLP profile ↗
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3ranked-venue papers
2as first author
0since 2021 · last 2014
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 3 · 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%
Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › sequence analysis
sequence assembly
0.112008
Sequence-specific reconstruction from fragmentary databases using seed sequences: implementation and validation on SAGE, proteome and generic sequencing data · Bioinform. 2008
Bioinformatics and computational biology › genomics › repetitive DNA analysis
tandem repeat analysis
0.112006
TRAP: automated classification, quantification and annotation of tandemly repeated sequences · Bioinform. 2006
Bioinformatics and computational biology › sequence analysis › repeat detection
tandem repeat annotation
0.112006
TRAP: automated classification, quantification and annotation of tandemly repeated sequences · Bioinform. 2006
Information retrieval
similarity search
0.012008
Sequence-specific reconstruction from fragmentary databases using seed sequences: implementation and validation on SAGE, proteome and generic sequencing data · Bioinform. 2008
Bioinformatics and computational biology
genome annotation
0.012006
TRAP: automated classification, quantification and annotation of tandemly repeated sequences · Bioinform. 2006

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

similarity search · 0.2recursive assembly · 0.2tandem repeats finder · 0.1
YearPublicationVenuePosition
2014 KVFinder: steered identification of protein cavities as a PyMOL plugin
abstract
BACKGROUND: The characterization of protein binding sites is a major challenge in computational biology. Proteins interact with a wide variety of molecules and understanding of such complex interactions is essential to gain deeper knowledge of protein function. Shape complementarity is known to be important in determining protein-ligand interactions. Furthermore, these protein structural features have been shown to be useful in assisting medicinal chemists during lead discovery and optimization. RESULTS: We developed KVFinder, a highly versatile and easy-to-use tool for cavity prospection and spatial characterization. KVFinder is a geometry-based method that has an innovative customization of the search space. This feature provides the possibility of cavity segmentation, which alongside with the large set of customizable parameters, allows detailed cavity analyses. Although the main focus of KVFinder is the steered prospection of cavities, we tested it against a benchmark dataset of 198 known drug targets in order to validate our software and compare it with some of the largely accepted methods. Using the one click mode, we performed better than most of the other methods, staying behind only of hybrid prospection methods. When using just one of KVFinder's customizable features, we were able to outperform all other compared methods. KVFinder is also user friendly, as it is available as a PyMOL plugin, or command-line version. CONCLUSION: KVFinder presents novel usability features, granting full customizable and highly detailed cavity prospection on proteins, alongside with a friendly graphical interface. KVFinder is freely available on http://lnbio.cnpem.br/bioinformatics/main/software/.
Saulo Henrique Pires de Oliveira, Felipe A. N. Ferraz, Rodrigo V. Honorato, José Xavier-Neto, Tiago José P. Sobreira, Paulo S. L. de Oliveira
BMC Bioinform.5
2008 Sequence-specific reconstruction from fragmentary databases using seed sequences: implementation and validation on SAGE, proteome and generic sequencing data
abstract
MOTIVATION: DNA assembly programs classically perform an all-against-all comparison of reads to identify overlaps, followed by a multiple sequence alignment and generation of a consensus sequence. If the aim is to assemble a particular segment, instead of a whole genome or transcriptome, a target-specific assembly is a more sensible approach. GenSeed is a Perl program that implements a seed-driven recursive assembly consisting of cycles comprising a similarity search, read selection and assembly. The iterative process results in a progressive extension of the original seed sequence. GenSeed was tested and validated on many applications, including the reconstruction of nuclear genes or segments, full-length transcripts, and extrachromosomal genomes. The robustness of the method was confirmed through the use of a variety of DNA and protein seeds, including short sequences derived from SAGE and proteome projects. AVAILABILITY: GenSeed is available under the GNU General Public License at http://www.coccidia.icb.usp.br/genseed/
Tiago José P. Sobreira, Arthur Gruber
Bioinform.1
2006 TRAP: automated classification, quantification and annotation of tandemly repeated sequences
abstract
Abstract Summary: TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files. Availability: TRAP is available under the GNU General Public License at Contact: [email protected] Supplementary Information: Supplementary data are available at
Tiago José P. Sobreira, Alan M. Durham, Arthur Gruber
Bioinform.1