Whole-genome analysis for backfat thickness in a tropically adapted, composite cattle breed from Brazil.

Backfat thickness affects the preservation of the beef carcass after slaughter and confers organoleptic characteristics assessed by the consumer. One of the breeding goals for Canchim, a tropically adapted breed, is to comprehensively increase fat thickness. Our goals were to identify genomic regions associated with backfat in Canchim populations and validate the association of single nucleotide polymorphisms (SNPs) overlapping previously identified QTL regions known to affect fat deposition. Fifteen animals with lower and 15 animals with higher residues for backfat, according to a linear model using the SAS GLM procedure, were selected from a population of 1171 animals and genotyped using the BovineSNP50 BeadChip. Initial analysis revealed more than 100 SNPs that discriminated the tails of phenotypic distribution. One extended region of association included the centromeric region of chromosome (Chr) 14. Because this region overlapped with QTL from previous reports, we developed SNP assays to interrogate two linkage disequilibrium blocks, one in the centromeric region and another in the middle region of Chr 14 to confirm the association. The analysis validated the presence of specific haplotypes affecting fat thickness.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining.

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one’s ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

The Star STING server: a multiplatform environment for protein structure analysis.

Star STING is the latest version of the STING suite of programs and corresponding database. We report on five important aspects of this package that have acquired some new characteristics, designed to add key advantages to the whole suite: 1) availability for most popular platforms and browsers, 2) introduction of the STING_DB quality assessment, 3) improvement in algorithms for calculation of three STING parameters, 4) introduction of five new STING modules, and 5) expansion of the existing modules. Star STING is freely accessible at: http://sms.cbi.cnptia.embrapa.br/SMS/, http://trantor.bioc.columbia.edu/SMS, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://www.ar.embnet.org/SMS.

Predicting enzyme class from protein structure using Bayesian classification

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.

Building multiple sequence alignments with a flavor of HSSP alignments

Homology-derived secondary structure of proteins (HSSP) is a well-known database of multiple sequence alignments (MSAs) which merges information of protein sequences and their three-dimensional structures. It is available for all proteins whose structure is deposited in the PDB. It is also used by STING and (Java)Protein Dossier to calculate and present relative entropy as a measure of the degree of conservation for each residue of proteins whose structure has been solved and deposited in the PDB. However, if the STING and (Java)Protein Dossier are to provide support for analysis of protein structures modeled in computers or being experimentally solved but not yet deposited in the PDB, then we need a new method for building alignments having a flavor of HSSP alignments (myMSAr). The present study describes a new method and its corresponding databank (SH2QS--database of sequences homologue to the query [structure-having] sequence). Our main interest in making myMSAr was to measure the degree of residue conservation for a given query sequence, regardless of whether it has a corresponding structure deposited in the PDB. In this study, we compare the measurement of residue conservation provided by corresponding alignments produced by HSSP and SH2QS. As a case study, we also present two biologically relevant examples, the first one highlighting the equivalence of analysis of the degree of residue conservation by using HSSP or SH2QS alignments, and the second one presenting the degree of residue conservation for a structure modeled in a computer, which , as a consequence, does not have an alignment reported by HSSP.

PDB-Metrics: a web tool for exploring the PDB contents

PDB-Metrics (http://sms.cbi.cnptia.embrapa.br/SMS/pdb_metrics/index.html) is a component of the Diamond STING suite of programs for the analysis of protein sequence, structure and function. It summarizes the characteristics of the collection of protein structure descriptions deposited in the Protein Data Bank (PDB) and provides a Web interface to search and browse the PDB, using a variety of alternative criteria. PDB-Metrics is a powerful tool for bioinformaticians to examine the data span in the PDB from several perspectives. Although other Web sites offer some similar resources to explore the PDB contents, PDB-Metrics is among those with the most complete set of such facilities, integrated into a single Web site. This program has been developed using SQLite, a C library that provides all the query facilities of a database management system

The Star STING server: a multiplatform environment for protein structure analysis

Star STING is the latest version of the STING suite of programs and corresponding database. We report on five important aspects of this package that have acquired some new characteristics, designed to add key advantages to the whole suite: 1) availability for most popular platforms and browsers, 2) introduction of the STING_DB quality assessment, 3) improvement in algorithms for calculation of three STING parameters, 4) introduction of five new STING modules, and 5) expansion of the existing modules. Star STING is freely accessible at: http://sms.cbi.cnptia.embrapa.br/SMS/, http://trantor.bioc.columbia.edu/SMS, http://www.es.embnet.org/SMS/, http://gibk26.bse.kyutech.ac.jp/SMS/ and http://www.ar.embnet.org/SMS.

STING Contacts: a web-based application for identification and analysis of amino acid contacts within protein structure and across protein interfaces.

UNLABELLED: Amino acid contacts in terms of atomic interactions are essential factors to be considered in the analysis of the structure of a protein and its complexes. Consequently, molecular biologists do require specific tools for the identification and visualization of all such contacts. Graphical contacts (GC) and interface forming residue graphical contacts (IFRgc) presented here, calculate atomic contacts among amino acids based on a table of predefined pairs of the atom types and their distances, and then display them using number of different forms. The inventory of currently listed contact types by GC and IFRgc include hydrogen bonds (in nine different flavors), hydrophobic interactions, charge-charge interactions, aromatic stacking and disulfide bonds. Such extensive catalog of the interactions, representing the forces that govern protein folding, stability and binding, is the key feature of these two applications. GC and IFRgc are part of STING Millennium Suite. AVAILABILITY: http://sms.cbi.cnptia.embrapa.br/SMS, http://trantor.bioc.columbia.edu/SMS, http://mirrors.rcsb.org//SMS, http://www.es.embnet.org/SMS and http://www.ar.embnet.org/SMS (Options: Graphical Contacts and IFR Graphical Contacts).

Defining 3D residue environment in protein structures using SCORPION and FORMIGA.

SUMMARY: Two web-based applications to analyze amino acids three-dimensional (3D) local environment within protein structures-SCORPION and FORMIGA-are presented. SCORPION and FORMIGA produce a graphical presentation for simple statistical data showing the frequency of residue occurrence within a given sphere (defined here as the 3D contacts). The center of that sphere is placed at the Calpha and at the last heavy atom in the side chain of the selected amino acid. Further depth of detail is given in terms of a secondary structure to which the profiled amino acid belongs. Results obtained with those two applications are relevant for estimating the importance of the amino acid 3D local environment for protein folding and stability. Effectively, SCORPION and FORMIGA construct knowledge-based force fields. The difference between SCORPION and FORMIGA is in that the latter operates on protein interfaces, while the former only functions for a single protein chain. Both applications are implemented as stand-alone components of STING Millennium Suite. AVAILABILITY: http://sms.cbi.cnptia.embrapa.br/SMS, http://trantor.bioc.columbia.edu/SMS, http://mirrors.rcsb.org/SMS, http://www.es.embnet.org/SMS and http://www.ar.embnet.org/SMS. [options: Scorpion, Formiga]

ConSSeq: a web-based application for analysis of amino acid conservation based on HSSP database and within context of structure.

SUMMARY: A web-based application to analyze protein amino acids conservation-Consensus Sequence (ConSSeq) is presented. ConSSeq graphically represents information about amino acid conservation based on sequence alignments reported in homology-derived structures of proteins. Beyond the relative entropy for each position in the alignment, ConSSeq also presents the consensus sequence and information about the amino acids, which are predominant at each position of the alignment. ConSSeq is part of the STING Millennium Suite and is implemented as a Java Applet. AVAILABILITY: http://sms.cbi.cnptia.embrapa.br/SMS/STINGm/consseq/, http://trantor.bioc.columbia.edu/SMS/STINGm/consseq/, http://mirrors.rcsb.org//SMS/STINGm/consseq/, http://www.es.embnet.org/SMS/STINGm/consseq/ and http://www.ar.embnet.org/SMS/STINGm/consseq/

Fast EM-like methods for maximum "a posteriori" estimates in emission tomography.

The maximum-likelihood (ML) approach in emission tomography provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. The expectation-maximization (EM) algorithm is an iterative algorithm for maximizing the Poisson likelihood in emission computed tomography that became very popular for solving the ML problem because of its attractive theoretical and practical properties. Recently, (Browne and DePierro, 1996 and Hudson and Larkin, 1994) block sequential versions of the EM algorithm that take advantage of the scanner's geometry have been proposed in order to accelerate its convergence. In Hudson and Larkin, 1994, the ordered subsets EM (OS-EM) method was applied to the ML problem and a modification (OS-GP) to the maximum a posteriori (MAP) regularized approach without showing convergence. In Browne and DePierro, 1996, we presented a relaxed version of OS-EM (RAMLA) that converges to an ML solution. In this paper, we present an extension of RAMLA for MAP reconstruction. We show that, if the sequence generated by this method converges, then it must converge to the true MAP solution. Experimental evidence of this convergence is also shown. To illustrate this behavior we apply the algorithm to positron emission tomography simulated data comparing its performance to OS-GP.