PSS-SQL: Protein Secondary Structure - Structured Query Language

Mrozek, Dariusz; Wieczorek, Dominika; Małysiak-Mrozek, Bożena; Kozielski, Stanisław

doi:10.1109/iembs.2010.5627303

Cited by 12 publications

(3 citation statements)

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“…the primary, secondary and tertiary level. In [33] and [34], we reported the PSS-SQL (Protein Secondary Structure -Structured Query Language) for searching protein similarities on the basis of their secondary structures in the Microsoft SQL Server relational database. The newest version of the PSS-SQL search engine [35] utilizes a multi-threaded alignment procedure, which allows effective querying on DBMSs hosted on computers with multi-core CPUs.…”

Section: B Related Workmentioning

confidence: 99%

Protein Construction-Based Data Partitioning Scheme for Alignment of Protein Macromolecular Structures Through Distributed Querying in Federated Databases

Mrozek

Kwiendacz

2020

IEEE Trans.on Nanobioscience

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Section: B Related Workmentioning

confidence: 99%

Protein Construction-Based Data Partitioning Scheme for Alignment of Protein Macromolecular Structures Through Distributed Querying in Federated Databases

Mrozek

Kwiendacz

2020

IEEE Trans.on Nanobioscience

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“…The BioSQL [25], which incorporates modules of the BioJava project [26], focuses on biomolecular sequences and features, their annotation, a reference taxonomy, and ontologies. Several extensions to the SQL language, including PSS-SQL [27,28] and the query language developed by Hammel and Patel [29] and Tata et al [30], were proposed for searching protein similarities on the basis of protein secondary structures. These works show how protein data can be stored in relational tables.…”

Section: Related Workmentioning

confidence: 99%

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment

Mrozek

Dąbek

2019

Molecules

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Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.

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“…Protein-QL allows to formulate simple queries that operate on the primary, secondary and tertiary level. Finally, in Małysiak- Mrozek et al (2012) and Mrozek et al (2010), we reported the PSS-SQL (Protein Secondary Structure -Structured Query Language) that allows to search for protein similarities on the basis of secondary structures. The search engine of the PSS-SQL utilized a single-thread alignment procedure, which we found insufficient for effective processing in the era of multi-core CPUs.…”

Section: Related Workmentioning

confidence: 99%

An efficient and flexible scanning of databases of protein secondary structures

et al. 2015

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Protein secondary structure describe protein construction in terms of regular spatial shapes, including alpha-helices, beta-strands, and loops, which protein amino acid chain can adopt in some of its regions. This information is supportive for protein classification, functional annotation, and 3D structure prediction. The relevance of this information and the scope of its practical applications cause the requirement for its effective storage and processing. Relational databases, widely-used in commercial systems in recent years, are one of the serious alternatives honed by years of experience, enriched with developed technologies, equipped with the declarative SQL query language, and accepted by the large community of programmers. Unfortunately, relational database management systems are not designed for efficient storage and processing of biological data, such as protein secondary structures. In this paper, we present a new search method implemented in the search engine of the PSS-SQL language. The PSS-SQL allows formulation of queries against a relational database in order to find proteins having secondary structures similar to the structural pattern specified by a user. In the paper, we will show how the search process can be accelerated by multiple scanning of the Segment Index and parallel implementation of the alignment procedure using multiple threads working on multiple-core CPUs.

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PSS-SQL: Protein Secondary Structure - Structured Query Language

Cited by 12 publications

References 11 publications

Protein Construction-Based Data Partitioning Scheme for Alignment of Protein Macromolecular Structures Through Distributed Querying in Federated Databases

Protein Construction-Based Data Partitioning Scheme for Alignment of Protein Macromolecular Structures Through Distributed Querying in Federated Databases

Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment

An efficient and flexible scanning of databases of protein secondary structures

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