Methods for Retrieving and Searching Biological Data
August 17, 2023Due to the massive accumulation of biological data, the field of bioinformatics has advanced significantly in recent decades. Essential to this process is the ability to efficiently retrieve and search this data, thereby allowing researchers to make critical associations, predict molecular functions, and decipher biological phenomena. This article serves as a guide to understanding the primary methods used for retrieving and searching biological data, catering to both professionals and general readers.
Databases for Biological Data
1. GenBank
GenBank, initiated in 1982, is one of the world’s most comprehensive and widely accessed nucleotide sequence databases. GenBank, run by the National Center for Biotechnology Information (NCBI), a division of the U.S. National Library of Medicine, contains a wide range of genetic data from various species, including humans and microbes.
Contents: GenBank compiles DNA sequences from various research articles, direct submissions from scientists, and large-scale sequencing projects, including the Human Genome Project.
Features: GenBank goes beyond mere sequence storage. It also offers tools for annotation, which attach meaningful information to raw sequence data. Using the NCBI’s Entrez search and retrieval system, scientists can extract valuable information about gene locations, their associated functions, and even literature references.
Significance: By providing a freely accessible repository, GenBank promotes transparency and collaboration in the scientific community. Its breadth and depth are instrumental for evolutionary biology research, medical genetics, and even forensic analysis.
2. EMBL (European Molecular Biology Laboratory) Nucleotide Sequence Database
The European Bioinformatics Institute (EBI), a division of EMBL, is responsible for creating and maintaining the EMBL database, a pillar in the European biological data landscape.
Contents: Similar to GenBank, EMBL stores a vast collection of high-quality nucleotide sequences and associated annotations. It incorporates data from scientific articles, direct researcher submissions, and large sequencing initiatives.
Collaboration: A significant feature of EMBL is its collaboration with GenBank and DDBJ. These three principal databases form the International Nucleotide Sequence Database Collaboration (INSDC), ensuring that sequence data submitted to one database becomes accessible to all. This collaborative effort guarantees the global consistency and availability of data.
Significance: EMBL’s database is a crucial tool for European researchers, and its close collaboration with international counterparts ensures that data discrepancies are minimized, and global researchers can access consistent information.
3. Protein Data Bank (PDB)
While GenBank and EMBL focus on nucleotide sequences, the PDB provides detailed information about the 3D structures of large biological molecules.
Contents: The PDB primarily includes data on the spatial arrangements of proteins and nucleic acids, derived from experimental methods like X-ray crystallography, NMR spectroscopy, and, more recently, cryo-electron microscopy.
Features: Each entry in PDB is associated with a plethora of data, including the molecular structure, experimental details, and literature references. Users can visualize these structures using various software tools, enhancing their understanding of molecular functions.
Significance: Insights into molecular structures are paramount for multiple fields, including drug design, protein engineering, and understanding enzymatic functions. PDB provides an invaluable resource for researchers in these domains.
Other Notable Databases
DDBJ (DNA Data Bank of Japan): Based in Japan, DDBJ is the third pillar of the INSDC. Holding analogous data as GenBank and EMBL, DDBJ focuses on serving the Asia-Pacific region’s research community.
Swiss-Prot: Part of the Universal Protein Resource (UniProt), Swiss-Prot is a curated protein sequence database, ensuring high levels of accuracy and reliability in protein sequence and function annotation.
TIGR (The Institute for Genomic Research): Though TIGR has now transformed into the J. Craig Venter Institute, it previously made significant contributions to microbial genomics and developed multiple genomic databases, aiding microbial and plant researchers.
This detailed exposition offers a more in-depth view of the databases that are the backbone of modern bioinformatics, facilitating countless discoveries and innovations.
Methods and Tools for Data Retrieval and Search
1. BLAST (Basic Local Alignment Search Tool): Probably the most renowned tool, BLAST allows researchers to compare an input sequence against a database, finding regions of local similarity. It aids in inferring functional and evolutionary relationships.
2. FASTA: Like BLAST, FASTA is used for comparing sequences but uses a different algorithm. While BLAST looks for local alignments, FASTA looks for the best global alignment.
3. SRS (Sequence Retrieval System): This is a network- and platform-independent data integration software that allows seamless querying across different databases and retrieval of biological data.
Interplay of DNA/RNA and Protein Data
The intricate link between nucleic acids and proteins is the crux of molecular biology. For instance, researchers can use mRNA sequences in GenBank to predict protein sequences and then delve into PDB to find similar 3D structures. Such a unified approach enables comprehensive insights into the molecular biology of an organism, aiding in tasks from evolutionary studies to drug discovery.
The vastness of biological data brings challenges:
Data Volume: The rapid influx of data necessitates tools that can quickly scan vast databases, ensuring timely data retrieval.
Data Quality: As with any large-scale project, ensuring the accuracy and reliability of data is paramount.
Interoperability: Since biological data is stored across various databases worldwide, tools and methods should be compatible with multiple formats and standards.
Conclusion
The ongoing explosion of biological data has made efficient retrieval and search mechanisms more critical than ever. While we have robust databases and tools at our disposal, the field is ever-evolving, requiring constant adaptation and innovation. Whether you are a professional in bioinformatics or just starting out, understanding these methods is pivotal to harnessing the power of biological data.
