Biological databases

 Biological databases

A database is a computerized archive used to store and organize data in such a way that information can be retrieved easily via a variety of search criteria. Databases are composed of computer hardware and software for data management. The chief objective of the development of a database is to organize data in a set of structured records to enable easy retrieval of information. Each record, also called an entry, should contain a number of fields that hold the actual data items, for example, fields for names, phone numbers, addresses, dates. To retrieve a particular record from the database, a user can specify a particular piece of information, called value, to be found in a particular field and expect the computer to retrieve the whole data record. This process is called making a query.

Biological databases are libraries of biological sciences, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis.

Based on the contents, biological databases are divided into three categories: primary databases, secondary databases, and specialized databases.

Primary databases contain original biological data. They are archives of raw sequence or structural data submitted by the scientific community. GenBank and Protein Data Bank (PDB) are examples of primary databases.

 Secondary databases contain computationally processed or manually curated information, based on original information from primary databases. Translated protein sequence databases containing functional annotation belong to this category. Examples are SWISS-Prot and Protein Information Resources (PIR) (successor of Margaret Dayhoff’s Atlas of Protein Sequence and Structure .

Specialized databases are those that cater to a particular research interest. For example, Flybase, HIV sequence database, and Ribosomal Database Project are databases that specialize in a particular organism or a particular type of data.

Primary Databases

 There are three major public sequence databases that data produced and submitted by researchers worldwide: GenBank, the European Molecular Biology Laboratory (EMBL) database and the DNA Data Bank of Japan (DDBJ), which are all freely available on the Internet. Most of the data in the databases are contributed directly by authors with a minimal level of annotation. A small number of sequences, especially those published in the 1980s, were entered manually from published literature by database management staff.

Presently, sequence submission to either GenBank, EMBL, or DDBJ is a precondition for publication in most scientific journals to ensure the fundamental molecular data to be made freely available. These three public databases closely collaborate and exchange new data daily. They together constitute the International Nucleotide Sequence Database Collaboration. This means that by connecting to any one of the three databases, one should have access to the same nucleotide sequence data. Although the three databases all contain the same sets of raw data, each of the individual databases has a slightly different kind of format to represent the data.

PDB is  only one centralized database for the three-dimensional structures of biological macromolecules, the This database archives atomic coordinates of macromolecules (both proteins and nucleic acids) determined by x-ray crystallography and NMR. It uses a flat file format to represent protein name, authors, experimental details, secondary structure, cofactors, and atomic coordinates. The web interface of PDB also provides viewing tools for simple image manipulation.

Sequence annotation information in the primary database is often minimal.

Secondary databases

Secondary databases contain computationally processed sequence information derived from the primary databases. The amount of computational processing work varies greatly among the secondary databases; some are simple archives of translated sequence data from identified open reading frames in DNA, where as others provide additional annotation and information related to higher levels of information regarding structure and functions.

A prominent example of secondary databases is SWISS-PROT, which provides detailed sequence annotation that includes structure, function, and protein family assignment. The sequence data are mainly derived from TrEMBL, a database of translated nucleic acid sequences stored in the EMBL database. The annotation of each entry is carefully curated by human experts and thus is of good quality. The protein annotation includes function, domain structure, catalytic sites, cofactor binding, posttranslational modification, metabolic pathway information, disease association, and similarity with other sequences. Much of this information is obtained from scientific literature and entered by database curators. The annotation provides significant added value to each original sequence record. The data record also provides cross referencing links to other online resources of interest. Other features such as very low redundancy and high level of integration with other primary and secondary databases make SWISS-PROT very popular among biologists.

Combination of SWISS-PROT, TrEMBL, and PIR led to the creation of the UniProt database, which has larger coverage than any one of the three databases while at the same time maintaining the original SWISS-PROT feature of low redundancy, cross-references, and a high quality of annotation.

Secondary databases  relate to protein family classification according to functions or structures. The Pfam and Blocks databases contain aligned protein sequence information as well as derived motifs and patterns, which can be used for classification of protein families and inference of protein functions. 

The DALI database  is a protein secondary structure database that is vital for protein structure classification and threading analysis  to identify distant evolutionary relationships among proteins.

Specialized Databases

Specialized databases normally serve a specific research community or focus on a particular organism. The content of these databases may be sequences or other types of information. The sequences in these databases may overlap with a primary database, but may also have new data submitted directly by authors. Because they are often curated by experts in the field, they may have unique organizations and additional annotations associated with the sequences. Many genome databases that are taxonomic specific fall within this category. Examples include Flybase, WormBase, AceDB, and TAIR . In addition, there are also specialized databases that contain original data derived from functional analysis. For example, GenBank EST database and Microarray Gene Expression Database at the European Bioinformatics Institute(EBI) are some of the gene expression databases available.

Current biological databases use  three types of database structures: flat files, relational, and object oriented. Many biological databases use flat file format. This system involves minimum amount of database design and the search output can be easily understood by working biologists.The main barrier to linking different biological databases is format incompatibility.The heterogeneous database structures limit communication between databases. One solution to networking the databases is to use a specification language called Common Object Request Broker Architecture(COBRA), which allows database programs at different locations to communicate in a network through an “interface broker” without having to understand each other’s database structure. A similar protocol called eXtensible Markup Language (XML) also helps in bridging databases. In this format, each biological record is broken down into small, basic components that are labeled with a hierarchical nesting of tags. This database structure significantly improves the distribution and exchange of complex sequence annotations between databases. 

Pitfalls of Biological Databases

Over reliance on sequence information and related annotations, without understanding the reliability of the information.

High levels of redundancy in the primary sequence databases. Steps have been taken to reduce the redundancy. The National Center for Biotechnology Information (NCBI) has now created a nonredundant database, called RefSeq, in which identical sequences from the same organism and associated sequence fragments are merged into a single entry.

There are many errors in sequence databases.

Annotations of genes can also occasionally be false or incomplete. All these types of errors can be passed on to other databases, causing propagation of errors.

Most errors in nucleotide sequences are caused by sequencing errors. Some of these errors cause frameshifts that make whole gene identification difficult or protein translation impossible. Sometimes, gene sequences are contaminated with sequences from cloning vectors.

Retrieval systems for biological databases

The most popular retrieval systems for biological databases are Entrez and Sequence Retrieval Systems (SRS) that provide access to multiple databases for retrieval of integrated search results.

The NCBI developed and maintains Entrez, a biological database retrieval system. It is a gateway that allows text-based searches for a wide variety of data, including annotated genetic sequence information, structural information, as well as citations and abstracts, full papers, and taxonomic data. The key feature of Entrez is its ability to integrate information, which comes from cross-referencing between NCBI databases based on preexisting and logical relationships between individual entries.

Sequence retrieval system(SRS) is a retrieval system maintained by the EBI, which is comparable to NCBI Entrez. It is not as integrated as Entrez, but allows the user to query multiple databases simultaneously, another good example of database integration. It also offers direct access to certain sequence analysis applications such as sequence similarity searching and Clustal sequence alignment.

References:https://en.wikipedia.org/wiki/Biological_database

Essential Bioinformatics by Jin Xiong