Sequence database

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In the field of bioinformatics, a sequence database is a type of biological database that is composed of a large collection of computerized ("digital") nucleic acid sequences, protein sequences, or other polymer sequences stored on a computer. The UniProt database is an example of a protein sequence database. As of 2013 it contained over 40 million sequences and is growing at an exponential rate. [1] Historically, sequences were published in paper form, but as the number of sequences grew, this storage method became unsustainable.

Contents

Searching in a sequence database involves looking for similarities between a genomic/protein sequence and a query string and, finding the sequence in the database that "best" matches the target sequence (based on criteria which vary depending on the search method). The number of matches/hits is used to formulate a score that determines the similarity between the sequence query and the sequences in the sequence database. [2] The main goal is to have a good balance between the two criteria.

History

1950

The need for sequence databases originated in 1950 when Fredrick Sanger reported the primary structure of insulin. He won his second Nobel Prize for creating methods for sequencing nucleic acids, and his comparative approach is what sparked other protein biochemists to begin collecting amino acid sequences. Thus marking the beginning of molecular databases. [3]

1960

In 1965 Margaret Dayhoff and her team at the National Biomedical Research Foundation (NBRF) published "The Atlas of Protein Sequence and Structure". They put all know protein sequences in the Atlas, even unpublished material. This can be seen as the first attempt to create a molecular database. They made use of the newly computerized (1964) Medical Literature Analysis and Retrieval System (MEDLARS) at the National Institutes of Health (NIH). The team used computers to store the data but had to manually type and proofread each sequence, which had a high cost in time and money. [3]

In 1966 the team released the second edition of the Atlas, double the size of the first. It contained about 1000 sequences, and this time was coined as an information explosion. The National Biomedical Research Foundation (NBRF) was on the cutting edge of utilizing computers for medicine and biology at this time. Dayhoff and her team made use of their facilities for determining amino acid sequences of protein molecules in mainframe computers. The number of discovered sequences continued to grow allowing for a deeper comparative analysis of proteins than ever before. This led to many developments such as, probabilistic models of amino acid substitutions, sequence aligning and phylogenetic trees of evolutionary relationships of proteins. [3]

1970

Entire sequencing process became fully automated. [3]

1980

The first nucleotide sequence database was created. Previously known as the European Molecular Biology Laboratory (EMBL) Nucleotide Sequence Data Library (now known as European Nucleotide archive). Human Genome Project began in 1988. The project's goal was sequence and map all the genes in a human which required the capability to create and utilize a large sequence database. [4]

Present day

We now have many sequence databases, tools for using them and easy access to them. One of the largest being GenBank which contains over 2 billion sequences. [3]

Timeline

Timeline for the creation of sequence databases. Sequence Database Timeline.png
Timeline for the creation of sequence databases.

Current issues

Storage & redundancy

Records in sequence databases are deposited from a wide range of sources, from individual researchers to large genome sequencing centers. As a result, the sequences themselves, and especially the biological annotations attached to these sequences, may vary in quality. There is much redundancy, as multiple labs may submit numerous sequences that are identical, or nearly identical, to others in the databases. [5]

Many annotations of the sequences are based not on laboratory experiments, but on the results of sequence similarity searches for previously annotated sequences. Once a sequence has been annotated based on similarity to others, and itself deposited in the database, it can also become the basis for future annotations. This can lead to a transitive annotation problem because there may be several such annotation transfers by sequence similarity between a particular database record and actual wet lab experimental information. [6] Therefore, care must be taken when interpreting the annotation data from sequence databases.

Scoring methods

Most of the current database search algorithms rank alignment by a score, which is usually a particular scoring system. [7] The solution towards solving this issue is found by making a variety of scoring systems available to suit to the specific problem.

Alignment statistics

When using a searching algorithm we often produce an ordered list which can often carry a lack of biological significance. [8]

See also

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Genomics</span> Discipline in genetics

Genomics is an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes. A genome is an organism's complete set of DNA, including all of its genes as well as its hierarchical, three-dimensional structural configuration. In contrast to genetics, which refers to the study of individual genes and their roles in inheritance, genomics aims at the collective characterization and quantification of all of an organism's genes, their interrelations and influence on the organism. Genes may direct the production of proteins with the assistance of enzymes and messenger molecules. In turn, proteins make up body structures such as organs and tissues as well as control chemical reactions and carry signals between cells. Genomics also involves the sequencing and analysis of genomes through uses of high throughput DNA sequencing and bioinformatics to assemble and analyze the function and structure of entire genomes. Advances in genomics have triggered a revolution in discovery-based research and systems biology to facilitate understanding of even the most complex biological systems such as the brain.

<span class="mw-page-title-main">Sequence alignment</span> Process in bioinformatics that identifies equivalent sites within molecular sequences

In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences. Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as calculating the distance cost between strings in a natural language or in financial data.

<span class="mw-page-title-main">National Center for Biotechnology Information</span> Database branch of the US National Library of Medicine

The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine (NLM), a branch of the National Institutes of Health (NIH). It is approved and funded by the government of the United States. The NCBI is located in Bethesda, Maryland, and was founded in 1988 through legislation sponsored by US Congressman Claude Pepper.

In bioinformatics, sequence analysis is the process of subjecting a DNA, RNA or peptide sequence to any of a wide range of analytical methods to understand its features, function, structure, or evolution. Methodologies used include sequence alignment, searches against biological databases, and others.

<span class="mw-page-title-main">Nucleic acid sequence</span> Succession of nucleotides in a nucleic acid

A nucleic acid sequence is a succession of bases signified by a series of a set of five different letters that indicate the order of nucleotides forming alleles within a DNA or RNA (GACU) molecule. By convention, sequences are usually presented from the 5' end to the 3' end. For DNA, the sense strand is used. Because nucleic acids are normally linear (unbranched) polymers, specifying the sequence is equivalent to defining the covalent structure of the entire molecule. For this reason, the nucleic acid sequence is also termed the primary structure.

<span class="mw-page-title-main">Protein family</span> Group of evolutionarily-related proteins

A protein family is a group of evolutionarily related proteins. In many cases, a protein family has a corresponding gene family, in which each gene encodes a corresponding protein with a 1:1 relationship. The term "protein family" should not be confused with family as it is used in taxonomy.

BioJava is an open-source software project dedicated to provide Java tools to process biological data. BioJava is a set of library functions written in the programming language Java for manipulating sequences, protein structures, file parsers, Common Object Request Broker Architecture (CORBA) interoperability, Distributed Annotation System (DAS), access to AceDB, dynamic programming, and simple statistical routines. BioJava supports a huge range of data, starting from DNA and protein sequences to the level of 3D protein structures. The BioJava libraries are useful for automating many daily and mundane bioinformatics tasks such as to parsing a Protein Data Bank (PDB) file, interacting with Jmol and many more. This application programming interface (API) provides various file parsers, data models and algorithms to facilitate working with the standard data formats and enables rapid application development and analysis.

<span class="mw-page-title-main">UniProt</span> Database of protein sequences and functional information

UniProt is a freely accessible database of protein sequence and functional information, many entries being derived from genome sequencing projects. It contains a large amount of information about the biological function of proteins derived from the research literature. It is maintained by the UniProt consortium, which consists of several European bioinformatics organisations and a foundation from Washington, DC, United States.

The Protein Information Resource (PIR), located at Georgetown University Medical Center, is an integrated public bioinformatics resource to support genomic and proteomic research, and scientific studies. It contains protein sequences databases

The European Bioinformatics Institute (EMBL-EBI) is an intergovernmental organization (IGO) which, as part of the European Molecular Biology Laboratory (EMBL) family, focuses on research and services in bioinformatics. It is located on the Wellcome Genome Campus in Hinxton near Cambridge, and employs over 600 full-time equivalent (FTE) staff. Institute leaders such as Rolf Apweiler, Alex Bateman, Ewan Birney, and Guy Cochrane, an adviser on the National Genomics Data Center Scientific Advisory Board, serve as part of the international research network of the BIG Data Center at the Beijing Institute of Genomics.

Computational genomics refers to the use of computational and statistical analysis to decipher biology from genome sequences and related data, including both DNA and RNA sequence as well as other "post-genomic" data. These, in combination with computational and statistical approaches to understanding the function of the genes and statistical association analysis, this field is also often referred to as Computational and Statistical Genetics/genomics. As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets, computational studies have become one of the most important means to biological discovery.

InterPro is a database of protein families, protein domains and functional sites in which identifiable features found in known proteins can be applied to new protein sequences in order to functionally characterise them.

<span class="mw-page-title-main">BLOSUM</span>

In bioinformatics, the BLOSUM matrix is a substitution matrix used for sequence alignment of proteins. BLOSUM matrices are used to score alignments between evolutionarily divergent protein sequences. They are based on local alignments. BLOSUM matrices were first introduced in a paper by Steven Henikoff and Jorja Henikoff. They scanned the BLOCKS database for very conserved regions of protein families and then counted the relative frequencies of amino acids and their substitution probabilities. Then, they calculated a log-odds score for each of the 210 possible substitution pairs of the 20 standard amino acids. All BLOSUM matrices are based on observed alignments; they are not extrapolated from comparisons of closely related proteins like the PAM Matrices.

BLAT is a pairwise sequence alignment algorithm that was developed by Jim Kent at the University of California Santa Cruz (UCSC) in the early 2000s to assist in the assembly and annotation of the human genome. It was designed primarily to decrease the time needed to align millions of mouse genomic reads and expressed sequence tags against the human genome sequence. The alignment tools of the time were not capable of performing these operations in a manner that would allow a regular update of the human genome assembly. Compared to pre-existing tools, BLAT was ~500 times faster with performing mRNA/DNA alignments and ~50 times faster with protein/protein alignments.

<span class="mw-page-title-main">MicrobesOnline</span>

MicrobesOnline is a publicly and freely accessible website that hosts multiple comparative genomic tools for comparing microbial species at the genomic, transcriptomic and functional levels. MicrobesOnline was developed by the Virtual Institute for Microbial Stress and Survival, which is based at the Lawrence Berkeley National Laboratory in Berkeley, California. The site was launched in 2005, with regular updates until 2011.

SUPERFAMILY is a database and search platform of structural and functional annotation for all proteins and genomes. It classifies amino acid sequences into known structural domains, especially into SCOP superfamilies. Domains are functional, structural, and evolutionary units that form proteins. Domains of common Ancestry are grouped into superfamilies. The domains and domain superfamilies are defined and described in SCOP. Superfamilies are groups of proteins which have structural evidence to support a common evolutionary ancestor but may not have detectable sequence homology.

Single nucleotide polymorphism annotation is the process of predicting the effect or function of an individual SNP using SNP annotation tools. In SNP annotation the biological information is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is typically performed based on the available information on nucleic acid and protein sequences.

References

  1. ↑ Cochrane, G.; Karsch-Mizrachi, I.; Nakamura, Y. (23 November 2010). "The International Nucleotide Sequence Database Collaboration". Nucleic Acids Research. 39 (Database): D15–D18. doi:10.1093/nar/gkq1150. PMC   3013722 . PMID   21106499.
  2. ↑ Sung, Wing-Kin (2010). Algorithms in bioinformatics : a practical introduction. Boca Raton: Chapman & Hall/CRC Press. p. 109. ISBN   9781420070330.
  3. 1 2 3 4 5 Hagen, Joel B. (2011), Hamacher, Michael; Eisenacher, Martin; Stephan, Christian (eds.), "The Origin and Early Reception of Sequence Databases", Data Mining in Proteomics: From Standards to Applications, Methods in Molecular Biology, Totowa, NJ: Humana Press, vol. 696, pp. 61–77, doi:10.1007/978-1-60761-987-1_4, ISBN   978-1-60761-987-1, PMID   21063941 , retrieved 5 May 2022
  4. ↑ "History < EMBL-EBI". www.ebi.ac.uk. Retrieved 5 May 2022.
  5. ↑ Sikic, K.; Carugo, O. (2010). "Protein sequence redundancy reduction: comparison of various method". Bioinformation. 5 (6): 234–9. doi:10.6026/97320630005234. PMC   3055704 . PMID   21364823.
  6. ↑ Iliopoulos, I.; Tsoka, S.; Andrade, MA.; Enright, AJ.; Carroll, M.; Poullet, P.; Promponas, V.; Liakopoulos, T.; et al. (April 2003). "Evaluation of annotation strategies using an entire genome sequence". Bioinformatics. 19 (6): 717–26. doi: 10.1093/bioinformatics/btg077 . PMID   12691983.
  7. ↑ Altschul, Stephen; Boguski, Mark; Gish, Warren; Wootton, John (1994). "Issues in searching molecular sequence databases" (PDF). Nature Genetics. Nature Publishing Group. 6 (2): 119–129. doi:10.1038/ng0294-119. PMID   8162065. S2CID   270160.
  8. ↑ Altschul, Stephen; Boguski, Mark; Gish, Warren; Wootton, John (1994). "Issues in searching molecular sequence databases" (PDF). Nature Genetics. Nature Publishing Group. 6 (2): 119–129. doi:10.1038/ng0294-119. PMID   8162065. S2CID   270160.

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