Sequence database: Difference between revisions
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== Search == | == Search == | ||
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.<ref>{{cite book |last1=Sung |first1=Wing-Kin |title=Algorithms in bioinformatics : a practical introduction |date=2010 |publisher=Chapman & Hall/CRC Press |location=Boca Raton |isbn=9781420070330 |page=109 |url=https://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/}}</ref> The main goal is to have a good balance between the two criteria. | Searching in a sequence database involves looking for [[sequence similarity|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.<ref>{{cite book |last1=Sung |first1=Wing-Kin |title=Algorithms in bioinformatics : a practical introduction |date=2010 |publisher=Chapman & Hall/CRC Press |location=Boca Raton |isbn=9781420070330 |page=109 |url=https://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/}}</ref> The main goal is to have a good balance between the two criteria. | ||
== History == | == History == | ||
=== 1950 === | === 1950 === | ||
The need for sequence databases originated in 1950 when | The need for sequence databases originated in 1950 when [[Frederick 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.<ref name=":0">{{Citation |last=Hagen |first=Joel B. |title=The Origin and Early Reception of Sequence Databases |date=2011 |url=https://doi.org/10.1007/978-1-60761-987-1_4 |work=Data Mining in Proteomics: From Standards to Applications |pages=61–77 |editor-last=Hamacher |editor-first=Michael |series=Methods in Molecular Biology |volume=696 |place=Totowa, NJ |publisher=Humana Press |language=en |doi=10.1007/978-1-60761-987-1_4 |pmid=21063941 |isbn=978-1-60761-987-1 |access-date=2022-05-05 |editor2-last=Eisenacher |editor2-first=Martin |editor3-last=Stephan |editor3-first=Christian|url-access=subscription }}</ref> | ||
=== 1960 === | === 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.<ref name=":0" /> | 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.<ref name=":0" /> | ||
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 | 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 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.<ref name=":0" /> | ||
=== | === 1970s === | ||
{{see also|DNA sequencing#Early DNA sequencing methods}} | |||
The entire [[sequencing]] process became fully automated.<ref name=":0" /> | |||
=== 1980 === | === 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.<ref>{{Cite web |title=History < EMBL-EBI |url=https://www.ebi.ac.uk/history |access-date=2022-05-05 |website=www.ebi.ac.uk}}</ref> | 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.<ref>{{Cite web |title=History < EMBL-EBI |url=https://www.ebi.ac.uk/history |access-date=2022-05-05 |website=www.ebi.ac.uk}}</ref> | ||
=== Present day === | === Present day === | ||
| Line 32: | Line 32: | ||
=== Storage & redundancy === | === 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 | 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 [[annotation]]s attached to these sequences, may vary in quality. There is much [[Redundancy (linguistics)|redundancy]], as multiple labs may submit numerous sequences that are identical, or nearly identical, to others in the databases.<ref name="Sikic-2010">{{Cite journal | last1 = Sikic | first1 = K. | last2 = Carugo | first2 = O. | title = Protein sequence redundancy reduction: comparison of various method | journal = Bioinformation | volume = 5 | issue = 6 | pages = 234–9 | year = 2010 | doi = 10.6026/97320630005234| pmid = 21364823 | pmc=3055704}}</ref> | ||
Many | Many [[DNA annotation|sequence annotation]]s are based not on laboratory experiments, but on the results of [[sequence similarity]] [[similarity search|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.<ref name="Iliopoulos-2003">{{Cite journal | last1 = Iliopoulos | first1 = I. | last2 = Tsoka | first2 = S. | last3 = Andrade | first3 = MA. | last4 = Enright | first4 = AJ. | last5 = Carroll | first5 = M. | last6 = Poullet | first6 = P. | last7 = Promponas | first7 = V. | last8 = Liakopoulos | first8 = T. | last9 = Palaios | first9 = G. | last10 = Pasquier | first10 = C | last11 = Hamodrakas | first11 = S | last12 = Tamames | first12 = J | last13 = Yagnik | first13 = A. T. | last14 = Tramontano | first14 = A | last15 = Devos | first15 = D | last16 = Blaschke | first16 = C | last17 = Valencia | first17 = A | last18 = Brett | first18 = D | last19 = Martin | first19 = D | last20 = Leroy | first20 = C | last21 = Rigoutsos | first21 = I | last22 = Sander | first22 = C | last23 = Ouzounis | first23 = C. A. | title = Evaluation of annotation strategies using an entire genome sequence | journal = Bioinformatics | volume = 19 | issue = 6 | pages = 717–26 |date=April 2003 | doi = 10.1093/bioinformatics/btg077| pmid = 12691983 | display-authors = 8 | doi-access = free }}</ref> Therefore, care must be taken when interpreting the annotation data from sequence databases. | ||
=== Scoring methods === | === Scoring methods === | ||
Most of the current database search algorithms rank alignment by a score, which is usually a particular scoring system.<ref>{{cite journal |title=Issues in searching molecular sequence databases|last1=Altschul |first1=Stephen |last2=Boguski |first2=Mark |last3=Gish |first3=Warren |last4=Wootton |first4=John |journal=Nature Genetics |year=1994 |volume=6 |issue=2 |pages=119–129 |url=https://www.nature.com/articles/ng0294-119.pdf |publisher=Nature Publishing Group|doi=10.1038/ng0294-119 |pmid=8162065 |s2cid=270160 }}</ref> The solution towards solving this issue is found by making a variety of scoring systems available to suit to the specific problem. | Most of the current database search algorithms rank [[sequence alignment|alignment]] by a score, which is usually a particular scoring system.<ref>{{cite journal |title=Issues in searching molecular sequence databases|last1=Altschul |first1=Stephen |last2=Boguski |first2=Mark |last3=Gish |first3=Warren |last4=Wootton |first4=John |journal=Nature Genetics |year=1994 |volume=6 |issue=2 |pages=119–129 |url=https://www.nature.com/articles/ng0294-119.pdf |publisher=Nature Publishing Group|doi=10.1038/ng0294-119 |pmid=8162065 |s2cid=270160 }}</ref> The solution towards solving this issue is found by making a variety of scoring systems available to suit to the specific problem. | ||
=== Alignment statistics === | === Alignment statistics === | ||
[[Searching algorithm]] often produce an ordered list lacking biological significance.<ref>{{cite journal |title=Issues in searching molecular sequence databases|last1=Altschul |first1=Stephen |last2=Boguski |first2=Mark |last3=Gish |first3=Warren |last4=Wootton |first4=John |journal=Nature Genetics |year=1994 |volume=6 |issue=2 |pages=119–129 |url=https://www.nature.com/articles/ng0294-119.pdf |publisher=Nature Publishing Group|doi=10.1038/ng0294-119 |pmid=8162065 |s2cid=270160 }}</ref> | |||
==See also== | ==See also== | ||
* | * {{annotated link|FASTA format}} | ||
* | * {{annotated link|Similarity Matrix of Proteins|SIMAP}} | ||
* | * {{annotated link|List of biological databases}} | ||
* | * {{annotated link|Bioinformatics}} | ||
== References == | == References == | ||
| Line 53: | Line 53: | ||
==External links== | ==External links== | ||
* [http://www.ebi.ac.uk/Databases/ European Bioinformatics Institute databases] | * [http://www.ebi.ac.uk/Databases/ European Bioinformatics Institute databases] | ||
* [https://www.ncbi.nlm.nih.gov/ | * [https://www.ncbi.nlm.nih.gov/datasets/genome// NCBI completely sequenced genomes] | ||
* [http://www.yeastgenome.org/ Stanford Saccharomyces Genome Database] | * [http://www.yeastgenome.org/ Stanford Saccharomyces Genome Database] | ||
*[https://www.ncbi.nlm.nih.gov/protein Protein], the [[National Institute of Health|NIH]] protein database, a collection of sequences from several sources, including translations from annotated coding regions in [[GenBank]], [[RefSeq]] and [[Third Party Annotation|TPA]], as well as records from [[SwissProt]], [[Protein Information Resource|PIR]], PRF, and [[Protein Data Bank|PDB]] | *[https://www.ncbi.nlm.nih.gov/protein Protein], the [[National Institute of Health|NIH]] protein database, a collection of sequences from several sources, including translations from annotated coding regions in [[GenBank]], [[RefSeq]] and [[Third Party Annotation|TPA]], as well as records from [[SwissProt]], [[Protein Information Resource|PIR]], PRF, and [[Protein Data Bank|PDB]] | ||
Latest revision as of 10:18, 18 November 2025
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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.
Search
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 Frederick 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 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]
1970s
Script error: No such module "Labelled list hatnote". The 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
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 sequence annotations 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
Searching algorithm often produce an ordered list lacking biological significance.[8]
See also
References
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External links
- European Bioinformatics Institute databases
- NCBI completely sequenced genomes
- Stanford Saccharomyces Genome Database
- Protein, the NIH protein database, a collection of sequences from several sources, including translations from annotated coding regions in GenBank, RefSeq and TPA, as well as records from SwissProt, PIR, PRF, and PDB
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