The SWISS-PROT protein sequence data bank and its supplement TrEMBL

By Amos Bairoch, Rolf Apweiler. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ben M. Webb
2. Burkhard Rost
3. Yang Zhang
4. Sheng Wang
5. Narayanan Eswar
6. Andrej Sali
7. Jinbo Xu
8. Jianlin Cheng

Citations in the corpus

• Better prediction of functional effects for sequence variants (2015, neutral): “In addition to these, SWISS-PROT [22] annotations and SIFT [10] predictions were included in SNAP2, if available.”
• The Pfam protein families database (2007, methodology): “The taxonomic information for each protein match is extracted from the SWISS-PROT/TrEMBL databases (13).”
• RaptorX-Property: a web server for protein structure property prediction (2016, background): “The second section shows predicted results (4), with the first row showing input sequence, and the remaining rows showing the prediction of 3-state secondary structure (SS3), 8-state secondary structure (SS8), 3-state solvent accessibility (ACC), and order/disorder regions (DISO) with ‘*’ indicating”
• Evolution of function in protein superfamilies, from a structural perspective. (2001, methodology): “For the identi®cation of non-enzymes in SWISS-PROT, any entry matching one or more of the following criteria was ignored (a) contains the word ``hypothetical'' in the description (DE) or keyword (KW) lines (b) has ``FUNCTION: NOT KNOWN'' in the comment (CC) lines, (c) has no keywords nor ``FUNCTION'”
• Investigating Semantic Similarity Measures Across the Gene Ontology: The Relationship Between Sequence and Annotation (2003, background): “As they form a standard vocabulary across many biological resources such as SWISS-PROT (Bairoch and Apweiler, 2000), this shared understanding provides a valuable, computationally accessible form of the community’s knowledge about these attributes.”
• Amino acid?base interactions: a three-dimensional analysis of protein?DNA interactions at an atomic level (2001, background): “From these, monomers were identified on the basis of quaternary structure assignments in their corresponding SWISS-PROT (20) or Protein Quaternary Structure (PQS) database entries (21).”
• Non-classical protein secretion in bacteria (2005, methodology): “A set of 690 extracellular proteins from Firmicutes (Grampositive) and a set of 2185 extracellular proteins from Proteobacteria (Gram-negative) were extracted from the SwissProt database based on annotations in the feature table (FT) and comments line (CC) [52].”
• What is Bioinformatics? A Proposed Definition and Overview of the Field (2001, background): “At present there are about 400,000 known protein sequences [3], with a typical bacterial protein containing approximately 300 amino acids.”
• Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles (2002, methodology): “0 were constructed primarily by running the BLAST program [1] against the NR (non-redundant) database [3, 10], with standard default parameters (E=10.”
• Predicting transmembrane beta-barrels in proteomes. (2004, methodology): “html)ÐSetROCcomp: 145 outer- and 188 inner-membrane proteins taken from SWISS-PROT (51) that were”
• LocTree3 prediction of localization (2014, unknown): “For all proteins with experimentally known localization, we generated PSI-BLAST profiles using an 80% non-redundant database combining UniProt (1) and PDB (27) with two iterations and E-value ≤ 10−3.”
• SMART 4.0: towards genomic data integration (2004, methodology): “In addition to the Swiss-Prot and spTrembl databases (3), which have been used by SMART since its inception, SMART's source sequence databases now include all available Ensembl proteomes (2).”
• Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (2005, background): “!) according to Release 43.6 (June 21 2004) of SWISS-PROT (http://www.expasy.org/sprot/relotes/relstat.html).”
• Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network (2006, methodology): “To generate a negative dataset, we created non-epitopes using random peptides of length 20 residues from the proteins in SwissProt.(32) All the random peptides that are identical to B-cell”