Pfam: clans, web tools and services

By Robert D. Finn, Jaina Mistry, Benjamin Schuster-Böckler, Sam Griffiths-Jones, Volker Hollich, Timo Lassmann, Simon Moxon, Mhairi Marshall, Ajay Khanna, Richard Durbin and 3 more. 2005

2,350 citations recorded by the source corpus.

Pfam is a database of protein families that currently contains 7973 entries (release 18.0). A recent development in Pfam has enabled the grouping of related families into clans. Pfam clans are described in detail, together with the new associated web pages. Improvements to the range of Pfam web tools and the first set of Pfam web services that allow programmatic access to the database and associated tools are also presented. Pfam is available on the web in the UK (), the USA (), France () and Sweden ().

Subfields: Molecular Biology, Spectroscopy

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Citations in the corpus

The Pfam protein families database in 2019 (2018, background): “To tackle such cases, Pfam entries that are known to be evolutionarily related are grouped together into Pfam Clans (8).”
The Pfam protein families database (2011, methodology): “It was available in a previous version of the Pfam website (18) but was removed during development of the new website.”
The Pfam protein families database: towards a more sustainable future (2015, background): “Clans were first introduced in Pfam in 2005 to enable the grouping of related Pfam entries (14).”
Data growth and its impact on the SCOP database: new developments (2007, background): “This relationship was detected only by RPS-BLAST through Pfam families PF03819 and PF08761, both being linked to this SCOP super-family.”
A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 (2010, neutral): “Of these databases, CDD contains the domains imported from COG, Pfam and SMART, and hence is relatively much more complete [57].”
Microbial Degradation of Lignocellulosic Biomass (2013, background): “The XlnR transcriptional activator belongs to the class of zinc binuclear cluster domain pro‐ teins (PF00172) [113].”
Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners (2007, methodology): “Most methods begin by annotating protein sequences with domains that can be defined by Pfam, SCOP, CDD, or other domain databases [63–65].”
Database resources of the National Center for Biotechnology Information (2011, methodology): “The CDD (56) contains over 40 000 PSI-BLAST-derived position-specific score matrices representing domains taken from the simple modular architecture research tool (Smart) (57), Pfam (58), TIGRFAM (59), and from domain alignments derived from COGs and Protein Clusters.”
Database resources of the National Center for Biotechnology Information (2006, methodology): “The CDD (61) contains >46000 PSI-BLAST-derived Position Specific Score Matrices representing domains taken from the Simple Modular Architecture Research Tool (62), Pfam (63), TIGRFAM (64) and from domain alignments derived from COGs and Protein Clusters.”
Some remarks on protein attribute prediction and pseudo amino acid composition (2010, background): “Since then, a series of new protein FunD databases were established, such as COG (Tatusov et al., 2003) , KOG (Tatusov et al., 2003) , SMART (Letunic et al., 2006) , Pfam (Finn et al., 2006) , and CDD (Marchler-Bauer et al., 2007) .”
New developments in the InterPro database (2007, methodology): “In addition, new database cross-references to CluSTr (15) and Pfam clans (5) have been included, and entries link to the IntAct molecular interaction database (16) where manually curated examples of domain–domain interactions are available.”
Query-Dependent Banding (QDB) for Faster RNA Similarity Searches (2007, neutral): “org) [18,19].”