ArticleNucleic acids research2006
MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences.
Article in Nucleic acids research, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Efficient mining gapped sequential patterns for motifs in biological sequences.BMC systems biology · 2013Article
- Predicting DNA-binding locations and orientation on proteins using knowledge-based learning of geometric properties.Proteome science · 2011Article
- WildSpan: mining structured motifs from protein sequences.Algorithms for molecular biology : AMB · 2011Article
- Predicting RNA-binding residues from evolutionary information and sequence conservation.BMC genomics · 2010Article
- seeMotif: exploring and visualizing sequence motifs in 3D structures.Nucleic acids research · 2009Article
- E1DS: catalytic site prediction based on 1D signatures of concurrent conservation.Nucleic acids research · 2008Article
- Article
- iPDA: integrated protein disorder analyzer.Nucleic acids research · 2007Article
- Identification of hot regions in protein-protein interactions by sequential pattern mining.BMC bioinformatics · 2007Article
Corrections and comments
- Erratum issued
- Republished as
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein-protein interactions directly from sequences. The web service is available at http://biominer.bime.ntu.edu.tw/magiicpro and a mirror site at http://biominer.cse.yzu.edu.tw/magiicpro.
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