4.8 Article

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences

Journal

NUCLEIC ACIDS RESEARCH
Volume 40, Issue D1, Pages D252-D260

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkr1189

Keywords

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Funding

  1. National Institutes of Health [GM07689, LM010101, RR016464, R01GM079689]
  2. National Science Foundation [1005223, 0829916]
  3. Division of Computing and Communication Foundations [0829916] Funding Source: National Science Foundation
  4. Div Of Biological Infrastructure [1005223] Funding Source: National Science Foundation
  5. NATIONAL CENTER FOR RESEARCH RESOURCES [P20RR016464] Funding Source: NIH RePORTER
  6. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM079689] Funding Source: NIH RePORTER
  7. NATIONAL LIBRARY OF MEDICINE [R01LM010101] Funding Source: NIH RePORTER

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Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300 000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

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