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

نویسندگان

  • Tian Mi
  • Jerlin Camilus Merlin
  • Sandeep Deverasetty
  • Michael R. Gryk
  • Travis J. Bill
  • Andrew W. Brooks
  • Logan Y. Lee
  • Viraj Rathnayake
  • Christian A. Ross
  • David P. Sargeant
  • Christy L. Strong
  • Paula Watts
  • Sanguthevar Rajasekaran
  • Martin R. Schiller
چکیده

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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عنوان ژورنال:

دوره 40  شماره 

صفحات  -

تاریخ انتشار 2012