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AlignACE

W-AlignACE

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:: Introduction ::

Position weight matrices (PWMs) are widely used to depict the DNA binding preferences of transcription factors (TFs) in computational biology and regulatory genomics. Thus, leaning an accurate PWM to characterize the binding sites of a TF is a fundamental problem that plays an important role in modeling regulatory motifs and discovering the binding targets of TFs. We believe that the accurate PWM of a TF shall yield the maximum likelihood of simultatneously observing both its binding sequences and the associated gene expression or ChIP-chip values. We hence developed a new approach to learning PWMs via a sequence weighting scheme. The new learning approach  has been incorporated into the popular motif finding program AlignACE, and the motified program is called W-AlignACE. The large-scale tests have demonsrated that W-AlignACE is an effective tool for discovering TF binding sites from gene expression or ChIP-chip data and, in particular, has the ability to find very weak motifs.

Due to the stochastic nature of Gibbs sampling, the program W-AlignACE ( as well as AlignACE) may produce different outputs in different runs. This issue still exits even when running W-AlignACE with the same random seed but in the different platforms (because different random generators may be employed).

:: Run W-AlignACE ::

:: STEP 1 ::

>> Please input DNA sequences below in the FASTA format: (Example file; the maximum file size is 50KB; for details, see here)


>> or submit DNA sequences by uploading a file:
(Example file)



:: STEP 2 ::

>> Please set the parameters for W-AlignACE:

Number of columns to align:

Number of sites to expect:

Fractional background GC content:


:: STEP 3 ::

>>Submit a query, or reset the above inputs:
 

:: References ::
  • X. Chen and T. Jiang. An improved Gibbs sampling method for motif discovery via sequence weighting. Proc. of Computational System Bioinformatics, 239-247, 2006.
  • X. Chen, L. Guo, Z. Fan, and T. Jiang. Learning position weight matrices from sequence and expression data. Acceptted by CSB 2007.
:: Contact ::
If you have any question or comment with this program, please send me an email.
 
COPYRIGHT (C) 2006, Bioinformatrics Group @ Math.SPMS.NTU. ALL RIGHT RESERVED


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