bcrank                package:BCRANK                R Documentation

_B_C_R_A_N_K: _p_r_e_d_i_c_t_i_n_g _b_i_n_d_i_n_g _s_i_t_e _c_o_n_s_e_n_s_u_s _f_r_o_m _r_a_n_k_e_d _D_N_A _s_e_q_u_e_n_c_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function implements an algorithm for detection of short DNA
     sequences that are overrepresented in some part of the list.
     Starting from some initial consensus DNA sequence coded in IUPAC
     symbols, the method uses a heuristic search to improve the
     consensus until a local optimum is found.

_U_s_a_g_e:

     bcrank(fafile, startguesses=c(), restarts=10, length=10, 
         reorderings=500, silent=FALSE, plot.progress=FALSE, do.search=TRUE)

_A_r_g_u_m_e_n_t_s:

  fafile: a ranked fasta file containing DNA sequences.

startguesses: a character vector with consensus sequences in IUPAC
          coding to be used as starting sequences in the search. If
          empty, random start guesses will be generated.

restarts: number restarts of the algorithm when using random start
          guesses.

  length: legth of random start guess.

reorderings: number of random reorderings of the DNA sequences
          performed when calculating score.

  silent: reports progress status if FALSE. 

plot.progress: if TRUE, the progress is displayed in a plot.

do.search: if FALSE, no search is performed. In that case the start
          guesses are assigned with scores and reported as results.

_V_a_l_u_e:

     The method returns an objcet of class 'BCRANKresult-class'.

_A_u_t_h_o_r(_s):

     Adam Ameur, adam.ameur@lcb.uu.se

_R_e_f_e_r_e_n_c_e_s:

     Ameur, A., Rada-Iglesias, A., Komorowski, J., Wadelius, C. Novel
     algorithm and ChIP-analysis identifies candidate functional SNPs.
     Submitted

_S_e_e _A_l_s_o:

     'BCRANKresult-class'

_E_x_a_m_p_l_e_s:

     ## Load example fasta file  
     fastaFile <- system.file("Exfiles/USF1_small.fa", package = "BCRANK") 
     ## Run BCRANK
     ## Not run: BCRANKout <- bcrank(fastaFile, restarts=20)

     ## Show BCRANK results
     toptable(BCRANKout)
     ## The top scoring result
     topMotif <- toptable(BCRANKout,1)
     ## Plot BCRANK search path
     plot(topMotif)
     ## Position Weight Matrix
     pwm(topMotif, normalize=FALSE)

