code: def best_kmers(dt, response, sequence, k=6, consider_shift=True, n_cores=1,
                 seq_align="start", trim_seq_len=None):
      y = dt[response]
      seq = dt[sequence]
      if trim_seq_len is not None:
          seq = pad_sequences(seq, align=seq_align, maxlen=trim_seq_len)
          seq = [s.replace("N", "") for s in seq]
      dt_kmer = kmer_count(seq, k)
      Xsp = csc_matrix(dt_kmer)
      en = ElasticNet(alpha=1, standardize=False, n_splits=3)
      en.fit(Xsp, y)
      nonzero_kmers = dt_kmer.columns.values[en.coef_ != 0].tolist()
      def find_next_best(dt_kmer, y, selected_kmers, to_be_selected_kmers, consider_shift=True):
          F, pval = f_regression(dt_kmer[to_be_selected_kmers], y)
          kmer = to_be_selected_kmers.pop(pval.argmin())
          selected_kmers.append(kmer)
          def select_criterion(s1, s2, consider_shift=True):
              if hamming_distance(s1, s2) <= 1:
                  return False
              if consider_shift and hamming_distance(s1[1:], s2[:-1]) == 0:
                  return False
              if consider_shift and hamming_distance(s1[:-1], s2[1:]) == 0:
                  return False
              return True
          to_be_selected_kmers = [ckmer for ckmer in to_be_selected_kmers
                                  if select_criterion(ckmer, kmer, consider_shift)]
          if len(to_be_selected_kmers) == 0:
              return selected_kmers
          else:
              lm = LinearRegression()
              lm.fit(dt_kmer[selected_kmers], y)
              y_new = y - lm.predict(dt_kmer[selected_kmers])
              return find_next_best(dt_kmer, y_new, selected_kmers, to_be_selected_kmers, consider_shift)
      selected_kmers = find_next_best(dt_kmer, y, [], nonzero_kmers, consider_shift)
      return selected_kmers