Supplementary Information for "HTS-IBIS: fast and accurate inference of binding site
motifs from HT-SELEX data", by Orenstein and Shamir
Details of the algorithm
Choice of cycle
The cycle is chosen based on Kullback-Leibler divergence score. For each cycle c from 1 to the last
and for each k between 6 to 8 (inclusive) the score is:
𝑛∙4𝑘−6
𝐾𝐿(𝑐, 𝑘, 𝑛) = ∑ 𝑓𝑖𝑐 log 𝑓𝑓𝑖𝑐
𝑖=𝑜
𝑖0
Where fic is the frequency of the i-th most frequent k-mer in cycle c (starting from 1). f0c is the
sum of the frequencies of the rest of the k-mers. Laplace correction is applied to avoid zero
frequencies. The default value of n is 100. The first cycle for which there is a k with a score above
0.1 is chosen. If that condition is not met in any cycle, the last one is chosen.
Choice of k
K is searched in the range 6-8. The same score as in the cycle choosing process is used, and the
value of k with the highest score is chosen. The same process was used in (Slattery et al. 2011).
Seed finding
For each k-mer the sum of its corrected count in the chosen cycle and the previous cycle is
calculated, and the one with the highest sum is the seed. If the count of a k-mer is greater than 4
times or smaller by 4 times than the count of its reverse complement, both are given the lower
count of the two. If the k-mer is a 'sticky k-mer', i.e. has at least k-2 occurrences of the same
nucleotide, then its count is reduced in the same manner.
Model generation
This process mimics (Orenstein et al. 2013). A PWM of length 3*k-10 is generated, where the k
middle positions correspond to the seed. Each k-mer among the most-frequent 20·4k-4 k-mers is
aligned to the seed. The best orientation and offset (up to k-5 positions) is chosen if the number
of matches is at least 5. Otherwise, the k-mer is discarded. The real count of each k-mer is added
to the corresponding nucleotide at each position of the k-mer. After extension and trimming of
the matrix (see below), these aggregated counts are normalized to get a probability distribution
in each column.
Extension phase
Extend the matrix according to counts of longer k-mers that contain the seed. For all oligos
containing the seed k-mer and k-5 positions at each side, the count of the oligo is added to the
flanking positions in the matrix for the corresponding nucleotides.
Trimming phase
Trim uninformative side positions. Define the start position as the first position that does not
conation a zero-count nucleotide, has a total count of at least 80 and the information content of
the next position is at least 0.1. If no such position exists and the core starts at positions i, return
i+2. The information content for vector (v1, v2, v3, v4) (where i vi = 1) is defined as 2+i vi log(vi).
The end position is determined analogously.
Performance evaluation
Seed finding evaluation
We evaluated the seed finding process by comparing it to two gold-standards. The chosen seed is
compared to (i) the top-ranking 8-mer in a PBM experiment on the same protein; (ii) the
published seeds in (Jolma et al. 2013). If the chosen seed fits the gold standard in offset up to two
with at most two mismatches, then it is considered a match. In the cases where Jolma et al.
provided several seeds, a fit to one of them is enough to declare it a match. When there were
several PBM experiments on the same TF, an arbitrary PBM experiment on the same protein was
paired to each HT-SELEX experiment (a total of 237 paired experiments). The number of HT-SELEX
experiments is 547.
Binding prediction evaluation
We tested the accuracy of models inferred by each method in predicting in vitro and in vivo
binding. In vitro binding was measured on PBM experiments (Robasky and Bulyk 2011) and in
vivo on ChIP-seq experiments (Landt et al. 2012). The model, in PWM format, was used to rank all
PBM probe sequences or ChIP-seq peaks in an experiment on the same protein. For each
sequence an occupancy score was calculated, which is the sum of the probability of the protein
to bind over all positions (Tanay 2006). This score is used to rank all sequences. For sequence s
and PWM Θ of length k, the occupancy score is
f (s, )  t 0
| s| k

k
i 1
i [st  i ]
Where Θi(x) is the probability of base x in position i of the PWM. The ranking due to the
occupancy score is compared to the original ranking according to the binding intensity. The
positive set in PBM is selected as in (Chen et al. 2007); all other probes are the negative set. The
top 500 peaks were considered the positive set, and sequences of the same length 300-bp
downstream composed the negative set. For each peak, the 250bp around its center was used. A
model inferred from an HT-SELEX experiment was tested on all PBM experiments on the same TF
(a total of 344 paired experiments) and all ChIP-seq experiments on the same TF (a total of 59
paired experiments). For each PBM experiment, the AUC is reported. When there are several
ChIP-seq experiments on the same TF, the average AUC is reported.
Implementation details
The method is implemented efficiently in Java. Each nucleotide is coded by 2 bits. K-mers of the
maximum k (8) are counted, and counts of lower k-mers are derived from these counts. When
run on machine with a single core of an Intel Xeon CPU E5410 @2.33 GHz with 6 MB of cache and
16 GB of memory typical running time is ≤5 seconds and typical amount of peak memory usage is
2.5 MB.
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