Explanation for each column:

Label: An assigned label for each motif.

count: The number of subsequences used in the MSA to estimate the PWM. This is $N_j$ as in the definition of Likelihood ratio scores. Each subsequence in the MSA comes from different sequences in the dataset.

LRS: Likelihood ratio score.

E-value: The statistical significance of the motif as the number of subsequence covered in the input sequences and the control sequences are essentially the same. This is done the same way as STREME: the p-value times the number of discovered motifs; the p-value is obtained via Fisher-exact-test.

Likelihood ratio scores:

Let $P_j$ be the position frequency matrix estimated from the $j$th learned motif and $N_j$ be the number of sequences used in the estimation of $P_j$. The likelihood ratio score of the $j$th learned motif of length $L$ is $$ \sum_{n=1}^{N_j}\sum_{\ell=1}^L \sum_{\alpha} \unicode{x1D7D9}\left[s_n[\ell]=\alpha\right] \, P_j[\alpha,\ell]\, \ln \frac{P_j[\alpha,\ell]}{B[\alpha]}$$ where $B[\alpha]$ is the background frequency of nucleotide $\alpha$, $s_n$ the $n$th substring used in estimating $P_j$, and $\unicode{x1D7D9}[\cdot]$ is the indicator function. In this experiment, $B[\alpha]=1/4,\,\forall \alpha$.

Number of input sequences: {{num_seq}} {% for i in range(discovered[1]|length) %} {% endfor %} {% for d in range(discovered|length) %} {% for i in range(discovered[d]|length) %} {% endfor %} {% endfor %}

Discovered motifs
Label	count	LRS	E-value	Logo
D{{ d_indices[d][i] }}	{{msa_count[d][i]}}	{{ssc[d][i]}}	{{evalues[d][i]}}	Learned PWM