  Sequencing technologies are prone to errors, making error correction (EC)
necessary for downstream applications. EC tools need to be manually configured
for optimal performance. We find that the optimal parameters (e.g., k-mer size)
are both tool- and dataset-dependent. Moreover, evaluating the performance
(i.e., Alignment-rate or Gain) of a given tool usually relies on a reference
genome, but quality reference genomes are not always available. We introduce
Lerna for the automated configuration of k-mer-based EC tools. Lerna first
creates a language model (LM) of the uncorrected genomic reads; then,
calculates the perplexity metric to evaluate the corrected reads for different
parameter choices. Next, it finds the one that produces the highest alignment
rate without using a reference genome. The fundamental intuition of our
approach is that the perplexity metric is inversely correlated with the quality
of the assembly after error correction. Results: First, we show that the best
k-mer value can vary for different datasets, even for the same EC tool. Second,
we show the gains of our LM using its component attention-based transformers.
We show the model's estimation of the perplexity metric before and after error
correction. The lower the perplexity after correction, the better the k-mer
size. We also show that the alignment rate and assembly quality computed for
the corrected reads are strongly negatively correlated with the perplexity,
enabling the automated selection of k-mer values for better error correction,
and hence, improved assembly quality. Additionally, we show that our
attention-based models have significant runtime improvement for the entire
pipeline -- 18X faster than previous works, due to parallelizing the attention
mechanism and the use of JIT compilation for GPU inferencing.
