Sharma, A., Jain, P., Mahgoub, A., Zhou, Z., Mahadik, K., and Chaterji, S. (2022). Lerna: Transformer Architectures for Configuring Error Correction Tools for Short-and Long-Read Genome Sequencing. BMC bioinformatics, BioMed Central.
Students
Atul Sharma, Pranjal Jain, Ashraf Mahgoub, Zihan Zhou
Abstract
Background:
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, and then, based on this LM, calculates a metric called 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.
Therefore, Lerna leverages the perplexity metric for automated tuning of k-mer
sizes without needing a reference genome.
Results:
First, we show that the best k-mer value can vary for different datasets,
even for the same EC tool. This motivates our design that automates k-mer size
selection without using a reference genome. 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.
We validate our approach on both short and long reads. 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.
Conclusion:
Lerna improves de novo genome assembly by optimizing EC tools.
Our code is made available in a public repository at:
https://github.com/icanforce/lerna-genomics