DeepConsensus 1.1.0

• DeepConsensus v1.1 introduces a new model that improves coverage of telomere regions achieved through improved filtering of the training data with CHM13 high confidence regions.
• Improved yield at empirical Q30 from 187.1% in v1.0 to 194.4% in v1.1, relative to ccs baseline of 100%. This was achieved through improvements to the attention layer in the model.
• Updated the training tutorial for training on TPUs that users can use as a proof-of-concept to develop a training setup.
• This release evaluates performance using an updated HG002 truth assembly. We have re-evaluated previous releases with this updated dataset and updated Q30 yields accordingly.
• Thanks to Sergey Koren (@skoren) from NIH, NHGRI and the T2T consortium for invaluable feedback on the coverage of telomeric regions.
• Thanks to Daniel Liu (@Daniel-Liu-c0deb0t) for incorporating prior knowledge/sparsity in the attention layer of the model, which significantly improved the accuracy and Q30 yield.
• Thanks to Armin Töpfer (@armintoepfer), Aaron Wenger (@amwenger), and William Rowell (@williamrowell) at PacBio for advice and collaboration.