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Abstract Sequence alignment is an essential method in bioinformatics and the basis of many analyses, including phylogenetic inference, ancestral sequence reconstruction, and gene annotation. Sequencing artifacts and errors made during genome assembly, such as abiological frameshifts and incorrect early stop codons, can impact downstream analyses leading to erroneous conclusions in comparative and functional genomic studies. More significantly, while indels can occur both within and between codons in natural sequences, most amino-acid- and codon-based aligners assume that indels only occur between codons. This mismatch between biology and alignment algorithms produces suboptimal alignments and errors in downstream analyses. To address these issues, we present COATi, a statistical, codon-aware pairwise aligner that supports complex insertion–deletion models and can handle artifacts present in genomic data. COATi allows users to reduce the amount of discarded data while generating more accurate sequence alignments. COATi can infer indels both within and between codons, leading to improved sequence alignments. We applied COATi to a dataset containing orthologous protein-coding sequences from humans and gorillas and conclude that 41% of indels occurred between codons, agreeing with previous work in other species. We also applied COATi to semiempirical benchmark alignments and find that it outperforms several popular alignment programs on several measures of alignment quality and accuracy. 

Several species of Polistes paper wasp are well known for their social hierarchies and the ability for individual wasps to modulate their social behaviors based on recognizable facial features of other wasps. For example, wasps that observe an aggressive social interaction between two other wasps will later behave differently toward the winner and loser of that interaction. Being able to alter the physical appearance of wasps~(e.g., with paint) has allowed for testing hypothetical roles of individual recognition in hierarchy formation, which is how researchers know that wasps are attending to faces specifically. However, these physical methods are limited in their scope. Social insects who respond to visual stimuli from other insects have been shown to give the same responses to playbacks of video recordings of those stimuli, which suggests that there may be a role for generative methods in social-insect research. Being able to computationally change the faces of individual wasps in a video recording of wasp social interactions would greatly expand the experimental toolbox of the behavioral researcher. Toward this end, we evaluate the use of an existing annotation-free model for image animation by motion transfer, the thin-plate spline motion model, for creating realistic videos that depict the face of a paper wasp performing behaviors recorded by another. Not needing to pre-define important landmarks is a strength of this method for this application space, but we find that "deep faking wasps" poses unique and non-trivial problems that still need to be solved before off-the-shelf motion transfer models can be used in the insect behavioral laboratory.