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Abstract Communication signals by both human and non-human animals are often interrupted in nature. One advantage of multimodal cues is to maintain the salience of interrupted signals. We studied a frog that naturally can have silent gaps within its call. Using video/audio-playbacks, we presented females with interrupted mating calls with or without a simultaneous dynamic (i.e., inflating and deflating) vocal sac and tested whether multisensory cues (noise and/or dynamic vocal sac) inserted into the gap can compensate an interrupted call. We found that neither inserting white noise into the silent gap of an interrupted call nor displaying the dynamic vocal sac in that same gap restored the attraction of the call equivalent to that of a complete call. Simultaneously presenting a dynamic vocal sac along with noise in the gap, however, compensated the interrupted call, making it as attractive as a complete call. Our results demonstrate that the dynamic visual sac compensates for noise interference. Such novel multisensory integration suggests that multimodal cues can provide insurance against imperfect sender coding in a noisy environment, and the communication benefits to the receiver from multisensory integration may be an important selective force favoring multimodal signal evolution. 

Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data.

 

Discovering patterns from a set of text or, more generally, categorical data is an important problem in many disciplines such as biomedical research, linguistics, artificial intelligence and sociology. We consider here the well-known ‘market basket’ problem that is often discussed in the data mining community, and is also quite ubiquitous in biomedical research. The data under consideration are a set of ‘baskets’, where each basket contains a list of ‘items’. Our goal is to discover ‘themes’, which are defined as subsets of items that tend to co-occur in a basket. We describe a generative model, i.e. the theme dictionary model, for such data structures and describe two likelihood-based methods to infer themes that are hidden in a collection of baskets. We also propose a novel sequential Monte Carlo method to overcome computational challenges. Using both simulation studies and real applications, we demonstrate that the new approach proposed is significantly more powerful than existing methods, such as association rule mining and topic modelling, in detecting weak and subtle interactions in the data.