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An important unanswered question in chromatin biology is the extent to which long-range looping interactions change across developmental models, genetic perturbations, drug treatments, and disease states. Computational tools for rigorous assessment of cell type-specific loops across multiple biological conditions are needed. We present 3DeFDR, a simple and effective statistical tool for classifying dynamic loops across biological conditions from Chromosome-Conformation-Capture-Carbon-Copy (5C) and Hi-C data. Our work provides a statistical framework and open-source coding libraries for sensitive detection of cell type-specific loops in high-resolution 5C and Hi-C data from multiple cellular conditions.

Background With the growing adult population using electronic hearing devices such as cochlear implants or hearing aids, there is an increasing worldwide need for auditory training (AT) to promote optimal device use. However, financial resources and scheduling conflicts make clinical AT infeasible. Objective To address this gap between need and accessibility, we primarily aimed to develop a mobile health (mHealth) app called Speech Banana for AT. The app would be substantially more affordable and portable than clinical AT; would deliver a validated training model that is reflective of modern techniques; and would track users’ progress in speech comprehension, providing greater continuity between periodic in-person visits. To improve international availability, our secondary aim was to implement the English language training model into Korean as a proof of concept for worldwide usability. Methods A problem- and objective-centered Design Science Research Methodology approach was adopted to develop the Speech Banana app. A review of previous literature and computer-based learning programs outlined current AT gaps, whereas interviews with speech pathologists and users clarified the features that were addressed in the app. Past and present users were invited to evaluate the app via community forums and the System Usability Scale. Results Speech Banana has been implemented in English and Korean languages for iPad and web use. The app comprises 38 lessons, which include analytic exercises pairing visual and auditory stimuli, and synthetic quizzes presenting auditory stimuli only. During quizzes, users type the sentence heard, and the app provides visual feedback on performance. Users may select a male or female speaker and the volume of background noise, allowing for training with a range of frequencies and signal-to-noise ratios. There were more than 3200 downloads of the English iPad app and almost 100 downloads of the Korean app; more than 100 users registered for the web apps. The English app received a System Usability Scale rating of “good” from 6 users, and the Korean app received a rating of “OK” from 16 users. Conclusions Speech Banana offers AT accessibility with a validated curriculum, allowing users to develop speech comprehension skills with the aid of a mobile device. This mHealth app holds potential as a supplement to clinical AT, particularly in this era of global telemedicine.

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The mammalian genome is connected into tens of thousands of long-range looping interactions critically linked to spatiotemporal gene expression regulation. An important unanswered question is to what extent looping interactions change across developmental models, genetic perturbations, drug treatments, and disease states.Although methods exist for calling loops in single biological conditions, there is a severe shortage of computational tools for rigorous assessment of cell type-specific looping interactions across multiple biological conditions. Here we present 3DeFDR, a simple and effective statistical tool for classifying dynamic looping interactions across biological conditions from Chromosome-Conformation-Capture-Carbon-Copy (5C) data. 3DeFDR parses chromatin contacts into invariant and cell type-specific classes by thresholding on differences in modeled interaction strength signal across two or three cellular states.Thresholds are iteratively adjusted based on a target empirical false discovery rate computed between real and simulated 5C maps. 3DeFDR enables the sensitive detection of high-confidence looping interactions and markedly reduces false positives when benchmarked against a classic analysis of variance (ANOVA) test, our newly formulated parametric likelihood ratio test (3DLRT), and the leading Hi-C differential interaction caller diffHic. 3DeFDR also sensitively and specifically calls loops in Mbscale genomic regions parsed from Hi-C data. Our work provides a statistical framework and an open-source coding library for identifying dynamic long-range looping interactions in high-resolution 5C data from multiple cellular conditions.