Supplementary materials for "A Model for Data-Driven Sonification Using Soundscapes," a poster presented at the ACM Intelligent User Interfaces Conference in 2015.

We provide: 1. An example "input" soundscape, exemplifying an existing soundscape that can be provided by a user 2. A clip of the "output" soundscape that was rendered for a dataset, using the above input soundscape and the mapping policy 3. A supplementary text describing the features of the input soundscape, sound groups, sound samples, and Twitter data, as well as the details of the mapping policy that were used to generate this output soundscape. This text also provides attribution for the sound files used in the sonification

End-user Development of Sonifications using Soundscapes

Designing sonifications requires knowledge in many domains including sound design, sonification design, and programming. Thus end users typically do not create sonifications on their own, but instead work with sonification experts to iteratively co-design their systems. However, once a sonification system is deployed there is little a user can do to make adjustments. In this work, we present an approach for sonification system design that puts end users in the control of the design process by allowing them to interactively generate, explore, and refine sonification designs. Our approach allows a user to start creating sonifications simply by providing an example soundscape (i.e., an example of what they might want their sonification to sound like), and an example dataset illustrating properties of the data they would like to sonify. The user is then provided with the ability to employ automated or semi-automated design of mappings from features of the data to soundscape controls. To make this possible, we describe formal models for soundscape, data, and sonification, and an optimization-based method for creating sonifications that is informed by design principles outlined in past auditory display research

A Model for Data-Driven Sonification Using Soundscapes

A sonification is a rendering of audio in response to data, and is used in instances where visual representations of data are impossible, difficult, or unwanted. Designing sonifications often requires knowledge in multiple areas as well as an understanding of how the end users will use the system. This makes it an ideal candidate for end-user development where the user plays a role in the creation of the design. We present a model for sonification that utilizes user-specified examples and data to generate cross-domain mappings from data to sound. As a novel contribution we utilize soundscapes (acoustic scenes) for these user-selected examples to define a structure for the sonification. We demonstrate a proof of concept of our model using sound examples and discuss how we plan to build on this work in the future

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of diseas

Genetic effects on gene expression across human tissues

Characterization of the molecular function of the human genome and its variation across individuals is essential for identifying the cellular mechanisms that underlie human genetic traits and diseases. The Genotype-Tissue Expression (GTEx) project aims to characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible. Here we describe genetic effects on gene expression levels across 44 human tissues. We find that local genetic variation affects gene expression levels for the majority of genes, and we further identify inter-chromosomal genetic effects for 93 genes and 112 loci. On the basis of the identified genetic effects, we characterize patterns of tissue specificity, compare local and distal effects, and evaluate the functional properties of the genetic effects. We also demonstrate that multi-tissue, multi-individual data can be used to identify genes and pathways affected by human disease-associated variation, enabling a mechanistic interpretation of gene regulation and the genetic basis of disease

End-user development of sonifications using soundscapes

Presented at the 21st International Conference on Auditory Display (ICAD2015), July 6-10, 2015, Graz, Styria, Austria.Designing sonifications requires knowledge in many domains including sound design, sonification design, and programming. Thus end users typically do not create sonifications on their own, but instead work with sonification experts to iteratively co-design their systems. However, once a sonification system is deployed there is little a user can do to make adjustments. In this work, we present an approach for sonification system design that puts end users in the control of the design process by allowing them to interactively generate, explore, and refine sonification designs. Our approach allows a user to start creating sonifications simply by providing an example soundscape (i.e., an example of what they might want their sonification to sound like), and an example dataset illustrating properties of the data they would like to sonify. The user is then provided with the ability to employ automated or semi-automated design of mappings from features of the data to soundscape controls. To make this possible, we describe formal models for soundscape, data, and sonification, and an optimization-based method for creating sonifications that is informed by design principles outlined in past auditory display research

Landscape of X chromosome inactivation across human tissues

X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of 'escape' from inactivation varying between genes and individuals. The extent to which XCI is shared between cells and tissues remains poorly characterized, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression and phenotypic traits. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI

Dynamic landscape and regulation of RNA editing in mammals

Adenosine-to-inosine (A-to-I) RNA editing is a conserved post-transcriptional mechanism mediated by ADAR enzymes that diversifies the transcriptome by altering selected nucleotides in RNA molecules. Although many editing sites have recently been discovered, the extent to which most sites are edited and how the editing is regulated in different biological contexts are not fully understood. Here we report dynamic spatiotemporal patterns and new regulators of RNA editing, discovered through an extensive profiling of A-to-I RNA editing in 8,551 human samples (representing 53 body sites from 552 individuals) from the Genotype-Tissue Expression (GTEx) project and in hundreds of other primate and mouse samples. We show that editing levels in non-repetitive coding regions vary more between tissues than editing levels in repetitive regions. Globally, ADAR1 is the primary editor of repetitive sites and ADAR2 is the primary editor of non-repetitive coding sites, whereas the catalytically inactive ADAR3 predominantly acts as an inhibitor of editing. Cross-species analysis of RNA editing in several tissues revealed that species, rather than tissue type, is the primary determinant of editing levels, suggesting stronger cis-directed regulation of RNA editing for most sites, although the small set of conserved coding sites is under stronger trans-regulation. In addition, we curated an extensive set of ADAR1 and ADAR2 targets and showed that many editing sites display distinct tissue-specific regulation by the ADAR enzymes in vivo. Further analysis of the GTEx data revealed several potential regulators of editing, such as AIMP2, which reduces editing in muscles by enhancing the degradation of the ADAR proteins. Collectively, our work provides insights into the complex cis- and trans-regulation of A-to-I editing

Co-expression networks reveal the tissue-specific regulation of transcription and splicing

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single tissue or small sets of tissues. Here, we built networks that additionally capture the regulation of relative isoform abundance and splicing, along with tissue-specific connections unique to each of a diverse set of tissues. We used the Genotype-Tissue Expression (GTEx) project v6 RNA sequencing data across 50 tissues and 449 individuals. First, we developed a framework called Transcriptome-Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the interplay between the regulation of splicing and transcription. We built TWNs for 16 tissues and found that hubs in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome. Next, we used a Bayesian biclustering model that identifies network edges unique to a single tissue to reconstruct Tissue-Specific Networks (TSNs) for 26 distinct tissues and 10 groups of related tissues. Finally, we found genetic variants associated with pairs of adjacent nodes in our networks, supporting the estimated network structures and identifying 20 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships of the human transcriptome across tissues

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types