HFM: Hierarchical Feature Moment Extraction for MultiOmic Data Visualization Sequencing the DNA of the estimated 7.5 billion living humans would generate 1.4 zettabytes of data. However, given current perread rendering techniques, just one DNA alignment file which is around 200 gigabytes can be resource intensive to visualize at arbitrary scale. Going from human DNA and RNA sequencing data to biological insight is a process that requires domain knowledge in addition to computational methods that are bound by time and space. We address these limitations by integrating a parallel outofcore feature extraction algorithm with a diskbased hierarchical data store that provides several orders of magnitude speedup for common analysis and visualization tasks. To demonstrate the effectiveness of our strategy, we have developed a webbased REST service that serves translated data to a realtime genomic viewer, which in turn renders standardized moments as stackedarea graphs of features in milliseconds for multiple samples using a familiar genome browser interface. Unlike perread techniques which read a variable number of rows from the sequence alignment file depending on the region of interest, our data structure returns a controllable data size of that region, making the technique ideally suited for visualization and macrolevel insight of large cohorts. The strategy works well for highcoverage single coordinatebased visualization but could be extended for use in other longrange visualization techniques. We detail our opensource CythonPython based implementation as well as our prototype webbased visualization tool and then compare the resulting performance and against established visualization tools.

Sparse Binary Relation Representations for Genome Graph Annotation Highthroughput DNA sequencing data is accumulating in public repositories, and efficient approaches for storing and indexing such data are in high demand. In recent research, several graph data structures have been proposed to represent large sets of sequencing data and allow for efficient query of sequences. In particular, the concept of colored de Bruijn graphs has been explored by several groups. While there has been good progress towards representing the sequence graph in small space, methods for storing a set of labels on top of such graphs are still not sufficiently explored. It is also currently not clear how characteristics of the input data, such as the sparsity and correlations of labels, can help to inform the choice of method to compress the labels. In this work, we present a systematic analysis of five different stateoftheart annotation compression schemes that evaluates key metrics on both artificial and realworld data and discusses how different data characteristics influence the compression performance. In addition, we present a new approach, MultiBRWT, that shows an up to 50% improvement in compression performance over the current stateoftheart and is adaptive to different kinds of input data. Using our comprehensive test datasets, we show that this improvement can be robustly reproduced for different representative realworld datasets.