MemAlign: A Memory Structure to Accelerate Gene Sequencing Since 2003, when the human reference genome was discovered, several applications found gene sequencing a promising mechanism to help improve their results. These include studying hereditary diseases, prenatal monitoring and others. Gene sequencing in its typical configuration consists of several elaborate processing stages, each performed by a separate software package. The intermediate results are transferred via large files between gene sequencing different steps, hence making gene sequencing a processing and IO demanding task. Taking advantage of advances memory speed and capacity and with the ultimate goal of pipelining the gene sequencing steps and avoiding utilizing file storage to communicate intermediate results, in this paper we present MemAlign, a novel presorted memory structure to pipeline the first and second processing steps of gene sequencing; Alignment and Sort. The number of memory locations of MemAlign corresponds the positions on the human reference genome. Combined with techniques to compress the alignment results, MemAlign essentially eliminates the Sort step by storing alignment results in the memory location that corresponds to the alignment position. MemAlign not only speeds up the combined processing time of Alignment and Sort, but also saves the considerable amount of storage required to store the intermediate results between the two steps.

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.