LysoPhD: predicting functional prophages in bacterial genomes from highthroughput sequencing Motivation: When a temperate bacteriophage (phage) integrates into the chromosome of its host or to remain as latent episomal DNA, the prophage plays an important role in bacterial virulence acquisition and increase. With the recent release of thousands of bacterial high throughput sequencing data, there has been a growing interest in analyzing the impact of interdependency between a prophage and its host. One of the essential tasks is to acquire and identify the complete sequence of the prophage, i.e., the functional prophages. However, no existing tools can identify functional prophages from bacterial genomes. A few tools can predict putative prophage in bacterial genomes, but they are unable to distinguish functional prophages from defective prophages. To reduce the cost and to relieve the tedious biological experiments for functional prophage analysis, computational methods for predicting functional prophages based on bacterial highthroughput sequencing data would be an effective alternation. Results: This paper presents a method, LysoPhD, the first tool to predict functional prophage sequences using bacterial HTS data automatically and accurately. The main idea is to obtain whole genome of functional prophages from the bacterial genome based on phage gene annotation and readsmediated contig connection. LysoPhD obtains whole genome sequences of functional prophages based on improved models. The obtained the sequences are verified by wet lab experiments. Specifically, LysoPhD is applied to a set of staphylococcus HTS data in the case studies. Ten functional prophages are predicted from 72 staphylococcus isolates. The 72 bacterial strains are then induced with Mitomycin C and subsequent deep sequencing verified of the 10 functional prophages. This shows that LysoPhD is a sensitive and accurate program for functional prophage prediction.

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.