Transcriptome data on the quantitative numbers of transcriptional variants expressed in primary cells offer essential clues into specific cellular functions and biological processes. We have previously collected transcriptomes from primary smooth muscle cells (SMC), interstitial cells of Cajal (ICC), and PDGFRα+ cells (fibroblast-like cells) isolated from murine jejunal and colonic smooth muscle and/or mucosal tissues as well as transcriptomes from the associated tissues (jejunal smooth muscle, colonic smooth muscle, and colonic mucosa). In this study, we have built the Smooth Muscle Transcriptome Browser (SMTB), https://med.unr.edu/physio/transcriptome, a web-based, graphical user interface that offers genetic references and expression profiles of all transcripts expressed at both the cellular (SMC, ICC, and PDGFRα+ cells) and tissue level (smooth muscle and mucosal tissue). This browser brings new insights into the cellular and biological functions of the cell types in gastrointestinal smooth muscle biology.

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-Nucleotide sequencing of genomic data is an important step towards building understanding of gene expression. Current limitations in sequencing limit the number of base pairs that can be processed to only several hundred at a time. Consequently, these sequenced substrings need to be assembled into the overall genome. However, the existence of insertions, deletions and substitutions can complicate the assembly of subsequences and confuse existing methods. What has been needed is an approach that deals with ambiguity in trying to match and assemble a genome from its sequenced subsequences. This research develops fuzzy similarity measures between subsequences that are then incorporated into an assembler based on fuzzy logic and fuzzy similarity measures. The research addresses the problem of extensive computation required by clustering data into meaningful groups. Preliminary evaluation of this approach in conjunction with K-Means clustering suggests that this approach is at least as good as standard approaches and in some cases better.

Abstract-Approximating 'real' disease transmission networks through genomic sequence comparisons among pathogenic isolates is increasingly feasible with the current growth in genomic sequence data. Here, we derive a network from over 4,200 globally distributed influenza A virus isolates based on alignment-free sequence comparisons. We then employ network mixing pattern analysis to examine transmission probabilities between isolates from different global regions, host types, subtypes and collection years. While we can not use our results to describe the complete global network of influenza A virus, we present a novel analytical process. In addition, we describe some of the characteristics of this subset of currently available data. Most notable results are the high levels of inter regional links and the important role that avian species seem to play in non human global transmission.

Recent advances in next generation sequencing are providing a number of large whole-genome sequence datasets stemming from globally distributed disease occurrences. This offers an unprecedented opportunity for epidemiological studies and the development of computationally efficient, robust tools for such studies. Here we present an analytic approach combining several existing tools that enables a quick, effective, and robust epidemiological analysis of large wholegenome datasets. In this report, our dataset contains over 4, 200 globally sampled Influenza A virus isolates from multiple host type, subtypes, and years. These sequences are compared using an alignment-free method that runs in linear time. This enables us to generate a disease transmission network where sequences serve as nodes, and high-degree sequence similarity as edges. Mixing patterns are then used to examine statistical probabilities of edge formation among different host types from different global regions and from different localities within Southeast Asia. Our results reflect notable amounts of inter-host and inter-regional transmission of Influenza A virus.