Streptococcus pneumoniae is an infectious pathogen responsible for millions of deaths worldwide. Diseases caused by this bacterium are classified as pneumococcal diseases. This pathogen colonizes the nasopharynx of its host asymptomatically, but overtime can migrate to sterile tissues and organs and cause infections. Pneumonia is currently the most common pneumococcal disease. Pneumococcal pneumonia is a global health concern and vastly affects children under the age of five as well as the elderly and individuals with pre-existing health conditions. S. pneumoniae has a large selection of virulence factors that promote adherence, invasion of host tissues, and allows it to escape host immune defenses. A clear understanding of S. pneumoniae’s virulence factors, host immune responses, and examining the current techniques available for diagnosis, treatment, and disease prevention will allow for better regulation of the pathogen and its diseases. In terms of disease prevention, other considerations must include the effects of age on responses to vaccines and vaccine efficacy. Ongoing work aims to improve on current vaccination paradigms by including the use of serotype-independent vaccines, such as protein and whole cell vaccines. Extending our knowledge of the biology of, and associated host immune response to S. pneumoniae is paramount for our improvement of pneumococcal disease diagnosis, treatment, and improvement of patient outlook.

Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the use of the Wolfram Language to tackle such problems in bioinformatics. The package particularly addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. We anticipate MathIOmica to not only help in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.

Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6 th leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population), and the lack of effective treatments and preventive methods. With an increase in life expectancy, the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal and AD age-related brain degeneration and memory loss. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles. Our study aims to identify temporal changes within gene expression profiles of healthy controls and AD subjects. We conducted a meta-analysis using publicly available microarray expression data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We then performed an analysis of variance, using a linear model that incorporated age, tissue type, sex, and disease state as effects, as well as study to account for batch effects, and included binary interactions between factors. Our results identified 3,735 statistically significant (Bonferroni adjusted p < 0.05) gene expression differences between AD and healthy controls, which we filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, transmission across chemical synapses and mitochondrial translation. Overall our approach allowed us to effectively combine multiple available microarray datasets and identify gene expression differences between AD and healthy individuals including full age and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve AD diagnostics and potentially treatment or prevention.

Multiple omics data are rapidly becoming available, necessitating the use of new methods to integrate different technologies and interpret the results arising from multimodal assaying. The MathIOmica package for Mathematica provides one of the first extensive introductions to the use of the Wolfram Language to tackle such problems in bioinformatics. The package particularly addresses the necessity to integrate multiple omics information arising from dynamic profiling in a personalized medicine approach. It provides multiple tools to facilitate bioinformatics analysis, including importing data, annotating datasets, tracking missing values, normalizing data, clustering and visualizing the classification of data, carrying out annotation and enumeration of ontology memberships and pathway analysis. We anticipate MathIOmica to not only help in the creation of new bioinformatics tools, but also in promoting interdisciplinary investigations, particularly from researchers in mathematical, physical science and engineering fields transitioning into genomics, bioinformatics and omics data integration.

2Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and 3 Prevention (CDC) as the 6 th leading cause of death in the United States. AD is a significant 4 health-care burden because of its increased occurrence (specifically in the elderly population) 5 and the lack of effective treatments and preventive methods. With an increase in life expectancy, 6 the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated 7 with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal 8 and AD age-related brain degeneration and memory loss. AD targets neuronal function and can 9 cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary 10 tangles. 11Our study aims to identify temporal changes within gene expression profiles of healthy controls 12 and AD subjects. We conducted a meta-analysis using publicly available microarray expression 13 data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported 14 donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 15 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We 16 then performed an analysis of variance, using a linear model that incorporated age, tissue type, 17 sex, and disease state as effects, as well as study to account for batch effects, and including 18 binary interaction between factors. Our results identified 3,735 statistically significant (Bonferroni 19 adjusted p<0.05) gene expression differences between AD and healthy controls, which we 20 filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to 21 obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative 22 diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic 23 vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, 24 transmission across chemical synapses and mitochondrial translation. 25 Overall our approach allowed us to effectively combine multiple available microarray datasets 26 and identify gene expression differences between AD and healthy individuals including full age 27 and tissue type considerations. Our findings provide potential gene and pathway associations 28 that can be targeted to improve AD diagnostics and potentially treatment or prevention. (US). 29