OMARC: An online multimedia application for training health care providers in the assessment of respiratory conditions.

OBJECTIVES: OMARC, a multimedia application designed to support the training of health care providers for the identification of common lung sounds heard in a patient's thorax as part of a health assessment, is described and its positive contribution to user learning is assessed. The main goal of OMARC is to effectively help health-care students become familiar with lung sounds as part of the assessment of respiratory conditions. In addition, the application must be easy to use and accessible to students and practitioners over the internet. SYSTEM DESCRIPTION: OMARC was developed using an online platform to facilitate access to users in remote locations. OMARC's unique contribution as an educational software tool is that it presents a narrative about normal and abnormal lung sounds using interactive multimedia and sample case studies designed by professional health-care providers and educators. Its interface consists of two distinct components: a sounds glossary and a rich multimedia interface which presents clinical case studies and provides access to lung sounds placed on a model of a human torso. OMARC's contents can be extended through the addition of sounds and case studies designed by health-care educators and professionals. VALIDATION AND RESULTS: To validate OMARC and determine its efficacy in improving learning and capture user perceptions about it, we performed a pilot study with ten nursing students. Participants' performance was measured through an evaluation of their ability to identify several normal and adventitious/abnormal sounds prior and after exposure to OMARC. Results indicate that participants are able to better identify different lung sounds, going from an average of 63% (S.D. 18.3%) in the pre-test evaluation to an average of 90% (S.D. of 11.5%) after practising with OMARC. Furthermore, participants indicated in a user satisfaction questionnaire that they found the application helpful, easy to use and that they would recommend it to other persons in their field. CONCLUSIONS: OMARC is an online multimedia application for training health care students in the assessment of respiratory conditions. The software integrates multimedia technology and health-care education concepts to facilitate learning, while being useful and easy to use. Results from a pilot study indicate that OMARC significantly helps to improve the capacity of the users to correctly identify lung sounds for different respiratory conditions. In addition, participants' opinions about OMARC were quite positive: users were likely to recommend the application to other persons in their field and found the application easy to use and helpful to better identify lung sounds.

DETECTION OF BACTERIAL SMALL TRANSCRIPTS FROM RNA-SEQ DATA: A COMPARATIVE ASSESSMENT.

Small non-coding RNAs (sRNAs) are regulatory RNA molecules that have been identified in a multitude of bacterial species and shown to control numerous cellular processes through various regulatory mechanisms. In the last decade, next generation RNA sequencing (RNA-seq) has been used for the genome-wide detection of bacterial sRNAs. Here we describe sRNA-Detect, a novel approach to identify expressed small transcripts from prokaryotic RNA-seq data. Using RNA-seq data from three bacterial species and two sequencing platforms, we performed a comparative assessment of five computational approaches for the detection of small transcripts. We demonstrate that sRNA-Detect improves upon current standalone computational approaches for identifying novel small transcripts in bacteria.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence.

BACKGROUND: Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated. RESULTS: In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%. CONCLUSION: We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.