BioCloud Search EnGene: Surfing Biological Data on the Cloud

The massive production and spread of biomedical data around the web introduces new challenges related to identify computational approaches for providing quality search and browsing of web resources. This papers presents BioCloud Search EnGene (BSE), a cloud application that facilitates searching and integration of the many layers of biological information offered by public large-scale genomic repositories. Grounding on the concept of dataspace, BSE is built on top of a cloud platform that severely curtails issues associated with scalability and performance. Like popular online gene portals, BSE adopts a gene-centric approach: researchers can find their information of interest by means of a simple “Google-like” query interface that accepts standard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: http://biocloud-unica.appspot.com/

GENDB : a second generation genome annotation system

Meyer F. GENDB : a second generation genome annotation system. Bielefeld (Germany): Bielefeld University; 2001.The advent of new high throughput technologies opens the road towards a new era of genome analysis. Data from high throughput sequencers, chip based RNA expression analysis and proteome analysis systems create the need for software systems to support new kinds of analysis and data. At the same time the focus of molecular research shifted from the analysis of single genes to the analysis of whole genomes, multiple high throughput sources of data are routinely used. Yet there is a shortage of software systems that help store, integrate and analyse the wealth of information now available. We describe the development of a new genome annotation system (GENDB) based on a relational database system and object oriented technology that helps with the analysis of this data. GENDB significantly reduces the storage and compute overhead of existing systems, while offering more flexibility. The ability to integrate new kinds of data and new methods of analysis is one of the primary design targets for GENDB. The GENDB system has been succesfully used in a number of genome projects

Exploring Strategies to Integrate Disparate Bioinformatics Datasets

Distinct bioinformatics datasets make it challenging for bioinformatics specialists to locate the required datasets and unify their format for result extraction. The purpose of this single case study was to explore strategies to integrate distinct bioinformatics datasets. The technology acceptance model was used as the conceptual framework to understand the perceived usefulness and ease of use of integrating bioinformatics datasets. The population of this study included bioinformatics specialists of a research institution in Lebanon that has strategies to integrate distinct bioinformatics datasets. The data collection process included interviews with 6 bioinformatics specialists and reviewing 27 organizational documents relating to integrating bioinformatics datasets. Thematic analysis was used to identify codes and themes related to integrating distinct bioinformatics datasets. Key themes resulting from data analysis included a focus on integrating bioinformatics datasets, adding metadata with the submitted bioinformatics datasets, centralized bioinformatics database, resources, and bioinformatics tools. I showed throughout analyzing the findings of this study that specialists who promote standardizing techniques, adding metadata, and centralization may increase efficiency in integrating distinct bioinformatics datasets. Bioinformaticians, bioinformatics providers, the health care field, and society might benefit from this research. Improvement in bioinformatics affects poistevely the health-care field which has a positive social change. The results of this study might also lead to positive social change in research institutions, such as reduced workload, less frustration, reduction in costs, and increased efficiency while integrating distinct bioinformatics datasets