[Evaluation on effects of chromogenic medium in rapid detection of Coliform and Escherichia coli].

OBJECTIVE: To evaluate the detective efficacy of Chromogenic Coliform and Escherichia Coli Agar (CCEA). METHODS: A new chromogenic medium CCEA prepared by Huankai laboratory was used to compare with a classical medium of violet red bile agar (VRBA), and other two Chromogenic media Agar I and Agar II by detecting separately 11 reference strains, thirteen sterile samples with Coliform or E.coli and other four samples, and the accordant rates of detection were observed. RESULTS: CCEA had the good selectivity. To seven kinds of quality strains in the resultant analysis, CCEA with VRBA and Agar I had not shown salience difference (P > 0.05), and CCEA with Agar II had significant difference (P < 0.05). CCEA showed more advantages than the Agar II. To thirteen sterile samples with Coliform or E.coli in resultant analysis, CCEA with Agar I and Agar II had shown no significant difference (P > 0.05), while CCEA with VRBA had significant difference (P < 0.05). CCEA might be more advantageous than the VRBA. In analysis of the four actual samples of Coliform, CCEA with VRBA, Agar I and Agar II showed no significant difference (P > 0.05). The accordant rates were 90%, 71.88%, 86.25% and 81.25% respectively, showing CCEA > Agar I > Agar II > VRBA. To two actual samples of E.coli in the resultant analysis, the CCEA with Agar I and Agar II had not shown significant difference (P > 0.05). The accordant rates were 100% respectively. CONCLUSIONS: The CCEA might be more advantageous than the VRBA, having the same efficacy as with Agar I and Agar II.

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.