The combination of the Hadoop MapReduce programming model and cloud computing allows biological scientists to analyze next-generation sequencing (NGS) data in a timely and cost-effective manner. Cloud computing platforms remove the burden of IT facility procurement and management from end users and provide ease of access to Hadoop clusters. However, biological scientists are still expected to choose appropriate Hadoop parameters for running their jobs. More importantly, the available Hadoop tuning guidelines are either obsolete or too general to capture the particular characteristics of bioinformatics applications. In this study, we aim to minimize the cloud computing cost spent on bioinformatics data analysis by optimizing the extracted significant Hadoop parameters. When using MapReduce-based bioinformatics tools in the cloud, the default settings often lead to resource underutilization and wasteful expenses. We choose k-mer counting, a representative application used in a large number of NGS data analysis tools, as our study case. Experimental results show that, with the fine-tuned parameters, we achieve a total of 4× speedup compared with the original performance (using the default settings). This paper presents an exemplary case for tuning MapReduce-based bioinformatics applications in the cloud, and documents the key parameters that could lead to significant performance benefits.

Pancreatic intraductal papillary mucinous neoplasias (IPMN) are increasingly detected in medical diary practice because of increased awareness of their existence and because of increased use of cross-sectional imaging studies. IPMN are diagnosed incidentally in most cases and are classified as branch-duct IPMN, main-duct IPMN and combined-type IPMN. The last two types show a more aggressive biological behavior and surgery is recommended. Moreover, there are four subtypes of neoplastic epithelium in these tumours (intestinal, pancreatobiliary, gastric and oncocytic), which determine differences in the natural history of these neoplasms and this also seems to have prognostic relevance. We report a case of a patient who underwent a pancreatoduodenectomy due to a combined-type IPMN and whose anatomopathological study revealed an intestinal subtype IPMN with high grade dysplasia and colloid carcinoma. We also review the literature and describe the main aspects of this particular type ofcystic pancreatic tumours.