Pergola: Boosting Visualization and Analysis of Longitudinal Data by Unlocking Genomic Analysis Tools

Comparison of high-throughput single-cell RNA sequencing data processing pipelines

With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

Protocol for Measuring Compulsive-like Feeding Behavior in Mice

Obesity is an important health problem with a strong environmental component that is acquiring pandemic proportion. The high availability of caloric dense foods promotes overeating potentially causing obesity. Animal models are key to validate novel therapeutic strategies, but researchers must carefully select the appropriate model to draw the right conclusions. Obesity is defined by an increased body mass index greater than 30 and characterized by an excess of adipose tissue. However, the regulation of food intake involves a close interrelationship between homeostatic and non-homeostatic factors. Studies in animal models have shown that intermittent access to sweetened or calorie-dense foods induces changes in feeding behavior. However, these studies are focused mainly on the final outcome (obesity) rather than on the primary dysfunction underlying the overeating of palatable foods. We describe a protocol to study overeating in mice using diet-induced obesity (DIO). This method can be applied to free choice between palatable food and a standard rodent chow or to forced intake of calorie-dense and/or palatable diets. Exposure to such diets is sufficient to promote changes in meal pattern that we register and analyze during the period of weight gain allowing the longitudinal characterization of feeding behavior in mice. Abnormal eating behaviors such as binge eating or snacking, behavioral alterations commonly observed in obese humans, can be detected using our protocol. In the free-choice procedure, mice develop a preference for the rewarding palatable food showing the reinforcing effect of this diet. Compulsive components of feeding are reflected by maintenance of feeding despite an adverse bitter taste caused by adulteration with quinine and by the negligence of standard chow when access to palatable food is ceased or temporally limited. Our strategy also enables to identify compulsive overeating in mice under a high-caloric regime by using limited food access and finally, we propose complementary behavioral tests to confirm the non-homeostatic food-taking triggered by these foods. Finally, we describe how to computationally explore large longitudinal behavioral datasets.

Pergola-web: a web server for the visualization and analysis of longitudinal behavioral data using repurposed genomics tools and standards

We present a new web application to query and visualize time-series behavioral data: the Pergola web-server. This server provides a user-friendly interface for exploring longitudinal behavioral data taking advantage of the Pergola Python library. Using the server, users can process the data applying some basic operations, such as binning or grouping, while formatting the data into existing genomic formats. Thanks to this repurposing of genomics standards, the application automatically renders an interactive data visualization based on sophisticated genome visualization tools. Our tool allows behavioral scientists to share, display and navigate complex behavioral data comprising multiple individuals and multiple data types, in a scalable and flexible manner. A download option allows for further analysis using genomic tools. The server can be a great resource for the field in a time where behavioral science is entering a data-intensive cycle thanks to high-throughput behavioral phenotyping platforms. Pergola is publicly available at http://pergola.crg.eu/.