We describe lessons learned from using the air:bit project to introduce more than 150 students in the Norwegian upper secondary school to computer programming, engineering and environmental sciences. In the air:bit project, students build and code a portable air quality sensor kit, and use their air:bit to collect data to investigate patterns in air quality in their local environment. When the project ended students had collected more than 400,000 measurements with their air:bit kits, and could describe local patterns in air quality. Students participate in all parts of the project, from soldering components and programming the sensors, to analyzing the air quality measurements. Following the project we conducted a survey to investigate the satisfaction of participating in the project, as well as learning outcomes in the different parts of the project to improve future versions. The results show that both teachers and students were very satisfied with participating in the project. While the students showed positive learning outcomes in electronics and simple programming parts of the project, there is room for improvement regarding more advanced programming tasks. We believe that our work provides insight for other similar maker-inspired projects, and guidelines for educators to incorporate computer programming in traditional science experiments.

Precision medicine brings the promise of more precise diagnosis and individualized therapeutic strategies from analyzing a cancer's genomic signature. Technologies such as high-throughput sequencing enable cheaper data collection at higher speed, but rely on modern data analysis platforms to extract knowledge from these high dimensional datasets. Since this is a rapidly advancing field, new diagnoses and therapies often require tailoring of the analysis. These pipelines are therefore developed iteratively, continuously modifying analysis parameters before arriving at the final results. To enable reproducible results it is important to record all these modifications and decisions made during the analysis process. We built a system, walrus, to support reproducible analyses for iteratively developed analysis pipelines. The approach is based on our experiences developing and using deep analysis pipelines to provide insights and recommendations for treatment in an actual breast cancer case. We designed walrus for the single servers or small compute clusters typically available for novel treatments in the clinical setting. walrus leverages software containers to provide reproducible execution environments, and integrates with modern version control systems to capture provenance of data and pipeline parameters. We have used walrus to analyze a patient's primary tumor and adjacent normal tissue, including subsequent metastatic lesions. Although we have used walrus for specialized analyses of whole-exome sequencing datasets, it is a general data analysis tool that can be applied in a variety of scientific disciplines.

We describe our citizen science approach and technologies designed to introduce students in upper secondary schools to computational thinking and engineering. Using an Arduino microcontroller and low-cost sensors we have developed the air:bit, a programmable sensor kit that students build and program to collect air quality data. In our course, students develop their own research questions regarding air quality before using their own air quality sensor kit to answer their respective questions. This project combines electronics and coding with natural sciences providing a truly interdisciplinary course. We have open-sourced the teaching materials including the building and coding instructions. In addition, students can contribute to our web-based platform for storing, visualizing, and exploring the collected air quality data. It also provides an open API for anyone to download air quality data collected by the students. Through the website, available at airbit.uit.no, students are motivated to contribute air quality data open to the public. We describe lessons learned from our pilot project in a Norwegian upper secondary school and how we are deploying it in 10 schools across Northern Norway. In the pilot, students successfully built and coded the air:bits, and after two months of data collection they could correctly describe local patterns in the air quality. We believe that by combining electronics and coding with the natural sciences we motivate students to engage in all scientific disciplines.

Scalability is increasingly important for bioinformatics analysis services, since these must handle larger datasets, more jobs, and more users. The pipelines used to implement analyses must therefore scale with respect to the resources on a single compute node, the number of nodes on a cluster, and also to cost-performance. Here, we survey several scalable bioinformatics pipelines and compare their design and their use of underlying frameworks and infrastructures. We also discuss current trends for bioinformatics pipeline development.

Although systemic immunity is critical to the process of tumor rejection, cancer research has largely focused on immune cells in the tumor microenvironment. To understand molecular changes in the patient systemic response (SR) to the presence of BC, we profiled RNA in blood and matched tumor from 173 patients. We designed a system (MIxT, Matched Interactions Across Tissues) to systematically explore and link molecular processes expressed in each tissue. MIxT confirmed that processes active in the patient SR are especially relevant to BC immunogenicity. The nature of interactions across tissues (i.e. which biological processes are associated and their patterns of expression) varies highly with tumor subtype. For example, aspects of the immune SR are underexpressed proportionally to the level of expression of defined molecular processes specific to basal tumors. The catalog of subtype-specific interactions across tissues from BC patients provides promising new ways to tackle or monitor the disease by exploiting the patient SR.

Biological data analysis is typically implemented using a pipeline that combines many data analysis tools and meta-databases. These pipelines must scale to very large datasets, and therefore often require parallel and distributed computing. There are many infrastructure systems for data-intensive computing. However, most biological data analysis pipelines do not leverage these systems. An important challenge is therefore to integrate biological data analysis frameworks with data-intensive computing infrastructure systems. In this paper, we describe how we have extended data-intensive computing systems to support unmodified biological data analysis tools. We also describe four approaches for integrating the extended systems with biological data analysis frameworks, and discuss challenges for such integration on production platforms. Our results demonstrate how biological data analysis pipelines can benefit from infrastructure systems for data-intensive computing.

Visualization is a key step in scientific analysis and understanding in many fields. Scientific studies often require development of software that produces visualizations. However, as a study proceeds, the software evolves, and both developers and expert users have to periodically ascertain how code modifications affect visualization output and hence the results of the study. To our knowledge, no current visualization framework enables tracking and comparison of the lineage of scientific visualizations. We describe an approach for comparing and maintaining the code for scientific analysis and modeling through interactive comparison of visualization output. We have realized this approach in a tool called Mr. Clean. This tool provides a framework for combining different visualization tools, interaction devices, and display middleware for visual comparisons on high-resolution displays. Mr. Clean also provides user-configurable interactions supported by many devices. We provide use cases and a requirement analysis for our approach, and we describe the design and implementation of Mr. Clean. Source code is available at: https://github.com/UniversityofTromso/mrclean.

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction. We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.

We present the design, implementation, and evaluation of Direct File System (DFS) for virtualized flash storage. Instead of using traditional layers of abstraction, our layers of abstraction are designed for directly accessing flash memory devices. DFS has two main novel features. First, it lays out its files directly in a very large virtual storage address space provided by FusionIO's virtual flash storage layer. Second, it leverages the virtual flash storage layer to perform block allocations and atomic updates. As a result, DFS performs better and is much simpler than a traditional Unix file system with similar functionalities. Our microbenchmark results show that DFS can deliver 94,000 I/O operations per second (IOPS) for direct reads and 71,000 IOPS for direct writes with the virtualized flash storage layer on FusionIO's ioDrive. For direct access performance, DFS is consistently better than ext3 on the same platform, sometimes by 20%. For buffered access performance, DFS is also consistently better than ext3, and sometimes by over 149%. Our application benchmarks show that DFS outperforms ext3 by 7% to 250% while requiring less CPU power.

Scalability of parallel applications on clusters and multi-clusters is often limited by communication performance. Message tracing can provide data for understanding bottlenecks, and for performance tuning. However, it requires collecting, storing, analyzing, and transferring potentially gigabytes of data. We have designed the EventSpace system for low overhead and high performance runtime collective communication trace analysis. EventSpace separates the perturbation and performance requirements of data collection, analysis, gathering sand visualization. Data collection overhead is low since the minimum amount of data is recorded and stored temporarily in main memory. The recorded data is either discarded or analyzed on demand using available cluster resources. Analysis is distributed for high performance, and coscheduled with the computation and communication system threads for low perturbation. Gathering of analyzed data is done using extensible collective communication operations, which can be tuned to trade off between performance and monitoring overhead. EventSpace was used to do run-time monitoring and analysis of collective communication micro-benchmarks run on clusters, multi-clusters, and multi-clusters with emulated WAN links. Performance data was collected, analyzed and gathered with 0-3% monitoring overhead.