Framing Apache Spark in life sciences.

Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.

Empowering spot detection in 2DE images by wavelet denoising.

Typical high-abundant proteins, including albumin, IgG, IgA and others, are the target of depletion methods usually applied to two-dimensional electrophoresis (2DE) of human biological fluids like serum and plasma. Detection of low-abundant proteins is of interest with regard to biomarkers for disease when being studied by 2DE or liquid chromatography-mass spectrometry (LC/MS). After depletion of very abundant proteins, serum samples consist of an enriched pool of low-abundant proteins that can be further studied without significant interferences, thus allowing for a full identification of the low abundant proteins, whose spots become now more visible. We have employed wavelet-based techniques and their derived denoisers to explore 2DE from disease-control human samples. We have pursued the goal of mimicking in silico the spot detection performance experimentally obtained by depletion methods, thus hoping to read through the critical high-abundant protein regions. Our results suggest that an efficient and effective computational tool has been added to other ones performing 2DE image analysis, such as decomposition and segmentation, but with the advantage of being specifically targeted to the depletion task.