The rhizosphere microbiome offers a range of ecosystem services to the plant, including nutrient acquisition and tolerance to (a)biotic stress. Here, analysing the data by Mendes et al. (2011), we show that short heat disturbances (50 or 80 °C, 1 h) of a soil suppressive to the root pathogenic fungus Rhizoctonia solani caused significant increase in alpha diversity of the rhizobacterial community and led to partial or complete loss of disease protection. A reassembly model is proposed where bacterial families that are heat tolerant and have high growth rates significantly increase in relative abundance after heat disturbance, while temperature-sensitive and slow-growing bacteria have a disadvantage. The results also pointed to a potential role of slow-growing, heat-tolerant bacterial families from Actinobacteria and Acidobacteria phyla in plant disease protection. In conclusion, short heat disturbance of soil results in rearrangement of rhizobacterial communities and this is correlated with changes in the ecosystem service disease suppression.

BackgroundOur previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinformatics by making its use and extension more simple through more and better application of high-level languages commonly used in bioinformatics, such as Python.ResultsThe novel application pyPaSWAS presents the parallel SW sequence alignment code fully packed in Python. It is a generic SW implementation running on several hardware platforms with multi-core systems and/or GPUs that provides accurate sequence alignments that also can be inspected for alignment details. Additionally, pyPaSWAS support the affine gap penalty. Python libraries are used for automated system configuration, I/O and logging. This way, the Python environment will stimulate further extension and use of pyPaSWAS.ConclusionspyPaSWAS presents an easy Python-based environment for accurate and retrievable parallel SW sequence alignments on GPUs and multi-core systems. The strategy of integrating Python with high-performance parallel compute languages to create a developer- and user-friendly environment should be considered for other computationally intensive bioinformatics algorithms.