The amount of erythrocyte-derived microRNAs (miRNAs) represents the majority of miRNAs expressed in whole blood. miR-451, miR-144, and miR-486, which are abundant in red blood cells (RBCs), are involved in the process of erythropoiesis and disease occurrence. Moreover, erythrocyte-derived miRNAs have been reported to be potential biomarkers of specific diseases. However, the function and underlying mechanisms of miRNAs derived from erythrocytes remain unclear. Based on a review of previously published literature, we discuss several possible pathways by which RBC miRNAs may function and propose that RBCs may serve as repositories of miRNAs in the circulatory system and participate in the regulation of gene expression mainly via the transfer of miRNAs from erythrocyte extracellular vesicles (EVs). In the whole blood, there are still other important cell types such as leukocytes and platelets harboring functional miRNAs, and hemolysis also exists, which limit the abundance of miRNAs as disease biomarkers, and thus, miRNA studies on RBCs may be impacted. In the future, the role of RBCs in the regulation of normal physiological functions of the body and the entire circulatory system under pathological states, if any, remains to be determined.

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights.

Motivation: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. Results: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. Availability: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Summary: With the advent of next-generation sequencing, traditional bioinformatics tools are challenged by massive raw metagenomic datasets. One of the bottlenecks of metagenomic studies is lack of large-scale and cloud computing suitable data analysis tools. In this paper, we proposed a Spark-based tool, called MetaSpark, to recruit metagenomic reads to reference genomes. MetaSpark benefits from the distributed data set (RDD) of Spark, which makes it able to cache data set in memory across cluster nodes and scale well with the datasets. Compared with previous metagenomics recruitment tools, MetaSpark recruited significantly more reads than many programs such as SOAP2, BWA and LAST and increased recruited reads by ∼4% compared with FR-HIT when there were 1 million reads and 0.75 GB references. Different test cases demonstrate MetaSpark’s scalability and overall high performance. Availability:https://github.com/zhouweiyg/metaspark Contact:bniu@sccas.cn, jingluo@ynu.edu.cn Supplementary information: