Genome-based therapeutic interventions for ß-type hemoglobinopathies.

For decades, various strategies have been proposed to solve the enigma of hemoglobinopathies, especially severe cases. However, most of them seem to be lagging in terms of effectiveness and safety. So far, the most prevalent and promising treatment options for patients with ß-types hemoglobinopathies, among others, predominantly include drug treatment and gene therapy. Despite the significant improvements of such interventions to the patient's quality of life, a variable response has been demonstrated among different groups of patients and populations. This is essentially due to the complexity of the disease and other genetic factors. In recent years, a more in-depth understanding of the molecular basis of the ß-type hemoglobinopathies has led to significant upgrades to the current technologies, as well as the addition of new ones attempting to elucidate these barriers. Therefore, the purpose of this article is to shed light on pharmacogenomics, gene addition, and genome editing technologies, and consequently, their potential use as direct and indirect genome-based interventions, in different strategies, referring to drug and gene therapy. Furthermore, all the latest progress, updates, and scientific achievements for patients with ß-type hemoglobinopathies will be described in detail.

Improving the Run-Time of Space-Efficient n-Gram Data Structures Using Apache Spark.

Storing information in memory efficiently is one of the most significant challenges in computer science. The two main factors that consist an efficient data structure is the reduction of space and time consumption. There is a plethora of different tools able to reduce the run-time of a process, and Apache Spark is one of these; it is a computing framework that is using clusters to execute a process. There are two key features in this software, a directed acyclic graph (DAG) that maps the execution process and the resilient distributed datasets (RDD), which allow large in-memory computations. In order to construct a data structure, which is space- and time-efficient, we have to utilize the corresponding framework. A comparison of the run-time improvement with the use of Spark is also provided. Finally, to prove the efficacy of this software tool, we construct a space-efficient data structure and compare the run-time with and without its use.

Documentation of clinically relevant genomic biomarker allele frequencies in the next-generation FINDbase worldwide database.

FINDbase (http://www.findbase.org) is a comprehensive data resource recording the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants underlying genetic disorders as well as pharmacogenomic biomarkers that can guide drug treatment. Here, we report significant new developments and technological advancements in the database architecture, leading to a completely revamped database structure, querying interface, accompanied with substantial extensions of data content and curation. In particular, the FINDbase upgrade further improves the user experience by introducing responsive features that support a wide variety of mobile and stationary devices, while enhancing computational runtime due to the use of a modern Javascript framework such as ReactJS. Data collection is significantly enriched, with the data records being divided in a Public and Private version, the latter being accessed on the basis of data contribution, according to the microattribution approach, while the front end was redesigned to support the new functionalities and querying tools. The abovementioned updates further enhance the impact of FINDbase, improve the overall user experience, facilitate further data sharing by microattribution, and strengthen the role of FINDbase as a key resource for personalized medicine applications and personalized public health.