The Intellectual Training Environment for Prolog Programming Language

In this work is described a new complex training system, named SPprolog, intended for training and self-training in logic programming language - Prolog. This system includes elements related to Prolog and logic programming, and the elements of independent, complex, self-sufficient training system which is capable considerably to increase the quality of self-training, and to be effective assistant in training. The most useful application of the system can be in distance education and self-training. The main elements of SPprolog system are: Functionally expanded (in comparison with existing systems) Prolog development environment, with the multipurpose code editor, the automated organization system of the personal tools, automated advice mode "Expert Advice", based on the incorporated expert system for cultivated, effective and optimized programming; Link to foreign Prolog programs compiler which allow to compile the program to independent executable; Built in intellectual, interactive, multimedia Prolog interpreter integrated with expert system and the elements of the intellectuality, allowing to lead detailed program interpretation, with popular and evident, explanation of the theory and mechanisms used in it, applying audiovisual effects to increase the level of naturalness of process of explanation; Full digital training course of Prolog programming language presented in the form of the matrix of knowledge and supplied system of consecutive knowledge reproduction for self-training and evaluation; an intensive course of training to the Prolog language and Spprolog system, based on the programmed, consecutive set of actions, allowing using the previous two mechanisms of sys-tem for popular and evident explanation of the main principles of work of system and Prolog language.training, prolog, environment, Spprolog

Technology dictates algorithms: Recent developments in read alignment

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to todays diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies

Unlocking capacities of genomics for the COVID-19 response and future pandemics

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing