SUMMARY: Legionella pneumophila has significantly contributed to multiple cases of pneumonia with a high rate of mortality globally. Its ability to exploit host mechanisms through several expressed virulence factors poses challenges for diagnosis, treatment, and outbreak control. To address this, we developed LegionProfiler, a computational tool that swiftly identifies virulence factor protein domains within genome assemblies of Legionella pneumophila serogroup 1 isolates and classifies them into high- or low-virulence groups. LegionProfiler automates the probing of genome assemblies for virulence-associated protein domains and determines the isolate's potential to cause severe pneumonia infection. The LegionProfiler workflow is made available through a user-friendly interface to enhance technical control of infectious sources and adds important insights to the general epidemiology of clinical isolates. It could also support the development of targeted therapeutic strategies that will improve patient treatment. AVAILABILITY AND IMPLEMENTATION: LegionProfiler is freely accessible as a web service at https://legionprofiler.uni-muenster.de, and can also be run locally in a Docker container. The source code can be found at https://imigitlab.uni-muenster.de/heiderlab/legionprofiler or at Zenodo (DOI:10.5281/zenodo.15592325). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The increasing demand for radio spectrum, spurred by an upsurge in interconnected devices, necessitates innovative management solutions. Cognitive Radio Networks (CRN) offer a promising approach to dynamically allocate spectrum resources. This study examines the Cooperative Spectrum Sensing (CCSS) architecture, leveraging cloud computing and big data analytics, to enhance CRN efficacy. We present a risk analysis identifying key vulnerabilities, explore regulatory considerations within the Canadian spectrum landscape, and propose an augmented architecture incorporating preventative and defensive strategies. We also discuss challenges encountered in the implementation phase and suggest future research trajectories for cloud-assisted CCSS systems.

Recent technological advancements and scientific discoveries have revolutionized the current era of genomics. Next-generation sequencing (NGS) technologies have led to tremendous reduction in the sequencing time and given rise to the production and collection of high volumes of genomic datasets. Predicting protein-coding genes from these copious genomic datasets is significant for the synthesis of protein and the understating of the regulatory function of the non-coding region. Methods have been developed to find protein-coding genes from the genome of organisms. Notwithstanding, the recent data explosion in genomics accentuates the need for more efficient algorithms for gene prediction. In this paper, we explore predictive analytics on genomic data. In particular, we present a scalable naïve Bayes-based algorithm that is deployed over a cluster of Apache Spark framework for efficient prediction of genes in the genome of eukaryotic organisms. Evaluation results on the human genome chromosome GRCh37 and GRCh38 show that effectiveness of our algorithm for predictive analytics on genomic data with high-performance computing. high sensitivity, specificity and accuracy.

The advent of high-throughput technologies such as Illumina HiSeq X, mass spectrometry, and microarray heralds a new era of big biological datasets in computational biology. This digital revolution in bioinformatics has generated unprecedented volumes of omics data (e.g., transcriptomes, genomes, proteomes, metabolomes) with various degrees of veracities and values. These deluge of omics data are awash with a wealth of information in the form of frequently repeated contiguous patterns-namely, sequence motifs. Sequence motifs are short repeated contiguous subsequences located in the promoter region of a genome sequence. On some occasions, users are interested in mining only a particular type of sequence motifs (e.g., palindromic motifs). In genomics, palindromes are sequences from the nucleotide bases from deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) strands that are symmetrical in the sense that they read exactly the same as their complementary sequences in the reverse direction. The use of classical constraints (e.g., anti-monotonic, succinct, and/or convertible constraints) allows users to specify their interest in the universal search space, and thus enhancing distinct and effective pruning of the search space- leading to a reduction in the computational time required for the mining process. Despite several attempts made by existing algorithms for mining palindromic motifs from DNA sequences, a major drawback stems from the high volumes of the DNA sequences leading to high complexities and turnaround time of the algorithms. To this end, we propose a parallel scalable sequential mining algorithm that exploits some features of anti-monotonic constraints-using the in-memory computing model of the Apache Spark framework deployed on a cluster of a homogeneous distributed-memory system-for mining palindromic motifs from high volumes of DNA sequences. To evaluate our algorithm, we obtained the human genome (Homo sapiens) assemblies GRCh37 patch 13 (hg19), which is of size 3.2 GB from the Ensembl data repository. It contains 104,763 protein-coding sequences and 24,513 non-coding sequences. Evaluation results show that our algorithm extracts accurate palindromic motifs using a short turnaround time.

Data explosion in bioinformatics in recent years has led to new challenges for researchers to develop novel techniques to discover new knowledge from the avalanche of omics data (e.g., genomics, proteomics, transcriptomics). These data are embedded with a wealth of information including frequently repeated patterns (i.e., sequence motifs). In genomics, deoxyribonucleic acid (DNA) sequence motifs are short repeated contiguous frequent subsequences located in the prompter region. Due to the high volume and various degrees of veracity of these DNA datasets generated by the next-generation sequencing techniques, sequence motif mining from DNA sequences poised a major challenge in bioinformatics. In this article, we present a distributed sequential algorithm—which uses the MapReduce programming model on a cluster of homogeneous distributed-memory system running on an Apache Spark computing framework—for DNA sequence motif mining. Experimental results show the effectiveness of our algorithm in Spark-based data analytics of sequence motifs in large omics data.

Big data has become ubiquitous as high volumes of wide varieties of valuable data of different veracities (e.g., precise, imprecise or uncertain data) are made available at a high velocity through fast throughput machines and techniques for data gathering and curation in many real life applications in various domains and application areas such as bioinformatics, biomedicine, finance, social networking, and weather forecasting. In bioinformatics, terabytes of deoxyribonucleic acid (DNA) sequences can now be generated within a few hours with the use of next generation sequencing (NGS) technologies such as Illumina HiSeq X and Illumina Genome Analyzer. Due to the nature of these NGS technologies, generated data are usually inherent with some noise or other forms of error. These uncertain data are embedded with a wealth of information in the form of frequent patterns. Mining frequently occurring patterns (e.g., motifs) from these big uncertain DNA sequences is a challenge in bioinformatics and biomedicine. Many existing algorithms are serial and mine DNA sequence motifs using precise data mining methods. Mining of motifs from big DNA sequences is a computationally intensive task because of the high volume and the associated uncertainty of these DNA sequences. In this paper, we propose a scalable algorithm for high performance computing on bioinformatics. Specifically, our parallel algorithm uses a fault-tolerant collection of resilient distributed datasets (RDDs) in Apache Spark computing framework to mine sequence motifs from uncertain big DNA data. Experimental results show that our algorithm extracts accurate motifs within a short time frame.