Investigation of long non-coding RNAs in extracellular vesicles from low-volume blood serum specimens of colorectal cancer patients.

Colorectal cancer (CRC) is the second most prevalent cancer type worldwide, which highlights the urgent need for non-invasive biomarkers for its early detection and improved prognosis. We aimed to investigate the patterns of long non-coding RNAs (lncRNAs) in small extracellular vesicles (sEVs) collected from low-volume blood serum specimens of CRC patients, focusing on their potential as diagnostic biomarkers. Our research comprised two phases: an initial exploratory phase involving RNA sequencing of sEVs from 76 CRC patients and 29 healthy controls, and a subsequent validation phase with a larger cohort of 159 CRC patients and 138 healthy controls. Techniques such as dynamic light scattering, transmission electron microscopy, and Western blotting were utilized for sEV characterization. Optimized protocol for sEV purification, RNA isolation and preamplification was applied to successfully sequence the RNA content of sEVs and validate the results by RT-qPCR. We successfully isolated sEVs from blood serum and prepared sequencing libraries from a low amount of RNA. High-throughput sequencing identified differential levels of 460 transcripts between CRC patients and healthy controls, including mRNAs, lncRNAs, and pseudogenes, with approximately 20% being lncRNAs, highlighting several tumor-specific lncRNAs that have not been associated with CRC development and progression. The validation phase confirmed the upregulation of three lncRNAs (NALT1, AL096828, and LINC01637) in blood serum of CRC patients. This study not only identified lncRNA profiles in a population of sEVs from low-volume blood serum specimens of CRC patients but also highlights the value of innovative techniques in biomolecular research, particularly for the detection and analysis of low-abundance biomolecules in clinical samples. The identification of specific lncRNAs associated with CRC provides a foundation for future research into their functional roles in cancer development and potential clinical applications.

CNproScan: Hybrid CNV detection for bacterial genomes.

Discovering copy number variation (CNV) in bacteria is not in the spotlight compared to the attention focused on CNV detection in eukaryotes. However, challenges arising from bacterial drug resistance bring further interest to the topic of CNV and its role in drug resistance. General CNV detection methods do not consider bacteria's features and there is space to improve detection accuracy. Here, we present a CNV detection method called CNproScan focused on bacterial genomes. CNproScan implements a hybrid approach and other bacteria-focused features and depends only on NGS data. We benchmarked our method and compared it to the previously published methods and we can resolve to achieve a higher detection rate together with providing other beneficial features, such as CNV classification. Compared with other methods, CNproScan can detect much shorter CNV events.

A degeneration-reducing criterion for optimal digital mapping of genetic codes.

Bioinformatics may seem to be a scientific field processing primarily large string datasets, as nucleotides and amino acids are represented with dedicated characters. On the other hand, many computational tasks that bioinformatics challenges are mathematical problems understandable as operations with digits. In fact, many computational tasks are solved this way in the background. One of the most widely used digital representations is mapping of nucleotides and amino acids with integers 0-3 and 0-20, respectively. The limitation of this mapping occurs when the digital signal of nucleotides has to be translated into a digital signal of amino acids as the genetic code is degenerated. This causes non-monotonies in a mapping function. Although map for reducing this undesirable effect has already been proposed, it is defined theoretically and for standard genetic codes only. In this study, we derived a novel optimal criterion for reducing the influence of degeneration by utilizing a large dataset of real sequences with various genetic codes. As a result, we proposed a new robust global optimal map suitable for any genetic code as well as specialized optimal maps for particular genetic codes.

Rapid Bacterial Species Delineation Based on Parameters Derived From Genome Numerical Representations.

Species delineation based on bacterial genomes is an essential part of the research of prokaryotes. In silico genome-to-genome comparison methods are computationally demanding, but much less tedious and error prone than the wet-lab methods. In this paper, we present a novel method for the delineation of bacterial genomes based on genomic signal processing. The proposed method uses numerical representations of whole bacterial genomes, phase signal and cumulated phase signal, from which four parameters are derived for each genome. The parameters characterize a genome and their calculation is independent of the other genomes comprising a delineation dataset. The delineation itself is processed as a calculation of the parameters' average similarity. The method was statistically verified on 1826 bacterial genomes. A similarity threshold of 96% was set based on the receiver operating characteristic curve that featured sensitivity of 99.78% and specificity of 97.25%. Additionally, comparative analysis on another 33 bacterial genomes was conducted using standard delineation tools as these tools were not able to process the dataset of 1826 genomes using desktop computer. The proposed method achieved comparable or better delineation results in comparison with the standard tools. Besides the excellent delineation results, another great advantage of the method is its small computational demands, which enables the delineation of thousands of genomes on a desktop computer. The calculation of the parameters takes tens of minutes for thousands of genomes. Moreover, they can be calculated in advance by creating a database, meaning the delineation itself is then completed in a matter of seconds.