Search Results (11)

The study aimed to identify the variants of SARS-CoV-2 (Severe Acute Respiratory Syndrome related coronavirus-2) virus isolates within the window of March 2021 to February 2022 in Bangladesh and investigate their comparative mutational profiles, preferences and phylogenetics. After the collection of the sample specimen and RNA extraction, the genome was sequenced using Illumina COVID Seq, and NGS data analysis was performed in DRAGEN COVID Lineage software (version 3.5.9). Among the 96 virus isolates, 24 (25%) were from Delta (clade 21A (n = 21) and 21J (n = 3)) and 72 (75%) were from Omicron (clade 20A (n = 6) and 20B (n = 66)). In Omicron and Delta, substitutions were much higher than deletions and insertions. High-frequency nucleotide change patterns were similar (for C > T, and A > G) in both of the variants, but different in some (i.e., G > T, G > A). Preferences for specific amino acids over the other amino acids in substitutions and deletions were observed to vary in different proteins of these variants. Phylogenetic analysis showed that the most ancestral variants were from clade 21A and clade 20A, and then the other variants emerged. The study demonstrates noteworthy variations of Omicron and Delta in mutational pattern and preferences for amino acids and protein, and further study on their biological functional impact might unveil the reason behind their mutational strategies and behavioral changes. Full article

The SARS-CoV-2-induced disease, COVID-19, remains a worldwide public health concern due to its high rate of transmission, even in vaccinated and previously infected people. In the endemic state, it continues to cause significant pathology. To elu- cidate the viral mutational changes and screen the emergence of new variants of concern, we conducted this study in Bangladesh. The viral RNA genomes extracted from 25 ran- domly collected samples of COVID-19-positive patients from March 2021 to February 2022 were sequenced using Illumina COVID Seq protocol and genomic data processing, as well as evaluations performed in DRAGEN COVID Lineage software. In this study, the percentage of Delta, Omicron, and Mauritius variants identified were 88%, 8%, and 4%, respectively. All of the 25 samples had 23,403 A>G (D614G, S gene), 3037 C>T (nsp3), and 14,408 C>T (nsp12) mutations, where 23,403 A>G was responsible for increased transmis- sion. Omicron had the highest number of unique mutations in the spike protein (i.e., sub- stitutions, deletions, and insertions), which may explain its higher transmissibility and immune-evading ability than Delta. A total of 779 mutations were identified, where 691 substitutions, 85 deletions, and 3 insertion mutations were observed. To sum up, our study will enrich the genomic database of SARS-CoV-2, aiding in treatment strategies along with understanding the virus’s preferences in both mutation type and mutation site for predicting newly emerged viruses’ survival strategies and thus for preparing to coun- teract them. Full article

A pseudogene is a non-functional copy of a protein-coding gene. Processed pseudogenes, which are created by the reverse transcription of mRNA and subsequent integration of the resulting cDNA into the genome, being a major pseudogene class, represent a significant challenge in genome analysis due to their high sequence similarity to the parent genes and their frequent absence in the reference genome. This homology can lead to errors in variant identification, as sequences derived from processed pseudogenes can be incorrectly assigned to parental genes, complicating correct variant calling. In this study, we quantified the occurrence of variant calling errors associated with pseudogenes, generated by the most popular germline variant callers, namely GATK-HC, DRAGEN, and DeepVariant, when analysing 30x human whole-genome sequencing data (n = 13,307). The results show that the presence of pseudogenes can interfere with variant calling, leading to false positive identifications of potentially clinically relevant variants. Compared to other approaches, DeepVariant was the most effective in correcting these errors. Full article

The cost-effectiveness of whole exome sequencing (WES) remains controversial due to variant call variability, necessitating sensitivity and specificity evaluation. WES was performed by three companies (AA, BB, and CC) using reference standards composed of DNA from hydatidiform mole and individual blood at various ratios. Sensitivity was assessed by the detection rate of null–homozygote (N–H) alleles at expected variant allelic fractions, while false positive (FP) errors were counted for unexpected alleles. Sensitivity was approximately 20% for in-house results from BB and CC and around 5% for AA. Dynamic Read Analysis for GENomics (DRAGEN) analyses identified 1.34 to 1.71 times more variants, detecting over 96% of in-house variants, with sensitivity for common variants increasing to 5%. In-house FP errors varied significantly among companies (up to 13.97 times), while DRAGEN minimized this variation. Despite DRAGEN showing higher FP errors for BB and CC, the increased sensitivity highlights the importance of effective bioinformatic conditions. We also assessed the potential effects of target enrichment and proposed optimal cutoff values for the read depth and variant allele fraction in WES. Optimizing bioinformatic analysis based on sensitivity and specificity from reference standards can enhance variant detection and improve the clinical utility of WES. Full article

Lipoprotein (a) (Lp(a)) is a risk factor for cardiovascular diseases and mainly regulated by the complex LPA gene. We investigated the types of variation in the LPA gene and their predictive performance on Lp(a) concentration. We determined the Kringle IV-type 2 (KIV-2) copy number (CN) using the DRAGEN LPA Caller (DLC) and a read depth-based CN estimator in 8351 short-read whole genome sequencing samples from the GENESIS-HD study. The pentanucleotide repeat in the promoter region was genotyped with GangSTR and ExpansionHunter. Lp(a) concentration was available in 4861 population-based subjects. Predictive performance on Lp(a) concentration was investigated using random forests. The agreement of the KIV-2 CN between the two specialized callers was high (r = 0.9966; 95% confidence interval [CI] 0.9965–0.9968). Allele-specific KIV-2 CN could be determined in 47.0% of the subjects using the DLC. Lp(a) concentration can be better predicted from allele-specific KIV-2 CN than total KIV-2 CN. Two single nucleotide variants, 4925G>A and rs41272114C>T, further improved prediction. The genetically complex LPA gene can be analyzed with excellent agreement between different callers. The allele-specific KIV-2 CN is more important for predicting Lp(a) concentration than the total KIV-2 CN. Full article

This study investigates multiple cyclic loading scenarios of non-oriented electrical steel sheets through both experimental and numerical approaches. The numerical simulations were conducted using Representative Volume Elements generated with DRAGen. DRAGen allowed for the generation of Representative Volume Elements with a non-cubic shape to cover the complete sheet thickness and enough grains to represent the material’s texture. The experimental results, on the other hand, are utilized to calibrate and validate a prediction model, highlighting the significance of accumulated plastic slip as a suitable parameter correlated with fatigue life. Using the accumulated plastic slip from the simulations, a fatigue fracture locus is introduced, which describes a 3D surface dependent on the maximum stress, fatigue life, and the fatigue stress ratio. The study shows reliable results for the fatigue life prediction using the calibrated fatigue fracture locus. While substantial progress has been made in predicting the fatigue life at multiple fatigue stress ratios, notable disparities between experimental and simulation results suggest the need for further investigations regarding the influence of the surface quality. This observation motivates ongoing research efforts aimed at refining simulation methodologies to better incorporate surface roughness effects. In summary, this study presents a validated model for predicting fatigue life in non-oriented electrical steel sheets, offering valuable insights into material behavior at different loading scenarios and informing future research directions for enhanced structural performance and durability. Full article

Rapid Whole Genome Sequencing (rWGS) represents a valuable exploration in critically ill pediatric patients. Early diagnosis allows care to be adjusted. We evaluated the feasibility, turnaround time (TAT), yield, and utility of rWGS in Belgium. Twenty-one unrelated critically ill patients were recruited from the neonatal intensive care units, the pediatric intensive care unit, and the neuropediatric unit, and offered rWGS as a first tier test. Libraries were prepared in the laboratory of human genetics of the University of Liège using Illumina DNA PCR-free protocol. Sequencing was performed on a NovaSeq 6000 in trio for 19 and in duo for two probands. The TAT was calculated from the sample reception to the validation of results. Clinical utility data were provided by treating physicians. A definite diagnosis was reached in twelve (57.5%) patients in 39.80 h on average (range: 37.05–43.7). An unsuspected diagnosis was identified in seven patients. rWGS guided care adjustments in diagnosed patients, including a gene therapy, an off-label drug trial and two condition-specific treatments. We successfully implemented the fastest rWGS platform in Europe and obtained one of the highest rWGS yields. This study establishes the path for a nationwide semi-centered rWGS network in Belgium. Full article

In December 2019, the COVID-19 disease started in Wuhan, China. The WHO declared a pandemic on 12 March 2020, and the disease started in Myanmar on 23 March 2020. In December 2020, different variants were brought worldwide, threatening global health. To counter those threats, Myanmar started the COVID-19 variant surveillance program in late 2020. Whole genome sequencing was done six times between January 2021 and March 2022. Among them, 83 samples with a PCR threshold cycle of less than 25 were chosen. Then, we used MiSeq FGx for sequencing and Illumina DRAGEN COVIDSeq pipeline, command line interface, GISAID, and MEGA version 7 for data analysis. In January 2021, no variant was detected. The second run, during the rise of cases in June 2021, showed Alpha, Delta, and Kappa variants. The third and the fourth runs in August and December showed only a Delta variant. Omicron and Delta variants were detected during the fifth run in January 2022. The sixth run in March 2022 showed only Omicron BA.2. Amino acid mutation at the receptor binding domain of Spike glycoprotein started since the second run coupling with high transmission, recurrence, and vaccine escape. We also found the mutation at the primer targets used in current RT-PCR platforms, but there was no mutation at the existing antiviral drug targets. The occurrence of multiple variants and mutations claimed vigilance at ports of entry and preparedness for effective control measures. Genomic surveillance with the observation of evolutionary data is required to predict imminent threats of the current disease and diagnose emerging infectious diseases. Full article

To provide precision medicine for better cancer care, researchers must work on clinical patient data, such as electronic medical records, physiological measurements, biochemistry, computerized tomography scans, digital pathology, and the genetic landscape of cancer tissue. To interpret big biodata in cancer genomics, an operational flow based on artificial intelligence (AI) models and medical management platforms with high-performance computing must be set up for precision cancer genomics in clinical practice. To work in the fast-evolving fields of patient care, clinical diagnostics, and therapeutic services, clinicians must understand the fundamentals of the AI tool approach. Therefore, the present article covers the following four themes: (i) computational prediction of pathogenic variants of cancer susceptibility genes; (ii) AI model for mutational analysis; (iii) single-cell genomics and computational biology; (iv) text mining for identifying gene targets in cancer; and (v) the NVIDIA graphics processing units, DRAGEN field programmable gate arrays systems and AI medical cloud platforms in clinical next-generation sequencing laboratories. Based on AI medical platforms and visualization, large amounts of clinical biodata can be rapidly copied and understood using an AI pipeline. The use of innovative AI technologies can deliver more accurate and rapid cancer therapy targets. Full article

SARS-CoV-2 variants surveillance is a worldwide task that has been approached with techniques such as Next Generation Sequencing (NGS); however, this technology is not widely available in developing countries because of the lack of equipment and limited funding in science. An option is to deploy a RT-qPCR screening test which aids in the analysis of a higher number of samples, in a shorter time and at a lower cost. In this study, variants present in samples positive for SARS-CoV-2 were identified with a RT-qPCR mutation screening kit and were later confirmed by NGS. A sample with an abnormal result was found with the screening test, suggesting the simultaneous presence of two viral populations with different mutations. The DRAGEN Lineage analysis identified the Delta variant, but there was no information about the other three mutations previously detected. When the sequenced data was deeply analyzed, there were reads with differential mutation patterns, that could be identified and classified in terms of relative abundance, whereas only the dominant population was reported by DRAGEN software. Since most of the software developed to analyze SARS-CoV-2 sequences was aimed at obtaining the consensus sequence quickly, the information about viral populations within a sample is scarce. Here, we present a faster and deeper SARS-CoV-2 surveillance method, from RT-qPCR screening to NGS analysis. Full article

In this study, a novel approach for generating Representative Volume Elements (RVEs) is introduced. In contrast to common generators, the new RVE generator is based on discrete methods to reconstruct synthetic microstructures, using simple methods and a modular structure. The plain and uncomplicated structure of the generator makes the extension with new features quite simple. It is discussed why certain features are essential for microstructural simulations. The discrete methods are implemented into a python tool. A Random Sequential Addition (RSA)-Algorithm for discrete volumes is developed and the tessellation is realized with a discrete tessellation function. The results show that the generator can successfully reconstruct realistic microstructures with elongated grains and martensite bands from given input data sets. Full article