Comparison of structural variant callers for massive whole-genome sequence data.

BACKGROUND: Detecting structural variations (SVs) at the population level using next-generation sequencing (NGS) requires substantial computational resources and processing time. Here, we compared the performances of 11 SV callers: Delly, Manta, GridSS, Wham, Sniffles, Lumpy, SvABA, Canvas, CNVnator, MELT, and INSurVeyor. These SV callers have been recently published and have been widely employed for processing massive whole-genome sequencing datasets. We evaluated the accuracy, sequence depth, running time, and memory usage of the SV callers. RESULTS: Notably, several callers exhibited better calling performance for deletions than for duplications, inversions, and insertions. Among the SV callers, Manta identified deletion SVs with better performance and efficient computing resources, and both Manta and MELT demonstrated relatively good precision regarding calling insertions. We confirmed that the copy number variation callers, Canvas and CNVnator, exhibited better performance in identifying long duplications as they employ the read-depth approach. Finally, we also verified the genotypes inferred from each SV caller using a phased long-read assembly dataset, and Manta showed the highest concordance in terms of the deletions and insertions. CONCLUSIONS: Our findings provide a comprehensive understanding of the accuracy and computational efficiency of SV callers, thereby facilitating integrative analysis of SV profiles in diverse large-scale genomic datasets.

Exploring the DNA methylome of Korean patients with colorectal cancer consolidates the clinical implications of cancer-associated methylation markers.

Aberrant DNA methylation plays a critical role in the development and progression of colorectal cancer (CRC), which has high incidence and mortality rates in Korea. Various CRC-associated methylation markers for cancer diagnosis and prognosis have been developed; however, they have not been validated for Korean patients owing to the lack of comprehensive clinical and methylome data. Here, we obtained reliable methylation profiles for 228 tumor, 103 adjacent normal, and two unmatched normal colon tissues from Korean patients with CRC using an Illumina Infinium EPIC array; the data were corrected for biological and experiment biases. A comparative methylome analysis confirmed the previous findings that hypermethylated positions in the tumor were highly enriched in CpG island and promoter, 5' untranslated, and first exon regions. However, hypomethylated positions were enriched in the open-sea regions considerably distant from CpG islands. After applying a CpG island methylator phenotype (CIMP) to the methylome data of tumor samples to stratify the CRC patients, we consolidated the previously established clinicopathological findings that the tumors with high CIMP signatures were significantly enriched in the right colon. The results showed a higher prevalence of microsatellite instability status and MLH1 methylation in tumors with high CMP signatures than in those with low or non-CIMP signatures. Therefore, our methylome analysis and dataset provide insights into applying CRC-associated methylation markers for Korean patients regarding cancer diagnosis and prognosis. [BMB Reports 2024; 57(3): 161-166].

Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy.

Simultaneous point-by-point raster scanning of optical and acoustic beams has been widely adapted to high-speed photoacoustic microscopy (PAM) using a water-immersible microelectromechanical system or galvanometer scanner. However, when using high-speed water-immersible scanners, the two consecutively acquired bidirectional PAM images are misaligned with each other because of unstable performance, which causes a non-uniform time interval between scanning points. Therefore, only one unidirectionally acquired image is typically used; consequently, the imaging speed is reduced by half. Here, we demonstrate a scanning framework based on a deep neural network (DNN) to correct misaligned PAM images acquired via bidirectional raster scanning. The proposed method doubles the imaging speed compared to that of conventional methods by aligning nonlinear mismatched cross-sectional B-scan photoacoustic images during bidirectional raster scanning. Our DNN-assisted raster scanning framework can further potentially be applied to other raster scanning-based biomedical imaging tools, such as optical coherence tomography, ultrasound microscopy, and confocal microscopy.

Coexistence of Spinal Intramedullary Tuberculoma and Multiple Intracranial Tuberculomas.

Spinal intramedullary tuberculoma remains a very rare entity of central nervous system tuberculosis. This is the same with the coexistence of spinal intramedullary and intracranial tuberculomas that remains extremely rare with less than 20 cases reported at present. Authors describe this uncommon case by analyzing a 65-year-old female patient who had past history of kidney transplantation due to stage 5 chronic kidney disease and pulmonary tuberculosis on medication. The patient experiences progressive paraplegia and numbness on both lower extremities. Magnetic resonance imaging demonstrated an intramedullary mass at T9-10 level and multiple intracranial enhancing nodules. Microsurgical resection of spinal intramedullary mass was performed and the lesion was histopathologically confirmed as Mycobacterium tuberculosis. Efficient diagnosis and management of this rare disease are reviewed along with previously reported cases.

An evolutionary Monte Carlo algorithm for predicting DNA hybridization.

Many DNA-based technologies, such as DNA computing, DNA nanoassembly and DNA biochips, rely on DNA hybridization reactions. Previous hybridization models have focused on macroscopic reactions between two DNA strands at the sequence level. Here, we propose a novel population-based Monte Carlo algorithm that simulates a microscopic model of reacting DNA molecules. The algorithm uses two essential thermodynamic quantities of DNA molecules: the binding energy of bound DNA strands and the entropy of unbound strands. Using this evolutionary Monte Carlo method, we obtain a minimum free energy configuration in the equilibrium state. We applied this method to a logical reasoning problem and compared the simulation results with the experimental results of the wet-lab DNA experiments performed subsequently. Our simulation predicted the experimental results quantitatively.