MSIsensor-ct: Microsatellite instability detection using cfDNA sequencing data

MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. RESULTS: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. AVAILABILITY: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online

Modeling the Determinants of PM2.5 in China Considering the Localized Spatiotemporal Effects: A Multiscale Geographically Weighted Regression Method

Many studies have identified the influences of PM2.5. However, very little research has addressed the spatiotemporal dependence and heterogeneity in the relationships between impact factors and PM2.5. This study firstly utilizes spatial statistics and time series analysis to investigate the spatial and temporal dependence of PM2.5 at the city level in China using a three-year (2015–2017) dataset. Then, a new local regression model, multiscale geographically weighted regression (MGWR), is introduced, based on which we measure the influence of PM2.5. A spatiotemporal lag is constructed and included in MGWR to account for spatiotemporal dependence and spatial heterogeneity simultaneously. Results of MGWR are comprehensively compared with those of ordinary least square (OLS) and geographically weighted regression (GWR). Experimental results show that PM2.5 is autocorrelated in both space and time. Compared with existing approaches, MGWR with a spatiotemporal lag (MGWRL) achieves a higher goodness-of-fit and a more significant effect on eliminating residual spatial autocorrelation. Parameter estimates from MGWR demonstrate significant spatial heterogeneity, which traditional global models fail to detect. Results also indicate the use of MGWR for generating local spatiotemporal dependence evaluations which are conditioned on various covariates rather than being simple descriptions of a pattern. This study offers a more accurate method to model geographic events

MDM4 was associated with poor prognosis and tumor-immune infiltration of cancers

Abstract MDM4 is one of the MDM protein family and is generally recognized as the key negative regulator of p53. As a cancer-promoting factor, it plays a non-negligible role in tumorigenesis and development. In this article, we analyzed the expression levels of MDM4 in pan-cancer through multiple databases. We also investigated the correlations between MDM4 expression and prognostic value, immune features, genetic mutation, and tumor-related pathways. We found that MDM4 overexpression is often accompanied by adverse clinical features, poor prognosis, oncogenic mutations, tumor-immune infiltration and aberrant activation of oncogenic signaling pathways. We also conducted transcriptomic sequencing to investigate the effect of MDM4 on transcript levels in colon cancer and performed qPCR to verify this. Finally, we carried out some in vitro experiments including colony formation assay, chemoresistance and senescence-associated β-galactosidase activity assay to study the anti-tumor treatment effect of small molecule MDM4 inhibitor, NSC146109. Our research confirmed that MDM4 is a prognostic biomarker and potential therapeutic target for a variety of malignancies

Genetic characterization of a Salmonella enterica serovar Typhimurium isolated from an infant with concurrent resistance to ceftriaxone, ciprofloxacin and azithromycin

ABSTRACT: Objectives: To investigate the resistance mechanism of a Salmonella Typhimurium (S. Typhimurium) isolated from a faecal sample of an infant, which exhibited concurrent resistance to ceftriaxone, ciprofloxacin and azithromycin. Methods: Antimicrobial susceptibility testing was performed by broth microdilution in two kinds of drug-sensitive plates. Antimicrobial resistance (AMR) genes were identified by whole genome sequencing and bioinformatics analysis. Genotyping of the strain was performed by multilocus sequence typing (MLST). Plasmid DNA was sequenced and analysed using plasmid bioinformatics tools. Results: The SH11G993 strain was resistant to 28 antibiotics and carried 54 AMR genes. MLST results showed that the strain belonged to a rare genotype. The plasmid profile and plasmid sequencing showed that the strain carried two resistance plasmids. The pSH11G993-1 carried 14 AMR genes (especially co-harboured blaCMY-2, mphA and ermB) and a variety of insertion sequences, belonging to the IncC. The pSH11G993-2 carried 3 AMR genes and 9 virulence genes, belonging to the IncFIB-FII, forming a novel resistance and virulence co-harbouring plasmid. Conclusions: Our findings highlight that continuously monitor the changes in antibiotic resistance patterns and research on the resistance mechanisms in potential human pathogens are imperative

How Big Data and High-performance Computing Drive Brain Science

Brain science accelerates the study of intelligence and behavior, contributes fundamental insights into human cognition, and offers prospective treatments for brain disease. Faced with the challenges posed by imaging technologies and deep learning computational models, big data and high-performance computing (HPC) play essential roles in studying brain function, brain diseases, and large-scale brain models or connectomes. We review the driving forces behind big data and HPC methods applied to brain science, including deep learning, powerful data analysis capabilities, and computational performance solutions, each of which can be used to improve diagnostic accuracy and research output. This work reinforces predictions that big data and HPC will continue to improve brain science by making ultrahigh-performance analysis possible, by improving data standardization and sharing, and by providing new neuromorphic insights. Keywords: Brain science, Big data, High-performance computing, Brain connectomes, Deep learnin

Gclust: A Parallel Clustering Tool for Microbial Genomic Data

The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust. Keywords: Microbial genome clustering, Parallelization, Sparse suffix array, Maximal exact match, Segment extensio