Search for: All records

Abstract Rapid development of transcriptome sequencing technologies has resulted in a data revolution and emergence of new approaches to study transcriptomic regulation such as alternative splicing, alternative polyadenylation, CRISPR knockout screening in addition to the regular gene expression. A full characterization of the transcriptional landscape of different groups of cells or tissues holds enormous potential for both basic science as well as clinical applications. Although many methods have been developed in the realm of differential gene expression analysis, they all geared towards a particular type of sequencing data and failed to perform well when applied in different types of transcriptomic data. To fill this gap, we offer a negative beta binomial t-test (NBBt-test). NBBt-test provides multiple functions to perform differential analyses of alternative splicing, polyadenylation, CRISPR knockout screening, and gene expression datasets. Both real and large-scale simulation data show superior performance of NBBt-test with higher efficiency, and lower type I error rate and FDR to identify differential isoforms and differentially expressed genes and differential CRISPR knockout screening genes with different sample sizes when compared against the current very popular statistical methods. An R-package implementing NBBt-test is available for downloading from CRAN ( https://CRAN.R-project.org/package=NBBttest ). 

ABSTRACT Colorectal cancer (CRC) is the second leading cause of cancer mortality worldwide. The dysbiotic gut microbiota and its metabolite secretions play a significant role in CRC development and progression. In this study, we identified microbial and metabolic biomarkers applicable to CRC using a meta-analysis of metagenomic datasets from diverse geographical regions. We used LEfSe, random forest (RF), and co-occurrence network methods to identify microbial biomarkers. Geographic dataset-specific markers were identified and evaluated using area under the ROC curve (AUC) scores and random effect size. Co-occurrence networks analysis showed a reduction in the overall microbial associations and the presence of oral pathogenic microbial clusters in CRC networks. Analysis of predicted metabolites from CRC datasets showed the enrichment of amino acids, cadaverine, and creatine in CRC, which were positively correlated with CRC-associated microbes ( Peptostreptococcus stomatis , Gemella morbillorum , Bacteroides fragilis , Parvimonas spp., Fusobacterium nucleatum , Solobacterium moorei , and Clostridium symbiosum ), and negatively correlated with control-associated microbes. Conversely, butyrate, nicotinamide, choline, tryptophan, and 2-hydroxybutanoic acid showed positive correlations with control-associated microbes ( P < 0.05). Overall, our study identified a set of global CRC biomarkers that are reproducible across geographic regions. We also reported significant differential metabolites and microbe-metabolite interactions associated with CRC. This study provided significant insights for further investigations leading to the development of noninvasive CRC diagnostic tools and therapeutic interventions. IMPORTANCE Several studies showed associations between gut dysbiosis and CRC. Yet, the results are not conclusive due to cohort-specific associations that are influenced by genomic, dietary, and environmental stimuli and associated reproducibility issues with various analysis approaches. Emerging evidence suggests the role of microbial metabolites in modulating host inflammation and DNA damage in CRC. However, the experimental validations have been hindered by cost, resources, and cumbersome technical expertise required for metabolomic investigations. In this study, we performed a meta-analysis of CRC microbiota data from diverse geographical regions using multiple methods to achieve reproducible results. We used a computational approach to predict the metabolomic profiles using existing CRC metagenomic datasets. We identified a reliable set of CRC-specific biomarkers from this analysis, including microbial and metabolite markers. In addition, we revealed significant microbe-metabolite associations through correlation analysis and microbial gene families associated with dysregulated metabolic pathways in CRC, which are essential in understanding the vastly sporadic nature of CRC development and progression.