Background: A novel data-driven Boolean model, namely, the fundamental Boolean model (FBM), has been proposed to draw genetic regulatory insights into gene activation, inhibition, and protein decay, published in 2018. This novel Boolean model facilitates the analysis of the activation and inhibition pathways. However, the novel model does not handle the situation well, where genetic regulation might require more time steps to complete.

Methods: Here, we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others. We denoted this extension model as the temporal fundamental Boolean model (TFBM) and related networks as the temporal fundamental Boolean networks (TFBNs). The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.

Results: We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets, which are in the form of short-time series. The data contain gene expression measurements for 13 GC-sensitive children under therapy for acute lymphoblastic leukaemia, and each sample has three time points: 0 hour (before GC treatment), 6/8 hours (after GC treatment) and 24 hours (after GC treatment).

Conclusion: We conclude that the proposed TFBM unlocks their predecessor’s limitation, i.e., FBM, that could help pharmaceutical agents identify any side effects on clinic-related data. New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.

Background: Renal cell carcinoma (RCC) is among the top adult cancers worldwide, with a challenging management due to lack of early diagnosis, therapy resistance, and diverse molecular background. Aberrant DNA methylation has been associated with RCC development due to transcription deregulation. We discovered potential DNA methylation-based biomarkers for stage I RCC in Caucasian population from The Cancer Genome Atlas (TCGA) database.

Methods: Patients’ clinical, methylation beta-value, and mRNA expression data were retrieved. Differential methylation and expression analysis were conducted to obtain differentially methylated CpG-gene pairs. Inversely correlated CpG-gene pairs between their expression and methylation levels were selected using Pearson’s correlation test and then screened for any recorded somatic mutations. Their biomarker capacities were analyzed using the Kaplan-Meier and receiver operating characteristic analysis, followed by protein network and functional enrichment analysis.

Results: We obtained differentially methylated CpGs in clear cell (KIRC) and papillary RCC (KIRP) but not chromophobe RCC (KICH). Six inversely correlated CpG-gene pairs with no reported cancer-associated mutations were selected. Prognostic values were found in ATXN1 and RFTN1 for KIRC, along with GRAMD1B and TM4SF19 for KIRP, while diagnostic values were found in VIM and RFTN1 for KIRC, along with TNFAIP6 and TM4SF19 for KIRP. Both subtypes showed enrichment of immune and metabolism-related pathways.

Conclusion: We discovered novel potential DNA methylation-based prognostic and diagnostic markers for early-stage RCC in Caucasian population. Validation by wet laboratory analysis and adjustments for confounding variables might be needed, considering our study limitation to specific race.

Background: DNA methylation is a key heritable epigenetic modification that plays a crucial role in transcriptional regulation and therefore a broad range of biological processes. The complex patterns of DNA methylation highlight the significance of the profiling the DNA methylation landscape.

Results: In this review, the main high-throughput detection technologies are summarized, and then the three trends of computational estimation of DNA methylation levels were analyzed, especially the expanding of the methylation data with lower coverage. Furthermore, the detection methods of differential methylation patterns for sequencing and array data were presented.

Conclusions: More and more research indicated the great importance of DNA methylation changes across different diseases, such as cancers. Although a lot of enormous progress has been made in understanding the role of DNA methylation, only few methylated genes or functional elements serve as clinically relevant cancer biomarkers. The bottleneck in DNA methylation advances has shifted from data generation to data analysis. Therefore, it is meaningful to develop machine learning models for computational estimation of methylation profiling and identify the potential biomarkers.

Background: Genome-wide association studies (GWAS) have identified thousands of genomic non-coding variants statistically associated with many human traits and diseases, including cancer. However, the functional interpretation of these non-coding variants remains a significant challenge in the post-GWAS era. Alternative polyadenylation (APA) plays an essential role in post-transcriptional regulation for most human genes. By employing different poly(A) sites, genes can either shorten or extend the 3′-UTRs that contain cis-regulatory elements such as miRNAs or RNA-binding protein binding sites. Therefore, APA can affect the mRNA stability, translation, and cellular localization of proteins. Population-scale studies have revealed many inherited genetic variants that potentially impact APA to further influence disease susceptibility and phenotypic diversity, but systematic computational investigations to delineate the connections are in their earliest states.

Results: Here, we discuss the evolving definitions of the genetic basis of APA and the modern genomics tools to identify, characterize, and validate the genetic influences of APA events in human populations. We also explore the emerging and surprisingly complex molecular mechanisms that regulate APA and summarize the genetic control of APA that is associated with complex human diseases and traits.

Conclusion: APA is an intermediate molecular phenotype that can translate human common non-coding variants to individual phenotypic variability and disease susceptibility.

Background: The advancement of genomics has progressed in lightning speed over the past two decades. Numerous large-scale genome sequencing initiatives were announced, along with the rise of the holistic precision medicine approach. However, the field of genomic medicine has now come to a bottleneck since genomic-phenomic interactions are not fully comprehended due to the complexity of the human systems biology and environmental influence, hence the emergence of human phenomics.

Results: This review attempts to provide an overview of the potential advantages of investigating the human phenomics of indigenous populations and the challenges ahead.

Conclusion: We believe that the indigenous populations serve as an ideal model to excavate our understanding of genomic-environmental-phenomics interactions.

Background: Inference of population structure is crucial for studies of human evolutionary history and genome-wide association studies. While several genomic regions have been reported to distort population structure analysis of European populations, no systematic analysis has been performed on non-European continental groups and with the latest human genome assembly.

Methods: Using the 1000 Genomes Project high coverage whole-genome sequencing data from four major continental groups (Europe, East Asia, South Asia, and Africa), we developed a statistical framework and systematically detected genomic regions with unusual contributions to the inference of population structure for each of the continental groups.

Results: We identified and characterized 27 unusual genomic regions mapped to GRCh38, including 13 regions around centromeres, 2 with chromosomal inversions, 8 under natural selection, and 4 with unknown causes. Excluding these regions would result in a more interpretable population structure inferred by principal components analysis and ADMIXTURE analysis.

Conclusions: Unusual genomic patterns in certain regions can distort the inference of population structure. Our compiled list of these unusual regions will be useful for many population-genetic studies, including those from non-European populations. Availability: The code to reproduce our results is available at the website of Github (/dwuab/UnRegFinder).