Size matters: how sample size affects the reproducibility and specificity of gene set analysis

Acute promyelocytic leukemia with PML/RARA (bcr1, bcr2 and bcr3) transcripts in a pediatric patient

Patients with acute promyelocytic leukemia (APL) exhibit the t(15;17)(q24.1;q21.2) translocation that produces the promyelocytic leukemia (PML)/retinoic acid receptor α (RARA) fusion gene. Different PML breakpoints yield three alternative molecular transcripts, bcr1, bcr2 and bcr3. The present study reports the simultaneous presence of three PML/RARA transcripts in a pediatric female patient diagnosed with APL, according to the clinical characteristics, immunophenotype and karyotype of the patient. The simultaneous presence of the PML/RARA transcripts were detected using reverse transcription-quantitative PCR (RT-qPCR). This was confirmed with HemaVision-28N Multiplex RT-qPCR, HemaVision-28Q qualitative RT-qPCR and the AmpliSeq RNA Myeloid Panel. To the best of our knowledge, the pediatric patient described in the present study is the first case found to exhibit all three PML/RARA transcripts (bcr1, bcr2 and bcr3). Additionally, a microarray analysis was performed to determine the expression profile, potential predictive biomarkers and the implications of this uncommon finding. According to the information obtained from molecular monitoring, the results reported in the present study were associated with a good patient prognosis. In addition, upregulated genes that are rare in acute myeloid leukemia were identified, and these genes may be promising diagnostic biomarkers for further study. For example, CCL-1 is present in leukemic stem cells, causing treatment failure and relapse, and α- and β-defensins have been reported exclusively in chronic myeloid leukemia. However, the results of the present study confirmed that they may also be present in APL. Thus, these findings suggested a possible signaling pathway that involves the PML/RARA oncoprotein in APL.

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients' progression in lung cancer.

METHOD

Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.

RESULTS

A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.

CONCLUSIONS

Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.

HLA-G Gene Variability Is Associated with Papillary Thyroid Carcinoma Morbidity and the HLA-G Protein Profile

Human leukocyte antigen (HLA)-G is an immune checkpoint molecule that is highly expressed in papillary thyroid carcinoma (PTC). The HLA-G gene presents several functional polymorphisms distributed across the coding and regulatory regions (5'URR: 5' upstream regulatory region and 3'UTR: 3' untranslated region) and some of them may impact HLA-G expression and human malignancy. To understand the contribution of the HLA-G genetic background in PTC, we studied the HLA-G gene variability in PTC patients in association with tumor morbidity, HLA-G tissue expression, and plasma soluble (sHLA-G) levels. We evaluated 185 PTC patients and 154 healthy controls. Polymorphic sites defining coding, regulatory and extended haplotypes were characterized by sequencing analyses. HLA-G tissue expression and plasma soluble HLA-G levels were evaluated by immunohistochemistry and ELISA, respectively. Compared to the controls, the G0104a(5'URR)G*01:04:04(coding)UTR-03(3'UTR) extended haplotype was underrepresented in the PTC patients, while G0104a(5'URR)G*01:04:01(coding)UTR-03(3'UTR) was less frequent in patients with metastatic and multifocal tumors. Decreased HLA-G tissue expression and undetectable plasma sHLA-G were associated with the G010102a(5'URR)G*01:01:02:01(coding)UTR-02(3'UTR) extended haplotype. We concluded that the HLA-G variability was associated with PTC development and morbidity, as well as the magnitude of the encoded protein expression at local and systemic levels.

T cell receptor β repertoires in patients with COVID-19 reveal disease severity signatures

Background

The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.

Methods

In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.

Results

We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.

Conclusions

Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq

Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.

Associations of VEGF Polymorphisms With Retinopathy of Prematurity

Purpose

This study investigated the associations between vascular endothelial growth factor (VEGF) polymorphisms and retinopathy of prematurity (ROP) risk.

Methods

Infants born prematurely at any time from 2009 to 2018 were included. Five single-nucleotide polymorphisms (SNPs) of VEGF were analyzed using real-time PCR in all infants. Multivariate logistic regression was applied to model the associations between VEGF polymorphisms and ROP susceptibility, severity, and premature clinicopathologic characteristics.

Results

A total of 334 patients were included and categorized into three groups: those without ROP, those with mild ROP (i.e., ROP not requiring treatment), and those with severe ROP (i.e., ROP for whom treatment was indicated). Among the female patients with ROP, those with VEGF rs3025035 CT (3.231-fold; 95% confidence interval [CI], 1.238-8.431) and a combination of CT and TT genotypes (2.643-fold; 95% CI, 1.056-6.619) exhibited significantly higher risks of severe ROP compared with those with wild-type genotypes. Female ROP infants with VEGF rs3025010 C (TC + CC) alleles had a lower risk of ROP stage ≥3 (odds ratio [OR] = 0.406; 95% CI, 0.165-0.999) than those with TT homozygotes. ROP patients with the VEGF rs10434 A allele (GA + AA) exhibited higher risks of necrotizing enterocolitis (OR = 2.750; 95% CI, 1.119-6.759) and lower risk of bronchopulmonary dysplasia (OR = 0.390; 95% CI, 0.173-0.877) than those with GG homozygotes did.

Conclusions

VEGF polymorphisms affect ROP risks differently in male and female infants. In female infants, VEGF rs3025035 with T alleles may predict ROP severity, and VEGF rs3025010 with C alleles may protect against severe ROP.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data

BACKGROUND

Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices.

RESULTS

We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.

Recent Progress in Genetics and Epigenetics Research on Diabetic Nephropathy in Malaysia

Diabetic nephropathy is a multifactorial disease. Gene susceptibility, as well as environmental exposure, plays an important role in disease progression. Malaysia is reported to be among the world's second-fastest-growing rates of kidney failure. Diabetic nephropathy has become the main cause of end-stage renal disease in Malaysia. This article is aimed at reviewing genetic studies conducted among diabetic nephropathy patients in the Malaysian population. This review was conducted by searching PubMed, MEDLINE, and Google Scholar databases to identify all relevant papers published in English from March 2022 to April 2022, using the following keywords: diabetes, type 2 diabetes, diabetic nephropathy, diabetic kidney disease, and Malaysia. The case-control study among diabetic patients with and without diabetic nephropathy showed a significant association with diabetic nephropathy in CNDP1, NOS3, and MnSOD genes. In the ethnic subgroup analysis, significant differences for diabetic nephropathy in terms of diabetes duration (≥10 years) were observed for CCL2 rs3917887, CCR5 rs1799987, ELMO1 rs74130, and IL8 rs4073. The IL8 rs4073 was associated only with the Indians, while the CCR5 rs1799987 was associated with the Chinese. In Malays, SLC12A3 Arg913Gln polymorphism and ICAM1 K469E (A/G) polymorphism were found to be associated with diabetic nephropathy. Studies on gene-environment interactions have suggested significant genetic and environmental factors such as smoking, waist circumference, and sex for eNOS rs2070744, PPARGC1A rs8192678, KCNQ1 rs2237895, and KCNQ1 rs2283228 with kidney disease. The genetic variants' contributions differed across ethnic groups. Therefore, a study to validate the genetic variants that are found to be associated with different ethnicities in Malaysia may be important in future studies.

Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis

Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes.

The role of gene set analysis is to identify groups of genes that are perturbed in a genomics experiment. There are many tools available for this task and they do not all test for the same types of changes. Here we propose a new way to carry out gene set analysis that involves first working out the distribution of the group effect in the gene set and then comparing this distribution to the equivalent distribution in other genes. Tests performed by existing tools for gene set analysis can be related to different comparisons in these distributions of group effects. A unified framework for gene set analysis provides for more explicit null hypotheses against which to test sets of genes for different types of responses to the experimental conditions. These results are more interpretable, because the group effect distributions can be compared visually, providing an indication of how the experimental effect differs between the gene sets.

Progeria and Aging-Omics Based Comparative Analysis

Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare "normal aging" (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.

The Endometrial Transcriptome of Metabolic and Inflammatory Pathways During the Window of Implantation Is Deranged in Infertile Obese Polycystic Ovarian Syndrome Women

Introduction and Aim: Obese women with polycystic ovarian syndrome (PCOS) have a reduced rate of spontaneous conception even when their cycles are ovulatory. Endometrial receptivity is an important factor for poor implantation and increased miscarriage rates. Mechanisms in which both pathologies modify the endometrium are not fully clarified. The aim of our study was to compare the endometrial transcriptomic profiles between infertile obese PCOS (O-PCOS) women and infertile normal weight subjects during the window of implantation in ovulatory menstrual cycles. Methods: We conducted a prospective transcriptomic analysis of the endometrium using RNA sequencing. In this way, potential endometrial mechanisms leading to the poor reproductive outcome in O-PCOS patients could be characterized. Endometrial samples during days 21-23 of the menstrual cycle were collected from infertile O-PCOS women (n = 11) and normal weight controls (n = 10). Subgroups were defined according to the ovulatory/anovulatory status in the natural cycles, and O-PCOS women were grouped into the O-PCOS ovulatory (O-PCOS-ovul) subgroup. RNA isolation, sequencing with library reparation, and subsequent RNAseq data analysis were performed. Results: Infertile O-PCOS patients had 610 differentially expressed genes (DEGs), after adjustment for multiple comparisons with normal weight infertile controls, related to obesity (MXRA5 and ECM1), PCOS (ADAMTS19 and SLC18A2), and metabolism (VNN1 and PC). In the ovulatory subgroup, no DEGs were found, but significant differences in canonical pathways and the upstream regulator were revealed. According to functional and upstream analyses of ovulatory subgroup comparisons, the most important biological processes were related to inflammation (TNFR1 signaling), insulin signaling (insulin receptor signaling and PI3/AKT), fatty acid metabolism (stearate biosynthesis I and palmitate biosynthesis I), and lipotoxicity (unfolded protein response pathway). Conclusions: We demonstrated that endometrial transcription in ovulatory O-PCOS patients is deranged in comparison with the control ovulatory endometrium. The most important pathways of differentiation include metabolism and inflammation. These processes could also represent potential mechanisms for poor embryo implantation, which prevent the development of a successful pregnancy. ClinicalTrials.gov ID: NCT03353948.

Venn Diagrams May Indicate Erroneous Statistical Reasoning in Transcriptomics

A common application of differential expression analysis is finding genes that are differentially expressed upon treatment in only one out of several groups of samples. One of the approaches is to test for significant difference in expression between treatment and control separately in the two groups, and then select genes that show statistical significance in one group only. This approach is then often combined with a gene set enrichment analysis to find pathways and gene sets regulated by treatment in only this group. Here we show that this procedure is statistically incorrect and that the interaction between treatment and group should be tested instead. Moreover, we show that gene set enrichment analysis applied to such incorrectly defined genes group-specific genes may result in misleading artifacts. Due to the presence of false negatives, genes significant in one, but not the other group are enriched in gene sets which correspond to the overall effect of the treatment. Thus, the results appear related to the problem at hand, but do not reflect the group-specific effect of a treatment. A literature search revealed that more than a quarter of papers which used a Venn diagram to illustrate the results of separate differential analysis have also applied this incorrect reasoning.

Network analysis of TCGA and GTEx gene expression datasets for identification of trait-associated biomarkers in human cancer

Advances in high-throughput sequencing technologies now yield unprecedented volumes of OMICs data with opportunities to conduct systematic data analyses and derive novel biological insights. Here, we provide protocols to perform differential-expressed gene analysis of TCGA and GTEx RNA-Seq data from human cancers, complete integrative GO and network analyses with focus on clinical and survival data, and identify differential correlation of trait-associated biomarkers.

For complete details on the use and execution of this protocol, please refer to Chen and MacDonald (2021).

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Protocols for the identification of trait-associated molecular correlates in cancer

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Differentially-expressed gene (DEG) analysis of TCGA and GTEx transcriptomic data

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Protocols for integrative network analysis of RNA-seq, clinical, and survival data

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Differential correlation of trait-associated biomarkers for hypothesis testing

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar's test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

Silver: Forging almost Gold Standard Datasets

Gene set analysis has been widely used to gain insight from high-throughput expression studies. Although various tools and methods have been developed for gene set analysis, there is no consensus among researchers regarding best practice(s). Most often, evaluation studies have reported contradictory recommendations of which methods are superior. Therefore, an unbiased quantitative framework for evaluations of gene set analysis methods will be valuable. Such a framework requires gene expression datasets where enrichment status of gene sets is known a priori. In the absence of such gold standard datasets, artificial datasets are commonly used for evaluations of gene set analysis methods; however, they often rely on oversimplifying assumptions that make them biased in favor of or against a given method. In this paper, we propose a quantitative framework for evaluation of gene set analysis methods by synthesizing expression datasets using real data, without relying on oversimplifying or unrealistic assumptions, while preserving complex gene-gene correlations and retaining the distribution of expression values. The utility of the quantitative approach is shown by evaluating ten widely used gene set analysis methods. An implementation of the proposed method is publicly available. We suggest using Silver to evaluate existing and new gene set analysis methods. Evaluation using Silver provides a better understanding of current methods and can aid in the development of gene set analysis methods to achieve higher specificity without sacrificing sensitivity.

Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution

Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.

Genetics of canine myxomatous mitral valve disease

Myxomatous mitral valve disease (MMVD) is the most common heart disease and cause of cardiac death in domestic dogs. MMVD is characterised by slow progressive myxomatous degeneration from the tips of the mitral valves onwards with subsequent mitral valve regurgitation, and left atrial and ventricular dilatation. Although the disease usually has a long asymptomatic period, in dogs with severe disease, mortality is typically secondary to left-sided congestive heart failure. Although it is not uncommon for dogs to survive long enough in the asymptomatic period to die from unrelated causes; a proportion of dogs rapidly advance into congestive heart failure. Heightened prevalence in certain breeds, such as the Cavalier King Charles Spaniel, has indicated that MMVD is under a genetic influence. The genetic characterisation of the factors that underlie the difference in progression of disease is of strong interest to those concerned with dog longevity and welfare. Advanced genomic technologies have the potential to provide information that may impact treatment, prevalence, or severity of MMVD through the elucidation of pathogenic mechanisms and the detection of predisposing genetic loci of major effect. Here we describe briefly the clinical nature of the disorder and consider the physiological mechanisms that might impact its occurrence in the domestic dog. Using results from comparative genomics we suggest possible genetic approaches for identifying genetic risk factors within breeds. The Cavalier King Charles Spaniel breed represents a robust resource for uncovering the genetic basis of MMVD.

Meta-analysis of gene expression patterns in Down syndrome highlights significant alterations in mitochondrial and bioenergetic pathways

Individuals with Down syndrome (DS) have an extra copy of chromosome 21. Clinical observations and preclinical studies both suggest that DS is associated with altered bioenergetic pathways. Several studies have reported that differentially expressed genes in DS are located not only on chromosome 21 but also on all other chromosomes. Numerous sets of microarray and RNA-seq data are publicly accessible through the Gene Expression Omnibus. We have conducted a meta-analysis on differentially expressed genes between DS and control subjects. Data deposited before July 1, 2020, were identified by using the search terms "Down syndrome" or "trisomy 21" and "human". Gene expression data were analyzed and normalized for each study. The mixed effect model was used to identify the differentially expressed genes. We conclude that in DS more than 60% of the genes located on chromosome 21 are significantly upregulated and none of them are downregulated. In addition, a significant dysregulation of genes occurs on all other chromosomes as well. Several of the upregulated genes in DS encode for important components of various bioenergetic pathways, for instance PFKL and ACLY. Genes involved in oxidative phosphorylation are mostly downregulated in DS. The gene expression alterations are consistent with the development of significant metabolic disturbances ("pseudohypoxia") in DS cells, which may explain some of the well-known functional defects (ranging from neuronal dysfunction to reduced exercise tolerance) associated with DS.

Comprehensive pathway enrichment analysis workflows: COVID-19 case study

The coronavirus disease 2019 (COVID-19) outbreak due to the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been classified as a pandemic disease by the World Health Organization on the 12th March 2020. This world-wide crisis created an urgent need to identify effective countermeasures against SARS-CoV-2. In silico methods, artificial intelligence and bioinformatics analysis pipelines provide effective and useful infrastructure for comprehensive interrogation and interpretation of available data, helping to find biomarkers, explainable models and eventually cures. One class of such tools, pathway enrichment analysis (PEA) methods, helps researchers to find possible key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. Since many software tools are available, it is not easy for non-computational users to choose the best one for their needs. In this paper, we highlight how to choose the most suitable PEA method based on the type of COVID-19 data to analyze. We aim to provide a comprehensive overview of PEA techniques and the tools that implement them.

Automation in the Life Science Research Laboratory

Protocols in the academic life science laboratory are heavily reliant on the manual manipulation of tools, reagents and instruments by a host of research staff and students. In contrast to industrial and clinical laboratory environments, the usage of automation to augment or replace manual tasks is limited. Causes of this 'automation gap' are unique to academic research, with rigid short-term funding structures, high levels of protocol variability and a benevolent culture of investment in people over equipment. Automation, however, can bestow multiple benefits through improvements in reproducibility, researcher efficiency, clinical translation, and safety. Less immediately obvious are the accompanying limitations, including obsolescence and an inhibitory effect on the freedom to innovate. Growing the range of automation options suitable for research laboratories will require more flexible, modular and cheaper designs. Academic and commercial developers of automation will increasingly need to design with an environmental awareness and an understanding that large high-tech robotic solutions may not be appropriate for laboratories with constrained financial and spatial resources. To fully exploit the potential of laboratory automation, future generations of scientists will require both engineering and biology skills. Automation in the research laboratory is likely to be an increasingly critical component of future research programs and will continue the trend of combining engineering and science expertise together to answer novel research questions.

Role of miRNA-mRNA Interaction in Neural Stem Cell Differentiation of Induced Pluripotent Stem Cells

miRNA regulates the expression of protein coding genes and plays a regulatory role in human development and disease. The human iPSCs and their differentiated progenies provide a unique opportunity to identify these miRNA-mediated regulatory mechanisms. To identify miRNA-mRNA regulatory interactions in human nervous system development, well characterized NSCs were differentiated from six validated iPSC lines and analyzed for differentially expressed (DE) miRNome and transcriptome by RNA sequencing. Following the criteria, moderated t statistics, FDR-corrected p-value ≤ 0.05 and fold change-absolute (FC-abs) ≥2.0, 51 miRNAs and 4033 mRNAs were found to be significantly DE between iPSCs and NSCs. The miRNA target prediction analysis identified 513 interactions between 30 miRNA families (mapped to 51 DE miRNAs) and 456 DE mRNAs that were paradoxically oppositely expressed. These 513 interactions were highly enriched in nervous system development functions (154 mRNAs; FDR-adjusted p-value range: 8.06 × 10^-15-1.44 × 10^-4). Furthermore, we have shown that the upregulated miR-10a-5p, miR-30c-5p, miR23-3p, miR130a-3p and miR-17-5p miRNA families were predicted to down-regulate several genes associated with the differentiation of neurons, neurite outgrowth and synapse formation, suggesting their role in promoting the self-renewal of undifferentiated NSCs. This study also provides a comprehensive characterization of iPSC-generated NSCs as dorsal neuroepithelium, important for their potential use in in vitro modeling of human brain development and disease.

Gene Set Analysis: Challenges, Opportunities, and Future Research

Gene set analysis methods are widely used to provide insight into high-throughput gene expression data. There are many gene set analysis methods available. These methods rely on various assumptions and have different requirements, strengths and weaknesses. In this paper, we classify gene set analysis methods based on their components, describe the underlying requirements and assumptions for each class, and provide directions for future research in developing and evaluating gene set analysis methods.

Prognostic Biomarkers in Endometrial Cancer: A Systematic Review and Meta-Analysis

Endometrial cancer (EC) is the sixth most common cancer in women worldwide and its mortality is directly associated with the presence of poor prognostic factors driving tumor recurrence. Stratification systems are based on few molecular, and mostly clinical and pathological parameters, but these systems remain inaccurate. Therefore, identifying prognostic EC biomarkers is crucial for improving risk assessment pre- and postoperatively and to guide treatment decisions. This systematic review gathers all protein biomarkers associated with clinical prognostic factors of EC, recurrence and survival. Relevant studies were identified by searching the PubMed database from 1991 to February 2020. A total number of 398 studies matched our criteria, which compiled 255 proteins associated with the prognosis of EC. MUC16, ESR1, PGR, TP53, WFDC2, MKI67, ERBB2, L1CAM, CDH1, PTEN and MMR proteins are the most validated biomarkers. On the basis of our meta-analysis ESR1, TP53 and WFDC2 showed potential usefulness for predicting overall survival in EC. Limitations of the published studies in terms of appropriate study design, lack of high-throughput measurements, and statistical deficiencies are highlighted, and new approaches and perspectives for the identification and validation of clinically valuable EC prognostic biomarkers are discussed.