A pipeline for RNA-seq based eQTL analysis with automated quality control procedures

Genetic transcriptional regulation profiling of cartilage reveals pathogenesis of osteoarthritis

BACKGROUND

Genome-wide association studies (GWAS) have identified more than one hundred risk loci for osteoarthritis (OA). Identifying the effector genes and deciphering the underlying regulatory mechanisms are of great importance but remains challenging due to limited availability of OA-related tissue data. This study aims to address this issue by generating a cartilage expression quantitative trait loci (eQTLs) and a functional fine-mapping resource.

METHODS

We performed cis-eQTL analysis using genomics and cartilage transcriptomics data from 204 patients with OA (largest sample size to date). Cell type-interaction eQTL analysis (ci-eQTL) was conducted to explore the chondrocyte subtype dependency of eQTL effects. Co-localization analysis was used to nominate effector genes of OA GWAS risk loci. A deciphering pipeline was established to identify candidate causal variants in eQTL loci that regulate gene expression through the alteration of chromatin accessibility or disruption of transcription factors (TFs) binding to regulatory elements.

FINDINGS

We identified 3352 independent eQTLs for 3109 genes, 120 eQTL-gene pairs showed chondrocyte subtype dependency. We identified 19 new OA risk genes. We identified 117 causal eQTLs exhibiting allele-specific open chromatin (ASoC) and 547 eQTLs involved in transcription factor binding disruption (TBD). Functional validation showed that the T allele of the OA risk variant rs11750646 enhances the AR binding affinity to an open chromatin region, thereby promoting the expression of the OA-related gene PIK3R1.

INTERPRETATION

Our findings provide insights into the unique regulatory landscape of cartilage and elucidate potential mechanisms underlying OA pathogenesis.

FUNDING

This work was supported by National Natural Science Foundation of China (32470639, 82372458, and 82170896); Science Fund for Distinguished Young Scholars of Shaanxi Province (2025JC-JCQN-054); Innovation Capability Support Program of Shaanxi Province (2022TD-44, 2024RS-CXTD-86); Key Research and Development Project of Shaanxi Province (2023-YBSF-180); China Postdoctoral Science Foundation (2024M752561); and the Fundamental Research Funds for the Central Universities.

A compendium of methods: Searching allele specific expression via RNA sequencing

Diploid mammalian genome has paired alleles for each gene; typically allowing for equal expression of the two alleles within the cell/tissue. However, genetic regulatory elements and epigenetic modifications can disrupt this equality, leading to preferential expression of one allele. Examining high-confidence allele-specific expression (ASE) is vital for understanding genetic variations and their impact on major diseases like cancers and diabetes. ASE analysis not only aids in disease prognosis and diagnosis but also helps to identify regulatory mechanisms operating within the genome. While advances in sequencing technologies have greatly improved our understanding of ASE, challenges remain in estimating it accurately. In this article, we reviewed methods for detecting ASE using both bulk RNASeq and single-cell RNASeq data to provide deeper insights beyond the mere prediction of ASE genes. Fundamentally, ASE detection methods are data-driven and can be classified according to type of data used. Some methods utilize both, DNA genotyping information and RNASeq while others rely solely on RNASeq data. This article offers a comparative analysis of these methods and compilation of repositories providing valuable insights.

Neonatal Encephalopathy: Novel Phenotypes and Genotypes Identified by Genome Sequencing

Background and Objectives

Neonatal encephalopathy (NE) is characterized by an abnormal level of consciousness with or without seizures in the neonatal period. It affects 1-6/1,000 live term newborns. We applied genome sequencing (GS) in term newborns with NE to investigate the underlying genetic causes.

Methods

We enrolled term newborns according to inclusion/exclusion criteria during their Neonatal Intensive Care admission. We performed GS trio and applied bioinformatic tools. We developed pipelines for manual filters. We applied in silico prediction tools, protein 3D modeling, and functional characterization to assess the pathogenicity of variants.

Results

Seventeen newborns fulfilled inclusion criteria. We identified 12 variants in 10 genes. We classified 4 variants in PPP2R5D, BCOR, CFL2, and SCN2A (previously established disease genes) as pathogenic/likely pathogenic; 7 variants in DST (previously established disease gene), STAB2, CELF4, SORCS2, CTNND2, and ASTN1 (5 candidate genes) as variants of uncertain significance (VUS); and one variant in STAB2 as likely benign. The CELF4 and ASTN1 copy number variants (CNVs) resulted in structural changes in protein 3D models. The functional characterization of SORCS2 VUS revealed disruption of SorCS2 dimer formation and confirmed its pathogenicity. The functional characterization of STAB2 variants updated their characterization from VUS/likely benign to benign. The CTNND2 VUS resulted in a shift in 3D protein structure. We were not able to perform protein 3D modeling and functional characterization of two DST VUS. We are not certain whether CTNND2 and DST variants may be causative of NE in our study.

Discussion

The diagnostic rate of research GS was 41% in our prospective study. We broaden the phenotypic spectrum of PPP2R5D-associated Hogue-Janssens syndrome 1, CFL2-associated nemaline myopathy 7, and BCOR-associated oculo-facio-cardio-dental syndrome to include NE and/or neonatal seizures. We identified 3 candidate genes (SORCS2, CELF4, ASTN1) that may cause NE. We believe that protein 3D modeling is an important tool to assess the pathogenicity of CNVs and may advance the discoveries of novel genetic diseases. However, functional characterization of missense variants is essential for discoveries of novel genetic diseases. It seems that GS can help identify more candidate genes compared with ES.

A brief guide to analyzing expression quantitative trait loci

Molecular quantitative trait locus (molQTL) mapping has emerged as an important approach for elucidating the functional consequences of genetic variants and unraveling the causal mechanisms underlying diseases or complex traits. However, the variety of analysis tools and sophisticated methodologies available for molQTL studies can be overwhelming for researchers with limited computational expertise. Here, we provide a brief guideline with a curated list of methods and software tools for analyzing expression quantitative trait loci, the most widely studied type of molQTL.

eQTL-Detect: nextflow-based pipeline for eQTL detection in modular format with sharable and parallelizable scripts

Bioinformatic pipelines are becoming increasingly complex with the ever-accumulating amount of Next-generation sequencing (NGS) data. Their orchestration is difficult with a simple Bash script, but bioinformatics workflow managers such as Nextflow provide a framework to overcome respective problems. This study used Nextflow to develop a bioinformatic pipeline for detecting expression quantitative trait loci (eQTL) using a DSL2 Nextflow modular syntax, to enable sharing the huge demand for computing power as well as data access limitation across different partners often associated with eQTL studies. Based on the results from a test run with pilot data by measuring the required runtime and computational resources, the new pipeline should be suitable for eQTL studies in large scale analyses.

postGWAS: A web server for deciphering the causality post the genome-wide association studies

While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.

METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images

BACKGROUND

Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administration (FDA), achieving promising results. However, current detection of MET dysregulation requires biopsy and gene sequencing, which is invasive, time-consuming and difficult to obtain tumor samples.

METHODS

To address the above problems, we developed a noninvasive and convenient deep learning (DL) model based on Computed tomography (CT) imaging data for prediction of MET dysregulation. We introduced the unsupervised algorithm RK-net for automated image processing and utilized the MedSAM large model to achieve automated tissue segmentation. Based on the processed CT images, we developed a DL model (METnet). The model based on the grouped convolutional block. We evaluated the performance of the model over the internal test dataset using the area under the receiver operating characteristic curve (AUROC) and accuracy. We conducted subgroup analysis on the basis of clinical data of the lung cancer patients and compared the performance of the model in different subgroups.

RESULTS

The model demonstrated a good discriminative ability over the internal test dataset. The accuracy of METnet was 0.746 with an AUC value of 0.793 (95% CI 0.714-0.871). The subgroup analysis revealed that the model exhibited similar performance across different subgroups.

CONCLUSIONS

METnet realizes prediction of MET dysregulation in NSCLC, holding promise for guiding precise tumor diagnosis and treatment at the molecular level.

The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease

The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.

A landscape of gene expression regulation for synovium in arthritis

The synovium is an important component of any synovial joint and is the major target tissue of inflammatory arthritis. However, the multi-omics landscape of synovium required for functional inference is absent from large-scale resources. Here we integrate genomics with transcriptomics and chromatin accessibility features of human synovium in up to 245 arthritic patients, to characterize the landscape of genetic regulation on gene expression and the regulatory mechanisms mediating arthritic diseases predisposition. We identify 4765 independent primary and 616 secondary cis-expression quantitative trait loci (cis-eQTLs) in the synovium and find that the eQTLs with multiple independent signals have stronger effects and heritability than single independent eQTLs. Integration of genome-wide association studies (GWASs) and eQTLs identifies 84 arthritis related genes, revealing 38 novel genes which have not been reported by previous studies using eQTL data from the GTEx project or immune cells. We further develop a method called eQTac to identify variants that could affect gene expression by affecting chromatin accessibility and identify 1517 regions with potential regulatory function of chromatin accessibility. Altogether, our study provides a comprehensive synovium multi-omics resource for arthritic diseases and gains new insights into the regulation of gene expression.

An integrative pipeline for circular RNA quantitative trait locus discovery with application in human T cells

MOTIVATION

Molecular quantitative trait locus (QTL) mapping has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by genome-wide association studies. Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of various human diseases. However, a well-developed computational pipeline for circRNA QTL (circQTL) discovery is still lacking.

RESULTS

We introduce an integrative method for circQTL mapping and implement it as an automated pipeline based on Nextflow, named cscQTL. The proposed method has two main advantages. Firstly, cscQTL improves the specificity by systematically combining outputs of multiple circRNA calling algorithms to obtain highly confident circRNA annotations. Secondly, cscQTL improves the sensitivity by accurately quantifying circRNA expression with the help of pseudo references. Compared to the single method approach, cscQTL effectively identifies circQTLs with an increase of 20%-100% circQTLs detected and recovered all circQTLs that are highly supported by the single method approach. We apply cscQTL to a dataset of human T cells and discover genetic variants that control the expression of 55 circRNAs. By colocalization tests, we further identify circBACH2 and circYY1AP1 as potential candidates for immune disease regulation.

AVAILABILITY AND IMPLEMENTATION

cscQTL is freely available at: https://github.com/datngu/cscQTL and https://doi.org/10.5281/zenodo.7851982.

Affordable, accurate and unbiased RNA sequencing by manual library miniaturization: A case study in barley

We present an easy-to-reproduce manual miniaturized full-length RNA sequencing (RNAseq) library preparation workflow that does not require the upfront investment in expensive lab equipment or long setup times. With minimal adjustments to an established commercial protocol, we were able to manually miniaturize the RNAseq library preparation by a factor of up to 1:8. This led to cost savings for miniaturized library preparation of up to 86.1% compared to the gold standard. The resulting data were the basis of a rigorous quality control analysis that inspected: sequencing quality metrics, gene body coverage, raw read duplications, alignment statistics, read pair duplications, detected transcripts and sequence variants. We also included a deep dive data analysis identifying rRNA contamination and suggested ways to circumvent these. In the end, we could not find any indication of biases or inaccuracies caused by the RNAseq library miniaturization. The variance in detected transcripts was minimal and not influenced by the miniaturization level. Our results suggest that the workflow is highly reproducible and the sequence data suitable for downstream analyses such as differential gene expression analysis or variant calling.

BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing

MOTIVATION

While many pipelines have been developed for calling genotypes using RNA-sequencing (RNA-Seq) data, they all have adapted DNA genotype callers that do not model biases specific to RNA-Seq such as allele-specific expression (ASE).

RESULTS

Here, we present Bayesian beta-binomial mixture model (BBmix), a Bayesian beta-binomial mixture model that first learns the expected distribution of read counts for each genotype, and then deploys those learned parameters to call genotypes probabilistically. We benchmarked our model on a wide variety of datasets and showed that our method generally performed better than competitors, mainly due to an increase of up to 1.4% in the accuracy of heterozygous calls, which may have a big impact in reducing false positive rate in applications sensitive to genotyping error such as ASE. Moreover, BBmix can be easily incorporated into standard pipelines for calling genotypes. We further show that parameters are generally transferable within datasets, such that a single learning run of less than 1 h is sufficient to call genotypes in a large number of samples.

AVAILABILITY AND IMPLEMENTATION

We implemented BBmix as an R package that is available for free under a GPL-2 licence at https://gitlab.com/evigorito/bbmix and https://cran.r-project.org/package=bbmix with accompanying pipeline at https://gitlab.com/evigorito/bbmix_pipeline.

An Adaptive Weighted Attention-Enhanced Deep Convolutional Neural Network for Classification of MRI images of Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed. Thus, capturing the features of the areas accurately where the lesions changed became a problem.

METHOD

We propose a deep learning framework that combines multi-scale attention guidance and multi-branch feature processing modules to diagnose PD by learning sMRI T2 slice features. In this scheme, firstly, to achieve effective feature transfer and gradient descent, a deep convolutional neural network framework based on dense block is designed. Next, an Adaptive Weighted Attention algorithm is proposed, whose pursers is to extract multi branch and even diverse features. Finally, Dropout layer and SoftMax layer are added to the network structure to obtain good classification results and rich and diverse feature information. The Dropout layer is used to reduce the number of intermediate features to increase the orthogonality between features of each layer. The activation function SoftMax increases the flexibility of the neural network by increasing the degree of fitting to the training set and converting linear to nonlinear.

RESULTS

The best performance of the proposed method an accuracy of 92%, a sensitivity of 94%, specificity of 90% and a F1 score of 95% respectively for identifying PD and HC.

CONCLUSION

Experiments show that the proposed method can successfully distinguish PD and NC. Good classification results were obtained in PD diagnosis classification task and compared with advanced research methods.

Label-free Quantitative Proteomic Analysis of Cerebrospinal Fluid and Serum in Patients With Relapse-Remitting Multiple Sclerosis

Background: The lack of effective serum and cerebrospinal fluid (CSF) biomarkers remains a barrier to early diagnosis and treatment of multiple sclerosis (MS). The study is to identify the diagnostic biomarkers of serum and CSF in patients who suffered MS.

Methods: At first, we performed differential analysis of CSF and serum proteomics on control and relapse-remitting multiple sclerosis (RRMS) patients. Secondly, CSF and serum’s differential proteins were compared, in order to identify the significative proteins. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed on the differential proteins in serum and CSF respectively to clarify their common biological functions and pathways.

Results: At the first step, in CSF, 73 proteins were significantly differentially expressed in the RRMS set compared with the controls. In serum, 22 proteins were differentially expressed. Secondly, we found MMP2 C8G and CFH were the same high expression trend in CSF and serum. Finally, we found the differential proteins in serum and CSF are mostly participated in biological processes: immuno-inflammatory response, neuronal development, cell adhesion and signaling.

Conclusion: MMP2, C8G and CFH may participate in the pathogenesis of RRMS, which are the potential diagnostic biomarkers of the disease.

DEEMD-SPP: A Novel Framework for Emotion Recognition Based on EEG Signals

Electroencephalography (EEG) is one of the most widely-used biosignal capturing technology for investigating brain activities, cognitive diseases, and affective disorders. To understand the underlying principles of brain activities and affective disorders using EEG data, one of the fundamental tasks is to accurately identify emotions from EEG signals, which has attracted huge attention in the field of affective computing. To improve the accuracy and effectiveness of emotion recognition based on EEG data, previous studies have successfully developed numerous feature extraction methods and classifiers. Among them, ensemble empirical mode decomposition (EEMD) is an efficient signal decomposition technique for extracting EEG features. It can alleviate the mode-mixing problem by adding white noise to the source signal. However, there remain some issues when applying this method to recognition tasks. As the added noise cannot be filtered completely, spurious modes are generated due to the residual noise. Therefore, it is crucial to perform intrinsic mode function (IMF) selection to find the most valuable IMF components that represent brain activities. Furthermore, the number of decomposed IMFs is various to different original signals, thus how to unify feature dimensions needs better solutions. To solve these issues, we propose a novel forecasting framework, named DEEMD-SPP, to identify emotions from EEG signals, based on the combination of denoising ensemble empirical mode decomposition (DEEMD) and Spatial Pyramid Pooling Network (SPP-Net). First, DEEMD is proposed to decompose the EEG signals, which effectively eliminates residual noise in the IMFs and selects the most valuable IMFs. Second, time-domain and frequency-domain features are extracted from the selected IMFs. Finally, SPP-net is employed as the classifier to recognize emotions, which can effectively transform various-sized feature maps into fixed-sized feature vectors through the pyramid pooling layer. The experimental results demonstrate that our proposed DEEMD-SPP framework can effectively reduce the effect of spike-in white noise, accurately extract EEG features, and significantly improve the performance of emotion recognition.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.