The Ensembl Genome Browser: Strategies for Accessing Eukaryotic Genome Data

The Genome Explorer genome browser

Are two adjacent genes in the same operon? What are the order and spacing between several transcription factor binding sites? Genome browsers are software data visualization and exploration tools that enable biologists to answer questions such as these. In this paper, we report on a major update to our browser, Genome Explorer, that provides nearly instantaneous scaling and traversing of a genome, enabling users to quickly and easily zoom into an area of interest. The user can rapidly move between scales that depict the entire genome, individual genes, and the sequence; Genome Explorer presents the most relevant detail and context for each scale. By downloading the data for the entire genome to the user's web browser and dynamically generating visualizations locally, we enable fine control of zoom and pan functions and real-time redrawing of the visualization, resulting in smoother and more intuitive exploration of a genome than is possible with other browsers. Further, genome features are presented together, in-line, using familiar graphical depictions. In contrast, many other browsers depict genome features using data tracks, which have low information density and can visually obscure the relative positions of features. Genome Explorer diagrams have a high information density that provides larger amounts of genome context and sequence information to be presented in a given-sized monitor than for tracks-based browsers. Genome Explorer provides optional data tracks for the analysis of large-scale data sets and a unique comparative mode that aligns genomes at orthologous genes with synchronized zooming.

IMPORTANCE

Genome browsers provide graphical depictions of genome information to speed the uptake of complex genome data by scientists. They provide search operations to help scientists find information and zoom operations to enable scientists to view genome features at different resolutions. We introduce the Genome Explorer browser, which provides extremely fast zooming and panning of genome visualizations and displays with high information density.

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for NSCLC. This study aims to identify the lncRNAs and mRNAs related to survival of non-small cell lung cancer (NSCLC). The expression profile data of lung adenocarcinoma and lung squamous cell carcinoma were downloaded in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. A total of eight survival related long non-coding RNAs (lncRNAs) and 262 survival related mRNAs were filtered. By gene set enrichment analysis, 17 significantly correlated Gene Ontology signal pathways and 14 Kyoto Encyclopedia of Genes and Genomes signal pathways were screened. Based on the clinical survival and prognosis information of the samples, we screened eight lncRNAs and 193 mRNAs by single factor Cox regression analysis. Further single and multifactor Cox regression analysis were performed, 30 independent prognostication-related mRNAs were obtained. The PPI network was further constructed. We then performed the machine learning algorithms (Least absolute shrinkage and selection operator, Recursive feature elimination, and Random forest) to screen the optimized DEGs combination, and a total of 17 overlapping mRNAs were obtained. Based on the 17 characteristic mRNAs obtained, we firstly built a Nomogram prediction model, and the ROC values of training set and testing set were 0.835 and 0.767, respectively. By overlapping the 17 characteristic mRNAs and PPI network hub genes, three genes were obtained: CDC6, CEP55, TYMS, which were considered as key factors associated with survival of NSCLC. The in vitro experiments were performed to examine the effect of CDC6, CEP55, and TYMS on NSCLC cells. Finally, the lncRNAs-mRNAs networks were constructed.

Computational investigations into structure and function impact of novel mutations identified in targeted exons from ovarian cancer cell lines

The lack of sensitive and specific biomarkers for ovarian cancer leads to late stage diagnosis of the disease in a majority of the cases. Mutation accumulation is the basis for cancer progression, thus identifying mutations is an important step in the disease diagnosis. In the present study, a comprehensive analysis of fifteen Next Generation Sequencing samples from thirteen ovarian cancer cell lines was carried out for the identification of new mutations. The study revealed eight clinically significant novel mutations in six ovarian cancer oncogenes, viz. SMARCA4, ARID1A, PPP2R1A, CTNNB1, DICER1 and PIK3CA. In-depth computational analysis revealed that the mutations affected the structure of the proteins in terms of stability, solvent accessible surface area and molecular dynamics. Moreover, the mutations were present in functionally significant domains of the proteins, thereby adversely affecting the protein functionality. PPI network for SMARCA4, CTNNB1, DICER1, PIK3CA, PPP2R1A and ARID1A showed that these genes were involved in certain significant pathways affecting various hallmarks of cancer. For further validation, in vitro studies were performed that revealed hypermutability of the CTNNB1 gene. Through this study we have identified some key mutations and have analysed their structural and functional impact. The study establishes some key mutations, which can be potentially explored as biomarker and drug target.Communicated by Ramaswamy H. Sarma.

Apolipoprotein E (APOE) Haplotypes in Healthy Subjects from Worldwide Macroareas: A Population Genetics Perspective for Cardiovascular Disease, Neurodegeneration, and Dementia

Human APOE is a 299-amino acid long protein expressed and secreted in several tissues and body districts, where it exerts different functions mainly related to lipid metabolism, with specific activities around cholesterol transport and absorption/elimination. It has three main isoforms, determined by the pair of mutations rs7412-C/T and rs429358-C/T, which gives rise to the functionally different APOE variants ε2, ε3, and ε4. These have a distinct impact on lipid metabolism and are differentially implicated in Alzheimer’s disease and neurodegeneration, cardiovascular disease, and dyslipidemia. A plethora of other single nucleotide variants along the sequence of the APOE gene have been studied in cohorts of affected individuals, where they also modulate the influence of the three main isoforms to determine the risk of developing the disease. However, no contextual analysis of gene-long haplotypes has been carried out so far, and never extensively in cohorts of healthy individuals from different worldwide populations. Leveraging a rich population genomics dataset, this study elucidates the distribution of APOE variants and haplotypes that are shared across populations and to specific macroareas, revealing a variety of risk-allele associations that distinguish specific ancestral backgrounds and can be leveraged for specific ancestry-informed screenings in medicine and public health.

Computational studies reveal co-occurrence of two mutations in IL7R gene of high-grade serous carcinoma patients

Major cause of mortality in ovarian cancer can be attributed to a lack of specific and sensitive biomarkers for diagnosis and prognosis of the disease. Uncovering the mutations in genes involved in crucial oncogenic pathways is a key step in discovery and development of novel biomarkers. Whole exome sequencing (WES) is a powerful method for the detection of cancer driver mutations. The present work focuses on identifying functionally damaging mutations in patients with high-grade serous ovarian carcinoma (HGSC) through computational analysis of WES. In this study, WES data of HGSC patients was retrieved from the genomic literature available in sequence read archive, the variants were identified and comprehensive structural and functional analysis was performed. Interestingly, I66T and V138I mutations were found to be co-occurring in the IL7R gene in four out of five HGSC patient samples investigated in this study. The V138I mutation was located in the fibronectin type-3 domain and computationally assessed to be causing disruptive effects on the structure and dynamics of IL7R protein. This mutation was found to be co-occurring with the neutral I66T mutation in the same domain which compensated the disruptive effects of V138I variant. These comprehensive studies point to a hitherto unexplored significant role of the IL7R gene in ovarian carcinoma. It is envisaged that the work will lay the foundation for the development of a novel biomarker with potential application in molecular profiling and in estimation of the disease prognosis.Communicated by Ramaswamy H. Sarma.

Mouse Genome Informatics (MGI): latest news from MGD and GXD

The Mouse Genome Informatics (MGI) database system combines multiple expertly curated community data resources into a shared knowledge management ecosystem united by common metadata annotation standards. MGI's mission is to facilitate the use of the mouse as an experimental model for understanding the genetic and genomic basis of human health and disease. MGI is the authoritative source for mouse gene, allele, and strain nomenclature and is the primary source of mouse phenotype annotations, functional annotations, developmental gene expression information, and annotations of mouse models with human diseases. MGI maintains mouse anatomy and phenotype ontologies and contributes to the development of the Gene Ontology and Disease Ontology and uses these ontologies as standard terminologies for annotation. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are MGI's two major knowledgebases. Here, we highlight some of the recent changes and enhancements to MGD and GXD that have been implemented in response to changing needs of the biomedical research community and to improve the efficiency of expert curation. MGI can be accessed freely at http://www.informatics.jax.org .

Tumor Mutation Burden-Associated LINC00638/miR-4732-3p/ULBP1 Axis Promotes Immune Escape via PD-L1 in Hepatocellular Carcinoma

Tumor mutation burden (TMB) is associated with immune infiltration, while its underlying mechanism in hepatocellular carcinoma (HCC) remains unclear. A long noncoding RNA (lncRNA)-related competitive endogenous RNA (ceRNA) network can regulate various tumor behaviors, and research about its correlation with TMB and immune infiltration is warranted. Data were downloaded from TCGA and ArrayExpress databases. Cox analysis and machine learning algorithms were employed to establish a lncRNA-based prognostic model for HCC. We then developed a nomogram model to predict overall survival and odds of death for HCC patients. The association of this prognostic model with TMB and immune infiltration was also analyzed. In addition, a ceRNA network was constructed by using DIANA-LncBasev2 and the starBase database and verified by luciferase reporter and colocalization analysis. Multiplex immunofluorescence was applied to determine the correlation between ULBP1 and PD-L1. An eight-lncRNA (SLC25A30-AS1, HPN-AS1, LINC00607, USP2-AS1, HCG20, LINC00638, MKLN1-AS and LINC00652) prognostic score model was constructed for HCC, which was highly associated with TMB and immune infiltration. Next, we constructed a ceRNA network, LINC00638/miR-4732-3p/ULBP1, that may be responsible for NK cell infiltration in HCC with high TMB. However, patients with high ULBP1 possessed a poorer prognosis. Using multiplex immunofluorescence, we found a significant correlation between ULBP1 and PD-L1 in HCC, and patients with high ULBP1 and PD-L1 had the worst prognosis. In brief, the eight-lncRNA model is a reliable tool to predict the prognosis of HCC patients. The LINC00638/miR-4732-3p/ULBP1 axis may regulate immune escape via PD-L1 in HCC with high TMB.

Genetic variants in STAT4 and their interactions with environmental factors for the incidence of hepatocellular carcinoma

BACKGROUND

A recent genome-wide association study (GWAS) has posed STAT4 as a promising susceptibility gene for hepatocellular carcinoma (HCC). However, the most significant variant in this GWAS, rs7574865, yielded inconsistent results.

OBJECTIVE

This study, in a Southern Chinese population, was aimed to clarify the roles in HCC incidence of the rs7574865 and other two potentially functional variants, rs897200 and rs1031507 in STAT4.

METHODS

This study enrolled 631 new HCC cases and 631 cancer-free controls. The genetic association was estimated using the multivariate logistic regression model. The pairwise gene-environment interactions were assessed using the multiplicative term in regression model and the "Delta" method for the additive scale.

RESULTS

In the multivariate analysis, the rs7574865 TT genotype conferred a decreased risk of HCC compared to the GG genotype (adjusted OR = 0.62, 95%CI = 0.38∼0.99). The significant association of rs7574865 was also observed under the additive genetic model, with an adjusted OR of 0.81 (95%CI = 0.65∼0.99). Nevertheless, other two variants alone showed no significant association, as well as the haplotypes and genetic risk scores. Further analysis indicated a potential interaction between the rs897200 and alcohol drinking (P= 0.048 and 0.072 for additive and multiplicative interactions, respectively). Drinkers with the rs897200 CT+CC genotypes presented an increased disease-risk, as compared with non-drinkers carrying the TT genotype (adjusted OR = 1.68, 95%CI = 1.11∼2.54).

CONCLUSIONS

The variant in STAT4, rs7574865, serves as a potential marker for predicting incidence of HCC. The rs897200 variant possibly interplays with alcohol drinking to alter HCC risk in the Southern Chinese, but warrants further investigation.

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

BACKGROUND

Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq).

RESULTS

In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface ( https://unibind.uio.no/ ), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions.

CONCLUSIONS

UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

Potential treatment for chronic myeloid leukemia using microRNA: in silico comparison between plants and human microRNAs in targeting BCR-ABL1 gene

Background

Chronic myeloid leukemia (CML) is a myeloproliferative disorder characterized by the expression of the BCR-ABL1 fusion gene. Tyrosine kinase inhibitors (TKI) are used to treat CML, but mutations in the tyrosine kinase domain contribute to CML chemo-resistance. Therefore, finding alternative molecular-targeted therapy is important for the comprehensive treatment of CML. MicroRNAs (miRNA) are small non-coding regulatory RNAs which suppress the expression of their target genes by binding to the 3′ untranslated region (3′UTR) of the target mRNA. Hypothetically, the miRNA-mRNA interaction would suppress BCR-ABL1 expression and consequently reduce and inhibit CML cell proliferation. Thus, our objective was to determine the target interaction of human and plant miRNAs targeting the 3′UTR region of BCR-ABL1 in terms of miRNA binding conformity, protein interaction network, and pathways using in silico analysis. The 3′UTR sequence of BCR-ABL1 is obtained from Ensembl Genome Browser while the binding conformity was determined using the PsRNATarget Analysis Server, RNA22, Target Rank Server, and DIANA TOOLS. Protein-protein interaction network and pathway analysis are determined using STRING, Cytoscape, and KEGG pathway analysis.

Results

Five plants and five human miRNAs show strong binding conformity with 3′UTR of BCR-ABL1. The strongest binding conformity was shown by Oryza sativa’s Osa-miR1858a and osa-miR1858b with −24.4 kcal/mol folding energy and a p value of 0.0077. Meanwhile, in human miRNA, the hsa-miR-891a-3p shows the highest miTG score of 0.99 with −12 kcal/mol folding energy and a p value of 0.037. Apart from ABL1, osa-miR1858a/osa-miR1858b and hsa-miR891a-3p also target other 720 and 645 genes, respectively. The interaction network of Osa-miR1858a/osa-miR1858b and hsa-miR891a-3p identifies nineteen and twelve ABL1’s immediate neighboring proteins, respectively. The pathways analysis focuses on the RAS, MAPK, CML, and hematopoietic cell lineage pathway.

Conclusion

Both plant and human miRNAs tested in this study could be a potential therapeutic prospect in CML treatment, but thermodynamically, osa-miR1858a/osa-miR1858b binding to ABL1 is more favorable. However, it is important to carry out more research in vitro and in vivo and clinical studies to assess its efficacy as a targeted therapy for CML.

Graphical abstract

Chloroquine and Hydroxychloroquine Interact Differently with ACE2 Domains Reported to Bind with the Coronavirus Spike Protein: Mediation by ACE2 Polymorphism

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection inducing coronavirus disease 2019 (COVID-19) is still an ongoing challenge. To date, more than 95.4 million have been infected and more than two million deaths have been officially reported by the WHO. Angiotensin-converting enzyme (ACE) plays a key role in the disease pathogenesis. In this computational study, seventeen coding variants were found to be important for ACE2 binding with the coronavirus spike protein. The frequencies of these allele variants range from 3.88 × 10^-3 to 5.47 × 10^-6 for rs4646116 (K26R) and rs1238146879 (P426A), respectively. Chloroquine (CQ) and its metabolite hydroxychloroquine (HCQ) are mainly used to prevent and treat malaria and rheumatic diseases. They are also used in several countries to treat SARS-CoV-2 infection inducing COVID-19. Both CQ and HCQ were found to interact differently with the various ACE2 domains reported to bind with coronavirus spike protein. A molecular docking approach revealed that intermolecular interactions of both CQ and HCQ exhibited mediation by ACE2 polymorphism. Further explorations of the relationship and the interactions between ACE2 polymorphism and CQ/HCQ would certainly help to better understand the COVID-19 management strategies, particularly their use in the absence of specific vaccines or drugs.

Improving the Gene Ontology Resource to Facilitate More Informative Analysis and Interpretation of Alzheimer's Disease Data

The analysis and interpretation of high-throughput datasets relies on access to high-quality bioinformatics resources, as well as processing pipelines and analysis tools. Gene Ontology (GO, geneontology.org) is a major resource for gene enrichment analysis. The aim of this project, funded by the Alzheimer's Research United Kingdom (ARUK) foundation and led by the University College London (UCL) biocuration team, was to enhance the GO resource by developing new neurological GO terms, and use GO terms to annotate gene products associated with dementia. Specifically, proteins and protein complexes relevant to processes involving amyloid-beta and tau have been annotated and the resulting annotations are denoted in GO databases as 'ARUK-UCL'. Biological knowledge presented in the scientific literature was captured through the association of GO terms with dementia-relevant protein records; GO itself was revised, and new GO terms were added. This literature biocuration increased the number of Alzheimer's-relevant gene products that were being associated with neurological GO terms, such as 'amyloid-beta clearance' or 'learning or memory', as well as neuronal structures and their compartments. Of the total 2055 annotations that we contributed for the prioritised gene products, 526 have associated proteins and complexes with neurological GO terms. To ensure that these descriptive annotations could be provided for Alzheimer's-relevant gene products, over 70 new GO terms were created. Here, we describe how the improvements in ontology development and biocuration resulting from this initiative can benefit the scientific community and enhance the interpretation of dementia data.