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We present a genome assembly from an individual male Thyatira batis (the peach-blossom moth; Arthropoda; Insecta; Lepidoptera; Drepanidae). The genome sequence is 315 megabases in span. The majority of the assembly (99.68%) is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. The mitochondrial genome was also assembled and is 15.4 kilobases in length. Gene annotation of this assembly on Ensembl has identified 12,238 protein coding genes.

Genomicus is a database and web-server dedicated to comparative genomics in eukaryotes. Its main functionality is to graphically represent the conservation of genomic blocks between multiple genomes, locally around a specific gene of interest or genome-wide through karyotype comparisons. Since 2010 and its first release, Genomicus has synchronized with 60 Ensembl releases and seen the addition of functions that have expanded the type of analyses that users can perform. Today, five public instances of Genomicus are supporting a total number of 1029 extant genomes and 621 ancestral reconstructions from all eukaryotes kingdoms available in Ensembl and Ensembl Genomes databases complemented with four additional instances specific to taxonomic groups of interest. New visualization and query tools are described in this manuscript. Genomicus is freely available at http://www.genomicus.bio.ens.psl.eu/genomicus.

The European Bioinformatics Institute (EMBL-EBI) maintains a comprehensive range of freely available and up-to-date molecular data resources, which includes over 40 resources covering every major data type in the life sciences. This year's service update for EMBL-EBI includes new resources, PGS Catalog and AlphaFold DB, and updates on existing resources, including the COVID-19 Data Platform, trRosetta and RoseTTAfold models introduced in Pfam and InterPro, and the launch of Genome Integrations with Function and Sequence by UniProt and Ensembl. Furthermore, we highlight projects through which EMBL-EBI has contributed to the development of community-driven data standards and guidelines, including the Recommended Metadata for Biological Images (REMBI), and the BioModels Reproducibility Scorecard. Training is one of EMBL-EBI’s core missions and a key component of the provision of bioinformatics services to users: this year's update includes many of the improvements that have been developed to EMBL-EBI’s online training offering.

Clinical decision support systems (CDSS) that make use of algorithms based on intelligent systems, such as machine learning or deep learning, they suffer from the fact that often the methods used are hard to interpret and difficult to understand on how some decisions are made; the opacity of some methods, sometimes voluntary due to problems such as data privacy or the techniques used to protect intellectual property, makes these systems very complicated. Besides this series of problems, the results obtained also suffer from the poor possibility of being interpreted; in the clinical context therefore it is required that the methods used are as accurate as possible, transparent techniques and explainable results. In this work the problem of the development of cervical cancer is treated, a disease that mainly affects the female population. In order to introduce advanced machine learning techniques in a clinical decision support system that can be transparent and explainable, a robust, accurate ensemble method is presented, in terms of error and sensitivity linked to the classification of possible development of the aforementioned pathology and advanced techniques are also presented of explainability and interpretability (Explanaible Machine Learning) applied to the context of CDSS such as Lime and Shapley. The results obtained, as well as being interesting, are understandable and can be implemented in the treatment of this type of problem.

Bull fertility is a key factor for successful reproductive performance in dairy cattle. Since the semen from a single bull can be used to inseminate hundreds of cows, one subfertile bull could have a major impact on herd reproductive efficiency. We have previously identified five genomic regions, located on BTA8 (72.2 Mb), BTA9 (43.7 Mb), BTA13 (60.2 Mb), BTA17 (63.3 Mb), and BTA27 (34.7 Mb), that show large dominance effects on bull fertility. Each of these regions explained about 5-8% of the observed differences in sire conception rate between Holstein bulls. Here, we aimed to identify candidate causal variants responsible for this variation using targeted sequencing (10 Mb per region). For each genomic region, two DNA pools were constructed from n ≈ 20 high-fertility and n ≈ 20 low-fertility Holstein bulls. The DNA-sequencing analysis included reads quality control (using FastQC), genome alignment (using BWA and ARS-UCD1.2), variant calling (using GATK) and variant annotation (using Ensembl). The sequencing depth per pool varied from 39× to 51×. We identified a set of nonsense mutations, missense mutations, and frameshift variants carried by low-fertility bulls. Notably, some of these variants were classified as strong candidate causal variants, i.e., mutations with deleterious effects located on genes exclusively/highly expressed in testis. Genes affected by these candidate causal variants include AK9, TTLL9, TCHP, and FOXN4. These results could aid in the development of novel genomic tools that allow early detection and culling of subfertile bull calves.

The identification of disease-causal variants is non-trivial. By mapping population variation from over 448,000 exome and genome sequences to over 81,000 experimental structures and homology models of the human proteome, we have calculated both regional intolerance to missense variation (Missense Tolerance Ratio, MTR), using a sliding window of 21–41 codons, and introduce a new 3D spatial intolerance to missense variation score (3D Missense Tolerance Ratio, MTR3D), using spheres of 5–8 Å. We show that the MTR3D is less biased by regions with limited data and more accurately identifies regions under purifying selection than estimates relying on the sequence alone. Intolerant regions were highly enriched for both ClinVar pathogenic and COSMIC somatic missense variants (Mann–Whitney U test P < 2.2 × 10−16). Further, we combine sequence- and spatial-based scores to generate a consensus score, MTRX, which distinguishes pathogenic from benign variants more accurately than either score separately (AUC = 0.85). The MTR3D server enables easy visualisation of population variation, MTR, MTR3D and MTRX scores across the entire gene and protein structure for >17,000 human genes and >42,000 alternative alternate transcripts, including both Ensembl and RefSeq transcripts. MTR3D is freely available by user-friendly web-interface and API at http://biosig.unimelb.edu.au/mtr3d/., Graphical Abstract Graphical AbstractMTR3D.

The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.

Through a case study of Jad (pseudonym), a music learner with autism spectrum disorder (ASD), I sought to understand his experiences as he engaged in peer scaffolding activities of a choral ensemble. The study illuminated the role of intersubjectivity (or shared understanding) in socially mediated music learning within an environment of inclusion. Through inclusive, play-full, intersubjective attunement of younger children to Jad, he increasingly took on a role as an empathetic teacher-helper, initially with his younger sister, then with other young children, then with the entire ensemble. Jad also increasingly displayed musical agency through physical movement during music-making, contributing to others’ understanding and musical agency. The findings describe intersections of play with intersubjectivity, focusing on learner attunement to affect and emotion in fostering an inclusive music education experience. Making space for peer scaffolding and playfulness within this music learning environment fostered shared understanding and empathy among all learners, including one with ASD.

© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. The GENCODE project annotates human and mouse genes and transcripts supported by experimental data with high accuracy, providing a foundational resource that supports genome biology and clinical genomics. GENCODE annotation processes make use of primary data and bioinformatic tools and analysis generated both within the consortium and externally to support the creation of transcript structures and the determination of their function. Here, we present improvements to our annotation infrastructure, bioinformatics tools, and analysis, and the advances they support in the annotation of the human and mouse genomes including: the completion of first pass manual annotation for the mouse reference genome; targeted improvements to the annotation of genes associated with SARS-CoV-2 infection; collaborative projects to achieve convergence across reference annotation databases for the annotation of human and mouse protein-coding genes; and the first GENCODE manually supervised automated annotation of lncRNAs. Our annotation is accessible via Ensembl, the UCSC Genome Browser and https://www.gencodegenes.org. National Human Genome Research Institute of the National Institutes of Health [U41HG007234]; the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health; Wellcome Trust [WT108749/Z/15/Z, WT200990/Z/16/Z]; European Molecular Biology Laboratory; Swiss National Science Foundation through the National Center of Competence in Research ‘RNA & Disease’ (to R.J.); Medical Faculty of the University of Bern (to R.J). Funding for open access charge: National Institutes of Health.

The latest developments in precision medicine allow the modulation of therapeutic approaches in different pathologies on the basis of the specific molecular characterization of the patient. This review of the literature coupled with in silico analysis was to provide a selected screening of interactions between single-nucleotide polymorphisms (SNPs) and drugs (repurposed, investigational, and biological agents) showing efficacy and toxicityin counteracting Covid-19 infection. In silico analysis of genetic variants related to each drug was performed on such databases as PharmGKB, Ensembl Genome Browser, www.drugs.com, and SNPedia, with an extensive literature review of papers (to May 10, 2020) on Covid-19 treatments using Medline, Embase, International Pharmaceutical Abstracts, PharmGKB, and Google Scholar. The clinical relevance of SNPs, known as both drug targets and markers, considering genetic variations with known drug responses, and the therapeutic consequences are discussed. In the context of clinical treatment of Covid-19, including infection prevention, control measures, and supportive care, this review highlights the importance of a personalized approach in the final selection of therapy, which is probably essential in the management of the Covid-19 pandemic.

Better understanding of molecular mechanisms for kidney stone formation is required to improve management of kidney stone disease with better therapeutic outcome. Recent kidney stone research has indicated critical roles of a group of proteins, namely ‘stone modulators’, in promotion or inhibition of the stone formation. Nevertheless, such information is currently dispersed and difficult to obtain. Herein, we present the kidney stone modulator database (StoneMod), which is a curated resource by obtaining necessary information of such stone modulatory proteins, which can act as stone promoters or inhibitors, with experimental evidence from previously published studies. Currently, the StoneMod database contains 10, 16, 13, 8 modulatory proteins that affect calcium oxalate crystallization, crystal growth, crystal aggregation, and crystal adhesion on renal tubular cells, respectively. Informative details of each modulatory protein and PubMed links to the published articles are provided. Additionally, hyperlinks to other protein/gene databases (e.g., UniProtKB, Swiss-Prot, Human Protein Atlas, PeptideAtlas, and Ensembl) are made available for the users to obtain additional in-depth information of each protein. Moreover, this database provides a user-friendly web interface, in which the users can freely access to the information and/or submit their data to deposit or update. Database URL:https://www.stonemod.org.

LBDs, as the plant-specific gene family, play essential roles in lateral organ development, plant regeneration, as well as abiotic stress and pathogen response. However, the number and characteristic of LBD genes in Pyscomitrella patens were still obscure. This study was performed to identify the LBD family gene in moss and to determine the expression profiles of LBDs under the abiotic and pathogen stress. Complete genome sequences and transcriptomes of P. patens were downloaded from the Ensembl plant database. The hidden Markov model-based profile of the conserved LOB domain was submitted as a query to identify all potential LOB domain sequences with HMMER software. Expression profiles of PpLBDs were obtained based on the GEO public database and qRT-PCR analysis. In this study, a total of 31 LBDs were identified in the P. patens genome, divided into two classes based on the presence of the leucine zipper-like coiled-coil motif. A phylogenetic relationship was obtained between 31 proteins from P. patens and 43 proteins from the Arabidopsis thaliana genome, providing insights into their conserved and potential functions. Furthermore, the exon–intron organization of each PpLBD were analyzed. All PpLBD contain the conserved DNA binding motif (CX2CX6CX3C zinc finger-like motif), and were predicted to be located in cell nuclear. The 31 PpLBD genes were unevenly assigned to 18 out of 27 chromosomes based on the physical positions. Among these genes, PpLBD27 was not only remarkably highest expressed in desiccation, but also a susceptible gene to pathogens through jasmonic acid-mediated signaling pathway. Most of PpLBDs were up-regulated with the treatment of mannitol. These results showed they were differentially induced and their potential functions in the environmental stimulus of the early terrestrial colonizers. Despite significant differences in the life cycle in P. patens and flowering plants, their functions involved in abiotic and biotic stress-regulated by LBDs have been identified and appear to be conserved in the two lineages. These results provided a comprehensive analysis of PpLBDs and paved insights into studies aimed at a better understanding of PpLBDs.

Diabetic retinopathy (DR) is a condition that develops after long-lasting and poorly handled diabetes and is presently the main reason for blindness among elderly and youth. Peroxisome proliferator-activated receptors (PPARs) are nuclear receptors that are involved in carbohydrate and fatty-acid metabolism and have also been associated with DR. Three PPAR isoforms are known: PPARG, PPARA, and PPARD. In the present study, we retrieved articles reporting associations between PPARs and DR from PubMed database and compiled the data in two catalogues, for human and animal models. Extracted data was then complemented with additional relevant genomic information. Seven retrieved articles reported testing an association between PPARs with DR in human. Four of them concluded association of PPARG and PPARA with DR in European and Asian populations, having a protective role on DR development. One study reported pathogenic role of PPARG, while two articles reported no association between PPARG and DR among Indian and Chinese populations. Six retrieved articles reported testing of involvement of PPARG and PPARA in DR in animal models, including mouse and rat. The review includes case-control studies, meta-analysis, expression studies, animal models, and cell line studies. Despite a large number of documented sequence variants of the PPAR genes available in genome browsers, researchers usually focus on a small set of previously reported variants. Data extraction from Ensembl genome browser revealed several sequence variants with predicted deleterious effect on protein function which present candidates for further experimental validation. Results of the present analysis will enable more holistic approach for understanding of PPARs in DR development. Additionally, developed catalogues present a baseline for standardized reporting of PPAR-phenotype association in upcoming studies.

We are entering the era of personalized medicine in which an individual's genetic makeup will eventually determine how a doctor can tailor his or her therapy. Therefore, it is becoming critical to understand the genetic basis of common diseases, for example, which genes predispose and rare genetic variants contribute to diseases, and so on. Our study focuses on helping researchers, medical practitioners, and pharmacists in having a broad view of genetic variants that may be implicated in the likelihood of developing certain diseases. Our focus here is to create a comprehensive database with mobile access to all available, authentic and actionable genes, SNPs, and classified diseases and drugs collected from different clinical and genomics databases worldwide, including Ensembl, GenCode, ClinVar, GeneCards, DISEASES, HGMD, OMIM, GTR, CNVD, Novoseek, Swiss‐Prot, LncRNADisease, Orphanet, GWAS Catalog, SwissVar, COSMIC, WHO, and FDA. We present a new cutting‐edge gene‐SNP‐disease‐drug mobile database with a smart phone application, integrating information about classified diseases and related genes, germline and somatic mutations, and drugs. Its database includes over 59 000 protein‐coding and noncoding genes; over 67 000 germline SNPs and over a million somatic mutations reported for over 19 000 protein‐coding genes located in over 1000 regions, published with over 3000 articles in over 415 journals available at the PUBMED; over 80 000 ICDs; over 123 000 NDCs; and over 100 000 classified gene‐SNP‐disease associations. We present an application that can provide new insights into the information about genetic basis of human complex diseases and contribute to assimilating genomic with phenotypic data for the availability of gene‐based designer drugs, precise targeting of molecular fingerprints for tumor, appropriate drug therapy, predicting individual susceptibility to disease, diagnosis, and treatment of rare illnesses are all a few of the many transformations expected in the decade to come.

A myriad of predicted proteins have been described at the genome scale, but their existence has not been confirmed at the protein level. These proteins that are predicted to be expressed from an open-reading frame (ORF) but for which translation has not been demonstrated are known as hypothetical proteins and constitute major fraction of the human proteome. In this study, we aim to identify and characterize hypothetical proteins from human tumor cell lines, viz., HeLa, MCF7, and BT474, thus providing the analytical basis for their expression. We used gel electrophoresis followed by in-gel digestion of the selected protein lanes and subsequent LC–MS/MS analysis of protein tryptic digests. ENSEMBL genome browser was used for genomic alignment. On search against human hypothetical protein data from NCBI database, 110 common proteins were identified across the three selected cells lines. Out of these, 88 proteins were already functionally characterized and remaining 22 were still found to be unreviewed in UniProt, lacking the evidence of expression at the protein level. To explore them further, following HPP guidelines, 15 proteins were selected and aligned against human reference genome. Five hypothetical proteins were confirmed as isoforms of known proteins. We conclude that the proteomic approach used would serve as a suitable tool to validate the existence of predicted or hypothetical proteins at the protein level. The MS proteomics data have been deposited to the ProteomeXchange Consortium via PRIDE with the data set identifiers PXD014258.

Differences in individual drug responses are obstacles in breast cancer (BRCA) treatment, so predicting responses would help to plan treatment strategies. The accumulation of cancer molecular profiling and drug response data provide opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs in BRCA. This study evaluated drug responses with a multi-omics integrated system that depended on long non-coding RNAs (lncRNAs). We identified drug response-related lncRNAs (DRlncs) by combining expression data of lncRNA, microRNA, messenger RNA, methylation levels, somatic mutations, and the survival data of cancer patients treated with drugs. We constructed an integrated and computational multi-omics approach to identify DRlncs for diverse chemotherapeutic drugs in BRCA. Some DRlncs were identified with Adriamycin, Cytoxan, Tamoxifen, and all samples for BRCA patients. These DRlncs showed specific features regarding both expression and computational accuracies. The DRlnc-gene co-expression networks were constructed and analyzed. Key DRlncs, such as HOXA-AS2 (Ensembl: ENSG00000253552), in the drug Adriamycin were characterized. The experimental analysis also suggested that HOXA-AS2 (Ensembl: ENSG00000253552) was a key DRlnc in Adriamycin drug resistance in BRCA patients. Some DRlncs were associated with survival and some specific functions. A possible mechanism of DRlnc HOXA-AS2 (Ensembl: ENSG00000253552) in the Adriamycin drug response for BRCA resistance was inferred. In summary, this study provides a framework for lncRNA-based evaluation of clinical drug responses in BRCA. Understanding the underlying molecular mechanisms of drug responses will facilitate improved responses to chemotherapy and outcomes of BRCA treatment.

Background RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. Results In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEQC consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods. Conclusion In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

Motivation Tracking disease outbreaks by whole-genome sequencing leads to the collection of large samples of closely related sequences. Five years ago, we published a method to accurately compute all pairwise distances for such samples by indexing each sequence. Since indexing is slow, we now ask whether it is possible to achieve similar accuracy when indexing only a single sequence. Results We have implemented this idea in the program phylonium and show that it is as accurate as its predecessor and roughly 100 times faster when applied to all 2678 Escherichia coli genomes contained in ENSEMBL. One of the best published programs for rapidly computing pairwise distances, mash, analyzes the same dataset four times faster but, with default settings, it is less accurate than phylonium. Availability and implementation Phylonium runs under the UNIX command line; its C++ sources and documentation are available from github.com/evolbioinf/phylonium. Supplementary information Supplementary data are available at Bioinformatics online.

Thanks to huge advances in sequencing technologies, genomic resources are increasingly being generated and shared by the scientific community. The quality of such public resources are therefore of critical importance. Errors due to contamination are particularly worrying; they are widespread, propagate across databases, and can compromise downstream analyses, especially the detection of horizontally-transferred sequences. However we still lack consistent and comprehensive assessments of contamination prevalence in public genomic data. Here we applied a standardized procedure for foreign sequence annotation to 43 published arthropod genomes from the widely used Ensembl Metazoa database. This method combines information on sequence similarity and synteny to identify contaminant and putative horizontally-transferred sequences in any genome assembly, provided that an adequate reference database is available. We uncovered considerable heterogeneity in quality among arthropod assemblies, some being devoid of contaminant sequences, whereas others included hundreds of contaminant genes. Contaminants far outnumbered horizontally-transferred genes and were a major confounder of their detection, quantification and analysis. We strongly recommend that automated standardized decontamination procedures be systematically embedded into the submission process to genomic databases.

The 2020 Nucleic Acids Research Database Issue contains 148 papers spanning molecular biology. They include 59 papers reporting on new databases and 79 covering recent changes to resources previously published in the issue. A further ten papers are updates on databases most recently published elsewhere. This issue contains three breakthrough articles: AntiBodies Chemically Defined (ABCD) curates antibody sequences and their cognate antigens; SCOP returns with a new schema and breaks away from a purely hierarchical structure; while the new Alliance of Genome Resources brings together a number of Model Organism databases to pool knowledge and tools. Major returning nucleic acid databases include miRDB and miRTarBase. Databases for protein sequence analysis include CDD, DisProt and ELM, alongside no fewer than four newcomers covering proteins involved in liquid–liquid phase separation. In metabolism and signaling, Pathway Commons, Reactome and Metabolights all contribute papers. PATRIC and MicroScope update in microbial genomes while human and model organism genomics resources include Ensembl, Ensembl genomes and UCSC Genome Browser. Immune-related proteins are covered by updates from IPD-IMGT/HLA and AFND, as well as newcomers VDJbase and OGRDB. Drug design is catered for by updates from the IUPHAR/BPS Guide to Pharmacology and the Therapeutic Target Database. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been revised, updating 305 entries, adding 65 new resources and eliminating 125 discontinued URLs; so bringing the current total to 1637 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

We present a genome assembly from an individual female Vanessa cardui (the painted lady; Arthropoda; Insecta; Lepidoptera; Nymphalidae). The genome sequence is 425 megabases in span. The majority of the assembly is scaffolded into 32 chromosomal pseudomolecules, with the W and Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 12,821 protein coding genes., Species taxonomy Background Genome sequence report Gene annotation Methods Sample acquisition and nucleic acid extraction Sequencing Genome assembly Ethical/compliance issues Data availability Author information References

Endothelial PAS domain-containing protein 1 (EPAS1), also HIF2α, is an alpha subunit of hypoxia-inducible transcription factor (HIF), which mediates cellular and systemic response to hypoxia. EPAS1 has an important role in the transcription of many hypoxia-responsive genes, however, it has been less researched than HIF1α. The aim of this study was to integrate an increasing number of data on EPAS1 into a map of diverse OMICs elements. Publications, databases, and bioinformatics tools were examined, including Ensembl, MethPrimer, STRING, miRTarBase, COSMIC, and LOVD. The EPAS1 expression, stability, and activity are tightly regulated on several OMICs levels to maintain complex oxygen homeostasis. In the integrative EPAS1 map we included: 31 promoter-binding proteins, 13 interacting miRNAs and one lncRNA, and 16 post-translational modifications regulating EPAS1 protein abundance. EPAS1 has been associated with various cancer types and other diseases. The development of neuroendocrine tumors and erythrocytosis was shown to be associated with 11 somatic and 20 germline variants. The integrative map also includes 12 EPAS1 target genes and 27 interacting proteins. The study introduced the first integrative map of diverse genomics, transcriptomics, proteomics, regulomics, and interactomics data associated with EPAS1, to enable a better understanding of EPAS1 activity and regulation and support future research.

We present a genome assembly from an individual male Lycaena phlaeas (the small copper; Arthropoda; Insecta; Lepidoptera; Lycaenidae). The genome sequence is 420 megabases in span. The whole of the assembly is scaffolded into 24 chromosomal pseudomolecules, with the Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 12,147 protein coding genes.

The Ensembl Variant Effect Predictor (VEP) is a freely available, open source tool for the annotation and filtering of genomic variants. It predicts variant molecular consequence using the Ensembl/GENCODE or RefSeq gene sets. It also reports phenotype associations from databases such as ClinVar, allele frequencies from studies including gnomAD, and predictions of deleteriousness from tools such as Sorting Intolerant From Tolerant (SIFT) and Combined Annotation Dependent Depletion (CADD). Ensembl VEP includes filtering options to customise variant prioritisation. It is well supported and updated roughly quarterly to incorporate the latest gene, variant and phenotype association information. Ensembl VEP analysis can be performed using a highly configurable, extensible command-line tool, a Representational State Transfer (REST) application programming interface (API) and a user-friendly web interface. These access methods are designed to suit different levels of bioinformatics experience and meet different needs in terms of data size, visualisation and flexibility. In this tutorial, we will describe performing variant annotation using the Ensembl VEP web tool, which enables sophisticated analysis through a simple interface. This article is protected by copyright. All rights reserved.

We present a genome assembly from an individual male Melitaea cinxia (the Glanville fritillary; Arthropoda; Insecta; Lepidoptera; Nymphalidae). The genome sequence is 499 megabases in span. The complete assembly is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. Gene annotation of this assembly on Ensembl has identified 13,666 protein coding genes.

Hypoxia-inducible factor (HIF) family of transcription factors (HIF1A, EPAS1, and HIF3A) are regulators of the cellular response to hypoxia. They have been shown to be involved in development of various diseases such as cancer, diabetes, and erythrocytosis. A complete map of connections between HIF family of genes with various omics types has not yet been developed. The main aim of the present analysis was to construct the integrative map of genomic elements associated with HIF1A gene and prioritize potentially deleterious variants. Various genomic databases and bioinformatics tools were used, including Ensembl, MirTarBase, STRING, Cytoscape, MethPrimer, CADD, SIFT, and UALCAN. Integrative HIF1A gene map was visualized and includes transcriptional and post-transcriptional regulators, downstream targets, and genetic variants. One CpG island overlaps transcription start site of the HIF1A gene. Out of over 450 missense variants, four have predicted deleterious effect on protein function by at least five bioinformatics tools. Currently there are 85 miRNAs reported to target HIF1A. HIF1A downstream targets include protein-coding genes, long noncoding RNAs, and microRNAs (hypoxamiRs). The study presents the first integration of heterogeneous molecular interactions associated with HIF1A gene enabling a holistic view of the gene and lays the groundwork for supplementing the data in the future.

The study seeks to compare the transcriptomic response of pancreatic and breast cancer cells to Anarcadic Acid and Olaparib via the preparation of Pancreatic Cancer Cell Culture which involves the seeding of PANC-1 cells in 6-well plates (5× 105 cells per well). 24hours later, cells will be untreated or treated by 5mM anacardic acid, 2mM olaparib or a combination of anacardic acid (5mM) and olaparib (2mM) for 48hours; after which Pancreatic Cancer Cell’s mRNA Library will be Prepared and Sequenced using the Illumina TruSeq™ RNA Sample Prep Kit v2. Samples will be sequenced on the Illumina HiSeq 2500, 2× 100bp paired-end reads, to a minimum depth of 30 million reads per sample. Thereafter, the Computational Analyses of Pancreatic Cancer RNA-seq Data will be done by obtaining a total of 240 million obtained reads of high quality clean tags which will then be mapped and annotated via human reference genome using Bioconductor package biomaRt (http://www.bioconductor.org) (Durinck et al 2009). Mapped reads with mapping quality 10 or more will be defined as uniquely mapped reads and used in the downstream analyses. Biological networks and pathways related to anachardic acid, olaparib and the combination will be analyzed with Ingenuity Pathway Analysis (IPA) software (Qiagen, CA, USA). The lists of all genes identified in gene expression analysis will be uploaded into the IPA software. For the analysis of networks and pathways, the cutoff values will be set at P≤ 1× 10−5 and FC≥ |2| respectively.Validation of RNA-seq Results by qRT-PCR via the expression of mRNA which will be determined in all 4 samples using Power SYBR® Green RNA-to-CT™ 1-Step Kit (Life Technologies, CA, USA). The Western blotting for the selected proteins will be performed, as described by Yue (Yue et al 2015). Thereafter, the Breast Cancer Cell Culture will be prepared and treated. Breast Cancer Cell’s mRNA RNA-seq will be prepared. The Truseq Stranded mRNA kit (Illumina) will be used to prepare mRNA libraries from 1 µg total RNA. Libraries will be confirmed on the Agilent 2100 Bioanalyzer and quantitated using the Illumina Library Quantification Kit, ABI Prism qPCR Mix from Kapa Biosystems and the ABI7900HT real-time PCR instrument. The differential Gene Expression will be analysed RNA-seq reads will be assembled according to the hg19.gtf annotation file (downloaded from ENSEMBL) (Flicek et al 2014) using Cufflinks (version 2.2.1) (Trapnell et al 2012). For each comparison, both cufflinks assemblies shall be merged, and the resulting merged gtf file serves as the transcript input for differential gene expression analysis in Gene Ontology and KEGG pathways. For three of the comparisons, a p-value cutoff ≤0.05 shall be used to determine differential expression. In-silico pathway and network analysis of differentially expressed genes shall be performed in MetaCore version 6.27 (GeneGO, Thomson Reuters, New York, N.Y.) (Bolser et al 2012). The results obtained will be statistically analysed. The results of RT-PCR shall be normalized to expression of GAPDH using the formula 2∆ CT. One-way ANOVA shall be used for comparing treatment with the combination of anacardic acid and olaparib to the untreated control. A P value less than 0.05 will be considered statistically significant.

Model organisms are essential to understanding the biological and disease consequences of human genome variation. Bioinformatics resources that support meaningful comparisons of mouse and human genotype-to-phenotype data and knowledge are needed to support the translation from bench to bedside and back again [1]. There is no genome variation resource for mouse comparable to resources available for human genome variation data such as EXAC [2], ClinVar [3], or ClinGen [4]. NCBI resources such as dbSNP and ClinVar no longer accept data from model organisms. While the European Variation Archive (EVA) serves a repository of SNP data for mouse, however, the resource does not accept imputed variation data or curated phenotype annotations associated with variation data that are central to data interpretation and analysis. Although the Mouse Genome Informatics database (MGI) [5] serves as a comprehensive mouse allele registry and curates information about the association of mouse variants with phenotypes and disease, the variation data in MGI are not currently available in format consistent with the Human Genome Variation Society (HGVS) standards [6]. The Mouse Variation Registry (MVAR) will represent the integration of all mouse genome variation data and includes processes to automatically canonicalize variants so that they are uniquely represented in the database with comprehensive annotation and their distribution across strains. The starting dataset used as input into MVAR was downloaded in VCF format [7] (as a 42GB gzipped file) from the Mouse Genomes Project [8] and contains about 81M Single-Nucleotide Variants (SNV), ~9M Deletions and ~8M Insertions. Other data will be obtained from MGI, the Mouse Mutant Repository Database (MMRDB), the Diversity Outbred Database (DODB), and from computationally imputed SNP data. The MVAR data ingest workflow has been developed to normalize, prepare and annotate input variation data. With the help of the GATK framework [9], the first step of the pipeline consists of normalizing i.e., left aligning each variant, and decomposing the multi-allelic variants (where there is more than one variation in a row of data). The next step in the pipeline is made with the use of the Ensembl Variant Effect Predictor (VEP) [10], which annotates the variation data with its corresponding HGVS nomenclature and existing external Id. The final step uses the Jannovar library [11] to enrich the data with Functional Consequence annotations. After the data has been pre-processed through the pipeline, they are inserted into a MySQL database with the help of custom tools developed to create the canonical variants representations. MVAR supports programmatic data access to the registry through an API for interoperability. This API is used by a user-friendly web-application with rich user interfaces to query the database and display results. The API is also available to be a resource for other services or applications over HTTP with JSON data payloads. Wide-used industry frameworks like Angular and Groovy Grails were leveraged to build the MVAR web application. To conclude, the lack of a comprehensive, annotated genome variation resource for mouse is a significant barrier to comparing variation and its biological consequences between mouse and human and limits the impact of many research and resource development programs. The MVAR project seeks to address this resource gap by bringing together investigators that have active projects in the area of genome variation in either mouse or human or both. Many of the investigators on this project have developed independent resources to curate or manage genome variation. This project aims to unify these efforts and build a common data resource. Future work will include the incorporation of structural variants into the MVAR registry.

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

SARS-CoV-2 infectivity is largely determined by the virus Spike protein binding to the ACE2 receptor. Meanwhile, marked infection rate differences were reported between populations and individuals. To understand the disease dynamic, we developed a computational approach to study the implications of both SARS-CoV-2 RBD mutations and ACE2 polymorphism on the stability of the virus-receptor complex. We used the 6LZG PDB RBD/ACE2 3D model, the mCSM platform, the LigPlot+ and PyMol software to analyze the data on SARS-CoV-2 mutations and ACE variants retrieved from GISAID and Ensembl/GnomAD repository. We observed that out of 351 RBD point mutations, 83% destabilizes the complex according to free energy (ΔΔG) differences. We also spotted variations in the patterns of polar and hydrophobic interactions between the mutations occurring in 15 out of 18 contact residues. Similarly, comparison of the effect on the complex stability of different ACE2 variants showed that the pattern of molecular interactions and the complex stability varies also according to ACE2 polymorphism. We infer that it is important to consider both ACE2 variants and circulating SARS-CoV-2 RBD mutations to assess the stability of the virus-receptor association and evaluate infectivity. This approach might offers a good molecular ground to mitigate the virus spreading.

Background Variant interpretation is dependent on transcript annotation and remains time consuming and challenging. There are major obstacles for historical data reuse and for interpretation of new variants. First, both RefSeq and Ensembl/GENCODE produce transcript sets in common use, but there is currently no easy way to translate between the two. Second, the resources often used for variant interpretation (e.g. ClinVar, gnomAD, UniProt) do not use the same transcript set, nor default transcript or protein sequence. Method Ensembl ran a survey in 2018 to sample attitudes to choosing one default transcript per locus, and to gather data on reference sequences used by the scientific community. This was publicised on the Ensembl and UCSC genome browsers, by email and on social media. Results The survey had 788 responses from 32 different countries, the results of which we report here. Conclusions We present our roadmap to create an effective default set of transcripts for resources, and for reporting interpretation of clinical variants., After decades of avoiding the demand to highlight one transcript per locus in Ensembl, we ran a survey 2018 to assay opinions across the scientific community. Ignoring the problem of ‘one transcript’ was not making the issue go away; many important genomic resources had instead adopted their own methods of selecting one transcript (e.g. HGMD, Ensembl, gnomAD, UniProt, ClinVar, etc.). Here we report our results and roadmap to create an effective default set of transcripts for resources, and for reporting interpretation of clinical variants.

OBJECTIVES The symptoms associated with microtia are ever-changing and not to stick to 1 pattern. The symptoms associated with microtia are constantly changing and are not set in stone. The aim of this article was to describe the various phenotypes from multiple systems found in microtitis patients included in the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources database, and to analyze possible pathogenic mutations. METHODS DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources is an interactive web-based database, which incorporates a suite of tools designed to aid the interpretation of genomic variants. The term "microtia" was used as the search term, and the data extracted from the DatabasE of genomiC varIation and Phenotype in Humans using Ensembl Resources for this study was updated until October 2020. Pearson chi-squared test was used to test associations between types of genomic variants and the pathogenicity of variants. RESULTS Of the 386 cases enrolled in the study, 99% (n = 382) had 1 or more associated abnormalities. The most frequently detected abnormalities were those of the face and neck (n = 362 [93.8% of all cases]); musculoskeletal system (n = 337 [87.3%]); and nervous system (n = 334 [86.5%]), followed by abnormalities of limbs (n = 252 [65.3%]); the eye (n = 212 [54.9%]); and the integument (n = 200 [51.8%]). Besides, a total of 479 genomic variants were determined, including sequence variants and copy number variants (loss and gain). The pathogenicity of loss-type variants was significantly higher among other types (P

PurposeThe widespread adoption of genomic testing for individuals with ophthalmic disorders has increased demand on diagnostic genomic services for these conditions. Moreover, the clinical utility of a molecular diagnosis for individuals with inherited ophthalmic disorders is increasingly placing pressure on the speed and accuracy of genomic testing.MethodsWe created EyeG2P, a publically available resource to assist diagnostic filtering of genomic datasets for ophthalmic conditions, utilising the Ensembl Variant Effect Predictor. We assessed the sensitivity of EyeG2P for 1234 individuals with a broad range of conditions, who had previously received a confirmed molecular diagnosis through routine genomic diagnostic approaches. For a prospective cohort of 83 individuals, we also assessed the precision of EyeG2P in comparision to routine genomic diagnostic approaches.ResultsWe observed that EyeG2P had a 99.5% sensitivity for genomic variants previously identified as a molecular diagnosis for 1234 individuals. EyeG2P enabled a significant increase in precision in comparison to routine testing strategies (pConclusionAutomated filtering of genomic variants through EyeG2P can increase the efficiency of diagnostic testing for individuals with a broad range of inherited ophthalmic disorders.