JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.

JASPAR (https://jaspar.elixir.no/) is a widely-used open-access database presenting manually curated high-quality and non-redundant DNA-binding profiles for transcription factors (TFs) across taxa. In this 10th release and 20th-anniversary update, the CORE collection has expanded with 329 new profiles. We updated three existing profiles and provided orthogonal support for 72 profiles from the previous release's UNVALIDATED collection. Altogether, the JASPAR 2024 update provides a 20% increase in CORE profiles from the previous release. A trimming algorithm enhanced profiles by removing low information content flanking base pairs, which were likely uninformative (within the capacity of the PFM models) for TFBS predictions and modelling TF-DNA interactions. This release includes enhanced metadata, featuring a refined classification for plant TFs' structural DNA-binding domains. The new JASPAR collections prompt updates to the genomic tracks of predicted TF binding sites (TFBSs) in 8 organisms, with human and mouse tracks available as native tracks in the UCSC Genome browser. All data are available through the JASPAR web interface and programmatically through its API and the updated Bioconductor and pyJASPAR packages. Finally, a new TFBS extraction tool enables users to retrieve predicted JASPAR TFBSs intersecting their genomic regions of interest.

OnTarget: in silico design of MiniPromoters for targeted delivery of expression.

MiniPromoters, or compact promoters, are short DNA sequences that can drive expression in specific cells and tissues. While broadly useful, they are of high relevance to gene therapy due to their role in enabling precise control of where a therapeutic gene will be expressed. Here, we present OnTarget (http://ontarget.cmmt.ubc.ca), a webserver that streamlines the MiniPromoter design process. Users only need to specify a gene of interest or custom genomic coordinates on which to focus the identification of promoters and enhancers, and can also provide relevant cell-type-specific genomic evidence (e.g. accessible chromatin regions, histone modifications, etc.). OnTarget combines the provided data with internal data to identify candidate promoters and enhancers and design MiniPromoters. To illustrate the utility of OnTarget, we designed and characterized two MiniPromoters targeting different cell populations relevant to Parkinson Disease.

SBILib: a handle for protein modeling and engineering.

SUMMARY: The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. AVAILABILITY AND IMPLEMENTATION: SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.

JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net/) is an open-access database containing manually curated, non-redundant transcription factor (TF) binding profiles for TFs across six taxonomic groups. In this 9th release, we expanded the CORE collection with 341 new profiles (148 for plants, 101 for vertebrates, 85 for urochordates, and 7 for insects), which corresponds to a 19% expansion over the previous release. We added 298 new profiles to the Unvalidated collection when no orthogonal evidence was found in the literature. All the profiles were clustered to provide familial binding profiles for each taxonomic group. Moreover, we revised the structural classification of DNA binding domains to consider plant-specific TFs. This release introduces word clouds to represent the scientific knowledge associated with each TF. We updated the genome tracks of TFBSs predicted with JASPAR profiles in eight organisms; the human and mouse TFBS predictions can be visualized as native tracks in the UCSC Genome Browser. Finally, we provide a new tool to perform JASPAR TFBS enrichment analysis in user-provided genomic regions. All the data is accessible through the JASPAR website, its associated RESTful API, the R/Bioconductor data package, and a new Python package, pyJASPAR, that facilitates serverless access to the data.

Linkage analysis identifies an isolated strabismus locus at 14q12 overlapping with FOXG1 syndrome region.

Strabismus is a common condition, affecting 1%-4% of individuals. Isolated strabismus has been studied in families with Mendelian inheritance patterns. Despite the identification of multiple loci via linkage analyses, no specific genes have been identified from these studies. The current study is based on a seven-generation family with isolated strabismus inherited in an autosomal dominant manner. A total of 13 individuals from a common ancestor have been included for linkage analysis. Among these, nine are affected and four are unaffected. A single linkage signal has been identified at an 8.5 Mb region of chromosome 14q12 with a multipoint LOD (logarithm of the odds) score of 4.69. Disruption of this locus is known to cause FOXG1 syndrome (or congenital Rett syndrome; OMIM #613454 and *164874), in which 84% of affected individuals present with strabismus. With the incorporation of next-generation sequencing and in-depth bioinformatic analyses, a 4 bp non-coding deletion was prioritised as the top candidate for the observed strabismus phenotype. The deletion is predicted to disrupt regulation of FOXG1, which encodes a transcription factor of the Forkhead family. Suggestive of an autoregulation effect, the disrupted sequence matches the consensus FOXG1 and Forkhead family transcription factor binding site and has been observed in previous ChIP-seq studies to be bound by Foxg1 in early mouse brain development. Future study of this specific deletion may shed light on the regulation of FOXG1 expression and may enhance our understanding of the mechanisms contributing to strabismus and FOXG1 syndrome.

JASPAR 2020: update of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions.

BACKGROUND: Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS: Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS: While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

GeneBreaker: Variant simulation to improve the diagnosis of Mendelian rare genetic diseases.

Mendelian rare genetic diseases affect 5%-10% of the population, and with over 5300 genes responsible for ∼7000 different diseases, they are challenging to diagnose. The use of whole-genome sequencing (WGS) has bolstered the diagnosis rate significantly. The effective use of WGS relies on the ability to identify the disrupted gene responsible for disease phenotypes. This process involves genomic variant calling and prioritization, and is the beneficiary of improvements to sequencing technology, variant calling approaches, and increased capacity to prioritize genomic variants with potential pathogenicity. As analysis pipelines continue to improve, careful testing of their efficacy is paramount. However, real-life cases typically emerge anecdotally, and utilization of clinically sensitive and identifiable data for testing pipeline improvements is regulated and limiting. We identified the need for a gene-based variant simulation framework that can create mock rare disease scenarios, utilizing known pathogenic variants or through the creation of novel gene-disrupting variants. To fill this need, we present GeneBreaker, a tool that creates synthetic rare disease cases with utility for benchmarking variant calling approaches, testing the efficacy of variant prioritization, and as an educational mechanism for training diagnostic practitioners in the expanding field of genomic medicine. GeneBreaker is freely available at http://GeneBreaker.cmmt.ubc.ca.

Human MiniPromoters for ocular-rAAV expression in ON bipolar, cone, corneal, endothelial, Müller glial, and PAX6 cells.

Small and cell-type restricted promoters are important tools for basic and preclinical research, and clinical delivery of gene therapies. In clinical gene therapy, ophthalmic trials have been leading the field, with over 50% of ocular clinical trials using promoters that restrict expression based on cell type. Here, 19 human DNA MiniPromoters were bioinformatically designed for rAAV, tested by neonatal intravenous delivery in mouse, and successful MiniPromoters went on to be tested by intravitreal, subretinal, intrastromal, and/or intravenous delivery in adult mouse. We present promoter development as an overview for each cell type, but only show results in detail for the recommended MiniPromoters: Ple265 and Ple341 (PCP2) ON bipolar, Ple349 (PDE6H) cone, Ple253 (PITX3) corneal stroma, Ple32 (CLDN5) endothelial cells of the blood-retina barrier, Ple316 (NR2E1) Müller glia, and Ple331 (PAX6) PAX6 positive. Overall, we present a resource of new, redesigned, and improved MiniPromoters for ocular gene therapy that range in size from 784 to 2484 bp, and from weaker, equal, or stronger in strength relative to the ubiquitous control promoter smCBA. All MiniPromoters will be useful for therapies involving small regulatory RNA and DNA, and proteins ranging from 517 to 1084 amino acids, representing 62.9-90.2% of human proteins.

Human progranulin-expressing mice as a novel tool for the development of progranulin-modulating therapeutics.

The granulin protein (also known as, and hereafter referred to as, progranulin) is a secreted glycoprotein that contributes to overall brain health. Heterozygous loss-of-function mutations in the gene encoding the progranulin protein (Granulin Precursor, GRN) are a common cause of familial frontotemporal dementia (FTD). Gene therapy approaches that aim to increase progranulin expression from a single wild-type allele, an area of active investigation for the potential treatment of GRN-dependent FTD, will benefit from the availability of a mouse model that expresses a genomic copy of the human GRN gene. Here we report the development and characterization of a novel mouse model that expresses the entire human GRN gene in its native genomic context as a single copy inserted into a defined locus (Hprt) in the mouse genome. We show that human and mouse progranulin are expressed in a similar tissue-specific pattern, suggesting that the two genes are regulated by similar mechanisms. Human progranulin rescues a phenotype characteristic of progranulin-null mice, the exaggerated and early deposition of the aging pigment lipofuscin in the brain, indicating that the two proteins are functionally similar. Longitudinal behavioural and neuropathological analyses revealed no significant differences between wild-type and human progranulin-overexpressing mice up to 18 months of age, providing evidence that long-term increase of progranulin levels is well tolerated in mice. Finally, we demonstrate that human progranulin expression can be increased in the brain using an antisense oligonucleotide that inhibits a known GRN-regulating micro-RNA, demonstrating that the transgene is responsive to potential gene therapy drugs. Human progranulin-expressing mice represent a novel and valuable tool to expedite the development of progranulin-modulating therapeutics.

Gene expression models based on transcription factor binding events confer insight into functional cis-regulatory variants.

MOTIVATION: Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions. RESULTS: We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R2 > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome. AVAILABILITY AND IMPLEMENTATION: The code and model training results are publicly available at https://github.com/wqshi/TF2Exp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework.

JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.

c-Myc is a novel Leishmania virulence factor by proxy that targets the host miRNA system and is essential for survival in human macrophages.

Leishmania species are intracellular protozoan pathogens that have evolved to successfully infect and deactivate host macrophages. How this deactivation is brought about is not completely understood. Recently, microRNAs (miRNAs) have emerged as ubiquitous regulators of macrophage gene expression that contribute to shaping the immune responses to intracellular pathogens. Conversely, several pathogens have evolved the ability to exploit host miRNA expression to manipulate host-cell phenotype. However, very little is known about the mechanisms used by intracellular pathogens to drive changes in host-cell miRNA abundance. Using miRNA expression profiling of Leishmania donovani-infected human macrophages, we show here that Leishmania infection induced a genome-wide down-regulation of host miRNAs. This repression occurred at the level of miRNA gene transcription, because the synthesis rates of primary miRNAs were significantly decreased in infected cells. miRNA repression depended on the host macrophage transcription factor c-Myc. Indeed, the expression of host c-Myc was markedly up-regulated by Leishmania infection, and c-Myc silencing reversed the miRNA suppression. Furthermore, c-Myc silencing significantly reduced intracellular survival of Leishmania, demonstrating that c-Myc is essential for Leishmania pathogenesis. Taken together, these findings identify c-Myc not only as being responsible for miRNA repression in Leishmania-infected macrophages but also as a novel and essential virulence factor by proxy that promotes Leishmania survival.

Evaluating the impact of single nucleotide variants on transcription factor binding.

Diseases and phenotypes caused by disrupted transcription factor (TF) binding are being identified, but progress is hampered by our limited capacity to predict such functional alterations. Improving predictions may be dependent on expanding the set of bona fide TF binding alterations. Allele-specific binding (ASB) events, where TFs preferentially bind to one of the two alleles at heterozygous sites, reveal the impact of sequence variations in altered TF binding. Here, we present the largest ASB compilation to our knowledge, 10 765 ASB events retrieved from 45 ENCODE ChIP-Seq data sets. Our analysis showed that ASB events were frequently associated with motif alterations of the ChIP'ed TF and potential partner TFs, allelic difference of DNase I hypersensitivity and allelic difference of histone modifications. For TF dimers bound symmetrically to DNA, ASB data revealed that central positions of the TF binding motifs were disproportionately important for binding. Lastly, the impact of variation on TF binding was predicted by a classification model incorporating all the investigated features of ASB events. Classification models using only DNase I hypersensitivity and sequence data exhibited predictive accuracy approaching the models with substantially more features. Taken together, the combination of ASB data and the classification model represents an important step toward elucidating regulatory variants across the human genome.

JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles.

JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release.

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research.

Virtually all the biological processes that occur inside or outside cells are mediated by protein-protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

ExplaiNN: interpretable and transparent neural networks for genomics.

Deep learning models such as convolutional neural networks (CNNs) excel in genomic tasks but lack interpretability. We introduce ExplaiNN, which combines the expressiveness of CNNs with the interpretability of linear models. ExplaiNN can predict TF binding, chromatin accessibility, and de novo motifs, achieving performance comparable to state-of-the-art methods. Its predictions are transparent, providing global (cell state level) as well as local (individual sequence level) biological insights into the data. ExplaiNN can serve as a plug-and-play platform for pretrained models and annotated position weight matrices. ExplaiNN aims to accelerate the adoption of deep learning in genomic sequence analysis by domain experts.

Refining the genomic determinants underlying escape from X-chromosome inactivation.

X-chromosome inactivation (XCI) epigenetically silences one X chromosome in every cell in female mammals. Although the majority of X-linked genes are silenced, in humans 20% or more are able to escape inactivation and continue to be expressed. Such escape genes are important contributors to sex differences in gene expression, and may impact the phenotypes of X aneuploidies; yet the mechanisms regulating escape from XCI are not understood. We have performed an enrichment analysis of transcription factor binding on the X chromosome, providing new evidence for enriched factors at the transcription start sites of escape genes. The top escape-enriched transcription factors were detected at the RPS4X promoter, a well-described human escape gene previously demonstrated to escape from XCI in a transgenic mouse model. Using a cell line model system that allows for targeted integration and inactivation of transgenes on the mouse X chromosome, we further assessed combinations of RPS4X promoter and genic elements for their ability to drive escape from XCI. We identified a small transgenic construct of only 6 kb capable of robust escape from XCI, establishing that gene-proximal elements are sufficient to permit escape, and highlighting the additive effect of multiple elements that work together in a context-specific fashion.

In silico discovery of small molecules for efficient stem cell differentiation into definitive endoderm.

Improving methods for human embryonic stem cell differentiation represents a challenge in modern regenerative medicine research. Using drug repurposing approaches, we discover small molecules that regulate the formation of definitive endoderm. Among them are inhibitors of known processes involved in endoderm differentiation (mTOR, PI3K, and JNK pathways) and a new compound, with an unknown mechanism of action, capable of inducing endoderm formation in the absence of growth factors in the media. Optimization of the classical protocol by inclusion of this compound achieves the same differentiation efficiency with a 90% cost reduction. The presented in silico procedure for candidate molecule selection has broad potential for improving stem cell differentiation protocols.