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Biology subjects

Lou, S.

Publications and source records attributed to Lou, S..

At least 19 recordsLinked to original sources

Xenograft supervision and joint methylation-fragment-end encoding define a reusable molecular scoring layer for cell-free tumor DNA

Circulating tumor DNA is often scarce in plasma, and regional methylation summaries can obscure informative differences between individual molecules. Here we present ctDNA-BERT, a Transformer that scores individual bisulfite reads without predefined differentially methylated regions by jointly encoding CpG methylation state, nucleotide context and the fragment 3' end. Xenografts provide molecule-level tumor supervision. The resulting frozen scorer supported tumor-fraction estimation in synthetic and patient-derived xenograft mixtures and enriched tumor-concordant methylation and chromatin-accessibility signals in independent hepatocellular carcinoma and stage III pan-cancer plasma without cohort-specific retraining. Features derived from selected reads discriminated stage I cancers from healthy controls in a retrospective cohort of 1,477 specimens, achieving an out-of-fold area under the receiver operating characteristic curve of 0.884. By separating molecular scoring from sample-level inference, ctDNA-BERT provides a reusable read-level scoring layer for cell-free DNA methylation analysis.

bioinformatics↗

Systematic discovery of single-cell protein networks in cancer with Shusi

Context-specific protein-protein interaction (PPI) drive heterogeneity of primary tumor, forming a formidable challenge to effective cancer therapy. However, systematically mapping and modeling these interactions at single-cell resolution across diverse cancer types remains an unmet need. Here, we present Shusi, a large language model-enhanced variational graph auto-encoder model trained on over single cells across 23 cancer types, to predict context-specific PPIs. Shusi outperforms existing state-of-the-art methods, as validated through orthogonal experimental evidence. Cancer-specific mutations are significantly enriched in Shusi-predicted networks, offering complementary insights to conventional marker gene-based approaches. Through systematic evaluations, we demonstrate strong associations between Shusi-predicted network topologies, genetic vulnerabilities, and therapeutic sensitivity. Finally, in acute myeloid leukemia (AML), a blood cancer where cell-state heterogeneity drives clinical resistance, Shusi pinpointed potential targets as actionable vulnerabilities of resistant leukemia subpopulations, as validated experimentally in primary AML. Shusi offers a deep-learning tool for implementing precision medicine based on context-specific protein network architecture.

bioinformatics↗

Single-cell genomics and regulatory networks for 388 human brains

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized [~]250 disease-risk genes and drug targets with associated cell types. Summary Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC="FIGDIR/small/585576v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@70b24org.highwire.dtl.DTLVardef@6c9eddorg.highwire.dtl.DTLVardef@131fae2org.highwire.dtl.DTLVardef@4e0f1e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

An AAV capsid reprogrammed to bind human Transferrin Receptor mediates brain-wide gene delivery

Developing vehicles that efficiently deliver genes throughout the human central nervous system (CNS) will broaden the range of treatable genetic diseases. We engineered an AAV capsid, BI-hTFR1, that binds human Transferrin Receptor (TfR1), a protein expressed on the blood-brain barrier (BBB). BI-hTFR1 was actively transported across a human brain endothelial cell layer and, relative to AAV9, provided 40-50 times greater reporter expression in the CNS of human TFRC knock-in mice. The enhanced tropism was CNS-specific and absent in wild type mice. When used to deliver GBA1, mutations of which cause Gaucher disease and are linked to Parkinsons disease, BI-hTFR1 substantially increased brain and cerebrospinal fluid glucocerebrosidase activity compared to AAV9. These findings establish BI-hTFR1 as a promising vector for human CNS gene therapy.

neuroscience↗

The pan-genome and local adaptation of Arabidopsis thaliana

Arabidopsis thaliana has been used as a model species for research in a diverse collection of plant species. However, previous studies based on single reference genomes and short-read sequencing data are restricted to detecting variable genes and large structural variation (SV) underlying local adaptation. Here we de novo assemble high-quality chromosomal genomes of 38 A. thaliana ecotypes (with 6 relict ones) using PacBio-HiFi long-read sequencing. From these newly assembled genomes, we annotate several thousand new genes through pan-genomic analysis in comparison to the previous reference genome. The identified variable genes are mainly enriched in and associated with ecological adaptation and this species substantially expands its gene repertoire for local adaptation. We construct a graph-based pan-genome and identify 62,525 SVs which overlap with 14,243 genes. These genes are enriched in multiple ecological adaptation functions, including secondary metabolic processes, enzyme regulation, and biotic/abiotic stimulus. For example, a 566 bp insertion in the promoter of the light-adaptation KNAT3 gene was specific to the high-altitude relict Tibet-0 ecotype. This SV reduces the expression level of KNAT3 and promotes A. thaliana adaptation to habitats high in light radiation. In addition, compared with the SNPs, the SVs identified in this study captured the missing heritability and we detected novel SV associations with environmental variables in their native range, highlighting the value of SVs in environmental adaptation. The genome resources presented here will help pinpoint genetic changes that include both SVs and the ecotype-specific genes for local adaptation of A. thaliana and increase our understanding of the molecular mechanisms in this model species to respond to varied habitats.

genomics↗

Capped nascent RNA sequencing reveals novel therapy-responsive enhancers in prostate cancer

Mounting evidence suggests that enhancer RNA (eRNA) transcription start sites (TSSs) provide higher sensitivity and specificity for enhancer identification than histone modifications and chromatin accessibility. The extent to which changes in eRNA transcription correspond to changes in enhancer activity, however, remains unclear. Here, we used precision run-on and capped RNA sequencing (PRO-cap) to assess changes in enhancer activity in response to treatment with the androgen receptor signaling inhibitor, enzalutamide (ENZ). We identified 6,189 high-confidence candidate enhancers in the human prostate cancer cell line, LNCaP; 853 of which demonstrated significant changes in activity in response to drug treatment. Notably, we found that 67% and 54% of drug-responsive enhancers did not show similar changes in activity in previous studies that utilized ChIP-seq and ATAC-seq, respectively. Strikingly, 79% of regions with increased eRNA transcription showed no other biochemical alterations, implying that PRO-cap can capture a set of precise changes in enhancer activity that classical approaches lack the sensitivity to detect. We performed in vivo functional validations of candidate enhancers and found that CRISPRi targeting of PRO-cap-specific drug-responsive enhancers impaired ENZ regulation of downstream target genes, suggesting that changes in eRNA TSSs mark true biological changes in enhancer activity with high sensitivity. Our study highlights the utility of using PRO-cap as a complementary approach to canonical biochemical methods for detecting precise changes in enhancer activity and, in particular, for better understanding disease progression and responses to treatment.

genomics↗

Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused millions of deaths worldwide. Many efforts have focused on unraveling the mechanism of the viral infection to develop effective strategies for treatment and prevention. Previous studies have provided some clarity on the protein-protein interaction linkages occurring during the life cycle of viral infection; however, we lack a complete understanding of the full interactome, comprising human miRNAs and protein-coding genes and co-infecting microbes. To comprehensively determine this, we developed a statistical modeling method using latent Dirichlet allocation (called MLCrosstalk, for multiple-layer crosstalk) to fuse many types of data to construct the full interactome of SARS-CoV-2. Specifically, MLCrosstalk is able to integrate samples with multiple layers of information (e.g., miRNA and microbes), enforce a consistent topic distribution on all data types, and infer individual-level linkages (i.e., differing between patients). We also implement a secondary refinement with network propagation to allow our microbe-gene linkages to address larger network structures (e.g., pathways). Using MLCrosstalk, we generated a list of genes and microbes linked to SARS-CoV-2. Interestingly, we found that two of the identified microbes, Rothia mucilaginosa and Prevotella melaninogenica, show distinct patterns representing synergistic and antagonistic relationships with the virus, respectively. We also identified several SARS-COV-2-associated pathways, including the VEGFA-VEGFR2 and immune response pathways, which may provide potential targets for drug design.

bioinformatics↗

DiNeR: a Differential Graphical Model for analysis of co-regulation Network Rewiring

BackgroundDuring transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. "co-binding changes") can affect the co-regulating associations between TFs (i.e. "rewiring the co-regulator network"). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied. MethodsTo address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. ResultsWe assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes. ConclusionsOur method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

bioinformatics↗

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise interventions effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

bioinformatics↗

TopicNet: a framework for measuring transcriptional regulatory network change

Next generation sequencing data highlights comprehensive and dynamic changes in the human gene regulatory network. Moreover, changes in regulatory network connectivity (network "rewiring") manifest different regulatory programs in multiple cellular states. However, due to the dense and noisy nature of the connectivity in regulatory networks, directly comparing the gains and losses of targets of key TFs is not that informative. Thus, here, we seek a abstracted lower-dimensional representation to understand the main features of network change. In particular, we propose a method called TopicNet that applies latent Dirichlet allocation (LDA) to extract meaningful functional topics for a collection of genes regulated by a TF. We then define a rewiring score to quantify the large-scale changes in the regulatory network in terms of topic change for a TF. Using this framework, we can pinpoint particular TFs that change greatly in network connectivity between different cellular states. This is particularly relevant in oncogenesis. Also, incorporating gene-expression data, we define a topic activity score that gives the degree that a topic is active in a particular cellular state. Furthermore, we show how activity differences can highlight differential survival in certain cancers.

bioinformatics↗

Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNAseq) can be used on sputum, but it can be challenging to interpret because sputum contains a complex and heterogeneous mixture of human cells and exogenous (microbial) material. In this study, we developed a methodology that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. We use this to relate bulk RNAseq data from 115 asthmatic patients with clinical information, microscope images, and single-cell profiles. First, we mapped sputum RNAseq to human and exogenous sources. Next, we decomposed the human reads into cell-expression signatures and fractions of these in each sample; we validated the decomposition using targeted single-cell RNAseq and microscopy. We observed enrichment of immune-system cells (neutrophils, eosinophils, and mast cells) in severe asthmatics. Second, we inferred microbial abundances from the exogenous reads and then associated these with clinical variables -- e.g., Haemophilus was associated with increased white blood cell count and Candida, with worse lung function. Third, we applied a generative model, Latent Dirichlet allocation (LDA), to identify patterns of gene expression and microbial abundances and relate them to clinical data. Based on this, we developed a method called LDA-link that connects microbes to genes using reduced-dimensionality LDA topics. We found a number of known connections, e.g. between Haemophilus and the gene IL1B, which is highly expressed by mast cells. In addition, we identified novel connections, including Candida and the calcium-signaling gene CACNA1E, which is highly expressed by eosinophils. These results speak to the mechanism by which gene-microbe interactions contribute to asthma and define a strategy for making inferences in heterogeneous and noisy RNAseq datasets.

genomics↗

Genome-wide analysis of GATA factors in moso bamboo (Phyllostachys edulis) unveils that PeGATAs regulate shoot rapid-growth and rhizome development

BackgroundMoso bamboo is well-known for its rapid-growth shoots and widespread rhizomes. However, the regulatory genes of these two processes are largely unexplored. GATA factors regulate many developmental processes, but its role in plant height control and rhizome development remains unclear.\n\nResultsHere, we found that bamboo GATA factors (PeGATAs) are involved in the growth regulation of bamboo shoots and rhizomes. Bioinformatics and evolutionary analysis showed that there are 31 PeGATA factors in bamboo, which can be divided into three subfamilies. Light, hormone, and stress-related cis-elements were found in the promoter region of the PeGATA genes. Gene expression of 12 PeGATA genes was regulated by phytohormone-GA but there was no correlation between auxin and PeGATA gene expression. More than 27 PeGATA genes were differentially expressed in different tissues of rhizomes, and almost all PeGATAs have dynamic gene expression level during the rapid-growth of bamboo shoots. These results indicate that PeGATAs regulate rhizome development and bamboo shoot growth partially via GA signaling pathway. In addition, PeGATA26, a rapid-growth negative regulatory candidate gene modulated by GA treatment, was overexpressed in Arabidopsis, and over-expression of PeGATA26 significantly repressed Arabidopsis primary root length and plant height. The PeGATA26 overexpressing lines were also resistant to exogenous GA treatment, further emphasizing that PeGATA26 inhibits plant height from Arabidopsis to moso bamboo via GA signaling pathway.\n\nConclusionsOur results provide an insight into the function of GATA transcription factors in regulating shoot rapid-growth and rhizome development, and provide genetic resources for engineering plant height.

plant biology↗

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗

The full-length transcriptome of Spartina alterniflora reveals the complexity of high salt tolerance in monocotyledonous halophyte

Spartina alterniflora (Spartina) is the only halophyte in the salt marsh. However, the molecular basis of its high salt tolerance remains elusive. In this study, we used PacBio full-length single molecule long-read sequencing and RNA-seq to elucidate the transcriptome dynamics of high salt tolerance in Spartina by salt-gradient experiments (0, 350, 500 and 800 mM NaCl). We systematically analyzed the gene expression diversity and deciphered possible roles of ion transporters, protein kinases and photosynthesis in salt tolerance. Moreover, the co-expression network analysis revealed several hub genes in salt stress regulatory networks, including protein kinases such as SaOST1, SaCIPK10 and three SaLRRs. Furthermore, high salt stress affected the gene expression of photosynthesis through down-regulation at the transcription level and alternative splicing at the post-transcriptional level. In addition, overexpression of two Spartina salt-tolerant genes SaHSP70-I and SaAF2 in Arabidopsis significantly promoted the salt tolerance of transgenic lines. Finally, we built the SAPacBio website for visualizing the full-length transcriptome sequences, transcription factors, ncRNAs, salt-tolerant genes, and alternative splicing events in Spartina. Overall, this study sheds light on the high salt tolerance mechanisms of monocotyledonous-halophyte and demonstrates the potential of Spartina genes for engineering salt-tolerant plants.

plant biology↗

High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification

Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored between one to 15 years in a biobank and show a high degree of the proteome pattern similarity between two types histological region of small FFPE samples, i.e. punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of certain degree of biological variations. Applying the method to two independent DLBCL cohorts we identified myeloperoxidase (MPO), a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.

biochemistry↗

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. In this work, we developed a framework that incorporates a denoising autoencoder and a supervised learning approach to identify gene signatures related to asthma severity. The autoencoder embeds high-dimensional gene expression data into a lower-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from gene expression data. We found that the weights on hidden units in this latent space correlate well with previously defined and clinically relevant clusters of patients. Moreover, pathway analysis based on each genes contribution to the hidden units showed significant enrichment in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary supervised classifier (based on random forest) for directly predicting asthma severity. The random-forest importance metric from this classifier identified a signature based on 50 key genes, which can predict severity with an AUROC of 0.81 and thus have potential as diagnostic biomarkers. Furthermore, the key genes could also be used for successfully estimating, via support-vector-machine regression, the FEV1/FVC ratios across patients, achieving pre- and post-treatment correlations of 0.56 and 0.65, respectively (between predicted and observed values). The 50 biomarker candidate genes can be found in supplementary. The source codes are freely available upon request.

bioinformatics↗

GRAM: A generalized model to predict the molecular effect of a non-coding variant in a cell-type specific manner

There has been much effort to prioritize genomic variants with respect to their impact on \"function\". However, function is often not precisely defined: Sometimes, it is the disease association of a variant; other times, it reflects a molecular effect on transcription or epigenetics. Here we coupled multiple genomic predictors to build GRAM, a generalized model, to predict a well-defined experimental target: the expression-modulating effect of a non-coding variant in a cell-specific manner. As a first step, we performed feature engineering: using a LASSO regularized linear model, we found transcription factor (TF) binding most predictive, especially for TFs that are hubs in the regulatory network; in contrast, evolutionary conservation, a popular feature in many other functional-impact predictors, has almost no contribution. Moreover, TF binding inferred from in vitro SELEX is as effective as that from in vivo ChIP-Seq. Second, we implemented GRAM integrating SELEX features and expression profiles. The program combines a universal regulatory score for a variant in a non-coding element with a modifier score reflecting the particular cell type. We benchmarked GRAM on a large-scale MPRA dataset in the GM12878 cell line, achieving a ROC score of [~]0.73; performance on the K562 cell line was similar. Finally, we evaluated the performance of GRAM on targeted regions using luciferase assays in MCF7 and K562 cell lines. We noted that changing the insertion position of the construct relative to the reporter gene gives very different results, highlighting the importance of carefully defining the functional target the model is predicting.\n\nAuthor SummaryNoncoding variants lie outside of protein-coding regions, and are found to have disease associations. However, knowledge on the molecular effect of these non-coding variants in a cell-specific context is very limited. Also, different output between multiple experiment platforms may introduce extra complexity in analyzing the molecular function of these variants. We developed GRAM, a generalized model to predict molecular effect of non-coding variants in multiple cell types for different experimental platforms. We first selected the most informative cell-independent SELEX transcription factor binding score on the variant locus as features and then combine cell-specific gene expression profile to build a multi-step prediction model. GRAM has been successfully tested on both MPRA and Luciferase assay, and on three different cell lines: GM12878, K562 and MCF7, shows high performance.

bioinformatics↗

RADAR: annotation and prioritization of variants in the post-transcriptional regulome of RNA-binding proteins

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation and disease. Their binding sites cover more of the genome than coding exons; nevertheless, most noncoding variant-prioritization methods only focus on transcriptional regulation. Here, we integrate the portfolio of ENCODE-RBP experiments to develop RADAR, a variant-scoring framework. RADAR uses conservation, RNA structure, network centrality, and motifs to provide an overall impact score. Then it further incorporates tissue-specific inputs to highlight disease-specific variants. Our results demonstrate RADAR can successfully pinpoint variants, both somatic and germline, associated with RBP-function dysregulation, that cannot be found by most current prioritization methods, for example variants affecting splicing.

genomics↗