Biology subjects

Cooper, D. N.

Publications and source records attributed to Cooper, D. N..

At least 19 recordsLinked to original sources

bioRxiv2026

Context-dependent variant interpretation from Mendelian disease to genetic predisposition: a proof-of-concept using LPL

As genomic sequencing evolves beyond rare disease diagnostics toward population screening and precision medicine, clinical variant interpretation is increasingly challenged by variants whose clinical consequences depend on biological context. Current frameworks, including the ACMG/AMP guidelines, generally assign a single classification to each variant regardless of inheritance state or genetic context, potentially failing to communicate context-dependent clinical consequences. Here, we address this issue using loss-of-function variants in LPL as a uniquely informative model system in which residual physiological LPL activity can be directly quantified in vivo. By systematically integrating published biallelic LPL genotypes, physiological measurements, functional studies, and clinical phenotypes, we identified a biologically meaningful transition at approximately 10% residual physiological LPL activity. Activity below this level was predominantly associated with classical childhood-onset familial chylomicronemia syndrome (FCS), whereas higher activity was associated with phenotypic attenuation and modifier-dependent clinical expression. Furthermore, heterozygous loss-of-function variants exhibited an estimated penetrance of 5-7% for severe hypertriglyceridemia. We therefore propose a context-dependent framework in which biallelic complete- or near-complete loss-of-function genotypes are interpreted as causative for FCS, whereas heterozygous variants are interpreted as predisposing to severe hypertriglyceridemia while retaining recognition of FCS carrier status. Together, our findings demonstrate that clinical variant interpretation should integrate available biological context--including, where relevant, allelic configuration, residual biological function, and penetrance--rather than rely on the intrinsic molecular consequence of the variant alone. More broadly, this framework provides a conceptual model for interpreting variants across the continuum from Mendelian disease to genetic predisposition in the era of precision medicine.

genetics↗

bioRxiv2026

Ancestry-specific performance of variant effect predictors in clinical variant classification

Predicting the effects of genetic variants and assessing prediction performance are key computational tasks in genomic medicine. It has been shown that well-calibrated variant effect predictors can be reliably used as evidence towards establishing pathogenicity (or benignity) of missense variants, thereby rendering these variants suitable for use in (or exclusion from) the genetic diagnosis of rare Mendelian conditions. However, most predictors have been trained or calibrated on data that may not be sufficiently representative to lead to similar performance across all genetic ancestries. This raises questions about the responsible deployment of these tools to improve human health. To better understand the utility of computational predictors, we set out to assess their ancestry-specific performance in terms of accuracy and evidence strength according to the ACMG/AMP guidelines. First, we determined that the expected count of rare variants in an individuals genome and the allele frequency distribution of these variants are the key confounders when evaluating a predictors performance across different genetic ancestries. Second, we found that a predictors accuracy itself inversely correlates with the allele frequency of the rare variant. After stratifying according to allele frequency, we show that established methods for predicting the pathogenicity of missense variants have comparable performance levels across major ancestry groups. Our results therefore support the wide deployment of such models in the context of genetic diagnosis and related applications.

bioinformatics↗

bioRxiv2025

Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI

Disruption of protein-protein interactions (PPIs) is a major mechanism of a variants deleterious effect. Computational tools are needed to assess such variants at scale, yet existing predictors rarely consider loss of specific interactions, particularly when variants perturb binding interfaces without significantly affecting protein stability. To address this problem, we present MutPred-PPI, a graph attention network that predicts interaction-specific (edgetic) effects of missense variants by operating on AlphaFold 3-based protein complex contact graphs with protein language model embeddings imposed upon nodes. We systematically evaluated our model with stringent group cross-validation as well as benchmark data recently collected within the IGVF Consortium. MutPred-PPI outperformed all baseline methods across all evaluation criteria, achieving an AUC of 0.85 on seen proteins and 0.72 on previously unseen proteins in cross-validation, demonstrating strong generalizability despite scarce training data. To demonstrate biomedical relevance, we applied MutPred-PPI to variants from ClinVar, HGMD, COSMIC, gnomAD, and two de novo neurodevelopmental disorder-linked datasets. Disease-associated variants from Clin-Var and HGMD showed strong enrichment for both quasi-null and edgetic effects, whereas population variants from gnomAD increasingly preserved interactions with higher allele frequencies. Notably, we observed a strong edgetic disruption signature in highly recurrent cancer variants from both the full COSMIC dataset and a subset of variants from oncogenes. Recurrent tumor suppressor gene variants and autism spectrum disorder-associated variants exhibited moderate quasi-null enrichment, whilst neurodevelopmental disorder-linked variants showed a weak edgetic disruption signature. These results indicate distinct PPI perturbation mechanisms across disease types and show that MutPred-PPI captures functionally relevant molecular effects of pathogenic variants.

bioinformatics↗

bioRxiv2025

When splicing is not all or none: Implications for variant classification

Variants with intermediate functional effects--those that neither eliminate nor fully maintain normal gene function--represent an underrecognized source of genetic complexity and constitute a grey zone that challenges variant classification. Here, we address this issue using GT>GC (+2T>C) 5' splice-site variants as a tractable model, as approximately 15% of such substitutions retain variable amounts of wild-type (WT) transcript. Using residual WT transcript as a quantitative readout, we first show that disease-associated GT>GC variants known to generate substantial WT transcript (for example, SPINK1 c.194+2T>C, HBB c.315+2T>C, and BRCA2 c.8331+2T>C) consistently complicate classification efforts. We then carried out a locus-wide assessment of all 26 GT>GC substitutions in CFTR, applying SpliceAI delta donor-loss scores as an initial screen and cross-checking classifications in expert-curated database. This evaluation indicated that three variants with low or intermediate SpliceAI scores are likely to retain WT transcript. Minigene analyses of four selected CFTR variants, together with additional examination of a BAP1 GT>GC variant with conflicting clinical interpretations, illustrate the methodological challenges in predicting and experimentally quantifying residual WT transcript. These results emphasize that (i) in silico predictors cannot reliably discriminate between complete and partial splice disruption and (ii) experimental systems differ in their ability to detect subtle splicing outcomes. Overall, our integrative analysis shows that GT>GC variants generating appreciable WT transcript exemplify a broader class of intermediate-effect alleles that complicate both computational prediction and experimental assessment, underscoring the need for classification frameworks that incorporate quantitative functional data and better capture the continuum of variant effects.

genetics↗

bioRxiv2025

Disease-specific variant pathogenicity prediction using multimodal biomedical language models

Missense variants play a key role in the diagnosis of genetic disorders and in disease risk prediction. Existing methods focus primarily on the prediction of variant effects in terms of their deleteriousness, without taking into account the disease-specific context, and are therefore limited in terms of their utility in real-world diagnosis and decision making. Here, we introduce disease-specific variant pathogenicity prediction (DIVA), a novel deep learning framework that directly predicts specific disease types alongside the probability of deleteriousness for missense variants. Our approach integrates information from two different modalities - protein sequence and disease-related textual annotations - encoded using two pre-trained language models and optimized within a contrastive learning paradigm designed to align variants with relevant diseases in the learned representation space. Our results demonstrate that DIVA outperforms baselines and provides accurate disease predictions with high relevance to clinically curated disease annotations for missense variants. Variant deleteriousness prediction is enhanced by incorporating AlphaMissense scores through learnable weights derived from protein function annotations, which additionally boosts DIVAs ability to accurately classify deleterious variants. Our work provides new insights into variant pathogenicity prediction with awareness of disease specificity, addressing a hitherto unmet need in relation to clinical variant interpretation.

bioinformatics↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2023

Integrating gene mutation spectra from tumors and the general population with gene expression topological networks to identify novel cancer driver genes

BackgroundUnderstanding the genetics underlying cancer development and progression is the most important goal of biomedical research to improve patient survival rates. Recently, researchers have proposed computationally combining the mutational burden with biological networks as a novel means to identify cancer driver genes. However, these approaches treated all mutations as having the same functional impact on genes and incorporated gene-gene interaction networks without considering tissue specificity, which may have hampered our ability to identify novel cancer drivers. MethodsWe have developed a framework, DGAT-cancer that integrates the predicted pathogenicity of somatic mutation in cancers and germline variants in the healthy population, with topological networks of gene expression in tumor tissues, and the gene expression levels in tumor and paracancerous tissues in predicting cancer drivers. These features were filtered by an unsupervised approach, Laplacian selection, and those selected were combined by Hotelling and Box-Cox transformations to score genes. Finally, the scored genes were subjected to Gibbs sampling to determine the probability that a given gene is a cancer driver. ResultsThis method was applied to nine types of cancer, and achieved the best area under the precision-recall curve compared to three commonly used methods, leading to the identification of 571 novel cancer drivers. One of the top genes, EEF1A1 was experimentally confirmed as a cancer driver of glioma. Knockdown of EEF1A1 led to a ~ 41-50% decrease in glioma size and improved the temozolomide sensitivity of glioma cells. ConclusionBy combining the pathogenic status of mutational spectra in tumors alongside the spectrum of variation in the healthy population, with gene expression in both tumors and paracancerous tissues, DGAT-cancer has significantly improved our ability to detect novel cancer driver genes.

bioinformatics↗

bioRxiv2023

An efficient error correction and accurate assembly tool for noisy long reads

Long read sequencing data, particularly those derived from the Oxford Nanopore (ONT) sequencing platform, tend to exhibit a high error rate. Here, we present NextDenovo, a highly efficient error correction and assembly tool for noisy long reads, which achieves a high level of accuracy in genome assembly. NextDenovo can rapidly correct reads; these corrected reads contain fewer errors than other comparable tools and are characterized by fewer chimeric alignments. We applied NextDenovo to the assembly of high quality reference genomes of 35 diverse humans from across the world using ONT Nanopore long read sequencing data. Based on these de novo genome assemblies, we were able to identify the landscape of segmental duplications and gene copy number variation in the modern human population. The use of the NextDenovo program should pave the way for population-scale long-read assembly, thereby facilitating the construction of human pan-genomes, using Nanopore long read sequencing data.

bioinformatics↗

bioRxiv2022

Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set

Gain-of-function (GOF) variants give rise to increased or novel protein functions whereas loss-of-function (LOF) variants lead to diminished protein function. GOF and LOF variants can result in markedly varying phenotypes, even when occurring in the same gene. However, experimental approaches for identifying GOF and LOF are generally slow and costly, whilst currently available computational methods have not been optimized to discriminate between GOF and LOF variants. We have developed LoGoFunc, an ensemble machine learning method for predicting pathogenic GOF, pathogenic LOF, and neutral genetic variants. LoGoFunc was trained on a broad range of gene-, protein-, and variant-level features describing diverse biological characteristics, as well as network features summarizing the protein-protein interactome and structural features calculated from AlphaFold2 protein models. We analyzed GOF, LOF, and neutral variants in terms of local protein structure and function, splicing disruption, and phenotypic associations, thereby revealing previously unreported relationships between various biological phenomena and variant functional outcomes. For example, GOF and LOF variants exhibit contrasting enrichments in protein structural and functional regions, whilst LOF variants are more likely to disrupt canonical splicing as indicated by splicing-related features employed by the model. Further, by performing phenome-wide association studies (PheWAS), we identified strong associations between relevant phenotypes and high-confidence predicted GOF and LOF variants. LoGoFunc outperforms other tools trained solely to predict pathogenicity or general variant impact for the identification of pathogenic GOF and LOF variants.

genetics↗

bioRxiv2022

Genome-wide detection of human variants that disrupt intronic branchpoints

Pre-mRNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Fifty-six rare variants in 44 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach has been available to efficiently detect such variants in next-generation sequencing (NGS) data. We established a comprehensive human genome-wide BP database by integrating existing BP data, and by generating new BP data from RNA-seq of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We in-depth characterize multiple features of BP in major and minor introns, and find that BP and BP-2 (two-nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, whilst being comparable to the exonic background. We develop BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition in NGS data. BPHunter retrospectively identifies 48 of the 56 known pathogenic BP mutations in which we summarize a strategy for prioritizing BP mutation candidates, and the remaining 8 all create AG dinucleotides between BP and acceptor site which is probably the reason for mis-splicing. We demonstrate the utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia, and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which we validate experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.

genetics↗

bioRxiv2021

Identification of Discriminative Gene-level and Protein-level Features Associated with Gain-of-Function and Loss-of-Function Mutations

Identifying whether a given genetic mutation results in a gene product with increased (gain-of-function; GOF) or diminished (loss-of-function; LOF) activity is an important step toward understanding disease mechanisms as they may result in markedly different clinical phenotypes. Here, we generated the first extensive database of all currently known germline GOF and LOF pathogenic mutations by employing natural language processing (NLP) on the available abstracts in the Human Gene Mutation Database. We then investigated various gene- and protein-level features of GOF and LOF mutations by applying machine learning and statistical analyses to identify discriminative features. We found that GOF mutations were enriched in essential genes, autosomal dominant inheritance, protein binding and interaction domains, whereas LOF mutations were enriched in singleton genes, protein-truncating variants, and protein core regions. We developed a user-friendly web-based interface that enables the extraction of selected subsets from the GOF/LOF database by a comprehensive set of annotated features, and downloading up-to-date versions (https://itanlab.shinyapps.io/goflof/). These results could ultimately improve our understanding of how mutations affect gene/protein function thereby guiding future treatment options.

genetics↗

bioRxiv2020

PIVOTAL: Prioritizing variants of uncertain significance with spatial genomic patterns in the 3D proteome

Variants of uncertain significance (VUS) have posed an increasingly prominent challenge to clinicians due to their growing numbers and difficulties in making clinical responses to them. Currently there are no existing methods that leverage the spatial relationship of known disease mutations and genomic properties for prioritizing variants of uncertain significance. More importantly, disease genes often associate with multiple clinically distinct diseases, but none of the existing variant prioritization methods provide clues as to the specific type of disease potentially associated with a given variant. We present PIVOTAL, a spatial neighborhood-based method using three-dimensional structural models of proteins, that significantly improves current variant prioritization tools and identifies potential disease etiology of candidate variants on a proteome scale. Using PIVOTAL, we made pathogenicity predictions for over 140,000 VUS and deployed a web application (http://pivotal.yulab.org) that enables users both to explore these data and to perform custom calculations.

genomics↗

bioRxiv2020

RNA secondary structure mediated by Alu insertion as a novel disease-causing mechanism

We previously identified a homozygous Alu insertion variant (Alu_Ins) in the 3-UTR of the SPINK1 gene as the cause of a novel pediatric disease. Although we established that Alu_Ins leads to the complete loss of SPINK1 mRNA expression, the precise mechanisms remained elusive. Here we aimed to elucidate these mechanisms through a hypothesis-driven approach. Initially, we speculated that Alu_Ins could independently disrupt mRNA 3 end formation and/or affect other post-transcriptional processes such as nuclear export and translation, due to its particular location. However, the presence of Alu_Ins in the 3-UTR resulted in only an ~50% reduction in luciferase reporter activity compared to the wild-type, suggesting the involvement of additional mechanisms. Using RepeatMasker, we identified two Alu elements within SPINK1s third intron, both of which resided in an orientation opposite to that of Alu_Ins. Through RNAfold predictions and full-length gene expression assays designed to examine orientation-dependent interactions between Alu repeats, we present evidence linking the detrimental effect of Alu_Ins to extensive double-stranded RNA structures formed between Alu_Ins and pre-existing intronic Alu sequences. Our results reveal a novel pathogenetic mechanism involving an Alu insertion, highlighting the importance of considering interactions between new and pre-existing Alu elements in inverted orientations within disease-associated genes.

genetics↗

bioRxiv2019

The experimentally obtained functional impact assessments of GT>GC 5' splice site variants differ markedly from those predicted

GT>GC 5 splice site (or +2T>C) variants have been frequently reported to cause human genetic disease. However, although we have demonstrated that GT>GC variants in human disease genes may not invariably be pathogenic, none of the currently available splicing prediction tools appear to be capable of reliably distinguishing those GT>GC variants that generate wild-type transcripts from those that do not. Recently, SpliceAI, a novel deep residual neural network tool, has been developed for splicing prediction. Methodologically distinct from previous approaches that either rely on human-engineered features and/or which focus on short nucleotide windows adjoining exon-intron boundaries, SpliceAI assesses splicing determinants by evaluating 10,000 nucleotides of flanking contextual sequence to predict the functional role in splicing of each position in the pre-mRNA transcript. Herein, we evaluated the performance of SpliceAI in the context of three datasets of GT>GC variants, all of which had been characterized functionally in terms of their impact on mRNA splicing. The first two datasets refer to our recently described "in vivo" dataset of 45 disease-causing GT>GC variants and the "in vitro" dataset of 103 GT>GC substitutions. The third dataset comprised 12 BRCA1 GT>GC variants that were recently analyzed by saturation genome editing. We processed all GT>GC variants using the default settings of SpliceAI. Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of the analyzed GT>GC variants revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue is that the impact of GT>GC (as well as GT>GA or +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.

genetics↗

bioRxiv2019

AMELIE 2 speeds up Mendelian diagnosis by matching patient phenotype & genotype to primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Trained clinicians can spend hours looking for the right publication/s supporting a single gene that best explains a patients disease. AMELIE (Automatic Mendelian Literature Evaluation) greatly accelerates this process. AMELIE parses all 29 million PubMed abstracts, downloads and further parses hundreds of thousands of full text articles in search of information supporting the causality and associated phenotypes of any published genetic variant. AMELIE then prioritizes patient candidate variants for their likelihood of explaining any patients given set of phenotypes. Diagnosis of singleton patients (without relatives exomes) is the most time-consuming scenario. AMELIE ranked the causative gene at the very top in 2/3 of 215 diagnosed singleton Mendelian patients. Evaluating only the top 11 AMELIE scored genes of 127 (median) candidate genes per patient results in rapid diagnosis for 90+% of cases. AMELIE-based evaluation of all cases is 3-19x more efficient than hand-curated database-based approaches. We replicate these results on a cohort of clinical cases from Stanford Childrens Health and the Manton Center for Orphan Disease Research. An analysis web portal with our most recent update, programmatic interface and code will be available at AMELIE.stanford.edu. A pilot run of the web portal has already served many thousands of job submissions from dozens of countries.

genetics↗

bioRxiv2019

5’ splice site GC>GT variants differ from GT>GC variants in terms of their functionality and pathogenicity

In the human genome, most 5 splice sites (~99%) employ the canonical GT dinucleotide whereas a small minority (~1%) use the non-canonical GC dinucleotide. The functionality and pathogenicity of 5 splice site GT>GC (i.e., +2T>C) variants have been extensively studied but we still know very little about 5 splice site GC>GT (+2C>T) variants. Herein, we sought to address this deficiency by performing a meta-analysis of identified +2C>T pathogenic variants together with a functional analysis of +2C>T substitutions using a cell culture-based full-length gene splicing assay. Our results establish a proof of concept that +2C>T variants are qualitatively different from +2T>C variants in terms of their functionality and pathogenicity and suggest that, in sharp contrast with +2T>C variants, most if not all +2C>T variants have no pathological relevance. Our findings have important implications for interpreting the clinical relevance of +2C>T variants but might also improve our understanding of the evolutionary basis of switching between GT and GC 5 splice sites in mammalian genomes.

genetics↗

bioRxiv2019

Common homozygosity for predicted loss-of-function variants reveals both redundant and advantageous effects of dispensable human genes

Humans homozygous or hemizygous for variants predicted to cause a loss of function of the corresponding protein do not necessarily present with overt clinical phenotypes. However, the set of effectively dispensable genes in the human genome has not yet been fully characterized. We report here 190 autosomal genes with 207 predicted loss-of-function variants, for which the frequency of homozygous individuals exceeds 1% in at least one human population from five major ancestry groups. No such genes were identified on the X and Y chromosomes. Manual curation revealed that 28 variants (15%) had been misannotated as loss-of-function, mainly due to linkage disequilibrium with different compensatory variants. Of the 179 remaining variants in 166 genes (0.82% of 20,232 genes), only 11 alleles in 11 genes had previously been confirmed experimentally to be loss-of-function. The set of 166 dispensable genes was enriched in olfactory receptor genes (41 genes), but depleted of genes expressed in a wide range of organs and in leukocytes. The 125 dispensable non-olfactory receptor genes displayed a relaxation of selective constraints both between species and within humans, consistent with greater redundancy. In total, 62 of these 125 genes were found to be dispensable in at least three human populations, suggesting possible evolution toward pseudogenes. Out of the 179 common loss-of-function variants, 72 could be tested for two neutrality selection statistics, and eight displayed robust signals of positive selection. These variants included the known FUT2 mutant allele conferring resistance to intestinal viruses and an APOL3 variant involved in resistance to parasitic infections. Finally, the 41 dispensable olfactory receptor genes also displayed a strong relaxation of selective constraints similar to that observed for the 341 non-dispensable olfactory receptor genes. Overall, the identification of 166 genes for which a sizeable proportion of humans are homozygous for predicted loss-of-function alleles reveals both redundancies and advantages of such deficiencies for human survival.\n\nSignificance statementHuman genes homozygous for seemingly loss of function (LoF) variants are increasingly reported in a sizeable proportion of individuals without overt clinical phenotypes. Here, we found 166 genes with 179 predicted LoF variants for which the frequency of homozygous individuals exceeds 1% in at least one of the populations present in databases ExAC and gnomAD. This set of putatively dispensable genes showed relaxation of selective constraints suggesting that a large number of these genes are undergoing pseudogenization. Eight of the common LoF variants displayed robust signals of positive selection including two variants located in genes involved in resistance to infectious diseases. The identification of dispensable genes will allow identifying functions that are, at least nowadays, redundant, or possibly advantageous, for human survival.

genetics↗

bioRxiv2019

Developmental gene expression differences between humans and mammalian models

Identifying the molecular programs underlying human organ development and how they differ from those in model species will advance our understanding of human health and disease. Developmental gene expression profiles provide a window into the genes underlying organ development as well as a direct means to compare them across species. We use a transcriptomic resource for mammalian organ development to characterize the temporal profiles of human genes associated with distinct disease classes and to determine, for each human gene, the similarity of its spatiotemporal expression with its orthologs in rhesus macaque, mouse, rat and rabbit. We find that half of human genes differ from their mouse orthologs in their temporal trajectories. These include more than 200 disease genes associated with brain, heart and liver disease, for which mouse models should undergo extra scrutiny. We provide a new resource that evaluates for every human gene its suitability to be modeled in different mammalian species.

genomics↗