Biology subjects

Brenner, S. E.

Publications and source records attributed to Brenner, S. E..

17 recordsLinked to original sources

bioRxiv2026

predNMD: prediction of nonsense-mediated mRNA decay for improved clinical variant pathogenicity classification

The clinical consequence of a stop-gain variant depends on whether it ablates protein production by triggering nonsense-mediated mRNA decay (NMD) or yields truncated protein with residual, dominant-negative, or gain-of-function activity. This distinction is the key branch-point in applying PVS1, the strongest ACMG/AMP pathogenic evidence criterion. Current PVS1 guidelines and proposed SVC v4.0 successors risk unwarranted evidence assignment by employing only the 50nt rule for NMD prediction, which is central but incompletely captures NMD biology. We developed predNMD, a random forest classifier trained on 5,304 nonsense variants from GTEx, TCGA, GEUVADIS, and GREGoR. Feature selection reduced 166 candidates to 20 final features, 7 new to NMD prediction, including m6A density and TranslationAI. For variants predicted to not trigger NMD, predNMD infers the likely protein truncation. predNMD predictions aligned with deliberate clinical decisions where ClinGen Variant Curation Expert Panels (VCEPs) drew on evidence leading them to diverge from standard decision tree. predNMD agreed with VCEP on 6 of the 8 stop-gain variants for which they declined full PVS1, though the variants were predicted to trigger NMD by the 50nt rule and in loss-of-function disease genes. In leave-one-chromosome-out cross-validation predNMD reached AUC=0.79, and on independent test set outperformed 50nt rule (AUC 0.78 vs 0.65), nearly doubling discriminative signal above random (0.28 vs 0.15). predNMD likewise effectively discriminated NMD targets on BRCA1 and BARD1 saturation genome-editing data, where RNA abundance reflects NMD. These results support replacing the 50nt rule in clinical variant classification with predNMD, available as precomputed predictions covering all 13,968,776 possible stop-gain SNVs in GRCh38, installable codes, Docker image, and at predNMD.org.

genomics↗

bioRxiv2026

Advances in protein function prediction from the fifth CAFA challenge

The Critical Assessment of Functional Annotation (CAFA) is a long-standing community effort to independently assess computational methods for protein function prediction, to highlight well-performing methodologies, to identify bottlenecks in the field, and to provide a forum for the dissemination of results and exchange of ideas. In its fifth round (CAFA5) of triennial challenges, a partnership with Kaggle Inc. facilitated participation from a large community of data scientists and computational biologists through a competitive prospective challenge on the crowdsourcing platform. In this work, we present an in-depth analysis of the submitted predictions and report improvements in accuracy over all methods from the previous CAFA challenges. We further introduce a new evaluation setting for proteins with pre-existing (incomplete) annotations and identify the need for methods that better leverage existing annotations to predict those that will be discovered later. Finally, we characterize the prospective evaluation framework by examining performance on a strict set of unpublished annotations and across intermediate database releases. Our results indicate that recent developments in the field, such as the availability of protein language models and accurately predicted 3D structures, as well as the growth of experimental annotations through biocuration, have all contributed to performance improvements. 1

bioinformatics↗

bioRxiv2026

Calibration of in-frame indel variant effect predictors for clinical variant classification

Insertions and deletions (indels) represent a substantial source of genetic variation in humans and are associated with a diverse array of functional consequences. Despite their prevalence and clinical importance, indels, particularly short in-frame indels, remain critically understudied compared to single nucleotide variants and are challenging to interpret clinically. While many computational predictors for missense variants have been rigorously evaluated and calibrated for clinical use, the clinical utility of tools for in-frame indels remains uncertain. To address this gap, we have calibrated in-frame indel prediction tools for clinical variant classification. We constructed a high-confidence dataset of in-frame indel variants ([≤] 50bp) from clinical and population databases and estimated the prior probability of pathogenicity of a rare in-frame indel observed in a disease-associated gene, and of an insertion and deletion separately. Using a previously developed statistical framework based on local posterior probabilities, we then established score thresholds for eight computational tools, corresponding to distinct evidence levels for pathogenic and benign classification according to ACMG/AMP guidelines. All in-frame indel predictors evaluated here reached multiple evidence levels of pathogenicity and/or benignity, demonstrating measurable clinical value. However, these models consistently exhibited lower performance levels compared to missense predictors, highlighting the need for improved computational approaches for indel classification.

bioinformatics↗

bioRxiv2024

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria

PurposeWe previously developed an approach to calibrate computational tools for clinical variant classification, updating recommendations for the reliable use of variant impact predictors to provide evidence strength up to Strong. A new generation of tools using distinctive approaches have since been released, and these methods must be independently calibrated for clinical application. MethodUsing our local posterior probability-based calibration and our established data set of ClinVar pathogenic and benign variants, we determined the strength of evidence provided by three new tools (AlphaMissense, ESM1b, VARITY) and calibrated scores meeting each evidence strength. Results All three tools reached the Strong level of evidence for variant pathogenicity and Moderate for benignity, though sometimes for few variants. Compared to previously recommended tools, these yielded at best only modest improvements in the tradeoffs of evidence strength and false positive predictions. ConclusionAt calibrated thresholds, three new computational predictors provided evidence for variant pathogenicity at similar strength to the four previously recommended predictors (and comparable with functional assays for some variants). This calibration broadens the scope of computational tools for application in clinical variant classification. Their new approaches offer promise for future advancement of the field.

bioinformatics↗

bioRxiv2024

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

BackgroundVariant interpretation is essential for identifying patients disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). ResultsThe Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. ConclusionsVIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. AvailabilityVIPdb version 2 is available at https://genomeinterpretation.org/vipdb

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↗

bioRxiv2022

Evidence-based calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for clinical use of PP3/BP4 criteria

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as Supporting level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (Supporting, Moderate, Strong, Very Strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tools scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved Supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying Moderate and several reached Strong evidence levels. One tool reached Very Strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

bioinformatics↗

bioRxiv2020

Dysregulation of hippocampal adult-born immature neurons disrupts a brain-wide network for spatial memory

Mounting evidence suggests that cognitive deficits associated with various neurological disorders may arise in part from a small population of dysregulated adult-born neurons in the dentate gyrus (DG). How these dysregulated adult-born neurons contribute to brain-wide network maladaptation and subsequent cognitive deficits remains unknown. Using an established mouse model with a small number of time-stamped dysregulated adult-born immature neurons and spatial memory deficits, we performed resting state functional magnetic resonance imaging and found that approximately 500 deficient immature neurons (<0.1% of total DG granule neurons) are sufficient to induce a significant decrease in the functional connectivity between DG and insular cortex (IC), two brain regions without direct anatomical connections. Furthermore, using a combination of rabies-based retrograde tracing and in vivo fiber photometry recording, we demonstrated that dysregulated adult-born neurons induce aberrant activity and synchrony in local hippocampal CA3 and CA1 regions, as well as distal medial-dorsal thalamus and IC regions during a spatial memory process. These results suggest that a few hundred dysregulated adult-born immature neurons can impact brain-wide network dynamics across several anatomically discrete regions and collectively contribute to impaired cognitive functions.

neuroscience↗

bioRxiv2020

Transcriptome analysis of alternative splicing-coupled nonsense-mediated mRNA decay in human cells reveals broad regulatory potential

To explore the regulatory potential of nonsense-mediated mRNA decay (NMD) coupled with alternative splicing, we globally surveyed the transcripts targeted by this pathway via RNA-Seq analysis of HeLa cells in which NMD had been inhibited. We identified putative NMD-targeted transcripts as those with a termination codon more than 50 nucleotides upstream of an exon-exon junction (premature termination as defined by the ‘50nt rule’) and that significantly increased in abundance upon NMD inhibition. We additionally controlled for potential transcriptional up-regulation by requiring the putative NMD targets to increase in abundance substantially more than the isoforms from the same gene that do not contain a premature termination codon. This resulted in a conservative set of 2,793 transcripts derived from 2,116 genes as physiological NMD targets (9.2% of expressed transcripts and >20% of alternatively spliced genes). Our analysis identified previously inferred unproductive isoforms and numerous heretofore-uncharacterized ones. NMD-targeted transcripts were derived from genes involved in many functional categories, and are particularly enriched for RNA splicing genes as well as for those harboring ultraconserved elements. By investigating the features of all transcripts impacted by NMD, we find that the 50nt rule is a strong predictor of NMD degradation while 3’ UTR length on its own generally has only a small effect in this human cell line. Additionally, thousands more transcripts without a premature termination codon in the main coding sequence contain a uORF and display significantly increased abundance upon NMD inhibition indicating potentially widespread regulation through decay coupled with uORF translation. Our results support that alternative splicing coupled with NMD is a prevalent post-transcriptional mechanism in human cells with broad potential for biological regulation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

bioRxiv2020

Perturbation robustness analyses reveal important parameters in variant interpretation pipelines

MotivationGenome sequencing is being used routinely in clinical and research applications, but subsequent variant interpretation pipelines can vary widely. A systematic approach for exploring parameter choices and selection plays an important role in designing robust pipelines for specific clinical applications. ResultsWe present a framework to be applied in scenarios with limited data whereby expert knowledge informs pipeline refinement. Starting from initial reference variant interpretation pipelines with commonly used parameters, we derived pipelines by perturbing the parameters one by one to determine which parameters can yield meaningful changes in a pipelines performance. We updated the reference pipeline by fixing the value of parameters which have small impact on the pipelines performance. Then we conducted new rounds of perturbation as the process converged, yielding a stable pipeline which is robust. We applied the framework for genetic disease prediction in de-identified exomes from a cohort of 138 individuals with rare Mendelian inborn errors of metabolism (IEMs) and systematically explored how perturbing different parameters affected the pipelines sensitivity and specificity. For this application, we perturbed commonly used parameters in variant interpretation pipelines, including choices of genes, variant callers, transcript models, databases of allele frequencies, databases of curated disease variants, and tools for variant impact prediction. Our analyses showed that choice of variant callers, variant impact prediction tools, MAF threshold, and MAF databases can meaningfully alter results from a pipeline. This work informs the development of exome analysis pipelines designed for newborn metabolic disorder screening and suggests the general application of perturbation analysis in genome interpretation pipeline design.

bioinformatics↗

bioRxiv2020

Networks of Splice Factor Regulation by Unproductive Splicing Coupled With Nonsense Mediated mRNA Decay

BackgroundNonsense mediated mRNA decay (NMD) is an RNA surveillance pathway that degrades aberrant transcripts harboring premature termination codons. This pathway, in conjunction with alternative splicing, regulates gene expression post-transcriptionally. Nearly all serine and arginine-rich (SR) proteins and many heterogeneous nuclear ribonucleoproteins (hnRNPs) produce isoforms that can be degraded by the NMD pathway. Many splicing factors have been reported to be regulated via alternative splicing coupled to NMD. However, it is still uncharacterized that to what extent NMD contributes to the regulation of splicing factors. ResultsHere, we characterized a regulatory network of splicing factors through alternative splicing coupled to NMD. Based upon an extensive literature search, we first assembled a network that encompasses the current knowledge of splice factors repressing or activating the expression of other splicing factors through alternative splicing coupled to NMD. This regulatory network is limited, including just a handful of well-studied splicing factors. To gain a more global and less biased overview, we examined the splicing factor-mRNA interactions from public crosslinking-immunoprecipitation (CLIP)-seq data, which provides information about protein-RNA interactions. A network view of these interactions reveals extensive binding among splicing regulators. We also found that splicing factors bind more frequently to transcripts of other splicing factors than to other genes. In addition, many splicing factors are targets of NMD, and might be regulated via alternative splicing coupled to NMD, which is demonstrated by the significant overlap between the experimental network and eCLIP-network. We found that hierarchy of the splicing-factor interaction network differs from the hierarchy observed for transcription factors. ConclusionThe extensive interaction between splicing factors and transcripts of other splicing factors suggests that the potential regulation via alternative splicing coupled with NMD is widespread. The splicing factor regulation is fundamentally different from that of transcription factors.

molecular biology↗

bioRxiv2020

DNA from dried blood spots yields high quality sequences for exome analysis

BackgroundDNA sequencing of archived dried blood spots (DBS) collected by newborn screening programs constitutes a potential health resource to study newborn disorders and understand genotype-phenotype relationships. However, its essential to verify that sequencing reads from DBS derived DNA are suitable for variant discovery. ResultsWe explored 16 metrics to comprehensively assess the quality of sequencing reads from 180 DBS and 35 whole blood (WB) samples. These metrics were used to assess a) mapping of reads to the reference genome, b) degree of DNA damage, and c) variant calling. Reads from both sets mapped with similar efficiencies, had similar overall DNA damage rates, measured by the mismatch rate with the reference genome, and produced variant calls sets with similar Transition-Transversion ratios. While evaluating single nucleotide changes that may have arisen from DNA damage, we observed that the A>T and T>A changes were more frequent in DNA from DBS than from WB. However, this did not affect the accuracy of variant calling, with DBS samples yielding a comparable count of high quality SNVs and indels in samples with at least 50x coverage. ConclusionsOverall, DBS DNA provided exome sequencing data of sufficient quality for clinical interpretation.

genomics↗

bioRxiv2020

StrVCTVRE: A supervised learning method to predict the pathogenicity of human structural variants.

BackgroundWhole genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and structural variant detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. ResultsTo address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. ConclusionsWe anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in patients where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is available at https://compbio.berkeley.edu/proj/strvctvre/.

genomics↗

bioRxiv2020

Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads

BackgroundInsertion and deletion sequencing errors are relatively common in next-generation sequencing data and produce long stretches of mistranslated sequence. These frameshifting errors can cause very serious damages to downstream data analysis of reads. However, it is possible to obtain more precise alignment of DNA sequences by taking into account both coding frame and sequencing errors estimated by quality scores. ResultsHere we designed and proposed a novel hidden Markov model (HMM)-based pairwise alignment algorithm, Meta-Align, that aligns DNA sequences in the protein space, incorporating quality scores from the DNA sequences and allowing frameshifts caused by insertions and deletions. Our model is based on both an HMM transducer of a pair HMM and profile HMMs for all possible amino acid pairs. A Viterbi algorithm over our model produces the optimal alignment of a pair of metagenomic reads taking into account all possible translating frames and gap penalties in both the protein space and the DNA space. To reduce the sheer number of states of this model, we also derived and implemented a computationally feasible model, leveraging the degeneracy of the genetic code. In a benchmark test on a diverse set of simulated reads based on BAliBASE we show that Meta-Align outperforms TBLASTX which compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database using the BLAST algorithm. We also demonstrate the effects of incorporating quality scores on Meta-Align. ConclusionsMeta-Align will be particularly effective when applied to error-prone DNA sequences. The package of our software can be downloaded at https://github.com/shravan-repos/Metaalign.

bioinformatics↗

bioRxiv2020

Polysome fractionation analysis reveals features important for human nonsense-mediated mRNA decay

Nonsense-mediated mRNA decay (NMD) is a translation-dependent mRNA surveillance pathway that eliminates transcripts with premature termination codons. Several studies have tried defined the features governing which transcripts are targeted to NMD. However, these approaches often rely on inhibiting core NMD factors, which often have roles in non-NMD processes within the cell. Based on reports that NMD-targeted transcripts are often bound by a single ribosome, we analyzed RNA-Seq data from a polysome fractionation experiment (TrlP-Seq) to characterize the features of NMD-targeted transcripts in human cells. This approach alleviates the need to inhibit the NMD pathway. We found that the exon junction complex (EJC) model, wherein an exon-exon junction located [≥]50 nucleotides downstream of a stop codon is predicted to elicit NMD, was a powerful predictor of transcripts with high abundance in the monosome fraction (bound by a single ribosome). This was also true for the presence of an upstream open reading frame. In contrast, as 3 UTR lengths increase, the proportion of transcripts that are most abundant in the monosome fraction does not increase. This suggests that either longer 3 UTRs do not consistently act as potent triggers of NMD or that the degradation of these transcripts is mechanistically different to other NMD-targeted transcripts. Of the ribosome-associated transcripts annotated as "non-coding", we find that a majority are bound by a single ribosome. Many of these transcripts increase in response to NMD inhibition, including the oncogenic SHNG15, suggesting many might be NMD targets. Finally, we found that retained intron transcripts without a premature termination codon are over-represented in the monosome fraction, suggesting an alternative mechanism is responsible for the low level of translation of these transcripts. In summary, our analysis finds that the EJC model is a powerful predictor of NMD-targeted transcripts, while the presence of a long 3 UTR is not.

molecular biology↗

bioRxiv2019

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

bioinformatics↗

bioRxiv2016

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology -- predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

bioinformatics↗