A powerful approach to identify replicable variants in genome-wide association studies.

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies.

Structural variation (SV) is an important form of genomic variation that influences gene function and expression by altering the structure of the genome. Although long-read data have been proven to better characterize SVs, SVs detected from noisy long-read data still include a considerable portion of false-positive calls. To accurately detect SVs in long-read data, we present SVDF, a method that employs a learning-based noise filtering strategy and an SV signature-adaptive clustering algorithm, for effectively reducing the likelihood of false-positive events. Benchmarking results from multiple orthogonal experiments demonstrate that, across different sequencing platforms and depths, SVDF achieves higher calling accuracy for each sample compared to several existing general SV calling tools. We believe that, with its meticulous and sensitive SV detection capability, SVDF can bring new opportunities and advancements to cutting-edge genomic research.

Hippocampal activity predicts contextual misattribution of false memories.

Failure of contextual retrieval can lead to false recall, wherein people retrieve an item or experience that occurred in a different context or did not occur at all. Whereas the hippocampus is thought to play a crucial role in memory retrieval, we lack understanding of how the hippocampus supports retrieval of items related to a target context while disregarding related but irrelevant information. Using direct electrical recordings from the human hippocampus, we investigate the neural process underlying contextual misattribution of false memories. In two large datasets, we characterize key physiological differences between correct and false recalls that emerge immediately prior to vocalization. By differentiating between false recalls that share high or low contextual similarity with the target context, we show that low-frequency activity (6 to 18 Hz) in the hippocampus tracks similarity between the current and retrieved context. Applying multivariate decoding methods, we were able to reliably predict the contextual source of the to-be-recalled item. Our findings elucidate one of the hallmark features of episodic memory: our ability to distinguish between memories that were formed on different occasions.

High-speed imaging reveals the bimodal nature of dense core vesicle exocytosis.

During exocytosis, the fusion of secretory vesicle with plasma membrane forms a pore that regulates release of neurotransmitter and peptide. Heterogeneity of fusion pore behavior has been attributed to stochastic variation in a common exocytic mechanism, implying a lack of biological control. Using a fluorescent false neurotransmitter (FFN), we imaged dense core vesicle (DCV) exocytosis in primary mouse adrenal chromaffin cells by total internal reflection fluorescence microscopy at millisecond resolution and observed strikingly divergent modes of release, with fast events lasting <30 ms and slow events persisting for seconds. Dual imaging of slow events shows a delay in the entry of external dye relative to FFN release, suggesting exclusion by an extremely narrow pore <1 nm in diameter. Unbiased comprehensive analysis shows that the observed variation cannot be explained by stochasticity alone, but rather involves distinct mechanisms, revealing the bimodal nature of DCV exocytosis. Further, loss of calcium sensor synaptotagmin 7 increases the proportion of slow events without changing the intrinsic properties of either class, indicating the potential for independent regulation. The identification of two distinct mechanisms for release capable of independent regulation suggests a biological basis for the diversity of fusion pore behavior.

Prog-Plot - a visual method to determine functional relationships for false discovery rate regression methods.

Multiple test corrections are a fundamental step in the analysis of differentially expressed genes, as the number of tests performed would otherwise inflate the false discovery rate (FDR). Recent methods for P-value correction involve a regression model in order to include covariates that are informative of the power of the test. Here, we present Progressive proportions plot (Prog-Plot), a visual tool to identify the functional relationship between the covariate and the proportion of P-values consistent with the null hypothesis. The relationship between the proportion of P-values and the covariate to be included is needed, but there are no available tools to verify it. The approach presented here aims at having an objective way to specify regression models instead of relying on prior knowledge.

A novel strategy for designing the magic shotguns for distantly related target pairs.

Due to its promising capacity in improving drug efficacy, polypharmacology has emerged to be a new theme in the drug discovery of complex disease. In the process of novel multi-target drugs (MTDs) discovery, in silico strategies come to be quite essential for the advantage of high throughput and low cost. However, current researchers mostly aim at typical closely related target pairs. Because of the intricate pathogenesis networks of complex diseases, many distantly related targets are found to play crucial role in synergistic treatment. Therefore, an innovational method to develop drugs which could simultaneously target distantly related target pairs is of utmost importance. At the same time, reducing the false discovery rate in the design of MTDs remains to be the daunting technological difficulty. In this research, effective small molecule clustering in the positive dataset, together with a putative negative dataset generation strategy, was adopted in the process of model constructions. Through comprehensive assessment on 10 target pairs with hierarchical similarity-levels, the proposed strategy turned out to reduce the false discovery rate successfully. Constructed model types with much smaller numbers of inhibitor molecules gained considerable yields and showed better false-hit controllability than before. To further evaluate the generalization ability, an in-depth assessment of high-throughput virtual screening on ChEMBL database was conducted. As a result, this novel strategy could hierarchically improve the enrichment factors for each target pair (especially for those distantly related/unrelated target pairs), corresponding to target pair similarity-levels.

Reducing false positive rate of docking-based virtual screening by active learning.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

On the use of tandem mass spectra acquired from samples of evolutionarily distant organisms to validate methods for false discovery rate estimation.

Estimating the false discovery rate (FDR) of peptide identifications is a key step in proteomics data analysis, and many methods have been proposed for this purpose. Recently, an entrapment-inspired protocol to validate methods for FDR estimation appeared in articles showcasing new spectral library search tools. That validation approach involves generating incorrect spectral matches by searching spectra from evolutionarily distant organisms (entrapment queries) against the original target search space. Although this approach may appear similar to the solutions using entrapment databases, it represents a distinct conceptual framework whose correctness has not been verified yet. In this viewpoint, we first discussed the background of the entrapment-based validation protocols and then conducted a few simple computational experiments to verify the assumptions behind them. The results reveal that entrapment databases may, in some implementations, be a reasonable choice for validation, while the assumptions underpinning validation protocols based on entrapment queries are likely to be violated in practice. This article also highlights the need for well-designed frameworks for validating FDR estimation methods in proteomics.

Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics.

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

Target-decoy false discovery rate estimation using Crema.

Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.

Expectation Cues and False Percepts Generate Stimulus-Specific Activity in Distinct Layers of the Early Visual Cortex.

Perception has been proposed to result from the integration of feedforward sensory signals with internally generated feedback signals. Feedback signals are believed to play an important role in driving false percepts, that is, seeing things that are not actually there. Feedforward and feedback influences on perception can be studied using layer-specific fMRI, which we used here to interrogate neural activity underlying high-confidence false percepts while healthy human participants (N = 25, male and female) performed a perceptual orientation discrimination task. Auditory cues implicitly signaled the most likely upcoming orientation (referred to here as expectations). These expectations induced orientation-specific templates in the deep and superficial layers of V2, without affecting perception. In contrast, the orientation of falsely perceived stimuli with high confidence was reflected in the middle input layers of V2, suggesting a feedforward signal contributing to false percepts. The prevalence of high-confidence false percepts was related to everyday hallucination severity in a separate online sample (N = 100), suggesting a possible link with abnormal perceptual experiences. These results reveal a potential feedforward mechanism underlying false percepts, reflected by spontaneous stimulus-like activity in the input layers of the visual cortex, independent of top-down signals reflecting cued orientations.SIGNIFICANCE STATEMENT False percepts have been suggested to arise through excessive feedback signals. However, feedforward contributions to false percepts have remained largely understudied. Laminar fMRI has been shown to be useful in distinguishing feedforward from feedback activity as it allows the imaging of different cortical layers. In the present study we demonstrate that although cued orientations are encoded in the feedback layers of the visual cortex, the content of the false percepts are encoded in the feedforward layers and did not rely on these cued orientations. This shows that false percepts can in principle emerge from random feedforward signals in the visual cortex, with possible implications for disorders hallmarked by hallucinations like schizophrenia and Parkinson's disease.

Control of false discoveries in grouped hypothesis testing for eQTL data.

BACKGROUND: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. RESULTS: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. CONCLUSION: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data.

QNetDiff: a quantitative measurement of network rewiring.

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data.

BACKGROUND: Controlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini-Hochberg (BH) and Benjamini-Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates. RESULTS: When there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures. CONCLUSION: The modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion.

Multiple hypothesis testing is an integral component of data analysis for large-scale technologies such as proteomics, transcriptomics, or metabolomics, for which the false discovery rate (FDR) and positive FDR (pFDR) have been accepted as error estimation and control measures. The pFDR is the expectation of false discovery proportion (FDP), which refers to the ratio of the number of null hypotheses to that of all rejected hypotheses. In practice, the expectation of ratio is approximated by the ratio of expectation; however, the conditions for transforming the former into the latter have not been investigated. This work derives exact integral expressions for the expectation (pFDR) and variance of FDP. The widely used approximation (ratio of expectations) is shown to be a particular case (in the limit of a large sample size) of the integral formula for pFDR. A recurrence formula is provided to compute the pFDR for a predefined number of null hypotheses. The variance of FDP was approximated for a practical application in peptide identification using forward and reversed protein sequences. The simulations demonstrate that the integral expression exhibits better accuracy than the approximate formula in the case of a small number of hypotheses. For large sample sizes, the pFDRs obtained by the integral expression and approximation do not differ substantially. Applications to proteomics data sets are included.

Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples.

Data-independent acquisition has seen breakthroughs that enable comprehensive proteome profiling using short gradients. As the proteome coverage continues to increase, the quality of the data generated becomes much more relevant. Using Spectronaut, we show that the default search parameters can be easily optimized to minimize the occurrence of false positives across different samples. Using an immunological infection model system to demonstrate the impact of adjusting search settings, we analyzed Mus musculus macrophages and compared their proteome to macrophages spiked withCandida albicans. This experimental system enabled the identification of "false positives" as Candida albicans peptides and proteins should not be present in the Mus musculus-only samples. We show that adjusting the search parameters reduced "false positive" identifications by 89% at the peptide and protein level, thereby considerably increasing the quality of the data. We also show that these optimized parameters incurred a moderate cost, only reducing the overall number of "true positive" identifications across each biological replicate by <6.7% at both the peptide and protein level. We believe the value of our updated search parameters extends beyond a two-organism analysis and would be of great value to any DIA experiment analyzing heterogeneous populations of cell types or tissues.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

Site-Specific Identification of Protein S-Acylation by IodoTMT0 Labeling and Immobilized Anti-TMT Antibody Resin Enrichment.

Protein S-acylation is a reversible post-translational modification (PTM). It is present on diverse proteins and has important roles in regulating protein function. Aminolysis with hydroxylamine is widely used in the global identification of the PTM. However, the identification is indirect. Distinct criteria have been used for identification, and the false discovery rate has not been addressed. Here, we report a site-specific method for S-acylation identification based on tagging of S-acylation sites with iodoTMT0. Efforts to improve the performance of the method and confidence of identification are discussed, highlighting the importance of reducing contaminant peptides and keeping the recovery rate consistent between aliquots with or without hydroxylamine treatment. With very stringent criteria, presumptive S-acylation sites of 269, 684, 695, and 780 were identified from HK2 cells, HK11 cells, mouse brain, and mouse liver samples, respectively. Among them, the newly identified protein S-acylation sites are equivalent to 34% of human and 24% of mouse S-acylation sites reported previously. In addition, false-positive rates for S-acylation identification and S-acylation abundances were estimated. Significant differences in S-acylation abundance were found from different samples (from 0.08% in HK2 cells to 0.76% in mouse brain), and the false-positive rates were significantly higher for samples with a low abundance of S-acylation.

Revisiting Cytomegalovirus Serology in Allogeneic Hematopoietic Cell Transplant Recipients.

BACKGROUND: Allogeneic hematopoietic cell transplant recipients (allo-HCTRs) with positive cytomegalovirus (CMV) serology may have false-positive results due to blood product transfusion-associated passive immunity. METHODS: This single-center cohort study included allo-HCTRs with negative baseline (at malignancy diagnosis) CMV serology and indeterminate/low-positive (CMV IgG titer, ≥0.6-<50 U/mL) pretransplant CMV serology with negative pretransplant plasma CMV DNAemia. The CMV status of those patients was reclassified from R+ to R- (CMVR- reclassification group). We compared those patients to allo-HCTRs with negative (CMV IgG titer <0.6 U/mL) pretransplant CMV IgG (CMVR- group). We describe the number and type of patients whose pretransplant CMV status was reclassified from indeterminate/positive to negative. We reviewed all plasma CMV DNAemia tests performed during the first 6 months posttransplant in both groups to assess the safety of this approach. RESULTS: Among 246 (84.5%) of 291 transplanted patients identified as CMVR+ pretransplant, 60 (24.4%) were reclassified from CMV serology indeterminate (N:10)/low-positive (N:50) to R-. Only 1 of 60 patients (1.67%) in the CMVR- reclassification group versus 3 of 44 (6.8%; P = .30) in the CMVR- group developed CMV DNAemia during the follow-up period. There were no significant differences in the number of CMV DNAemia tests performed, CMV DNAemia range, and time posttransplant between the 2 groups. CONCLUSIONS: One of 4 allo-HCT CMVR+ may be falsely flagged as R+, with significant impact on donor selection and prophylaxis administration. A 2-step approach including CMV serology testing at hematologic malignancy diagnosis in allo-HCT candidates and careful review of pretransplant CMV IgG titers may help correctly classify CMV serology status.

False-Reactive Fourth-Generation Human Immunodeficiency Virus Testing in Cancer Patients.

BACKGROUND: The fourth-generation (4th-gen) human immunodeficiency virus (HIV)-1/2 antibody/antigen (Ab/Ag) combination immunoassay currently used for HIV screening offers greater sensitivity than previous assays, but false-reactive results occur in up to 20% of patients. Large-scale observations in cancer patients are lacking. METHODS: We conducted a retrospective study of cancer patients seen at the University of Texas MD Anderson Cancer Center (March 2016-January 2023) who had reactive 4th-gen ARCHITECT HIV-1/2 Ab/Ag combination immunoassay results. We analyzed characteristics of patients with true-reactive and false-reactive results, defined based on Centers for Disease Control and Prevention criteria. RESULTS: A total of 43 637 patients underwent 4th-gen HIV screening, and 293 had reactive 4th-gen HIV test results. Twenty-one patients were excluded because they did not have cancer. Among the remaining 272 patients, 78 (29%) had false-reactive results. None of these patients experienced delays in their cancer treatment, but 26% experienced mental distress. Multivariate logistic regression analysis identified 5 predictors of having false-reactive results: age >60 years (adjusted odds ratio [aOR], 6.983; P < .0001), female sex (aOR, 6.060; P < .0001), race/ethnicity (Black: aOR, 0.274; Hispanic: aOR, 0.236; P = .002), syphilis coinfection (aOR, 0.046; P = .038), and plant alkaloids therapy (aOR, 2.870; P = .013). CONCLUSIONS: False-reactive 4th-gen HIV test results occur in almost one-third of cancer patients. Physicians should be aware of the high rates of false-reactive HIV screening results in this patient population. These findings may have implications for counseling regarding testing, especially among those at low risk for HIV infection.