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.

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.

Factors influencing blood tumor marker concentrations in the absence of neoplasia.

BACKGROUND: Tumor markers (TMs) are a heterogeneous group of molecules used in the diagnosis, prognosis and follow-up of cancer patients. During neoplastic differentiation, cells can either directly synthesize or induce the synthesis of TMs, and the release of these molecules into the bloodstream allows their quantification in biological fluids. Although very small concentrations of TMs are usually present in the serum or plasma of healthy subjects, increased concentrations may also be found in the presence of benign diseases or due to technical interference, producing false positive results. MATERIAL AND METHODS AND RESULTS: Our review analyses the causes of false positives described between January 1970 to February 2023 for the TMs most frequently used in clinical practice: α-fetoprotein (AFP), ß2-microglobulin (ß2-M), cancer antigen 15-3 (CA 15-3), cancer antigen CA 19-9 (CA 19-9), cancer antigen CA 72-4 (CA 72-4), cancer antigen 125 (CA 125), carcinoembryonic antigen (CEA), chromogranin A (CgA), choriogonadotropin (hCG), cytokeratin 19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), human epididymis protein 4 (HE4), serum HER2 (sHER2), squamous cell carcinoma antigen (SCCA), protein induced by vitamin K absence-II (PIVKA-II), Pro-gastrin-releasing peptide (Pro-GRP), prostate-specific antigen (PSA), Protein S-100 (S-100) and thyroglobulin (Tg). A total of 247 references were included. CONCLUSIONS: A better understanding of pathophysiological processes and other conditions that affect the concentration of TMs might improve the interpretation of results and their clinical application.

Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands.

Single-nucleotide variants (SNVs), pertinent to aging and disease, occur sporadically in the human genome, hence necessitating single-cell measurements. However, detection of single-cell SNVs suffers from false positives (FPs) due to intracellular single-stranded DNA damage and the process of whole-genome amplification (WGA). Here, we report a single-cell WGA method termed multiplexed end-tagging amplification of complementary strands (META-CS), which eliminates nearly all FPs by virtue of DNA complementarity, and achieved the highest accuracy thus far. We validated META-CS by sequencing kindred cells and human sperm, and applied it to other human tissues. Investigation of mature single human neurons revealed increasing SNVs with age and potentially unrepaired strand-specific oxidative guanine damage. We determined SNV frequencies along the genome in differentiated single human blood cells, and identified cell type-dependent mutational patterns for major types of lymphocytes.

An overlooked source of false positives in eDNA-based biodiversity assessment and management.

Biodiversity conservation and management in urban aquatic ecosystems is crucial to human welfare, and environmental DNA (eDNA)-based methods have become popular in biodiversity assessment. Here we report a highly overlooked source of significant false positives for eDNA-based biodiversity assessment in urban aquatic ecosystems supplied with treated wastewater - eDNA pollution originating from treated wastewater represents a noteworthy source of false positives. To investigate whether eDNA pollution is specific to a certain treatment or prevalent across methods employed by wastewater treatment plants, we conducted tests on effluent treated using three different secondary processes, both before and after upgrades to tertiary treatment. We metabarcoded eDNA collected from effluent immediately after full treatment and detected diverse native and non-native, commercial and ornamental fishes (48 taxa) across all treatment processes before and after upgrades. Thus, eDNA pollution occurred irrespective of the treatment processes applied. Release of eDNA pollution into natural aquatic ecosystems could translate into false positives for eDNA-based analysis. We discuss and propose technical solutions to minimize these false positives in environmental nucleic acid-based biodiversity assessments and conservation programs.

One pipeline to predict them all? On the prediction of alternative splicing from RNA-Seq data.

RNA-Seq has become the standard approach to quantify and compare gene expression and alternative splicing in different conditions. In many cases the limiting factor is not the sequencing itself but the bioinformatic analysis. A variety of software tools exist that predict alternative splicing patterns from RNA-Seq data, but surprisingly, a systematic comparison of the predictions obtained from different pipelines has not been performed. Here we compare results from frequently used bioinformatic tools using a high-quality RNA-Seq dataset. We show that there is little overlap in the splicing changes predicted by different tools and that GO-term analysis of the splicing changes predicted by the individual targets yields very different results. Validation of bioinformatic predictions by RT-PCR suggest a high number of false positives in the splicing changes predicated by each pipeline, which probably dominates GO-term analysis. The validation rate is strongly increased for targets predicted by several tools, offering a strategy to reduce false positives. Based on these results we offer some guidelines that may contribute to make alternative splicing predictions more reliable and may thus increase the impact of conclusions drawn from RNA-Seq studies. Furthermore, we created rmappet, a nextflow pipeline that performs alternative splicing analysis using rMATS and Whippet with subsequent overlapping of the results, enabling robust splicing analysis with only one command (https://github.com/didrikolofsson/rmappet/).

ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.

OBJECTIVES: To develop a deep learning (DL) for detection of brain metastasis (BM) that incorporates both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced DL) and evaluate it in a clinical cohort in comparison with human readers and DL using gradient-echo-based imaging only (GRE DL). MATERIALS AND METHODS: DL detection was developed using data from 200 patients with BM (training set) and tested in 62 (internal) and 48 (external) consecutive patients who underwent stereotactic radiosurgery and diagnostic dual-enhanced imaging (dual-enhanced DL) and later guide GRE imaging (GRE DL). The detection sensitivity and positive predictive value (PPV) were compared between two DLs. Two neuroradiologists independently analyzed BM and reference standards for BM were separately drawn by another neuroradiologist. The relative differences (RDs) from the reference standard BM numbers were compared between the DLs and neuroradiologists. RESULTS: Sensitivity was similar between GRE DL (93%, 95% confidence interval [CI]: 90-96%) and dual-enhanced DL (92% [89-94%]). The PPV of the dual-enhanced DL was higher (89% [86-92%], p < .001) than that of GRE DL (76%, [72-80%]). GRE DL significantly overestimated the number of metastases (false positives; RD: 0.05, 95% CI: 0.00-0.58) compared with neuroradiologists (RD: 0.00, 95% CI: - 0.28, 0.15, p < .001), whereas dual-enhanced DL (RD: 0.00, 95% CI: 0.00-0.15) did not show a statistically significant difference from neuroradiologists (RD: 0.00, 95% CI: - 0.20-0.10, p = .913). CONCLUSION: The dual-enhanced DL showed improved detection of BM and reduced overestimation compared with GRE DL, achieving similar performance to neuroradiologists. CLINICAL RELEVANCE STATEMENT: The use of deep learning-based brain metastasis detection with turbo spin-echo imaging reduces false positive detections, aiding in the guidance of stereotactic radiosurgery when gradient-echo imaging alone is employed. KEY POINTS: •Deep learning for brain metastasis detection improved by using both gradient- and turbo spin-echo contrast-enhanced MRI (dual-enhanced deep learning). •Dual-enhanced deep learning increased true positive detections and reduced overestimation. •Dual-enhanced deep learning achieved similar performance to neuroradiologists for brain metastasis counts.

Optimizing Research Output: How Can Psychological Research Methods Be Improved?

Recent evidence suggests that research practices in psychology and many other disciplines are far less effective than previously assumed, which has led to what has been called a "crisis of confidence" in psychological research (e.g., Pashler & Wagenmakers 2012). In response to the perceived crisis, standard research practices have come under intense scrutiny, and various changes have been suggested to improve them. The burgeoning field of metascience seeks to use standard quantitative data-gathering and modeling techniques to understand the reasons for inefficiency, to assess the likely effects of suggested changes, and ultimately to tell psychologists how to do better science. We review the pros and cons of suggested changes, highlighting the many complex research trade-offs that must be addressed to identify better methods.

Specificity of the local lymph node assay (LLNA) for skin sensitisation.

The local lymph node assay (LLNA) has provided a large dataset against which performance of non-animal approaches for prediction of skin sensitisation potential and potency can be assessed. However, a recent comparison of LLNA results with human data has argued that LLNA specificity is low, with many human non-sensitisers, particularly hydrophobic chemicals, being false positives. It has been suggested that such putative false positives result from hydrophobic chemicals causing cytotoxicity, which induces irritancy, in turn driving non-specific lymphocyte proliferation. This paper finds that the apparent reduced specificity of the LLNA largely reflects differences in definitions of the boundaries between weak skin sensitisers and non-sensitisers. A small number of LLNA false positives may be due to lymphocyte proliferation without skin sensitisation, but most alleged 'false' positives are in fact very weak sensitisers predictable from structure-activity considerations. The evidence does not support the hypothesis for hydrophobicity-induced false positives. Moreover, the mechanistic basis is untenable. Sound LLNA data, appropriately interpreted, remain a good measure of sensitisation potency, applicable across a wide hydrophilicity-hydrophobicity range. The standard data interpretation protocol enables detection of very low levels of sensitisation, irrespective of regulatory significance, but there is scope to interpret the data to give focus on regulatory significance.

Comparison and verification of two deep learning models for the detection of chest CT rib fractures.

BACKGROUND: A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures. PURPOSE: To examine the performance of two versions of deep-learning-based software tools in aiding radiologists in diagnosing rib fractures on chest computed tomography (CT) images. MATERIAL AND METHODS: In total, 123 patients (708 rib fractures) were included in this retrospective study. Two groups of radiologists with different experience levels retrospectively reviewed images for rib fractures in the concurrent mode aided with RibFrac-High Sensitivity (HS) and RibFrac-High Precision (HP). We compared their diagnostic performance against the reference standard in terms of sensitivity and positive predictive value (PPV). RESULTS: On a per-patient basis, RibFrac-HS exhibited a higher sensitivity compared with RibFrac-HP (mean difference=0.051, 95% CI=0.012-0.090; P = 0.011), whereas the latter significantly outperformed the former in terms of the PPV (mean difference=0.273, 95% CI=0.238-0.308; P < 0.0001). The use of RibFrac-HP significantly improved the junior and the senior groups' sensitivities respectively by 0.058 (95% CI=0.033-0.083; P < 0.0001) and 0.058 (95% CI=0.034-0.081; P < 0.0001), and decreased the diagnosis time by 206 s (95% CI=191-220; P < 0.0001) and 79 s (95% CI=67-92; P < 0.0001), respectively, when compared to no software assistance. CONCLUSION: The sensitivity and efficiency of radiologists in identifying rib fractures can be improved by using RibFrac-HS and/or RibFrac-HP. With an added module for false-positive suppression, RibFrac-HP maintains the sensitivity and increases the PPV in fracture detection compared to Rib-Frac-HS.

Improved YOLOv3 Integrating SENet and Optimized GIoU Loss for Occluded Pedestrian Detection.

Occluded pedestrian detection faces huge challenges. False positives and false negatives in crowd occlusion scenes will reduce the accuracy of occluded pedestrian detection. To overcome this problem, we proposed an improved you-only-look-once version 3 (YOLOv3) based on squeeze-and-excitation networks (SENet) and optimized generalized intersection over union (GIoU) loss for occluded pedestrian detection, namely YOLOv3-Occlusion (YOLOv3-Occ). The proposed network model considered incorporating squeeze-and-excitation networks (SENet) into YOLOv3, which assigned greater weights to the features of unobstructed parts of pedestrians to solve the problem of feature extraction against unsheltered parts. For the loss function, a new generalized intersection over unionintersection over groundtruth (GIoUIoG) loss was developed to ensure the areas of predicted frames of pedestrian invariant based on the GIoU loss, which tackled the problem of inaccurate positioning of pedestrians. The proposed method, YOLOv3-Occ, was validated on the CityPersons and COCO2014 datasets. Experimental results show the proposed method could obtain 1.2% MR-2 gains on the CityPersons dataset and 0.7% mAP@50 improvements on the COCO2014 dataset.

A Pilot Study to Detect Viable Salmonella spp. in Diarrheal Stool Using Viability Real-Time PCR as a Culture-Independent Diagnostic Tool in a Clinical Setting.

Frontline laboratories are adopting culture-independent diagnostic testing (CIDT) such as nucleic acid amplification tests (NAATs) due to numerous advantages over culture-based testing methods. Paradoxically, the viability of pathogens, a crucial factor determining active infections, cannot be confirmed with current NAATs alone. A recent development of viability PCR (vPCR) was introduced to mitigate this limitation associated with real-time PCR (qPCR) by using a DNA-intercalating dye to remove residual and dead cell DNA. This study assessed the applicability of the vPCR assay on diarrheal stools. Eighty-five diarrheal stools confirmed for Salmonellosis were tested via qPCR and vPCR using in-house primers and probe targeting the invA gene. vPCR-negative stools (Ct cut off > 31) were enriched in mannitol selenite broth (MSB) to verify low bacterial loads. vPCR assay showed ~89% sensitivity (qPCR- and vPCR-positive stools: 76/85). vPCR-negative stools (9/85; qPCR-positive: 5; qPCR-negative: 4) were qPCR- and culture-positive post-MSB-enrichment and confirmed the presence of low viable bacterial loads. Random sampling error, low bacterial loads, and receiving stools in batches could contribute to false negatives. This is a pilot study and further investigations are warranted to explore vPCR to assess pathogen viability in a clinical setting, especially when culture-based testing is unavailable.

The complex epistemological challenge of data curation in dietary metabarcoding: Comment on "The precautionary principle and dietary DNA metabarcoding: Commonly used abundance thresholds change ecological interpretation" by Littleford-Colquhoun et al. (2022).

In their article, Littleford-Colquhoun et al. (2022) advise against using arbitrary relative read abundance (RRA) thresholds (i.e., minimum sequence copy thresholds) for removing low-abundance sequences since they can increase false negative rates in dietary DNA metabarcoding data sets. The main criticisms presented against these widespread methods are that they (i) are arbitrary, often existing as standard values or defined based on researcher-selected delineations, (ii) are subjective, varying between studies and contexts, and, most problematically, (iii) result in the exclusion of true positives, particularly rarely consumed taxa, to the detriment of ecological insight. We commend the authors for presenting a refreshing and timely perspective on this often neglected topic, which is certainly in need of greater discussion following over a decade of significant advances in dietary metabarcoding. In this complex epistemological problem of false positives versus false negatives, we feel that several of the points raised deserve additional discussion. We address these aspects below, including measured approaches to data filtration and consistent representation of RRAs, and we welcome any further discourse to solidify or refute the concepts therein.

Current RT-qPCR to detect SARS-CoV-2 may give positive results for related coronaviruses.

Some weeks after the first CoVID-19 outbreak, the World Health Organization published some real-time PCR (qPCR) protocols developed by different health reference centers. These qPCR designs are being used worldwide to detect SARS-CoV-2 in the population, to monitor the prevalence of the virus during the pandemic. Moreover, some of these protocols to detect SARS-CoV-2 have widely been applied to environmental samples for epidemiological surveillance purposes. In the present work, the specificity of these currently used RT-qPCR designs was validated in vitro using SARS-CoV-2 and highly related coronaviral genomic sequences and compared to performance of the commercially available GPS™ CoVID-19 dtec-RT-qPCR Test. Assays performed with SARS-CoV-2-related genomes showed positive amplification when using some of these qPCR methods, indicating they may give SARS-CoV-2 false positives. This finding may be particularly relevant for SARS-CoV-2 monitoring of environmental samples, where an unknown pool of phylogenetically close-related viruses may exist.

The harmonic mean p-value for combining dependent tests.

Analysis of "big data" frequently involves statistical comparison of millions of competing hypotheses to discover hidden processes underlying observed patterns of data, for example, in the search for genetic determinants of disease in genome-wide association studies (GWAS). Controlling the familywise error rate (FWER) is considered the strongest protection against false positives but makes it difficult to reach the multiple testing-corrected significance threshold. Here, I introduce the harmonic mean p-value (HMP), which controls the FWER while greatly improving statistical power by combining dependent tests using generalized central limit theorem. I show that the HMP effortlessly combines information to detect statistically significant signals among groups of individually nonsignificant hypotheses in examples of a human GWAS for neuroticism and a joint human-pathogen GWAS for hepatitis C viral load. The HMP simultaneously tests all ways to group hypotheses, allowing the smallest groups of hypotheses that retain significance to be sought. The power of the HMP to detect significant hypothesis groups is greater than the power of the Benjamini-Hochberg procedure to detect significant hypotheses, although the latter only controls the weaker false discovery rate (FDR). The HMP has broad implications for the analysis of large datasets, because it enhances the potential for scientific discovery.

Types of Errors Hiding in Google Scholar Data.

Google Scholar (GS) is a free tool that may be used by researchers to analyze citations; find appropriate literature; or evaluate the quality of an author or a contender for tenure, promotion, a faculty position, funding, or research grants. GS has become a major bibliographic and citation database. For assessing the literature, databases, such as PubMed, PsycINFO, Scopus, and Web of Science, can be used in place of GS because they are more reliable. The aim of this study was to examine the accuracy of citation data collected from GS and provide a comprehensive description of the errors and miscounts identified. For this purpose, 281 documents that cited 2 specific works were retrieved via Publish or Perish software (PoP) and were examined. This work studied the false-positive issue inherent in the analysis of neuroimaging data. The results revealed an unprecedented error rate, with 279 of 281 (99.3%) examined references containing at least one error. Nonacademic documents tended to contain more errors than academic publications (U=5117.0; P<.001). This viewpoint article, based on a case study examining GS data accuracy, shows that GS data not only fail to be accurate but also potentially expose researchers, who would use these data without verification, to substantial biases in their analyses and results. Further work must be conducted to assess the consequences of using GS data extracted by PoP.

Clinical re-biopsy of segmental gains-the primary source of preimplantation genetic testing false positives.

PURPOSE: Does re-biopsy of blastocysts classified as abnormal (ABN) due to segmental aneuploidy (SA) have clinical utility? METHODS: The live birth (LB) outcomes of mosaic SAs, compared to other categories, were determined after transfer of 3084 PGT-A tested blastocysts. An initial 12-month trial thawed 111 blastocysts classified as ABN due to a SA for clinical re-biopsy, with an additional 58 from a subsequent 16-month revised protocol. Where re-biopsy failed to corroborate the original classification, blastocysts were reported as mosaic and suitable for clinical use. RESULTS: Segmental mosaics had a LB rate (54.1%) which was indistinguishable from that of euploid (53.7%). Numeric mosaics had statistically significant (P < 0.05) reduced LB rates compared to euploid, with high-level numerics (19.2%) also exhibiting a significant reduction compared to low level (42.3%). Of the initial 111 blastocysts with SAs, 85 could be re-biopsied. Segmental gains became suitable for re-biopsy at a high rate (90.9%), with 84.2% (16/19) of these reclassified as mosaic. Only 73.0% of deletions and complex changes were suitable for re-biopsy, of which 73.0% (46/63) were confirmed ABN. The subsequent 16-month period primarily focused on gains, confirming the high rate at which they can be reclassified as clinically useable. CONCLUSIONS: Blastocysts harboring mosaic segmental duplications, rather than SAs in general, are the primary source of false-positive PGT-A results and represent a category with a LB rate similar to that of euploid. A high degree of confidence in the reliability of PGT-A results can be maintained by performing confirmatory clinical TE biopsies.

Improving the sensitivity of cluster-based statistics for functional magnetic resonance imaging data.

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false-alarm rate, (b) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.

False and true positives in arthropod thermal adaptation candidate gene lists.

Genome-wide studies are prone to false positives due to inherently low priors and statistical power. One approach to ameliorate this problem is to seek validation of reported candidate genes across independent studies: genes with repeatedly discovered effects are less likely to be false positives. Inversely, genes reported only as many times as expected by chance alone, while possibly representing novel discoveries, are also more likely to be false positives. We show that, across over 30 genome-wide studies that reported Drosophila and Daphnia genes with possible roles in thermal adaptation, the combined lists of candidate genes and orthologous groups are rapidly approaching the total number of genes and orthologous groups in the respective genomes. This is consistent with the expectation of high frequency of false positives. The majority of these spurious candidates have been identified by one or a few studies, as expected by chance alone. In contrast, a noticeable minority of genes have been identified by numerous studies with the probabilities of such discoveries occurring by chance alone being exceedingly small. For this subset of genes, different studies are in agreement with each other despite differences in the ecological settings, genomic tools and methodology, and reporting thresholds. We provide a reference set of presumed true positives among Drosophila candidate genes and orthologous groups involved in response to changes in temperature, suitable for cross-validation purposes. Despite this approach being prone to false negatives, this list of presumed true positives includes several hundred genes, consistent with the "omnigenic" concept of genetic architecture of complex traits.