Highlights d Comprehensive map of TF occupancy in human tissues from DNase-seq footprints d Footprints contain genetic variants associated with changes in gene expression d Tissue-specific associations of footprints with genetic risk for complex traits
There is intense interest in mapping the tissue-specific binding sites of transcription factors in the human genome to reconstruct gene regulatory networks and predict functions for noncoding genetic variation. DNase-seq footprinting provides a means to predict the genome-wide binding sites for hundreds of transcription factors (TFs) simultaneously. However, despite the public availability of DNase-seq data for hundreds of samples, there is neither a unified analytical workflow nor a publicly accessible database providing the locations of footprints across all available samples. Here, we describe the implementation of a workflow for uniform processing of footprints using two state-of-the-art footprinting algorithms: Wellington and HINT. Our workflow then scans footprints for 1,530 sequence motifs to predict binding sites for 1,515 human transcription factors. We tested our workflow using 21 DNase-seq experiments of lymphoblastoid cell lines, generated by the ENCODE project. We trained a machine learning model to predict TF binding sites, integrating footprints with additional biologically-related features. This model achieved a maximum MCC of 0.423 and an AUC of 0.943 compared to ENCODE ChIP-seq data for 62 TFs in the same cell type. We applied our workflow to detect footprints in 206 DNase-seq experiments from ENCODE, spanning 27 human tissues. These footprints describe an expansive landscape of TF occupancy in the human genome. Across all tissues, we detected high-quality footprints spanning 9.8% of all nucleotides in the human genome with scores found to enrich for true positives. The highest tissue-specific coverage was observed for samples in the brain (4.4%), followed by extra-embryonic structure (2.6%) and skin (2.4%). In addition, we report a more lenient footprinting call set, providing some evidence of TF occupancy in at least one tissue for 34% of all genomic positions. Our cloud-based workflow and a database with all footprints and TF binding site predictions are available at www.trena.org.
Background: All-terrain vehicles are mostly used in poor driving environments. A key part of the suspension mechanism of all-terrain vehicles, the lower control arm, bears various loads when the vehicle is driving. This component is prone to be fatigue and failure, which affects the performance of the entire vehicle. Therefore, in order to improve the performance of all-terrain vehicles, the fatigue life of the lower control arm was studied based on the measured force load spectrum. Methods: Firstly, the finite element model of the lower control arm is established, the free modal simulation analysis is carried out, and the experimental research is carried out by building a modal test system. Then combining the calculated modal and experimental modal results, the finite element model is verified. Next, through the road load spectrum acquisition test in the automobile proving ground, the force time history of the lower control arm is obtained, and the signal is processed and analyzed to verify the reliability of the force load signal. On this basis, the boundary constraints of the lower control arm are established based on the actual working conditions of the all-terrain vehicle, and the dynamics simulation analysis is carried out with the measured force as input. Finally, according to stress-strain signal in dynamic analysis results, combining the modified local stress-strain method and the Landgrave damage criterion, the fatigue life of the lower control arm is calculated. Results: The minimum fatigue cycle life of the lower control arm on the test roads is 3.56×105 km, and its fatigue life meets the design and use requirements. Conclusions: The result shows that based on the actual driving load spectrum, the actual driving fatigue life can be calculated and forecasted more accurately.
Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS269). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS269 constructed with fine-mapping.
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