GenomicScores: seamless access to genomewide position-specific scores from R and Bioconductor

Puigdevall, Pau; Castelo, Robert

doi:10.1093/bioinformatics/bty311

Cited by 29 publications

(20 citation statements)

References 8 publications

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“…PhastCon scores were calculated with the GenomicScores package 52 and the phastCons100way.UCSC.hg19 package 53 in R, and summarized by taking the mean of the PhastCon scores of all the bases of either the promoter (200 bp upstream of the transcription start site to 100 bp downstream of the transcription start site) or the entire gene, as determined by the TxDb.Hsapiens.UCSC.hg19.knownGene package.…”

Section: Methodsmentioning

confidence: 99%

Intrinsic transcriptomic sex differences in human endothelial cells at birth and in adults are associated with coronary artery disease targets

Hartman

Kapteijn

Haitjema

et al. 2020

Sci Rep

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Sex differences in endothelial cell (EC) biology may reflect intrinsic differences driven by chromosomes or sex steroid exposure and gender differences accumulated over life. We analysed EC gene expression data from boy-girl twins at birth and in non-twin adults to detect sex differences at different stages of life, and show that 14-25% of the EC transcriptome is sex-biased. By combining data from both stages of life, we identified sex differences that are present at birth and maintained throughout life, and those that are acquired over life. Promisingly, we found that genes that present with an acquired sex difference in ECs are more likely to be targets of sex steroids. Annotating both gene sets with data from multiple genome-wide association studies (GWAS) revealed that genes with an intrinsic sex difference in ECs are enriched for coronary artery disease GWAS hits. This study underscores the need for treating sex as a biological variable. Abbreviations CAD Coronary artery disease DEG Differentially expressed gene EC Endothelial cell HAEC Human aortic endothelial cell HUVEC Human umbilical vein endothelial cell GWAS Genome-wide association study Endothelial cells (ECs) are located at the innermost lining of all vessels and are responsible for circulatory homeostasis. ECs are implicated in a plethora of functions, such as water and nutrient transfer, inflammation, vascular tension, hemostasis, and angiogenesis 1. Differences in EC biology between the sexes have been described, indicating that sex is an important variable in EC biology 2. Studies focussing on functional differences, i.e. migration, proliferation, and shear stress response, imply sex differences in regulation of key endothelial pathways, such as the redox system 3-5. Differences between female and male cells might be present from birth onwards, or acquired later in life because of sex steroid exposure and gender differences. Pinpointing master regulators herein has been difficult, as differences between the sexes may be caused by sex hormones and/or sex chromosomes, which are difficult to study separately or to dissect. Transcriptomic studies of female and male (endothelial) cells shed light on differentially expressed genes (DEGs) between the sexes and their regulators, but few have been performed thus far on ECs 4. Tissue-specific comparisons have been made using publicly available data from the

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Section: Methodsmentioning

confidence: 99%

Intrinsic transcriptomic sex differences in human endothelial cells at birth and in adults are associated with coronary artery disease targets

Hartman

Kapteijn

Haitjema

et al. 2020

Sci Rep

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“…The anchor regions of loops were overlapped and trimmed by the corresponding ATAseq peaks, then the phastCons60 scores for trimmed anchors were extracted by GenomicScores (64).…”

Section: Conservation Level Evaluation For Loopsmentioning

confidence: 99%

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model

et al. 2020

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Transcriptional enhancers commonly work over long genomic distances to precisely regulate spatiotemporal gene expression patterns. Dissecting the promoters physically contacted by these distal regulatory elements is essential for understanding developmental processes as well as the role of disease-associated risk variants. Modern proximity-ligation assays, like HiChIP and ChIA-PET, facilitate the accurate identification of long-range contacts between enhancers and promoters. However, these assays are technically challenging, expensive, and timeconsuming, making it difficult to investigate enhancer topologies, especially in uncharacterized cell types. To overcome these shortcomings, we therefore designed LoopPredictor, an ensemble machine learning model, to predict genome topology for cell types which lack longrange contact maps. To enrich for functional enhancer-promoter loops over common structural genomic contacts, we trained LoopPredictor with both H3K27ac and YY1 HiChIP data. What's more, the integration of several related multi-omics features facilitated identifying and annotating the predicted loops. LoopPredictor is able to efficiently identify cell type-specific enhancer mediated loops, and promoter-promoter interactions, with a modest feature input requirement.Comparable to experimentally generated H3K27ac HiChIP data, we found that LoopPredictor was able to identify functional enhancer loops. Furthermore, to explore the cross-species prediction capability of LoopPredictor, we fed mouse multi-omics features into a model trained on human data and found that the predicted enhancer loops outputs were highly conserved.LoopPredictor enables the dissection of cell type-specific long-range gene regulation, and can accelerate the identification of distal disease-associated risk variants.

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“…To support our analysis, we used the Bioconductor package GenomicScores, which conveniently provided genome-wide position-specific scores [30]. By loading a Bioconductor annotation package (phastCons100way.…”

Section: Conservation Of the Target Sitesmentioning

confidence: 99%

tRFTars: predicting the targets of tRNA-derived fragments

Xiao¹,

Gao²,

Huang³

et al. 2021

J Transl Med

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Background tRNA-derived fragments (tRFs) are 14–40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes. Methods We used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models. Results We first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977–0.983) in the training process and an AUC = 0.847 (0.83–0.861) in the test process. The model was applied to all the sites with perfect Watson–Crick complementarity to the seed in the 3′ untranslated region (3′-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a). Conclusions Predictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/, which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

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GenomicScores: seamless access to genomewide position-specific scores from R and Bioconductor

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 29 publications

References 8 publications

Intrinsic transcriptomic sex differences in human endothelial cells at birth and in adults are associated with coronary artery disease targets

Intrinsic transcriptomic sex differences in human endothelial cells at birth and in adults are associated with coronary artery disease targets

Predicting unrecognized enhancer-mediated genome topology by an ensemble machine learning model

tRFTars: predicting the targets of tRNA-derived fragments

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