scholarly journals HUGIn: Hi-C Unifying Genomic Interrogator

2017 ◽  
Author(s):  
Joshua S. Martin ◽  
Zheng Xu ◽  
Alex P. Reiner ◽  
Karen L. Mohlke ◽  
Patrick Sullivan ◽  
...  
Keyword(s):  
High Throughput ◽  
Regulatory Elements ◽  
Supplementary Information ◽  
Chromatin Conformation ◽  
Organization Studies ◽  
Web Browser ◽  
Functional Roles ◽  
Gwas Snps ◽  
Genome Wide ◽  
Coding Variants

AbstractMotivationHigh throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChlA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability.ResultsWe developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell liens. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTLs results and functional genomics data.AvailabilityHUGIn is available at http://yunliweb.its.unc.edu/[email protected] and [email protected] information:

scholarly journals DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome

2021 ◽  
Author(s):  
Yanrong Ji ◽  
Zhihan Zhou ◽  
Han Liu ◽  
Ramana V Davuluri
Keyword(s):  
Dna Sequences ◽  
Regulatory Elements ◽  
Ease Of Use ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Sequence Motifs ◽  
Semantic Relationship ◽  
Accurate Identification ◽  
Conserved Sequence ◽  
Genome Wide

Abstract Motivation Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. Results To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. Availability and implementation The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hi-Cpipe: a pipeline for high-throughput chromosome capture

2015 ◽  
Author(s):  
Giancarlo Castellano ◽  
François Le Dily ◽  
Antonio Hermoso Pulido ◽  
Miguel Beato ◽  
Guglielmo Roma
Keyword(s):  
High Throughput ◽  
Visual Inspection ◽  
Automated Analysis ◽  
Chromatin Conformation ◽  
Interaction Data ◽  
Alignment Quality ◽  
Bioinformatics Pipeline ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Analysis Workflow

Hi-Cpipe is a bioinformatics pipeline for the automated analysis of data generated by high-throughput chromatin conformation capture (HiC). The analysis workflow comprises steps of data formatting, genome alignment, quality control and filtering, identification of genome-wide chromatin interactions, visualization and statistics. An interactive browser enables visual inspection of interaction data and results.

scholarly journals Integration of methylation QTL and enhancer–target gene maps with schizophrenia GWAS summary results identifies novel genes

2019 ◽  
Vol 35 (19) ◽  
pp. 3576-3583 ◽  
Author(s):  
Chong Wu ◽  
Wei Pan
Keyword(s):  
Quantitative Trait ◽  
Large Scale ◽  
Target Gene ◽  
Association Studies ◽  
Regulatory Elements ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Novel Genes ◽  
Gene Pairs ◽  
Genome Wide

Abstract Motivation Most trait-associated genetic variants identified in genome-wide association studies (GWASs) are located in non-coding regions of the genome and thought to act through their regulatory roles. Results To account for enriched association signals in DNA regulatory elements, we propose a novel and general gene-based association testing strategy that integrates enhancer-target gene pairs and methylation quantitative trait locus data with GWAS summary results; it aims to both boost statistical power for new discoveries and enhance mechanistic interpretability of any new discovery. By reanalyzing two large-scale schizophrenia GWAS summary datasets, we demonstrate that the proposed method could identify some significant and novel genes (containing no genome-wide significant SNPs nearby) that would have been missed by other competing approaches, including the standard and some integrative gene-based association methods, such as one incorporating enhancer-target gene pairs and one integrating expression quantitative trait loci. Availability and implementation Software: wuchong.org/egmethyl.html Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting

2020 ◽  
Vol 36 (17) ◽  
pp. 4576-4582
Author(s):  
Yaobin Ke ◽  
Jiahua Rao ◽  
Huiying Zhao ◽  
Yutong Lu ◽  
Nong Xiao ◽  
...  
Keyword(s):  
Secondary Structure ◽  
High Throughput ◽  
Rna Secondary Structure ◽  
High Throughput Sequencing ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Synonymous Mutations ◽  
Periodic Distribution ◽  
Genome Wide ◽  
Extreme Gradient Boosting

Abstract Motivation RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage. Results Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods. Availability and implementation The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP. Supplementary information Supplementary data are available online.

scholarly journals Chromatin accessibility landscape and regulatory network of high-altitude hypoxia adaptation

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingxue Xin ◽  
Hui Zhang ◽  
Yaoxi He ◽  
Zhana Duren ◽  
Caijuan Bai ◽  
...  
Keyword(s):  
High Altitude ◽  
Regulatory Network ◽  
Cell Types ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Remarkable Case ◽  
Genome Wide ◽  
Altitude Adaptation ◽  
Time Courses ◽  
Coding Variants

Abstract High-altitude adaptation of Tibetans represents a remarkable case of natural selection during recent human evolution. Previous genome-wide scans found many non-coding variants under selection, suggesting a pressing need to understand the functional role of non-coding regulatory elements (REs). Here, we generate time courses of paired ATAC-seq and RNA-seq data on cultured HUVECs under hypoxic and normoxic conditions. We further develop a variant interpretation methodology (vPECA) to identify active selected REs (ASREs) and associated regulatory network. We discover three causal SNPs of EPAS1, the key adaptive gene for Tibetans. These SNPs decrease the accessibility of ASREs with weakened binding strength of relevant TFs, and cooperatively down-regulate EPAS1 expression. We further construct the downstream network of EPAS1, elucidating its roles in hypoxic response and angiogenesis. Collectively, we provide a systematic approach to interpret phenotype-associated noncoding variants in proper cell types and relevant dynamic conditions, to model their impact on gene regulation.

scholarly journals Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 950 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Malcolm Perry ◽  
Elizabeth Ing-Simmons
Keyword(s):  
Experimental Data ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Careful Analysis ◽  
Chromatin Conformation ◽  
Interaction Data ◽  
Genome Wide ◽  
Bioconductor Project ◽  
Basic Infrastructure

The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.

scholarly journals Infrastructure for genomic interactions: Bioconductor classes for Hi-C, ChIA-PET and related experiments

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 950 ◽  
Author(s):  
Aaron T. L. Lun ◽  
Malcolm Perry ◽  
Elizabeth Ing-Simmons
Keyword(s):  
Experimental Data ◽  
Open Source ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Careful Analysis ◽  
Chromatin Conformation ◽  
Interaction Data ◽  
Genome Wide ◽  
Bioconductor Project ◽  
Basic Infrastructure

The study of genomic interactions has been greatly facilitated by techniques such as chromatin conformation capture with high-throughput sequencing (Hi-C). These genome-wide experiments generate large amounts of data that require careful analysis to obtain useful biological conclusions. However, development of the appropriate software tools is hindered by the lack of basic infrastructure to represent and manipulate genomic interaction data. Here, we present the InteractionSet package that provides classes to represent genomic interactions and store their associated experimental data, along with the methods required for low-level manipulation and processing of those classes. The InteractionSet package exploits existing infrastructure in the open-source Bioconductor project, while in turn being used by Bioconductor packages designed for higher-level analyses. For new packages, use of the functionality in InteractionSet will simplify development, allow access to more features and improve interoperability between packages.

scholarly journals A genome-wide resource for high-throughput genomic tagging of yeast ORFs

2017 ◽  
Author(s):  
Matthias Meurer ◽  
Yuanqiang Duan ◽  
Ehud Sass ◽  
Ilia Kats ◽  
Konrad Herbst ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Protein Expression ◽  
High Throughput ◽  
Fluorescent Proteins ◽  
Regulatory Elements ◽  
Targeted Sequencing ◽  
Yeast Proteome ◽  
Genome Wide ◽  
A Genome

Here we describe a C-SWAT library for high-throughput tagging of Saccharomyces cerevisiae ORFs. It consists of 5661 strains with an acceptor module inserted after each ORF, which can be efficiently replaced with tags or regulatory elements. We validate the library with targeted sequencing and demonstrate its use by tagging the yeast proteome with bright fluorescent proteins, determining how sequences downstream of ORFs influence protein expression and localizing previously undetected proteins.

scholarly journals High-resolution genome-wide functional dissection of transcriptional regulatory regions in human

2017 ◽  
Author(s):  
Xinchen Wang ◽  
Liang He ◽  
Sarah Goggin ◽  
Alham Saadat ◽  
Li Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Association Studies ◽  
Regulatory Elements ◽  
Regulatory Sequence ◽  
Genome Wide Association Studies ◽  
Sequence Motifs ◽  
High Definition ◽  
Regulatory Regions ◽  
Genome Wide

AbstractGenome-wide epigenomic maps revealed millions of regions showing signatures of enhancers, promoters, and other gene-regulatory elements1. However, high-throughput experimental validation of their function and high-resolution dissection of their driver nucleotides remain limited in their scale and length of regions tested. Here, we present a new method, HiDRA (High-Definition Reporter Assay), that overcomes these limitations by combining components of Sharpr-MPRA2 and STARR-Seq3 with genome-wide selection of accessible regions from ATAC-Seq4. We used HiDRA to test ~7 million DNA fragments preferentially selected from accessible chromatin in the GM12878 lymphoblastoid cell line. By design, accessibility-selected fragments were highly overlapping (up to 370 per region), enabling us to pinpoint driver regulatory nucleotides by exploiting subtle differences in reporter activity between partially-overlapping fragments, using a new machine learning model SHARPR2. Our resulting maps include ~65,000 regions showing significant enhancer function and enriched for endogenous active histone marks (including H3K9ac, H3K27ac), regulatory sequence motifs, and regions bound by immune regulators. Within them, we discover ~13,000 high-resolution driver elements enriched for regulatory motifs and evolutionarily-conservednucleotides, and help predict causal genetic variants underlying disease from genome-wide association studies. Overall, HiDRA provides a general, scalable, high-throughput, and high-resolution approach for experimental dissection of regulatory regions and driver nucleotides in the context of human biology and disease.

scholarly journals Accurate Prediction of Genome-wide RNA Secondary Structure Profile Based On Extreme Gradient Boosting

2019 ◽  
Author(s):  
Yaobin Ke ◽  
Jiahua Rao ◽  
Huiying Zhao ◽  
Yutong Lu ◽  
Nong Xiao ◽  
...  
Keyword(s):  
Secondary Structure ◽  
High Throughput ◽  
Rna Secondary Structure ◽  
Pearson Correlation ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Synonymous Mutations ◽  
Periodic Distribution ◽  
Genome Wide ◽  
Extreme Gradient Boosting

AbstractMotivationMany studies have shown that RNA secondary structure plays a vital role in fundamental cellular processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. Identification of RNA secondary structure is a key step to understand the common mechanisms underlying the translation process. Recently, a few experimental methods were developed to measure genome-wide RNA secondary structure profile through high-throughput sequencing techniques, and have been successfully applied to genomes including yeast and human. However, these high-throughput methods usually have low precision and are hard to cover all nucleotides on the RNA due to limited sequencing coverage.ResultsIn this study, we developed a new method for the prediction of genome-wide RNA secondary structure profile (TH-GRASP) from RNA sequence based on eXtreme Gradient Boosting (XGBoost). The method achieves an prediction with areas under the receiver operating characteristic curve (AUC) values greater than 0.9 on three different datasets, and AUC of 0.892 by an independent test on the recently released Zika virus RNA dataset. These AUCs represent a consistent increase of >6% than the recently developed method CROSS trained by a shallow neural network. A further analysis on the 1000-Genome Project data showed that our predicted unpaired probability at mutations sites are highly correlated with the minor allele frequencies (MAF) of synonymous, non-synonymous mutations, and mutations in 3’ and 5’UTR with Pearson Correlation Coefficients all above 0.8. These PCCs are consistently higher than those generated by RNAplfold method. Moreover, an investigation over all human mRNA indicated a periodic distribution of the predicted unpaired probability on codons, and a decrease of paired probability in the boundary with 5’ and 3’ untranslated regions. These results highlighted TH-GRASP is effective to remove experimental noises and to have ability to make predictions on nucleotides with low or no coverage by fitting high-throughput genomic data for RNA secondary structure profiles, and also suggested that building model on high throughput experimental data might be a future direction to substitute analytical methods.AvailabilityThe TH-GRASP is available for academic use athttps://github.com/sysu-yanglab/TH-GRASP.Supplementary informationSupplementary data are available online.

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