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Computational Resources for the Compilation and Distribution of Transcription Fac (1R01GM084875-01A2)
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29 Research products, page 1 of 3

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  • Open Access English
    Authors: 
    Wenqiang Shi; Oriol Fornes; Wyeth W. Wasserman;
    Publisher: Oxford University Press
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Abstract Motivation Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions. Results We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R2 > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome. Availability and implementation The code and model training results are publicly available at https://github.com/wqshi/TF2Exp. Supplementary information Supplementary data are available at Bioinformatics online.

  • Research software . 2018
    Python
    Authors: 
    WassermanEmail author, Wyeth W.;
    Publisher: bio.tools
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    DEep learning for identifying Cis-Regulatory ElementS is an extension of the Deep Learning Tutorials developped by LISA lab (www.deeplearning.net/tutorial/). Although it is developped for the identification of CREs, it can also be used for other applications.

  • Publication . Article . Preprint . Other literature type . 2018
    Open Access English
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth W. Wasserman;
    Publisher: BioMed Central
    Country: Canada
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Background In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Results Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). Conclusion The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations. Electronic supplementary material The online version of this article (10.1186/s12859-018-2187-1) contains supplementary material, which is available to authorized users.

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Genome-wide predictions of cis-regulatory regions for all six cell types. (ZIP 20400 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    List of features used for each cell type. (ZIP 1.36 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: CIHR , NSERC , NIH | Computational Resources f... (1R01GM084875-01A2)

    This file contains supplemental Tables S1-S7, and supplemental Figures S1-S30. (PDF 27600 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    List of features used for each cell type. (ZIP 1.36 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    Predictions on CAGE supported bidirectional loci. AiA: Active in the FANTOM Enhancer Atlas; IiA: Inactive in the FANTOM Enhancer Atlas; NiA: Not included in the FANTOM Enhancer Atlas; Specific: Predicted cell-specific A-Es. (ZIP 4820 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    Predictions on CAGE supported bidirectional loci. AiA: Active in the FANTOM Enhancer Atlas; IiA: Inactive in the FANTOM Enhancer Atlas; NiA: Not included in the FANTOM Enhancer Atlas; Specific: Predicted cell-specific A-Es. (ZIP 4820 kb)

  • Open Access
    Authors: 
    Wenqiang Shi; Oriol Fornes; Wyeth W. Wasserman;
    Publisher: Zenodo
    Project: CIHR , NSERC , NIH | Computational Resources f... (1R01GM084875-01A2)

    AbstractDeciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. We hypothesize that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression. We present TF2Exp, the first gene-based framework (to our knowledge) to predict the impact of altered TF binding on personalized gene expression based on cis-regulatory variants. Using data from lymphoblastoid cell lines, TF2Exp models achieved suitable performance for 3,060 genes. Alterations within DNase I hypersensitive, CTCF-bound, and tissue-specific TF-bound regions were the greatest contributors to the models. Our cis-regulatory variant-based TF2Exp models performed as well as the state-of-the-art SNP-based models, both in cross-validation and external validation. In addition, unlike SNP-based models, our TF2Exp models have the unique advantages to evaluate impact of uncommon variants and distinguish the functional roles of variants in linkage disequilibrium, showing broader utility for future human genetic studies.

Advanced search in
Research products
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Computational Resources for the Compilation and Distribution of Transcription Fac (1R01GM084875-01A2)
Include:
The following results are related to Canada. Are you interested to view more results? Visit OpenAIRE - Explore.
29 Research products, page 1 of 3
  • Open Access English
    Authors: 
    Wenqiang Shi; Oriol Fornes; Wyeth W. Wasserman;
    Publisher: Oxford University Press
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Abstract Motivation Deciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. It has been hypothesized that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression levels. However, we lack a computational framework to understand and quantify such mechanistic contributions. Results We present TF2Exp, a gene-based framework to predict the impact of altered TF-binding events on gene expression levels. Using data from lymphoblastoid cell lines, TF2Exp models were applied successfully to predict the expression levels of 3196 genes. Alterations within DNase I hypersensitive, CTCF-bound and tissue-specific TF-bound regions were the greatest contributing features to the models. TF2Exp models performed as well as models based on common variants, both in cross-validation and external validation. Combining TF alteration and common variant features can further improve model performance. Unlike variant-based models, TF2Exp models have the unique advantage to evaluate the functional impact of variants in linkage disequilibrium and uncommon variants. We find that adding TF-binding events altered only by uncommon variants could increase the number of predictable genes (R2 > 0.05). Taken together, TF2Exp represents a key step towards interpreting the functional roles of cis-regulatory variants in the human genome. Availability and implementation The code and model training results are publicly available at https://github.com/wqshi/TF2Exp. Supplementary information Supplementary data are available at Bioinformatics online.

  • Research software . 2018
    Python
    Authors: 
    WassermanEmail author, Wyeth W.;
    Publisher: bio.tools
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    DEep learning for identifying Cis-Regulatory ElementS is an extension of the Deep Learning Tutorials developped by LISA lab (www.deeplearning.net/tutorial/). Although it is developped for the identification of CREs, it can also be used for other applications.

  • Publication . Article . Preprint . Other literature type . 2018
    Open Access English
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth W. Wasserman;
    Publisher: BioMed Central
    Country: Canada
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Background In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. Thus, Identifying active cis-regulatory regions in the human genome is critical for understanding gene regulation and assessing the impact of genetic variation on phenotype. The developments of high-throughput sequencing and machine learning technologies make it possible to predict cis-regulatory regions genome wide. Results Based on rich data resources such as the Encyclopedia of DNA Elements (ENCODE) and the Functional Annotation of the Mammalian Genome (FANTOM) projects, we introduce DECRES based on supervised deep learning approaches for the identification of enhancer and promoter regions in the human genome. Due to their ability to discover patterns in large and complex data, the introduction of deep learning methods enables a significant advance in our knowledge of the genomic locations of cis-regulatory regions. Using models for well-characterized cell lines, we identify key experimental features that contribute to the predictive performance. Applying DECRES, we delineate locations of 300,000 candidate enhancers genome wide (6.8% of the genome, of which 40,000 are supported by bidirectional transcription data), and 26,000 candidate promoters (0.6% of the genome). Conclusion The predicted annotations of cis-regulatory regions will provide broad utility for genome interpretation from functional genomics to clinical applications. The DECRES model demonstrates potentials of deep learning technologies when combined with high-throughput sequencing data, and inspires the development of other advanced neural network models for further improvement of genome annotations. Electronic supplementary material The online version of this article (10.1186/s12859-018-2187-1) contains supplementary material, which is available to authorized users.

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NSERC , CIHR , NIH | Computational Resources f... (1R01GM084875-01A2)

    Genome-wide predictions of cis-regulatory regions for all six cell types. (ZIP 20400 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    List of features used for each cell type. (ZIP 1.36 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: CIHR , NSERC , NIH | Computational Resources f... (1R01GM084875-01A2)

    This file contains supplemental Tables S1-S7, and supplemental Figures S1-S30. (PDF 27600 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    List of features used for each cell type. (ZIP 1.36 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    Predictions on CAGE supported bidirectional loci. AiA: Active in the FANTOM Enhancer Atlas; IiA: Inactive in the FANTOM Enhancer Atlas; NiA: Not included in the FANTOM Enhancer Atlas; Specific: Predicted cell-specific A-Es. (ZIP 4820 kb)

  • Open Access
    Authors: 
    Yifeng Li; Wenqiang Shi; Wyeth Wasserman;
    Publisher: figshare
    Project: NIH | Computational Resources f... (1R01GM084875-01A2), NSERC , CIHR

    Predictions on CAGE supported bidirectional loci. AiA: Active in the FANTOM Enhancer Atlas; IiA: Inactive in the FANTOM Enhancer Atlas; NiA: Not included in the FANTOM Enhancer Atlas; Specific: Predicted cell-specific A-Es. (ZIP 4820 kb)

  • Open Access
    Authors: 
    Wenqiang Shi; Oriol Fornes; Wyeth W. Wasserman;
    Publisher: Zenodo
    Project: CIHR , NSERC , NIH | Computational Resources f... (1R01GM084875-01A2)

    AbstractDeciphering the functional roles of cis-regulatory variants is a critical challenge in genome analysis and interpretation. We hypothesize that altered transcription factor (TF) binding events are a central mechanism by which cis-regulatory variants impact gene expression. We present TF2Exp, the first gene-based framework (to our knowledge) to predict the impact of altered TF binding on personalized gene expression based on cis-regulatory variants. Using data from lymphoblastoid cell lines, TF2Exp models achieved suitable performance for 3,060 genes. Alterations within DNase I hypersensitive, CTCF-bound, and tissue-specific TF-bound regions were the greatest contributors to the models. Our cis-regulatory variant-based TF2Exp models performed as well as the state-of-the-art SNP-based models, both in cross-validation and external validation. In addition, unlike SNP-based models, our TF2Exp models have the unique advantages to evaluate impact of uncommon variants and distinguish the functional roles of variants in linkage disequilibrium, showing broader utility for future human genetic studies.