scholarly journals FitHiChIP: Identification of significant chromatin contacts from HiChIP data

2018 ◽  
Author(s):  
Sourya Bhattacharyya ◽  
Vivek Chandra ◽  
Pandurangan Vijayanand ◽  
Ferhat Ay
Keyword(s):  
Immune Cell ◽  
Cell Types ◽  
Contact Analysis ◽  
Computational Method ◽  
Genomic Distance ◽  
Regulatory Regions ◽  
Uniform Coverage ◽  
Link Type ◽  
Genome Wide

Here we describe FitHiChIP (github.com/ay-lab/FitHiChIP), a computational method for identifying chromatin contacts among regulatory regions such as en-hancers and promoters from HiChIP/PLAC-seq data. FitHiChIP jointly models the non-uniform coverage and genomic distance scaling of HiChIP data, captures previously validated enhancer interactions for several genes including MYC and TP53, and recovers contacts genome-wide that are supported by ChIA-PET, pro-moter capture Hi-C and Hi-C data. FitHiChIP also provides a framework for differential contact analysis as showcased in a comparison of HiChIP data we have generated for two distinct immune cell types.

scholarly journals Integrative Genome-Wide Analysis of Long Noncoding RNAs in Diverse Immune Cell Types of Melanoma Patients

2018 ◽  
Vol 78 (15) ◽  
pp. 4411-4423 ◽  
Author(s):  
Lei Wang ◽  
Sara J. Felts ◽  
Virginia P. Van Keulen ◽  
Adam D. Scheid ◽  
Matthew S. Block ◽  
...  
Keyword(s):  
Immune Cell ◽  
Noncoding Rnas ◽  
Cell Types ◽  
Long Noncoding Rnas ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Melanoma Patients

scholarly journals OTME-5. Meningioma liquid biopsy specimens exhibit contrasting immune-cell landscapes across methylation-subtypes and estimated recurrence risk subgroups

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii14-ii14
Author(s):  
Grayson Herrgott ◽  
Ruicong She ◽  
Thais Sabedot ◽  
Michael Wells ◽  
Karam Asmaro ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Random Forest ◽  
Liquid Biopsy ◽  
Immune Cell ◽  
Cell Types ◽  
Recurrence Risk ◽  
Random Forest Classifier ◽  
Serum Samples ◽  
Biopsy Specimens ◽  
Genome Wide

Abstract Background Tumor-infiltrating immune cell compositions have been previously correlated to encouragement or inhibition of tumor growth. This association highlights immune-landscape profiling through non-invasive methods as a crucial step in approaches to treatment of patients with meningioma (MNG), a prevalent primary intracranial tumor. Genome-wide DNA methylation patterns can aid in definition and assessment of cell compositions in liquid biopsy serum specimens, and allow for development of machine-learning models with predictive capabilities. Methods We profiled the cfDNA methylome (EPIC array) in liquid biopsy specimens from patients with MNG (n = 63) and nontumor controls (n = 6). We conducted both unsupervised epigenome-wide and supervised analyses of the meningioma methylome. Estimation of immune cell composition was conducted using Python-based methodology, where a reference methylome atlas of chosen cell types (B-cells, CD4- and CD8T-cells, neutrophils, natural killer cells, monocytes, cortical neuron, vascular endothelial cells, and healthy meninge) was used to deconvolute the MNG samples. Recurrence risk was estimated using an existing methylation-based Random-Forest classifier previously reported and validated, adapted to our serum-based cohort through employment of translatable meningioma subgroup-specific methylation markers (differentially methylated probes). Results We identified four distinct genome-wide methylation subgroups (k-clusters) of MNG which presented differential tumor micro-environments across all cell types investigated. Application of the DNA methylation-based Random-Forest classifier allowed for categorization of primary MNG serum samples into estimated recurrence-risk subgroups. Significantly contrasting micro-environments for the subgroups were observed across several cell-types, with those MNG more likely to recur displaying depletion in cell types reported to improve anti-tumoral response in many tumors (e.g. T-Cells). Conclusions DNA methylation based deconvolution allowed for detection of contrasting tumor microenvironment compositions across MNG methylation subtypes and recurrence-risk estimation subgroups. These results suggest that microenvironment profiling can be informative of probable tumor behavior and prognostic outcomes, helping guide therapeutic approaches towards treatment of patients with MNG.

scholarly journals Identification of significant chromatin contacts from HiChIP data by FitHiChIP

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Sourya Bhattacharyya ◽  
Vivek Chandra ◽  
Pandurangan Vijayanand ◽  
Ferhat Ay
Keyword(s):  
Genetic Variants ◽  
Statistical Significance ◽  
Regulatory Elements ◽  
Computational Method ◽  
Genomic Distance ◽  
Uniform Coverage

Abstract HiChIP/PLAC-seq is increasingly becoming popular for profiling 3D chromatin contacts among regulatory elements and for annotating functions of genetic variants. Here we describe FitHiChIP, a computational method for loop calling from HiChIP/PLAC-seq data, which jointly models the non-uniform coverage and genomic distance scaling of contact counts to compute statistical significance estimates. We also develop a technique to filter putative bystander loops that can be explained by stronger adjacent loops. Compared to existing methods, FitHiChIP performs better in recovering contacts reported by Hi-C, promoter capture Hi-C and ChIA-PET experiments and in capturing previously validated promoter-enhancer interactions. FitHiChIP loop calls are reproducible among replicates and are consistent across different experimental settings. Our work also provides a framework for differential HiChIP analysis with an option to utilize ChIP-seq data for further characterizing differential loops. Even though designed for HiChIP, FitHiChIP is also applicable to other conformation capture assays.

scholarly journals A Catalogus Immune Muris of the mouse immune responses to diverse pathogens

2021 ◽  
Vol 12 (9) ◽  
Author(s):  
Céline Barlier ◽  
Diego Barriales ◽  
Alexey Samosyuk ◽  
Sascha Jung ◽  
Srikanth Ravichandran ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Responses ◽  
Biomedical Applications ◽  
Immune Cell ◽  
Cell Types ◽  
Computational Method ◽  
Functional Responses ◽  
Macrophage Cell ◽  
Functional States

AbstractImmunomodulation strategies are crucial for several biomedical applications. However, the immune system is highly heterogeneous and its functional responses to infections remains elusive. Indeed, the characterization of immune response particularities to different pathogens is needed to identify immunomodulatory candidates. To address this issue, we compiled a comprehensive map of functional immune cell states of mouse in response to 12 pathogens. To create this atlas, we developed a single-cell-based computational method that partitions heterogeneous cell types into functionally distinct states and simultaneously identifies modules of functionally relevant genes characterizing them. We identified 295 functional states using 114 datasets of six immune cell types, creating a Catalogus Immune Muris. As a result, we found common as well as pathogen-specific functional states and experimentally characterized the function of an unknown macrophage cell state that modulates the response to Salmonella Typhimurium infection. Thus, we expect our Catalogus Immune Muris to be an important resource for studies aiming at discovering new immunomodulatory candidates.

scholarly journals Integrative Genetics Analysis of Juvenile Idiopathic Arthritis Identifies Novel Loci

2020 ◽  
Author(s):  
Yun R. Li ◽  
Jin Li ◽  
Joseph T. Glessner ◽  
Jie Yang ◽  
Michael E. March ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Clinical Presentation ◽  
Immune Cell ◽  
Drug Repositioning ◽  
Cell Types ◽  
Disease Phenotypes ◽  
Genome Wide ◽  
Immune Mediated ◽  
Mhc Locus ◽  
Shared Genetic

Juvenile Idiopathic Arthritis (JIA) is the most common type of arthritis among children, encompassing a highly heterogeneous group of immune-mediated joint disorders, being classified into seven subtypes based on clinical presentation. To systematically understand the distinct and shared genetic underpinnings of JIA subtypes, we conducted a heterogeneity-sensitive GWAS encompassing a total of 1245 JIA cases classified into 7 subtypes and 9250 controls. In addition to the MHC locus, we uncovered 16 genome-wide significant loci, among which 15 were shared between at least two JIA subtypes, including 11 novel loci. Functional annotation indicates that candidate genes at these loci are expressed in diverse immune cell types. Further, based on the association results, the 7 JIA subtypes were classified into two groups, reflecting their autoimmune vs autoinflammatory nature. Our results suggest a common genetic mechanism underlying these subtypes in spite of their different clinical disease phenotypes, and that there may be drug repositioning opportunities for rare JIA subtypes.

scholarly journals Comparative analysis of genome-wide DNA methylation identifies patterns that associate with conserved transcriptional programs in osteosarcoma

2020 ◽  
Author(s):  
Lauren J. Mills ◽  
Milcah C. Scott ◽  
Pankti Shah ◽  
Anne R. Cunanan ◽  
Archana Deshpande ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Large Scale ◽  
Immune Cell ◽  
Cell Types ◽  
Structural Variations ◽  
Canine Osteosarcoma ◽  
Genome Wide ◽  
Highly Correlated ◽  
Murine Osteosarcoma ◽  
Methylation Patterns

AbstractOsteosarcoma is an aggressive tumor of the bone that primarily affects young adults and adolescents. Osteosarcoma is characterized by genomic chaos and heterogeneity. While inactivation of tumor suppressor p53 TP53 is nearly universal other high frequency mutations or structural variations have not been identified. Despite this genomic heterogeneity, key conserved transcriptional programs associated with survival have been identified across human, canine and induced murine osteosarcoma. The epigenomic landscape, including DNA methylation, plays a key role in establishing transcriptional programs in all cell types. The role of epigenetic dysregulation has been studied in a variety of cancers but has yet to be explored at scale in osteosarcoma. Here we examined genome-wide DNA methylation patterns in 24 human and 44 canine osteosarcoma samples identifying groups of highly correlated DNA methylation marks in human and canine osteosarcoma samples. We also link specific DNA methylation patterns to key transcriptional programs in both human and canine osteosarcoma. Building on previous work, we built a DNA methylation-based measure for the presence and abundance of various immune cell types in osteosarcoma. Finally, we determined that the underlying state of the tumor, and not changes in cell composition, were the main driver of differences in DNA methylation across the human and canine samples.SignificanceThis is the first large scale study of DNA methylation in osteosarcoma and lays the ground work for the exploration of DNA methylation programs that help establish conserved transcriptional programs in the context of different genomic landscapes.

scholarly journals FinaleDB: a browser and database of cell-free DNA fragmentation patterns

2020 ◽  
Author(s):  
Haizi Zheng ◽  
Michelle S Zhu ◽  
Yaping Liu
Keyword(s):  
Molecular Mechanisms ◽  
Cell Types ◽  
Cell Free Dna ◽  
Link Type ◽  
Diagnosis And Prognosis ◽  
Dna Database ◽  
Free Dna ◽  
Genome Wide ◽  
Fragmentation Patterns ◽  
Different Cell Types

AbstractSummaryCirculating cell-free DNA (cfDNA) is a promising biomarker for the diagnosis and prognosis of many diseases, including cancer. The genome-wide non-random fragmentation patterns of cfDNA are associated with the nucleosomal protection, epigenetic environment, and gene expression in the cell types that contributed to cfDNA. However, current progress on the development of computational methods and understanding of molecular mechanisms behind cfDNA fragmentation patterns is significantly limited by the controlled-access of cfDNA whole-genome sequencing (WGS) dataset. Here, we present FinaleDB (FragmentatIoN AnaLysis of cEll-free DNA DataBase), a comprehensive database to host thousands of uniformly processed and curated de-identified cfDNA WGS datasets across different pathological conditions. Furthermore, FinaleDB comes with a fragmentation genome browser, from which users can seamlessly integrate thousands of other omics data in different cell types to experience a comprehensive view of both gene-regulatory landscape and cfDNA fragmentation patterns.Availability and implementationFinaleDB service: http://finaledb.research.cchmc.org/ FinaleDB source code: https://github.com/epifluidlab/finaledb_portal and https://github.com/epifluidlab/[email protected]

scholarly journals Deconvolution of bulk blood eQTL effects into immune cell subpopulations

2019 ◽  
Author(s):  
R. Aguirre-Gamboa ◽  
N. de Klein ◽  
J. di Tommaso ◽  
A. Claringbould ◽  
U. Võsa ◽  
...  
Keyword(s):  
Whole Blood ◽  
Complex Disease ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Population Based ◽  
New Method ◽  
Cell Type ◽  
Link Type ◽  
Cell Subpopulations

AbstractExpression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, the current methods are labor-intensive and expensive. Here we introduce a new method, Decon2, a statistical framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) and consecutive deconvolution of cell type eQTLs (Decon-eQTL). The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell we can predict the proportions of 34 circulating cell types for 3,194 samples from a population-based cohort. Next we identified 16,362 whole blood eQTLs and assign them to a cell type with Decon-eQTL using the predicted cell proportions from Decon-cell. Deconvoluted eQTLs show excellent allelic directional concordance with those of eQTL(≥ 96%) and chromatin mark QTL (≥87%) studies that used either purified cell subpopulations or single-cell RNA-seq. Our new method provides a way to assign cell type effects to eQTLs from bulk blood, which is useful in pinpointing the most relevant cell type for a certain complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2), and as a web tool (www.molgenis.org/deconvolution).

scholarly journals A rapid and robust method for single cell chromatin accessibility profiling

2018 ◽  
Author(s):  
Xi Chen ◽  
Ricardo J Miragaia ◽  
Kedar Nath Natarajan ◽  
Sarah A Teichmann
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Single Cell Level ◽  
Cell Type ◽  
Cell Level ◽  
Regulatory Regions ◽  
Cell Type Specific ◽  
Accessible Chromatin

AbstractThe assay for transposase-accessible chromatin using sequencing (ATAC-seq) is widely used to identify regulatory regions throughout the genome. However, very few studies have been performed at the single cell level (scATAC-seq) due to technical challenges. Here we developed a simple and robust plate-based scATAC-seq method, combining upfront bulk Tn5 tagging with single-nuclei sorting. We demonstrated that our method worked robustly across various systems, including fresh and cryopreserved cells from primary tissues. By profiling over 3,000 splenocytes, we identify distinct immune cell types and reveal cell type-specific regulatory regions and related transcription factors.

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