scholarly journals eFORGE v2.0: updated analysis of cell type-specific signal in epigenomic data

2019 ◽  
Vol 35 (22) ◽  
pp. 4767-4769 ◽  
Author(s):  
Charles E Breeze ◽  
Alex P Reynolds ◽  
Jenny van Dongen ◽  
Ian Dunham ◽  
John Lazar ◽  
...  
Keyword(s):  
Supplementary Information ◽  
Supplementary Data ◽  
Cell Type ◽  
Web Tool ◽  
Methylation Analysis ◽  
450K Array ◽  
Composition Effects ◽  
Epigenome Editing ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

Abstract Summary The Illumina Infinium EPIC BeadChip is a new high-throughput array for DNA methylation analysis, extending the earlier 450k array by over 400 000 new sites. Previously, a method named eFORGE was developed to provide insights into cell type-specific and cell-composition effects for 450k data. Here, we present a significantly updated and improved version of eFORGE that can analyze both EPIC and 450k array data. New features include analysis of chromatin states, transcription factor motifs and DNase I footprints, providing tools for epigenome-wide association study interpretation and epigenome editing. Availability and implementation eFORGE v2.0 is implemented as a web tool available from https://eforge.altiusinstitute.org and https://eforge-tf.altiusinstitute.org/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Cell type‐specific DNA methylation analysis of the prefrontal cortex of patients with schizophrenia

2021 ◽  
Author(s):  
Junko Ueda ◽  
Miki Bundo ◽  
Yutaka Nakachi ◽  
Kiyoto Kasai ◽  
Tadafumi Kato ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Prefrontal Cortex ◽  
Cell Type ◽  
Methylation Analysis ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

scholarly journals SurfaceGenie: a web-based application for prioritizing cell-type-specific marker candidates

2020 ◽  
Vol 36 (11) ◽  
pp. 3447-3456 ◽  
Author(s):  
Matthew Waas ◽  
Shana T Snarrenberg ◽  
Jack Littrell ◽  
Rachel A Jones Lipinski ◽  
Polly A Hansen ◽  
...  
Keyword(s):  
Cell Surface ◽  
Specific Surface ◽  
Web Application ◽  
Rank Order ◽  
Surface Proteins ◽  
Supplementary Information ◽  
Live Cells ◽  
Specific Marker ◽  
Cell Type ◽  
Cell Type Specific

Abstract Motivation Cell-type-specific surface proteins can be exploited as valuable markers for a range of applications including immunophenotyping live cells, targeted drug delivery and in vivo imaging. Despite their utility and relevance, the unique combination of molecules present at the cell surface are not yet described for most cell types. A significant challenge in analyzing ‘omic’ discovery datasets is the selection of candidate markers that are most applicable for downstream applications. Results Here, we developed GenieScore, a prioritization metric that integrates a consensus-based prediction of cell surface localization with user-input data to rank-order candidate cell-type-specific surface markers. In this report, we demonstrate the utility of GenieScore for analyzing human and rodent data from proteomic and transcriptomic experiments in the areas of cancer, stem cell and islet biology. We also demonstrate that permutations of GenieScore, termed IsoGenieScore and OmniGenieScore, can efficiently prioritize co-expressed and intracellular cell-type-specific markers, respectively. Availability and implementation Calculation of GenieScores and lookup of SPC scores is made freely accessible via the SurfaceGenie web application: www.cellsurfer.net/surfacegenie. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals VarMap: a web tool for mapping genomic coordinates to protein sequence and structure and retrieving protein structural annotations

2019 ◽  
Vol 35 (22) ◽  
pp. 4854-4856 ◽  
Author(s):  
James D Stephenson ◽  
Roman A Laskowski ◽  
Andrew Nightingale ◽  
Matthew E Hurles ◽  
Janet M Thornton
Keyword(s):  
Protein Sequence ◽  
Structural Information ◽  
Protein Structures ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Web Tool ◽  
Genomic Variants ◽  
Structural Context ◽  
Pathogenic Variants ◽  
Transcript Evidence

Abstract Motivation Understanding the protein structural context and patterning on proteins of genomic variants can help to separate benign from pathogenic variants and reveal molecular consequences. However, mapping genomic coordinates to protein structures is non-trivial, complicated by alternative splicing and transcript evidence. Results Here we present VarMap, a web tool for mapping a list of chromosome coordinates to canonical UniProt sequences and associated protein 3D structures, including validation checks, and annotating them with structural information. Availability and implementation https://www.ebi.ac.uk/thornton-srv/databases/VarMap. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Ensemble learning for classifying single-cell data and projection across reference atlases

2020 ◽  
Vol 36 (11) ◽  
pp. 3585-3587
Author(s):  
Lin Wang ◽  
Francisca Catalan ◽  
Karin Shamardani ◽  
Husam Babikir ◽  
Aaron Diaz
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Status Quo ◽  
Supplementary Information ◽  
Published Data ◽  
Supplementary Data ◽  
Cell Type ◽  
Low Sensitivity ◽  
Project Data ◽  
Cell Data

Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals MAJIQ-SPEL: web-tool to interrogate classical and complex splicing variations from RNA-Seq data

2017 ◽  
Vol 34 (2) ◽  
pp. 300-302 ◽  
Author(s):  
Christopher J Green ◽  
Matthew R Gazzara ◽  
Yoseph Barash
Keyword(s):  
Experimental Validation ◽  
Ucsc Genome Browser ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Web Tool ◽  
Rt Pcr ◽  
Design Algorithm ◽  
Gene Isoforms ◽  
Downstream Analysis

Abstract Summary Analysis of RNA sequencing (RNA-Seq) data have highlighted the fact that most genes undergo alternative splicing (AS) and that these patterns are tightly regulated. Many of these events are complex, resulting in numerous possible isoforms that quickly become difficult to visualize, interpret and experimentally validate. To address these challenges we developed MAJIQ-SPEL, a web-tool that takes as input local splicing variations (LSVs) quantified from RNA-Seq data and provides users with visualization and quantification of gene isoforms associated with those. Importantly, MAJIQ-SPEL is able to handle both classical (binary) and complex, non-binary, splicing variations. Using a matching primer design algorithm it also suggests to users possible primers for experimental validation by RT-PCR and displays those, along with the matching protein domains affected by the LSV, on UCSC Genome Browser for further downstream analysis. Availability and implementation Program and code will be available athttp://majiq.biociphers.org/majiq-spel. Supplementary information Supplementary data are available atBioinformatics online.

scholarly journals Cell-type-specific DNA methylation analysis of the frontal cortices of mutant Polg1 transgenic mice with neuronal accumulation of deleted mitochondrial DNA

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Hiroko Sugawara ◽  
Miki Bundo ◽  
Takaoki Kasahara ◽  
Yutaka Nakachi ◽  
Junko Ueda ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Mitochondrial Dna ◽  
Transgenic Mice ◽  
Molecular Motor ◽  
Methylation Analysis ◽  
Behavioral Abnormalities ◽  
Gene Environment ◽  
Postmortem Brains ◽  
Cell Type Specific ◽  
Dna Methylation Analysis

AbstractBipolar disorder (BD) is a severe psychiatric disorder characterized by repeated conflicting manic and depressive states. In addition to genetic factors, complex gene–environment interactions, which alter the epigenetic status in the brain, contribute to the etiology and pathophysiology of BD. Here, we performed a promoter-wide DNA methylation analysis of neurons and nonneurons derived from the frontal cortices of mutant Polg1 transgenic (n = 6) and wild-type mice (n = 6). The mutant mice expressed a proofreading-deficient mitochondrial DNA (mtDNA) polymerase under the neuron-specific CamK2a promoter and showed BD-like behavioral abnormalities, such as activity changes and altered circadian rhythms. We identified a total of 469 differentially methylated regions (DMRs), consisting of 267 neuronal and 202 nonneuronal DMRs. Gene ontology analysis of DMR-associated genes showed that cell cycle-, cell division-, and inhibition of peptide activity-related genes were enriched in neurons, whereas synapse- and GABA-related genes were enriched in nonneurons. Among the DMR-associated genes, Trim2 and Lrpprc showed an inverse relationship between DNA methylation and gene expression status. In addition, we observed that mutant Polg1 transgenic mice shared several features of DNA methylation changes in postmortem brains of patients with BD, such as dominant hypomethylation changes in neurons, which include hypomethylation of the molecular motor gene and altered DNA methylation of synapse-related genes in nonneurons. Taken together, the DMRs identified in this study will contribute to understanding the pathophysiology of BD from an epigenetic perspective.

scholarly journals A Markov Random Field Model for Network-based Differential Expression Analysis of Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Hongyu Li ◽  
Zhichao Xu ◽  
Taylor Adams ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Random Field ◽  
Single Cell ◽  
Markov Random Field ◽  
Supplementary Information ◽  
Model Parameters ◽  
Type I ◽  
Cell Type ◽  
Data Set ◽  
Markov Random ◽  
Cell Type Specific

AbstractMotivationRecent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. However, the often-low sample size of single cell data limits the statistical power to identify DE genes.ResultsIn this article, we propose to borrow information through known biological networks. Our approach is based on a Markov Random Field (MRF) model to appropriately accommodate gene network information as well as dependencies among cells to identify cell-type specific DE genes. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DE genes than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls.AvailabilityThe algorithm is implemented in R. The source code can be downloaded at https://github.com/eddiehli/[email protected] informationSupplementary data are available online.

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