scholarly journals Meta‐analysis of massively parallel reporter assays enables prediction of regulatory function across cell types

2019 ◽  
Vol 40 (9) ◽  
pp. 1299-1313 ◽  
Author(s):  
Anat Kreimer ◽  
Zhongxia Yan ◽  
Nadav Ahituv ◽  
Nir Yosef
Keyword(s):  
Meta Analysis ◽  
Cell Types ◽  
Massively Parallel ◽  
Regulatory Function ◽  
Reporter Assays

scholarly journals Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks

2017 ◽  
Author(s):  
Joe Paggi ◽  
Andrew Lamb ◽  
Kevin Tian ◽  
Irving Hsu ◽  
Pierre-Louis Cedoz ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Transcriptional Activity ◽  
Cell Types ◽  
Massively Parallel ◽  
Neural Network Architecture ◽  
Regulatory Regions ◽  
Convolutional Networks ◽  
Transcriptional Regulatory ◽  
Reporter Assays

AbstractMassively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.

scholarly journals QuASAR-MPRA: Accurate allele-specific analysis for massively parallel reporter assays

2017 ◽  
Author(s):  
Cynthia A. Kalita ◽  
Gregory A. Moyerbrailean ◽  
Christopher Brown ◽  
Xiaoquan Wen ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
Regulatory Elements ◽  
Massively Parallel ◽  
Regulatory Function ◽  
Regulatory Sequences ◽  
Analysis Tools ◽  
Specific Analysis ◽  
Allele Specific ◽  
Reporter Assays

ABSTRACTMotivationThe majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRA), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets.ResultsWe have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data by Tewheyet al.(2016), we found 602 SNPs with significant (FDR 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high throughput reporter assays.Availabilityhttp://github.com/piquelab/QuASAR/tree/master/[email protected];[email protected]

scholarly journals Systematic Review and Meta-Analysis of In Vitro Anti-Human Cancer Experiments Investigating the Use of 5-Aminolevulinic Acid (5-ALA) for Photodynamic Therapy

2021 ◽  
Vol 14 (3) ◽  
pp. 229
Author(s):  
Yo Shinoda ◽  
Daitetsu Kato ◽  
Ryosuke Ando ◽  
Hikaru Endo ◽  
Tsutomu Takahashi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Photodynamic Therapy ◽  
Aminolevulinic Acid ◽  
Human Cancer ◽  
Meta Analysis ◽  
Protoporphyrin Ix ◽  
Cell Types ◽  
Amino Acid Derivative ◽  
Photodynamic Diagnosis

5-Aminolevulinic acid (5-ALA) is an amino acid derivative and a precursor of protoporphyrin IX (PpIX). The photophysical feature of PpIX is clinically used in photodynamic diagnosis (PDD) and photodynamic therapy (PDT). These clinical applications are potentially based on in vitro cell culture experiments. Thus, conducting a systematic review and meta-analysis of in vitro 5-ALA PDT experiments is meaningful and may provide opportunities to consider future perspectives in this field. We conducted a systematic literature search in PubMed to summarize the in vitro 5-ALA PDT experiments and calculated the effectiveness of 5-ALA PDT for several cancer cell types. In total, 412 articles were identified, and 77 were extracted based on our inclusion criteria. The calculated effectiveness of 5-ALA PDT was statistically analyzed, which revealed a tendency of cancer-classification-dependent sensitivity to 5-ALA PDT, and stomach cancer was significantly more sensitive to 5-ALA PDT compared with cancers of different origins. Based on our analysis, we suggest a standardized in vitro experimental protocol for 5-ALA PDT.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome

2018 ◽  
Author(s):  
Mehran Karimzadeh ◽  
Michael M. Hoffman
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Sites ◽  
Cell Types ◽  
Transcription Factor Binding ◽  
Regulatory Function ◽  
Factor Binding ◽  
Link Type ◽  
Genomic Regions ◽  
Factor Sequence

AbstractMotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).

Developmental expression of Sp1 in the mouse

1991 ◽  
Vol 11 (4) ◽  
pp. 2189-2199
Author(s):  
J D Saffer ◽  
S P Jackson ◽  
M B Annarella
Keyword(s):  
Housekeeping Genes ◽  
Cell Types ◽  
Immunohistochemical Localization ◽  
Developmental Expression ◽  
Regulatory Function ◽  
General Role ◽  
Protein Levels ◽  
Rna Analysis ◽  
Different Cell Types ◽  
Unexpected Difference

The expression of the trans-acting transcription factor Sp1 in mice was defined by a combination of RNA analysis and immunohistochemical localization of the Sp1 protein. Although ubiquitously expressed, there was an unexpected difference of at least 100-fold in the amount of Sp1 message in different cell types. Sp1 protein levels showed corresponding marked differences. Substantial variations in Sp1 expression were also found in some cell types at different stages of development. Sp1 levels appeared to be highest in developing hematopoietic cells, fetal cells, and spermatids, suggesting that an elevated Sp1 level is associated with the differentiation process. These results indicate that Sp1 has a regulatory function in addition to its general role in the transcription of housekeeping genes.

scholarly journals Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218073 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Dna Sequences ◽  
Genetic Variants ◽  
Network Models ◽  
Massively Parallel ◽  
Neural Network Models ◽  
Regulatory Dna Sequences ◽  
Reporter Assays ◽  
Regulatory Dna

scholarly journals Do Neural Stem Cells Have a Choice? Heterogenic Outcome of Cell Fate Acquisition in Different Injury Models

2019 ◽  
Vol 20 (2) ◽  
pp. 455 ◽  
Author(s):  
Felix Beyer ◽  
Iria Samper Agrelo ◽  
Patrick Küry
Keyword(s):  
Stem Cells ◽  
Neural Stem Cells ◽  
Cell Fate ◽  
Ex Vivo ◽  
Meta Analysis ◽  
Cell Types ◽  
Adult Brain ◽  
Repair Capacity ◽  
Cell Fate Decisions ◽  
Limiting Parameters

The adult mammalian central nervous system (CNS) is generally considered as repair restricted organ with limited capacities to regenerate lost cells and to successfully integrate them into damaged nerve tracts. Despite the presence of endogenous immature cell types that can be activated upon injury or in disease cell replacement generally remains insufficient, undirected, or lost cell types are not properly generated. This limitation also accounts for the myelin repair capacity that still constitutes the default regenerative activity at least in inflammatory demyelinating conditions. Ever since the discovery of endogenous neural stem cells (NSCs) residing within specific niches of the adult brain, as well as the description of procedures to either isolate and propagate or artificially induce NSCs from various origins ex vivo, the field has been rejuvenated. Various sources of NSCs have been investigated and applied in current neuropathological paradigms aiming at the replacement of lost cells and the restoration of functionality based on successful integration. Whereas directing and supporting stem cells residing in brain niches constitutes one possible approach many investigations addressed their potential upon transplantation. Given the heterogeneity of these studies related to the nature of grafted cells, the local CNS environment, and applied implantation procedures we here set out to review and compare their applied protocols in order to evaluate rate-limiting parameters. Based on our compilation, we conclude that in healthy CNS tissue region specific cues dominate cell fate decisions. However, although increasing evidence points to the capacity of transplanted NSCs to reflect the regenerative need of an injury environment, a still heterogenic picture emerges when analyzing transplantation outcomes in injury or disease models. These are likely due to methodological differences despite preserved injury environments. Based on this meta-analysis, we suggest future NSC transplantation experiments to be conducted in a more comparable way to previous studies and that subsequent analyses must emphasize regional heterogeneity such as accounting for differences in gray versus white matter.

scholarly journals Statistical considerations for the analysis of massively parallel reporter assays data

2020 ◽  
Vol 44 (7) ◽  
pp. 785-794
Author(s):  
Dandi Qiao ◽  
Corwin M. Zigler ◽  
Michael H. Cho ◽  
Edwin K. Silverman ◽  
Xiaobo Zhou ◽  
...  
Keyword(s):  
Massively Parallel ◽  
Reporter Assays
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