Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1043718 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26906158_1.fastq.gz | Fastqsanger.gz | |||
| SRR26906158_2.fastq.gz | Fastqsanger.gz | |||
| SRR26906159_1.fastq.gz | Fastqsanger.gz | |||
| SRR26906159_2.fastq.gz | Fastqsanger.gz | |||
| SRR26906160_1.fastq.gz | Fastqsanger.gz |
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Project description:Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors'(TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding event at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure transcription factor binding and mRNA expression from single cells CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these unique advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. Here, we introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. The package introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF function via CC data.
2024-01-01 | GSE248420 | GEO
Project description:Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions are recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high performance computing environment and to conduct downstream analyses.
2023-02-01 | GSE223926 | GEO
Project description:Calling cards technology using self-reporting transposons enables the identification of DNA-protein interactions through RNA sequencing . Here, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells by introducing a DNA barcode into the calling card itself. An additional barcode incorporated during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recover in vitro binding sites for four basic helix-loop-helix transcription factors with important roles in cell fate specification: ASCL1, MYOD1, NEUROD2, and NGN1. Further, simultaneous calling cards and transcriptional profiling during transcription factor overexpression identified both binding sites and gene expression changes for two of these factors. In sum, RNA-based identification of transcription factor binding sites and gene expression through barcoded self-reporting transposon calling cards and transcriptomes is an efficient and powerful method to infer gene regulatory networks in a population of cells.
2022-02-04 | GSE195992 | GEO
Project description:The ability to chronicle transcription factor binding events throughout the development of an organism would facilitate mapping of transcriptional networks that control cell fate decisions. We describe a method for permanently recording protein-DNA interactions in mammalian cells. We endow transcription factors with the ability to deposit a transposon into the genome near to where they bind. The transposon becomes a “Calling Card” the transcription factor leaves behind to record its visit to the genome. The locations of the Calling Cards can be determined by massively-parallel DNA sequencing. We show that the transcription factor SP1 fused to the piggyBac transposase directs insertion of the piggyBac transposon near SP1 binding sites. The locations of transposon insertions are highly reproducible, and agree with sites of SP1-binding determined by ChIP-seq. Genes bound by SP1 are more likely to be expressed in the HCT116 cell line we used, and SP1-bound CpG islands show a strong preference to be unmethylated. This method has the potential to trace transcription factor binding throughout cellular and organismal development in a way that has heretofore not been possible.
2011-12-31 | GSE34791 | GEO
Project description:The ability to chronicle transcription factor binding events throughout the development of an organism would facilitate mapping of transcriptional networks that control cell fate decisions. We describe a method for permanently recording protein-DNA interactions in mammalian cells. We endow transcription factors with the ability to deposit a transposon into the genome near to where they bind. The transposon becomes a M-bM-^@M-^\Calling CardM-bM-^@M-^] the transcription factor leaves behind to record its visit to the genome. The locations of the Calling Cards can be determined by massively-parallel DNA sequencing. We show that the transcription factor SP1 fused to the piggyBac transposase directs insertion of the piggyBac transposon near SP1 binding sites. The locations of transposon insertions are highly reproducible, and agree with sites of SP1-binding determined by ChIP-seq. Genes bound by SP1 are more likely to be expressed in the HCT116 cell line we used, and SP1-bound CpG islands show a strong preference to be unmethylated. This method has the potential to trace transcription factor binding throughout cellular and organismal development in a way that has heretofore not been possible. These data contain mapped insertion sites from the piggyBac (PB) transposon into HCT116 cell line and sequenced using an Illumina GAII analyzer. The first set contains the insertion sites of transposons mapped from the wild-type PB transposase and the second set contains the insertion sites of transposons mapped with the PB transposase fused to the transcription factor SP1. Other sequencing data present are ChIP-seq data for SP1 in the HCT116 cell line and RNA-seq data.
2011-12-31 | E-GEOD-34791 | biostudies-arrayexpress
Project description:Measuring transcription factor binding and gene expression using barcoded self-reporting transposon calling cards and transcriptomes
| PRJNA802849 | ENA
Project description:Calling Cards: a customizable platform to longitudinally record protein-DNA interactions over time in cells and tissues
| PRJNA929054 | ENA
Project description:In this study, single-cell sequencing of the BALF of CARDS patients yielded the following results: (1) IL1B+ neutrophils were significantly increased in the BALF of CARDS patients with exacerbated disease, which was associated with poor prognosis of CARDS patients, and this type of neutrophils underwent fatty acid metabolism reprogramming in comparison with other types of neutrophils, which may be related to their high-inflammatory response. (2) IL1B+ neutrophils promoted TNF secretion from hyperinflammatory type M1 macrophages through a caspase-dependent pathway, suggesting that neutrophil-macrophage interactions play an important role in the mechanism of CARDS development.
2024-05-15 | GSE240580 | GEO
Project description:Background: Standard Guthrie cards have been widely used to collect blood samples from neonates for newborn screening programs, and to a lesser extent, from normal controls and patients in research studies. Ease of blood collection (small quantity and less pain), transportation, and storage are the advantages of using these cards. It is believed that RNA obtained from these samples is of low quantity and degraded quality. However, we recently discovered that approximately 3,500 expressed genes can be detected from blood spot samples using in-house made, low resolution cDNA microarrays. Here, we established a new and improved methodology to acquire gene expression profiles from blood spot cards using commercially-available high resolution microarrays. We determined the optimal number of blood spot punches required for maximal RNA extraction, eliminated uses of trizol and chloroform for RNA extraction by using a modified protocol of the illustra Mini Spin Kit from GE-Whatm an, concentrated the quantity of RNA templates before amplification, improved amplification efficiency using the new Ribo-SPIA technology in WT-Ovation Pico System (WT-Pico) by NuGEN, before the samples were hybridized onto 4x44K whole human genome gene expression microarrays from Agilent. Nine dried blood spot samples were collected from a control population and stored at ~ -80 °C between 6 months to 2 years. High quality RNA was extracted from the buffy coat of the same individuals as a reference and processed using the standard Agilent microarray procedure. Commercially-available brain RNA was used as a positive control in both standard and new procedures for microarrays. Results: Three 3-mm punches produced the highest yield of total RNA using the non-trizol extraction method. Three to six nanogram per microliter of RNA can be concentrated and is sufficient to be amplified using the WT-Pico. Approximately 9,000 expressed genes can be detected after normalization and background correction of the microarray data. Conclusion: Genome-wide gene expression profile can be obtained from archived dried blood spot samples. Our new and improved methodology will add value to the perception of utilizing archival Guthrie cards eg. neonatal blood spot cards as unique biospecimens for molecular genomics and diagnostic studies of perinatal diseases such as pediatric cancers. Keywords: Gene Expression experiment Archival guthrie blood-spot cards may contain valuable data for epidemiological or other studies. Showing microarray data from guthrie blood-spot cards
2012-12-07 | E-GEOD-19919 | biostudies-arrayexpress
Project description:Background: Standard Guthrie cards have been widely used to collect blood samples from neonates for newborn screening programs, and to a lesser extent, from normal controls and patients in research studies. Ease of blood collection (small quantity and less pain), transportation, and storage are the advantages of using these cards. It is believed that RNA obtained from these samples is of low quantity and degraded quality. However, we recently discovered that approximately 3,500 expressed genes can be detected from blood spot samples using in-house made, low resolution cDNA microarrays. Here, we established a new and improved methodology to acquire gene expression profiles from blood spot cards using commercially-available high resolution microarrays. We determined the optimal number of blood spot punches required for maximal RNA extraction, eliminated uses of trizol and chloroform for RNA extraction by using a modified protocol of the illustra Mini Spin Kit from GE-Whatm an, concentrated the quantity of RNA templates before amplification, improved amplification efficiency using the new Ribo-SPIA technology in WT-Ovation Pico System (WT-Pico) by NuGEN, before the samples were hybridized onto 4x44K whole human genome gene expression microarrays from Agilent. Nine dried blood spot samples were collected from a control population and stored at ~ -80 °C between 6 months to 2 years. High quality RNA was extracted from the buffy coat of the same individuals as a reference and processed using the standard Agilent microarray procedure. Commercially-available brain RNA was used as a positive control in both standard and new procedures for microarrays. Results: Three 3-mm punches produced the highest yield of total RNA using the non-trizol extraction method. Three to six nanogram per microliter of RNA can be concentrated and is sufficient to be amplified using the WT-Pico. Approximately 9,000 expressed genes can be detected after normalization and background correction of the microarray data. Conclusion: Genome-wide gene expression profile can be obtained from archived dried blood spot samples. Our new and improved methodology will add value to the perception of utilizing archival Guthrie cards eg. neonatal blood spot cards as unique biospecimens for molecular genomics and diagnostic studies of perinatal diseases such as pediatric cancers. Keywords: Gene Expression experiment
2012-12-07 | GSE19919 | GEO