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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Cloudatac: A Cloud-based Framework For Atac-seq Data Analysis.

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Cloudatac: A Cloud-based Framework For Atac-seq Data Analysis.

Related Experiment Video

ATAC-Seq Optimization for Cancer Epigenetics Research

ATAC-Seq Optimization for Cancer Epigenetics Research

Published on: June 30, 2022

CloudATAC: a cloud-based framework for ATAC-Seq data analysis.

Avinash M Veerappa¹, M Jordan Rowley¹, Angela Maggio²

¹University of Nebraska Medical Center, Omaha, NE 68105 USA.

Briefings in Bioinformatics

|July 23, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Cloud ATAC offers an open-source, cloud-based framework for analyzing assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) data. This interactive platform streamlines complex epigenetic analyses for researchers, enhancing accessibility and interpretation of pooled-cell and single-cell data.

Keywords:

ATACseq Google Cloud NIH Cloud Lab NIH strides chromatin single-cell ATAC

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Area of Science:

Epigenetics and Genomics
Bioinformatics and Computational Biology
Cloud Computing in Life Sciences

Background:

Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) is crucial for genome-wide chromatin accessibility profiling.
Analyzing ATAC-seq data involves complex, interdependent bioinformatics pipelines, posing a challenge for non-specialists.
Limited computational resources often hinder comprehensive ATAC-seq data analysis.

Purpose of the Study:

To present Cloud ATAC, an open-source, cloud-based framework for streamlined ATAC-seq data analysis.
To provide an interactive learning platform for both pooled-cell and single-cell ATAC-seq data analysis using best practices.
To leverage cloud computing for scalable, flexible, and accessible epigenetic data analysis.

Main Methods:

Development of a cloud-based framework utilizing Google Cloud for on-demand computational resources.
Integration of Jupyter Notebooks for interactive learning, code execution, and data visualization.
Implementation of best practices for pooled-cell and single-cell ATAC-seq data processing and analysis.
Leveraging GPU instances to accelerate single-cell ATAC-seq framework runtime.

Main Results:

Cloud ATAC provides a scalable and flexible analysis framework for ATAC-seq data.
The interactive Jupyter Notebook environment enhances user learning and data interpretation.
GPU acceleration significantly reduces processing times for single-cell ATAC-seq analyses.
Publicly available source code and data facilitate reproducibility and adoption.

Conclusions:

Cloud ATAC democratizes complex ATAC-seq data analysis by providing an accessible, cloud-based solution.
The framework supports both pooled-cell and single-cell ATAC-seq, catering to diverse research needs.
This resource module, part of the NIGMS Sandbox, promotes cloud-based learning in bioinformatics.