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Related Concept Videos

RNA-seq03:21

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

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Related Experiment Video

Updated: Jun 13, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

Chromap Suite: an open-source single-binary platform for agentic multiomic RNA + ATAC profiling.

Ling-Hong Hung, Ka Yee Yeung

    Biorxiv : the Preprint Server for Biology
    |June 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Chromap Suite offers an open-source alternative for multiomic analysis, significantly improving speed and reducing memory usage compared to proprietary tools. This freely redistributable pipeline supports both researchers and AI agents for enhanced biological insights.

    Related Experiment Videos

    Last Updated: Jun 13, 2026

    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
    06:24

    Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

    Published on: March 12, 2021

    Area of Science:

    • Computational Biology
    • Genomics
    • Bioinformatics

    Background:

    • Single-cell multiomic profiling is crucial for understanding cellular regulatory states.
    • Existing analysis toolchains for multiomics, like Cell Ranger ARC, have limitations including proprietary software, restrictive licenses, and non-standard peak calling methods.
    • Integrating open-source tools like Chromap and MACS3 has been challenging due to incompatible runtimes.

    Purpose of the Study:

    • To develop a fully open-source, permissively licensed alternative for multiomic analysis.
    • To create a unified pipeline that integrates RNA and ATAC modalities using community-standard methods.
    • To provide a user-friendly platform for both human researchers and AI agents.

    Main Methods:

    • Extended Chromap with native BAM output, in-process narrow peak calling, and support for various input formats.
    • Reimplemented MACS3's narrow peak caller in C++ (libMACS3) to eliminate Python dependencies.
    • Integrated libchromap and libMACS3 into STAR Suite for concurrent RNA and ATAC analysis in a single binary.
    • Developed a Model Context Protocol (MCP) server and browser-based Launchpad for AI and human-driven analysis.

    Main Results:

    • Achieved a 2.12x speedup and 1.8x reduction in peak memory usage compared to Cell Ranger ARC on a 3K PBMC Multiome dataset.
    • The Chromap Suite platform completed analysis in 18 minutes 55 seconds with 44.6 GB peak resident memory.
    • Generated byte-identical MACS3 narrow peaks, ensuring compatibility with downstream ATAC analyses.
    • The integrated STAR Suite enables concurrent alignment, peak calling, and cell calling for both RNA and ATAC data.

    Conclusions:

    • Chromap Suite provides a unified, freely redistributable multiomic pipeline that is faster and more memory-efficient than proprietary alternatives.
    • The platform's open-source nature and MIT/BSD-3 licenses remove redistribution restrictions.
    • The MCP server and Launchpad facilitate straightforward operation by both researchers and AI agents.