High-throughput Mapping
High-throughput Mapping - Related Videos
08:48
High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization
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Cited by 5 •
2012
Genome assemblies based on massively parallel DNA sequencing technologies are usually highly fragmented. The development of physical chromosome maps can potentially improve genome assemblies. Here, we demonstrate innovative approaches to chromosome preparation, fluorescent in situ hybridization, and imaging that significantly increase throughput of the physical map development.
07:36
High-Throughput Analysis of Optical Mapping Data Using ElectroMap
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Cited by 13 •
2019
This protocol describes the setup and use of ElectroMap, a MATLAB-based open-source software platform for analysis of cardiac optical mapping data. ElectroMap provides a versatile high-throughput tool for analysis of optical mapping voltage and calcium datasets across a wide range of cardiac experimental models.
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
13:00
A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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Cited by 4 •
2009
Due to the transient nature of pre-mRNA, it can be difficult to isolate and study in vivo. Here, we present a novel in vitro approach to investigate RNA-protein interactions using a synthetic oligo pool that tiles across selected regions of pre-mRNA.
09:14
DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments
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Cited by 21 •
2016
We describe herein an assay by coupling DNA adenine methyltransferase identification (DamID) to high throughput sequencing (DamID-seq). This improved method provides a higher resolution and a wider dynamic range, and allows analyzing DamID-seq data in conjunction with other high throughput sequencing data such as ChIP-seq, RNA-seq, etc.
FAQs
Multiplexed assays allow many biological features or samples to be measured within a coordinated experiment, while molecular barcodes distinguish the resulting signals by feature, sample, or condition. This reduces the need to process each target separately and creates datasets that can be analyzed together. The combination is especially useful when comparing many cells, tissues, genomic regions, or population samples.
The connection depends on recording where a signal occurs or which experimental condition produced it. Imaging can preserve information about cellular or tissue position, whereas sequencing and associated barcoding can associate measurements with defined molecular targets or samples. Computational analysis then organizes these relationships, allowing researchers to compare patterns across locations, cell groups, tissues, or biological states.
Large parallel experiments produce many measurements whose biological meaning depends on their relationships to one another. Computational analysis organizes signals, links them to positions or conditions, and identifies patterns across the dataset. This step helps transform extensive measurements into interpretable maps that can reveal coordinated gene regulation, protein interactions, cellular organization, or variation among samples.
A workflow generally begins by selecting the biological features, locations, or conditions to compare. Researchers then generate measurements through automated or multiplexed assays, using molecular barcoding, sequencing, imaging, or a combination of these approaches. Finally, computational analysis organizes the results into maps and relates detected patterns to biological positions, sample groups, or experimental conditions.
Researchers would favor this approach when the question involves many features, interactions, cells, tissues, genomic regions, or samples and requires comparison across them. Parallel measurement can expose coordinated patterns that are difficult to recognize in isolated tests. These maps support systems-level investigations, disease studies, biomarker discovery, and models linking molecular patterns with biological function.
Applications include examining how genes are regulated, identifying protein interactions, describing cellular organization, and comparing biological variation among samples or populations. In disease investigation, the resulting patterns can help distinguish relevant molecular changes and support biomarker discovery. The data can also contribute to models that connect observed molecular distributions with broader biological functions.