Transcriptome Mapping
Transcriptome Mapping - Related Videos
10:45
iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution
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Cited by 203 •
2011
The spatial arrangement of RNA-binding proteins on a transcript is a key determinant of post-transcriptional regulation. Therefore, we developed individual-nucleotide resolution UV crosslinking and immunoprecipitation (iCLIP) that allows precise genome-wide mapping of the binding sites of an RNA-binding protein.
09:58
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Cited by 30 •
2020
Intrinsically disordered domains are important for oncogenic fusion transcription factor function. To therapeutically target these proteins, a more detailed understanding of the regulatory mechanisms employed by these domains is required. Here, we use transcriptomics to map important structural features of the intrinsically disordered EWS domain in Ewing sarcoma.
10:16
Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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2025
This article introduces a protocol for using DeepSpaceDB, a dynamic, interactive database for spatial transcriptomics, offering analysis workflows and examples to explore tissue organization and disease-related gene expression.
06:58
Fate Mapping
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2023
Fate mapping is a technique used to understand how embryonic cells divide, differentiate, and migrate during development. In classic fate mapping experiments, cells in different areas of an embryo are labeled with a chemical dye and then tracked to determine which tissues or structures they form. Technological improvements now allow for individual cells to be marked and traced throughout embryonic development and adulthood. This video reviews the concepts behind fate mapping, and then details a...
11:03
Motor Maps
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California One principle of brain organization is the topographic mapping of information. Especially in sensory and motor cortices, adjacent regions of the brain tend to represent information from adjacent parts of the body, resulting in maps of the body expressed on the surface of the brain. The primary sensory and motor maps in the brain surround a prominent sulcus known as the central sulcus. The cortex...
FAQs
RNA sequencing or another expression-profiling method first provides transcript measurements. Genome alignment places those sequences relative to genomic regions, transcript assembly organizes evidence into transcript structures, and quantification estimates their abundance. Combining these stages distinguishes genomic locations from expression levels, allowing a map to show both which transcripts are present and how strongly they are represented.
Transcript assembly organizes RNA-sequence evidence into distinct transcript structures, while splice-variant analysis separates alternative forms produced from the same genomic region. This matters because genes can generate multiple RNA products, and those products may differ in abundance across conditions. Mapping these forms therefore improves gene annotation and makes expression changes more biologically specific.
Transcript abundance can vary with location, timing, tissue composition, or experimental condition. Comparing maps across these contexts helps identify genes and noncoding transcripts whose activity changes, rather than treating all RNA as a single average signal. Such comparisons are useful for examining regulatory responses and distinguishing shared expression patterns from changes associated with a particular state.
DNA sequence describes genomic information, but it does not by itself show which regions are transcribed in a particular context. Transcriptome Mapping adds evidence about active coding and noncoding transcripts, transcript abundance, and splice forms. In genetics, this functional layer helps connect sequence information to gene regulation, development, disease-related states, and other biological outcomes.
A typical workflow combines expression profiling with three analytical stages: aligning RNA-derived sequences to the genome, assembling transcript structures, and quantifying transcript abundance. The order links measured RNA to genomic coordinates before abundance is compared across samples or conditions. The resulting dataset can support gene annotation and reveal expression patterns without relying on DNA sequence alone.
Researchers can apply these maps to annotate genes, investigate regulatory responses, compare healthy and diseased states, and study development or inheritance. They also help identify candidate therapeutic targets by showing which transcripts or expression changes are associated with a biological context. The value depends on interpreting transcript locations and abundance together rather than examining either feature in isolation.
By pairing transcript locations and abundance with genetic information, researchers can examine whether sequence variation is accompanied by altered expression or transcript usage. This connection provides a route from a DNA difference to a functional observation, supporting studies of disease mechanisms and inherited effects. It does not replace sequence analysis; instead, it adds evidence about how variation may influence gene activity.