Sequencing Data Visualization
Sequencing Data Visualization - Related Videos
10:58
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Cited by 12 •
2011
Visual analytics (VA) is a new approach of analyzing data interactively. In this video, we discuss the data overload problem brought on by high-throughput biological experiments, and propose VA as a solution to such problem. The video demonstrates analysis within and between immunological datasets using a VA tool called Tableau.
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Cited by 8 •
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Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
04:58
Introductory Analysis and Validation of CUT&RUN Sequencing Data
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Cited by 1 •
2024
This protocol guides bioinformatics beginners through an introductory CUT&RUN analysis pipeline that enables users to complete an initial analysis and validation of CUT&RUN sequencing data. Completing the analysis steps described here, combined with downstream peak annotation, will allow users to draw mechanistic insights into chromatin regulation.
08:15
Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton
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2023
Here, we present a protocol for converting phytoplankton microscopic images into vector graphics and repetitive patterns to enable visualization of shifts in phytoplankton taxa and biomass over 60 years. This protocol represents an approach that can be utilized for other plankton time series and datasets globally.
09:14
Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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Cited by 2 •
2018
Deep sequencing of yeast populations selected for positive yeast 2-hybrid interactions potentially yields a wealth of information about interacting partner proteins. Here, we describe the operation of specific bioinformatics tools and customized updated software to analyze sequence data from such screens.
FAQs
A sequencing view gains interpretive value by placing measurements against genomic coordinates or a reference sample. Aligned reads can be inspected alongside coverage, base quality, gene expression, or detected variants, allowing researchers to relate a signal to a specific genomic region. This coordinate-based organization supports pattern recognition and helps distinguish informative findings from sequencing-quality concerns.
Coverage and base-quality displays provide complementary information about sequencing results. Coverage shows how measurements are distributed across genomic regions, while base quality helps researchers evaluate the reliability of individual sequence observations. Examining both views can support quality assessment before interpreting variants, expression patterns, or other signals in a cancer sequencing study.
Comparing tumor sequence results with matched normal tissue helps researchers assess whether detected variants are associated with the tumor rather than also present in the normal sample. Visual comparison can therefore support investigation of somatic mutations and clarify patterns across samples. Comparing tumors from multiple patients adds context for identifying shared or differing genomic findings.
A practical workflow begins with sequencing results that include aligned reads or other measured features. Researchers then inspect relevant genomic regions, review coverage and base quality, and examine detected variants, expression, or copy-number changes in relation to coordinates or reference samples. The resulting views can guide validation, biological interpretation, and comparison across tumors.
The display should match the measurement being examined. Genome browsers are suited to inspecting aligned reads and signals across genomic coordinates, heat maps support comparisons of patterns such as gene-expression signatures, and summary charts present broader results across samples. Using these complementary formats helps researchers move from detailed regional inspection to comparative interpretation.
Visual comparisons across tumors or study time points can reveal patterns in mutations, copy-number changes, or gene-expression signatures that are relevant to cancer biology. These views help researchers examine how genomic findings differ among patients or change in relation to treatment response. They also provide a clear way to communicate validated observations and biological interpretations.