Disease Variant Modeling
Disease Variant Modeling - Related Videos
14:06
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Cited by 10 •
2012
Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.
06:02
Experimental Approaches for Biochemical Analysis of Glial Fibrillary Acidic Protein and Its Disease-associated Variants
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2025
This study outlines experimental methods to biochemically characterize GFAP and its disease-causing mutations, focusing on filament assembly, aggregation, and post-translational modification. High-purity GFAP proteins were analyzed, revealing insights into Alexander disease mechanisms that may provide a framework for studying potential therapeutic targets and understanding GFAP mutations and related disorders.
02:30
Histone Variants at the Centromere
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2020
Histone variants are the histone proteins with structural and sequence variations. These variants may be regarded as “mutant” forms that replace their canonical histone counterparts in the nucleosomes. Specific post-translational modifications on the histone variants enable further chromatin complexity and regulate tissue-specific gene expression. The most common histone variants are from histone H2A, H2B, and linker histone H1 families. However, several variants of histone H3 variants are also...
09:58
Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models
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Cited by 25 •
2016
Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.
00:06
In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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Cited by 43 •
2019
The goal of this protocol is to outline the design and performance of in vivo experiments in Drosophila melanogaster to assess the functional consequences of rare gene variants associated with human diseases.
FAQs
Researchers compare a variant’s predicted or measured effects with those of a reference sequence, then examine whether the difference aligns with a biological change relevant to disease. Evidence from gene expression, protein function, cellular behavior, or pathway activity can strengthen prioritization, but modeling primarily helps identify variants for further biological validation rather than serving as validation by itself.
Gene expression, protein function, cellular behavior, and pathway activity provide complementary readouts. Expression measurements indicate whether regulation may change, while protein and cellular assessments examine functional consequences at later biological levels. Pathway activity places an individual effect in a broader system. Selecting among these readouts lets investigators connect a modeled variant to a more specific biological outcome.
A reference sequence supplies the comparison baseline needed to interpret a predicted or measured variant effect. Defined conditions make the assessment more controlled by specifying the biological setting in which gene expression, protein function, cellular behavior, or pathway activity is examined. Without a consistent baseline and condition set, differences are harder to attribute to the variant or compare across models.
A practical workflow begins by selecting a disease-associated genetic difference and representing it in a computational model or experimental system. Investigators compare its predicted or measured effects with the reference sequence, assess one or more biological readouts, and interpret the results in relation to disease. Variants showing informative effects can then be prioritized for further biological validation.
Beyond explaining disease mechanisms, these models can support biomarker development and evaluation of potential therapeutic targets. A modeled variant may reveal a measurable biological change useful for tracking disease-related processes, or identify altered biology that warrants target assessment. The approach therefore connects variant interpretation with research decisions about what to measure, investigate, or validate next.
Biology researchers can apply this approach to both inherited and acquired disorders, because the modeled change is interpreted through its effects on biological systems rather than through sequence difference alone. Linking genotype to phenotype helps organize evidence across gene, protein, cellular, and pathway levels. This context supports disease-mechanism studies and helps prioritize variants for focused experimental follow-up.