Rna Variant Calling
Rna Variant Calling - Related Videos
18:10
Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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Cited by 40 •
2011
The present article describes the steps required to isolate and characterize RNA polymerase fidelity variants of RNA viruses and how to use mutation frequency data to confirm fidelity changes in tissue culture.
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.
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...
09:54
RNA-Seq
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2023
Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.
00:58
Eukaryotic RNA Polymerases
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2025
RNA Polymerase (RNAP) is conserved in all animals, with bacterial, archaeal, and eukaryotic RNAPs sharing significant sequence, structural, and functional similarities. Among the three eukaryotic RNAPs, RNA Polymerase II is most similar to bacterial RNAP in terms of both structural organization and folding topologies of the enzyme subunits. However, these similarities are not reflected in their mechanism of action. All three eukaryotic RNAPs require specific transcription factors, of which the...
FAQs
Splice-aware alignment accounts for reads that span exon boundaries created by RNA splicing. This helps distinguish genuine transcript sequences from apparent mismatches caused by placing spliced reads incorrectly against a reference genome. Accurate alignment is especially important when analyzing transcript-specific changes, because alignment artifacts can otherwise be mistaken for expressed variants.
Quality filters reduce false-positive calls caused by sequencing errors, alignment artifacts, and uneven transcript coverage. The analysis must evaluate whether a mismatch is supported reliably rather than accepting every difference from the reference. These filters improve confidence in detected single-nucleotide variants, insertions, and deletions, although RNA-seq evidence remains dependent on read quality and coverage.
A variant may be easier or harder to detect depending on whether its allele is expressed and how many reads cover the relevant transcript region. Uneven coverage can leave some changes poorly supported, while allele-specific expression can make one allele appear more frequently than another. Interpreting read counts therefore requires attention to expression patterns as well as sequence differences.
RNA variant calling can reveal both genomic variation expressed in transcripts and RNA editing events, but the RNA sequence alone does not automatically establish their origin. Comparison with DNA-based analyses helps determine whether an observed difference is supported at the genomic level or appears specifically in RNA. This distinction is important when connecting transcript changes to genetic mechanisms.
A typical workflow uses RNA sequencing reads, aligns them to a reference genome or transcriptome, evaluates mismatches and indel evidence, and applies quality filters. The analysis must also account for splicing, allele expression, and uneven coverage before interpreting candidate variants. The resulting calls represent sequence differences supported by the transcript data, rather than an unfiltered list of read mismatches.
RNA variant calling adds information about which genetic changes are expressed in transcripts and how transcript-specific variation may relate to proteins. In genetics, this can complement DNA-based analyses in inherited disease and cancer studies, while also supporting investigations of gene regulation and personalized medicine. Its value is greatest when sequence findings need to be connected with expressed molecular consequences.