Basic Information

Symbol
RIGI
RNA class
mRNA
Alias
RNA Sensor RIG-I RIG-I Antiviral Innate Immune Response Receptor RIG-I RIG-1 DDX58 RIG1 DEAD (Asp-Glu-Ala-Asp) Box Polypeptide 58 Retinoic Acid-Inducible Gene 1 Protein Retinoic Acid-Inducible Gene I Protein ATP-Dependent RNA Helicase DDX58 DExD/H-Box Helicase 58 DEAD Box Protein 58 RNA Helicase RIG-I DKFZp434J1111 FLJ13599 RLR-1 DEAD/H (Asp-Glu-Ala-Asp/His) Box Polypeptide Probable ATP-Dependent RNA Helicase DDX58 Retinoic Acid Inducible Gene I RIG-I-Like Receptor 1 EC 3.6.4.13 SGMRT2
Location (GRCh38)
Forensic tag(s)
Other applications

MANE select

Transcript ID
NM_014314.4
Sequence length
4628.0 nt
GC content
0.3991

Transcripts

ID Sequence Length GC content
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4622 nt 0.3992
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4457 nt 0.3976
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4641 nt 0.3991
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4480 nt 0.3982
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4613 nt 0.3993
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4638 nt 0.3991
GAACGUAGCUAGCUGCAAGCAGAGGCCGGCAUGACCACCGAGCAGCGAC… 4628 nt 0.3991
Summary

DEAD box proteins, characterized by the conserved motif Asp-Glu-Ala-Asp (DEAD), are putative RNA helicases which are implicated in a number of cellular processes involving RNA binding and alteration of RNA secondary structure. This gene encodes a protein containing RNA helicase-DEAD box protein motifs and a caspase recruitment domain (CARD). It is involved in viral double-stranded (ds) RNA recognition and the regulation of the antiviral innate immune response. Mutations in this gene are associated with Singleton-Merten syndrome 2. [provided by RefSeq, Aug 2020]

Forensic Context

A study in rats demonstrated that the RIGI (DEXD/H-box helicase 58) was core-enriched in downregulated immune-associated datasets in fetal cardiac fibroblasts compared with neonatal cardiac fibroblasts, identifying it as an innate immune response receptor whose expression is developmentally regulated [Perreault et al. DOI:10.1152/physiolgenomics.00074.2021]. A study in diverse RNA constructs demonstrated that computational models developed via a dual-crowdsourcing approach could predict RNA degradation patterns with high accuracy, with 41% of nucleotide-level predictions from the winning model within experimental error [Wayment-Steele et al. DOI:10.1038/s42256-022-00571-8].