#MALAT1

Critical Assessment of a Structure-Based Pipeline for Targeting the Long Noncoding RNA MALAT1 Long noncoding RNAs (lncRNAs) are increasingly recognized as druggable targets due to their conserved secondary/tertiary structures and regulatory roles in disease. A prototypical example is the MALAT...

🧬 lncRNA #MALAT1 as a case study to test docking on flexible #RNA targets for #drugdiscovery. HREX MD revealed two sites, whose druggability was assessed through ensemble docking of 21 diminazene derivatives and multiple scoring functions. @JCIM pubs.acs.org/doi/10.1021/...

Diagnostic value of long noncoding RNAs MIAT and MALAT1 in coronary artery disease: a systematic review and diagnostic accuracy test meta-analysis - BMC Cardiovascular Disorders BMC Cardiovascular Disorders - Long noncoding RNAs (lncRNAs) MALAT1 and MIAT modulate atherosclerotic progression and myocardial ischemia injury, through vascular endothelial dysfunction....

#MetaAnalysis reveals that lncRNAs #MALAT1 and #MIAT show moderate diagnostic accuracy for #CoronaryArteryDisease, with MALAT1 also linked to prognosis. Findings highlight their potential but call for standardization in clinical validation. #Biomarker link.springer.com/article/10.1...

🐟 New in NARMME: malat1 in zebrafish triggers signaling for retinal repair guided by Notch, Wnt & TGF-β pathways. In mammals, TGF-β blocks regeneration revealing a key difference in healing potential.

🔗 doi.org/10.1093/narm...

#Zebrafish #RetinaRepair #Malat1 #MolecularBiology #NARMME #OpenAccess

Identification of human pathways acting on nuclear non-coding RNAs using the Mirror forward genetic approach - Nature Communications Human pathways acting on nuclear ncRNAs have been refractory to forward genetics. Here, the authors develop a forward genetic approach that identifies such pathways and show DDX59 is required for mino...

A tour de force. Wonderful stuff! Congrats!
#ncRNA #MALAT1 #RNAExosome #RNaseP #NEXT #DDX59 #RNADecay #RNA #RNAsky

www.nature.com/articles/s41...

So we encourage to plot boxplots for scRNAseq qcing for #nUMI, #mito, #MALAT1 and #intronic content or a combination of them like Figure 6 in our publication!

Keep an eye to your sc dataset, 1)are there two clusters of same cell type one with and the other without #MALAT1 expression?
2)extreme values of #MALAT1 or #intronic reads is bad, null values indicate citosolic debris and extreme high values bare nuclei (dying/damaged cell)