Relevant papers about isomiRs and other novel small RNAs with functional relevance


  • Our approach can be adapted to many polyadenylation-based RT-qPCR technologies already exiting, providing a convenient way to distinguish long and short 3′-isomiRs.


Naturally existing isoforms of miR-222 have distinct functions: this work demonstrates the capacity for 3’ isomiRs to mediate differential functions, we contend more attention needs to be given to 3’ variance given the prevalence of this class of isomiR.

miR-142-3p isomiR: “We furthermore demonstrate that miRNA 5′-end variation leads to differential targeting and can thus broaden the target range of miRNAs.”

A highly expressed miR-101 isomiR is a functional silencing small RNA.

A challenge for miRNA: multiple isomiRs in miRNAomics.

miR-183-5p isomiR changes in breast cancer. Validated target regulation of new genes different from the reference miRNA.

A comprehensive survey of 3’ animal miRNA modification events and a possible role for 3’ adenylation in modulating miRNA targeting effectiveness.

PAPD5-mediated 3′ adenylation and subsequent degradation of miR-21 is disrupted in proliferative disease.

High-resolution analysis of the human retina miRNome reveals isomiR variations and novel microRNAs.

Sequence features of Drosha and Dicer cleavage sites affect the complexity of isomiRs.

Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types


A novel piRNA mechanism in regulating gene expression in highly differentiated somatic cells.

Differential and coherent processing patterns from small RNAs to detect changes in profiles of processing small RNAs.

Survey of 800+ datasets from human tissue and body fluid reveals XenomiRs are likely artifacts


Identification of factors involved in target RNA-directed microRNA degradation.


miRQC: work studying the accuracy and specificity of different technologies to detect miRNAs.

Important features affecting the detection of small RNA biomarkers: How the sample can affect the detection of biomarkers (like RIN value, concentration, …)

Comparison of alignment and normalization . I will take the message that TMM and DESeq/2 normalization are the best to avoid strong bias if we consider to have a small proportion of DE miRNAs. For the alignments, here you have another comparison for miRNAs annotation:

review of tools for detect miRNA-disease network.

review of tools for miRNA de-novo and interaction analysis

Evaluation of microRNA alignment techniques