Dataset Information


Differential expression of microRNAs in wildtype versus DCL1 mutants in Arabidopsis thaliana

ABSTRACT: Small RNA libraries of wildtype Arabidopsis thaliana and its mutant Dicer-like 1 (Dcl1) were constructed and sequenced for miRNA identification and expression analysis. The mutant data was used to validate novel miRNA predictions (from miRCat2 (Paicu et al. 2017), miRCat (Moxon et al. 2008), miRPlant (An et al. 2014) and miReap (, by calculating the log fold change between the mutant and the wildtype samples. Overall design: Seeds of Arabidopsis thaliana wildtype and dcl1-7 mutants were stratified and germinated. When plants were grown, leaves were harvested and genomic DNA were extracted as previously described (Amani et al. 2011) for genotyping. Heterozygous individuals were identified through PCR-sequencing for seed increase (mutants) and by phenotypes (wildtype). When the flower buds in both wildtype and mutant plants were visible but without opening flowers, the leaves of nine plants were harvested for three biological replicates. One replicate consists of a pool of leaves from three individual plants. Total RNA of both wildtype and mutant leaf tissue was extracted, then small RNA (sRNA) of each sample was further enriched and used for HD library construction. The small RNA libraries were sequenced and analyzed.

INSTRUMENT(S): Illumina HiSeq 2500 (Arabidopsis thaliana)

SUBMITTER: Irina Mohorianu  

PROVIDER: GSE90771 | GEO | 2017-04-17



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miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets.

Paicu Claudia C   Mohorianu Irina I   Stocks Matthew M   Xu Ping P   Coince Aurore A   Billmeier Martina M   Dalmay Tamas T   Moulton Vincent V   Moxon Simon S  

Bioinformatics (Oxford, England) 20170801 16

MicroRNAs are a class of ∼21-22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction alg  ...[more]

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