Monitor the efficiency of "WIND: A Workflow for pIRNAs aNd beyonD" for the identification of single-stranded (SS) spike-in piRNA-like molecules in smallRNA-seq
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ABSTRACT: Monitor the efficiency of "WIND: A Workflow for pIRNAs aNd beyonD" for the identification of single-stranded (SS) spike-in piRNA-like molecules in smallRNA-seq
Project description:For the evaluation of \\"WIND: A Workflow for pIRNAs aNd beyonD\\" performance and the transcriptomic approach on small-RNA identification, and particularly on piRNAs, a synthetic set of 4 piRNA-like molecules was used. Two non-methylated (SS-22 and SS-28) and two methylated (mSS-22 and mSS-28) of two different lengths (22 nt and 28 nt) were included. Spike-ins were chemically synthesised at Exiqon, the pool of 4 molecules was used at three different concentrations, with a final amount of 0.3 x 10^9 (dil_A), 0.3 x 10^10 (dil_B) and 0.3 x 10^11 (dil_C) molecules/ug of RNA. Library preparation and sequencing were done as previously described in Sellitto et al 2019 (doi: https://doi.org/10.3390/cells8111390)
Project description:For the evaluation of "WIND: A Workflow for pIRNAs aNd beyonD" performance and the transcriptomic approach on small-RNA identification, and particularly on piRNAs, two mouse Adult Cardiomyocytes (aCMs) were used.
Project description:WIND: A Workflow for pIRNAs aNd beyonD, for the evaluation of the performance in piRNAs high expression and low expression conditions, we used Human Testis RNAs (BioChain Institute Inc, Newark, CA, USA) and COLO 205 cell line RNAs (samples are available on ArrayExpress (E-MTAB-8115: Non_treated_Testis_1 and Non_treated_COLO205_1, Non_treated_COLO205_2, Non_treated_COLO205_3).
Project description:Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research
Project description:RNA-sequencing (RNA-seq) is a ubiquitous tool to profile genome-wide changes in gene expression. RNA-seq uses high-throughput sequencing technology to quantify the amount of RNA in a biological sample. With the increasing popularity of RNA-seq, many variations on the protocol have been proposed to extract unique and relevant information from biological samples. 3’ Tag-Seq (also called TagSeq, 3′ Tag-RNA-Seq, and Quant-Seq 3′ mRNA-Seq) is one RNA-seq variation, where the 3’ end of the transcript is selected and amplified to yield one copy of cDNA from each transcript in the biological sample.We present a simple, easy to use, and publicly available computational workflow to analyze 3’ Tag-Seq data. The workflow begins by trimming sequence adapters from raw FASTQ files. The trimmed sequence reads are checked for quality using FastQC, aligned to the reference genome, and read counts are obtained using STAR. Differential gene expression analysis is performed using DESeq2, based on differential analysis of gene count data. The outputs of this workflow are MA plots, tables of differentially expressed genes, and UpSet plots.This protocol is intended for users specifically interested in analyzing 3’ Tag-Seq data. As such, transcript length-based normalizations are not performed within the workflow. Future updates to this workflow could include custom analyses based on the gene counts table as well as data visualization enhancements.