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A computational workflow for the analysis of 3’ Tag-Seq data

ABSTRACT: 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.

ORGANISM(S): Candida albicans

PROVIDER: GSE200778 | GEO | 2022/10/13

REPOSITORIES: GEO

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Project description:Salmonella enterica serovar Typhimurium (S. typhimurium) bacteria cause an inflammatory and lethal infection in zebrafish embryos. To characterize the embryonic innate host response at the transcriptome level, we have extended and validated previous microarray data by Illumina next-generation sequencing analysis. Comparison of tag-based sequencing (DGE or Tag-Seq) with full transcript sequencing (RNA-Seq) showed a strong correlation of sequence read counts per transcript and an overlap of 241 transcripts differentially expressed in response to infection. A slightly lower overlap of 165 transcripts was observed with previous microarray data. Based on the combined sequencing-based and microarray-based transcriptome data we compiled an annotated reference set of infection-responsive genes in zebrafish embryos, encoding transcription factors, signal transduction proteins, cytokines and chemokines, complement factors, proteins involved in apoptosis and proteolysis, proteins with anti-microbial activities, as well as many known or novel proteins not previously linked to the immune response. Furthermore, by comparison of the deep sequencing data of S. typhimurium infection in zebrafish embryos with previous deep sequencing data of Mycobacterium marinum infection in adult zebrafish we derived a common set of innate host defense genes that are expressed both in the absence and presence of a fully developed adaptive immune system and that provide a valuable reference for future studies of host-pathogen interactions using zebrafish infection models. Zebrafish embryos were infected with Salmonella typhimurium (strain SL1027) by microinjection of DsRED-labeled bacteria into the caudal vein close to the urogenital opening after the onset of blood circulation (27 hpf). An equal volume of PBS was injected in the control group. RNA samples were collected at 8 hours post infection (hpi) and samples from triplicate infection experiments were pooled. DGE libraries from the RNA pools (1 M-NM-<g) of Salmonella-infected and control embryos were prepared using the DGE:Tag Profiling for NlaIII Sample Prep kit from Illumina as previously described (Hegedus et al., 2009). The libraries were sequenced in duplicate using 2 (Control 1; Salmonella infected 1) and 3 pmol (Control 2; Salmonella infected 2) of cDNA. Sequencing was performed using the Illumina Genome Analyzer II System (BaseClear B.V., Leiden, The Netherlands) according to the manufacturerM-bM-^@M-^Ys protocols. Image analysis, base calling, extraction of 17 bp tags and tag counting were performed using the Illumina pipeline. Tag counts from duplicate libraries were merged in silico.

Project description:The precise regulation of transcription requires the coordinated activity of numerous proteins and protein complexes. Although significant progress has been made in understanding the mechanisms that regulate transcription, many questions remain unresolved. Accurately defining the direct effects of transcriptional regulators is critical to addressing these questions. An effective approach for identifying the direct targets of transcriptional regulators is combining rapid protein depletion and quantification of newly synthesized RNA. The auxin-inducible degron (AID) system and thiol (SH)-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) are powerful methods to rapidly degrade a target protein and directly quantify newly synthesized RNA, respectively. Both methods have been widely applied to study transcriptional regulation. To address unresolved questions in transcription, we assembled an end-to-end workflow to deplete proteins of interest using the AID system and measure newly synthesized RNA using SLAM-seq in the model eukaryote, Saccharomyces cerevisiae. We provide an open-source, step-by-step protocol to support rapid implementation of this workflow. We include methods for targeted protein degradation, 4-thiouracil (4tU) incorporation, rapid methanol fixation, RNA purification, RNA alkylation, mRNA-seq library construction, and data analysis. Additionally, we demonstrate that this workflow can help define the direct effects of transcriptional regulators using the bromodomain and extra-terminal domain (BET) proteins, Bdf1 and Bdf2, as an example. We provide evidence that data generated using this workflow is robust to normalization using whole-cell spike-in or total read counts, correlates well with 4tU-seq data, and identifies extensive differential expression due to the depletion of Bdf1 and Bdf2. Taken together, this workflow will help address outstanding questions underlying the molecular basis of transcriptional regulation and other processes in S. cerevisiae and other eukaryotes.

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A computational workflow for the analysis of 3’ Tag-Seq data

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