Evidence map›Paper›PMID 36779816›Full record

ArticleCurrent protocols2023

A Computational Workflow for Analysis of 3' Tag-Seq Data.

Akshay D Paropkari, Priyanka S Bapat, Suzanne S Sindi, Clarissa J Nobile

Abstract read
In one paragraph

Article in Current protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Akshay D ParopkariQuantitative and Systems Biology Graduate Program, University of California, Merced, California.
Priyanka S BapatQuantitative and Systems Biology Graduate Program, University of California, Merced, California.
Suzanne S SindiDepartment of Applied Mathematics, School of Natural Sciences, University of California, Merced, California.
Clarissa J NobileDepartment of Molecular and Cell Biology, School of Natural Sciences, University of California, Merced, California.

Funding

Genetic regulation of inter- and intra-species microbial community formationR35GM124594 · NIGMS · UNIVERSITY OF CALIFORNIA, MERCED · PI NOBILE, CLARISSA JANE · 2017 to 2021
$2.0M
NIGMS NIH HHS R35 GM124594
6 · The paper itself

Abstract

RNA-sequencing (RNA-seq) is a gold-standard method 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 and aligned to the reference genome, and then 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, and thus normalizations based on transcript length 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. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Running the 3' Tag-Seq workflow Support Protocol: Generating genome indices.

Indexed as

Gene Expression ProfilingSoftwareRNA, MessengerRNA-SeqWorkflowRNA, Messenger3′ Tag-Seqcomputational pipelinedifferential gene expressionRNA sequencing

Identifiers

PMID36779816
PMCPMC9930165

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.