Transcriptional Analysis of RNA-seq Data

The RNA-seq data set in the NCBI Gene Expression Omnibus (GEO) database, accession number GSE118116, was analyzed to assess transcriptional units and compared to the meta-analysis of GSE18149, GSE33176, GSE41014, and GSE53636 carried out by Peña-Castillo et al. (44 (link)). Transcript abundance was visualized using the Broad Institute’s IGV viewer (95 (link)). Protein alignments were made in ClustalΩ (96 (link)) and then visualized in Jalview (97 (link)). FIJI software (98 (link)) was used to analyze microscopy images. Statistical analysis was carried out using SigmaPlot software version 13 (Systat Software Inc., Slough, UK; www.systatsoftware.com), and for each use, the test parameters are indicated in the text and/or figure legends. All data presented in figures correspond to one representative experiment consisting of at least three biological replicates; all experimental data were confirmed on at least two different occasions. CorelDraw 2018 (Corel Corporation, Ottawa, Canada) was used for figure preparation and ChemDraw (Perkin Elmer, Waltham, MA, USA) for chemical structure diagrams.

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Sherlock D, & Fogg P.C. (2022). Loss of the Rhodobacter capsulatus Serine Acetyl Transferase Gene, cysE1, Impairs Gene Transfer by Gene Transfer Agents and Biofilm Phenotypes. Applied and Environmental Microbiology, 88(19), e00944-22.

Publication 2022

SigmaPlot software version 13 CorelDraw 2018

Biological Gene expression Microscopy Protein Rna seq Transcriptional

Corresponding Organization : University of York

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Variable analysis

independent variables

Transcript abundance from the RNA-seq data set in the NCBI Gene Expression Omnibus (GEO) database, accession number GSE118116

dependent variables

Transcriptional units
Transcript abundance

control variables

Meta-analysis of the RNA-seq data sets GSE18149, GSE33176, GSE41014, and GSE53636

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