A primer for generating and using transcriptome data and gene sets

Abstract

Transcriptomic approaches have provided a growing set of powerful tools with which to study genome-wide patterns of gene expression. Rapidly evolving technologies enable analysis of transcript abundance data from particular tissues and even single cells. This Primer discusses methods that can be used to collect and profile RNAs from specific tissues or cells, process and analyze high-throughput RNA-sequencing data, and define sets of genes that accurately represent a category, such as tissue-enriched or tissue-specific gene expression.

Identification and Cloning of a CC-NBS-NBS-LRR Gene as a Candidate of Pm40 by Integrated Analysis of Both the Available Transcriptional Data and Published Linkage Mapping

“…In fact, the expression of the same gene exhibited slight differences in both expression level and trend in different races [ 51 ], and different rank position of this gene in cascade signaling pathway [ 52 ]. Therefore, the proportion of genes low abundance expression cannot be directly compared because of both low sequencing depth and stage-specific expression [ 53 , 54 ]. In this study, qRT-PCR analysis, which is usually treated as a powerful tool for the study of low abundance expression [ 55 ], shows that the expression of TraesCS7B01G164000 in L658 cells was upregulated after inoculation with Bgt and expression peaked at 6 hpi, which agreed with previous work showed that the genes expression influencing the infection of Bgt could occur within 12 hpi [ 6 ].…”

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

“…Because of inconsistencies in 16S and 18S rRNA gene copy number between di↵erent taxa (e.g., [48, 49, 50]), we further normalized abundance estimates to Z-scores during the period of interest. While not making disparate samples directly comparable [46, 51, 52], Z-scores standardize measurements already subject to variance stabilization via CPMcontig calculation and simplifies interpretation of changes in abundance during the sampling period. Instead of using raw abundance, the Z-score of each abundance estimate was calculated as a relative measure of the change in the size of the community of the respective taxonomic group over time.…”

Marine Microeukaryote Metatranscriptomics: Sample Processing and Bioinformatic Workflow Recommendations for Ecological Applications

“…These approaches make key assumptions about most genes not being differentially expressed, and care should be taken when comparing disparate marine ecosystems (e.g., surface and deep populations, or surface waters from eastern and western ocean basins). Normalization approaches that merely account for changes in sequence library and differences in contig length, such as Transcripts Per Million (TPM) (Wagner et al, 2012), may be better suited for large spatial or temporal field studies, although these methods cannot provide statistical estimates of differential expression and may retain transcript composition biases (Cockrum et al, 2020; see below: Consolidating assemblies). Flexible and adaptive statistical techniques that take into consideration zero-inflation, such as Tweedie models, may be useful for metatranscriptomic analyses and are worth exploring in marine omic datasets (Mallick et al, 2021).…”