scholarly journals De Novo Transcriptome Assembly and Gene Expression Profiling of the Copepod Calanus helgolandicus Feeding on the PUA-Producing Diatom Skeletonema marinoi

Marine Drugs ◽  
2020 ◽  
Vol 18 (8) ◽  
pp. 392 ◽  
Author(s):  
Sneha Asai ◽  
Remo Sanges ◽  
Chiara Lauritano ◽  
Penelope K. Lindeque ◽  
Francesco Esposito ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
De Novo ◽  
Differential Expression Analysis ◽  
Prorocentrum Minimum ◽  
Marine Phytoplankton ◽  
Mixed Solution ◽  
Methionine Metabolism ◽  
Skeletonema Marinoi ◽  
Calanus Helgolandicus

Diatoms are the dominant component of the marine phytoplankton. Several diatoms produce secondary metabolites, namely oxylipins, with teratogenic effects on their main predators, crustacean copepods. Our study reports the de novo assembled transcriptome of the calanoid copepod Calanus helgolandicus feeding on the oxylipin-producing diatom Skeletonema marinoi. Differential expression analysis was also performed between copepod females exposed to the diatom and the control flagellate Prorocentrum minimum, which does not produce oxylipins. Our results showed that transcripts involved in carbohydrate, amino acid, folate and methionine metabolism, embryogenesis, and response to stimulus were differentially expressed in the two conditions. Expression of 27 selected genes belonging to these functional categories was also analyzed by RT-qPCR in C. helgolandicus females exposed to a mixed solution of the oxylipins heptadienal and octadienal at the concentration of 10 µM, 15 µM, and 20 µM. The results confirmed differential expression analysis, with up-regulation of genes involved in stress response and down-regulation of genes associated with folate and methionine metabolism, embryogenesis, and signaling. Overall, we offer new insights on the mechanism of action of oxylipins on maternally-induced embryo abnormality. Our results may also help identify biomarker genes associated with diatom-related reproductive failure in the natural copepod population at sea.

scholarly journals Assembly-free rapid differential gene expression analysis in non-model organisms using DNA-protein alignment

2021 ◽  
Author(s):  
Anish M.S. Shrestha ◽  
Joyce Emlyn B. Guiao ◽  
Kyle Christian R. Santiago
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
De Novo ◽  
Transcriptome Assembly ◽  
Differential Expression Analysis ◽  
Homology Search ◽  
Model Organisms ◽  
Rna Seq ◽  
Protein Database

AbstractRNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. Conventional differential expression analysis for organisms without reference sequences requires performing computationally expensive and error-prone de-novo transcriptome assembly, followed by homology search against a high-confidence protein database for functional annotation. We propose a shortcut, where we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the protein database. Through experiments on simulated and real data, we show drastic reductions in run-time and memory usage, with no loss in accuracy. A Snakemake implementation of our workflow is available at:https://bitbucket.org/project_samar/samar

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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