On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves: Bowtie2 counts and kallisto abundances

Intra [1] and inter [2-5] study RNA-seq read datasets representing the varying brain compartments of foxes (n=24), as well as dogs (n=14) and wolves (n=6), as described in Lobo et al., (2022) (under review), were mapped to the dog reference transcriptome [6], which contained 26,107 annotated transcripts (Ensembl CanFam3.1, release 92) [7], using Bowtie2 v.2.3.4.1 [8] and using kallisto v0.46.1 [9]. Count data obtained following each mapping approach for each dataset had high correlations (Lobo et al., Figure S2). Bowtie2 counts were subsequently used in multiple differential analysis experiments in order to explore the effects of intra-condition count variation on the detection of differentially expressed transcripts. The individual count and abundance datasets for each corresponding RNA-seq dataset are available here.

A preprint of Lobo et al., 2022, currently under review for PLOS ONE, is available [10]. The preprint however does not contain reviewer requested information on simulations as this, along with other additions including an additional author RL, has been subsequently added during the review process. These additions will be made available following review via a link to the final paper. 

Related software to this project are:
1. CStone 
2. CSReadGen
3. CView 
4. ChimSim
5. TVScript <

General details of the projects involved are available: dog-wolf and chimerism.

References

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2. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, et al. Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. 2013;24: 484–499. doi:10.1007/s00335-013-9480-0

3. Fushan AA, Turanov AA, Lee SG, Kim EB, Lobanov A V, Yim SH, et al. Gene expression defines natural changes in mammalian lifespan. Aging Cell. 2015;14: 352–365. doi:10.1111/acel.12283

4. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172

5. Albert FW, Somel M, Carneiro M, Aximu-Petri A, Halbwax M, Thalmann O, et al. A Comparison of Brain Gene Expression Levels in Domesticated and Wild Animals. Akey JM, editor. PLoS Genet. 2012;8:e1002962. doi:10.1371/journal.pgen.1002962

6. Hoeppner MP, Lundquist A, Pirun M, Meadows JRS, Zamani N, Johnson J, et al. An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One. 2014;9(3):91172. doi:10.1371/journal.pone.0091172

7. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48: D682–D688. doi:10.1093/NAR/GKZ966

8. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012. doi:10.1038/nmeth.1923

9. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016 345. 2016;34: 525–527. doi:10.1038/nbt.3519

10. Lobo D, Godinho R, Archer JP. On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves. bioRxiv. 2022; 2022.01.24.477470. doi:10.1101/2022.01.24.477470