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- Vijay, Nagarjun, et al.
(författare)
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Challenges and strategies in transcriptome assembly and differential gene expression quantification. A comprehensive in silico assessment of RNA-seq experiments
- 2013
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Ingår i: Molecular Ecology. - : Wiley. - 0962-1083 .- 1365-294X. ; 22:3, s. 620-634
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Tidskriftsartikel (refereegranskat)abstract
- Transcriptome Shotgun Sequencing (RNA-seq) has been readily embraced by geneticists and molecular ecologists alike. As with all high-throughput technologies, it is critical to understand which analytic strategies are best suited and which parameters may bias the interpretation of the data. Here we use a comprehensive simulation approach to explore how various features of the transcriptome (complexity, degree of polymorphism p, alternative splicing), technological processing (sequencing error e, library normalization) and bioinformatic workflow (de novo vs. mapping assembly, reference genome quality) impact transcriptome quality and inference of differential gene expression (DE). We find that transcriptome assembly and gene expression profiling (EdgeR vs. BaySeq software) works well even in the absence of a reference genome and is robust across a broad range of parameters. We advise against library normalization and in most situations advocate mapping assemblies to an annotated genome of a divergent sister clade, which generally outperformed de novo assembly (Trans-Abyss, Trinity, Soapdenovo-Trans). Transcriptome complexity (size, paralogs, alternative splicing isoforms) negatively affected the assembly and DE profiling, whereas the effects of sequencing error and polymorphism were almost negligible. Finally, we highlight the challenge of gene name assignment for de novo assemblies, the importance of mapping strategies and raise awareness of challenges associated with the quality of reference genomes. Overall, our results have significant practical and methodological implications and can provide guidance in the design and analysis of RNA-seq experiments, particularly for organisms where genomic background information is lacking.
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| 2. |
- Wolf, Jochen B. W., et al.
(författare)
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Nonlinear Dynamics of Nonsynonymous (d(N)) and Synonymous (d(S)) Substitution Rates Affects Inference of Selection
- 2009
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Ingår i: Genome Biology and Evolution. - : Oxford University Press (OUP). - 1759-6653. ; 1, s. 308-319
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Tidskriftsartikel (refereegranskat)abstract
- Selection modulates gene sequence evolution in different ways by constraining potential changes of amino acid sequences (purifying selection) or by favoring new and adaptive genetic variants (positive selection). The number of nonsynonymous differences in a pair of protein-coding sequences can be used to quantify the mode and strength of selection. To control for regional variation in substitution rates, the proportionate number of nonsynonymous differences (d(N)) is divided by the proportionate number of synonymous differences (d(S)). The resulting ratio (d(N)/d(S)) is a widely used indicator for functional divergence to identify particular genes that underwent positive selection. With the ever-growing amount of genome data, summary statistics like mean d(N)/d(S) allow gathering information on the mode of evolution for entire species. Both applications hinge on the assumption that d(S) and mean d(S) (similar to branch length) are neutral and adequately control for variation in substitution rates across genes and across organisms, respectively. We here explore the validity of this assumption using empirical data based on whole-genome protein sequence alignments between human and 15 other vertebrate species and several simulation approaches. We find that d(N)/d(S) does not appropriately reflect the action of selection as it is strongly influenced by its denominator (d(S)). Particularly for closely related taxa, such as human and chimpanzee, d(N)/d(S) can be misleading and is not an unadulterated indicator of selection. Instead, we suggest that inconsistencies in the behavior of d(N)/d(S) are to be expected and highlight the idea that this behavior may be inherent to taking the ratio of two randomly distributed variables that are nonlinearly correlated. New null hypotheses will be needed to adequately handle these nonlinear dynamics.
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