Dataset Information

Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species.

ABSTRACT: BACKGROUND: How well does RNA-Seq data perform for quantitative whole gene expression analysis in the absence of a genome? This is one unanswered question facing the rapidly growing number of researchers studying non-model species. Using Homo sapiens data and resources, we compared the direct mapping of sequencing reads to predicted genes from the genome with mapping to de novo transcriptomes assembled from RNA-Seq data. Gene coverage and expression analysis was further investigated in the non-model context by using increasingly divergent genomic reference species to group assembled contigs by unique genes. RESULTS: Eight transcriptome sets, composed of varying amounts of Illumina and 454 data, were assembled and assessed. Hybrid 454/Illumina assemblies had the highest transcriptome and individual gene coverage. Quantitative whole gene expression levels were highly similar between using a de novo hybrid assembly and the predicted genes as a scaffold, although mapping to the de novo transcriptome assembly provided data on fewer genes. Using non-target species as reference scaffolds does result in some loss of sequence and expression data, and bias and error increase with evolutionary distance. However, within a 100 million year window these effect sizes are relatively small. CONCLUSIONS: Predicted gene sets from sequenced genomes of related species can provide a powerful method for grouping RNA-Seq reads and annotating contigs. Gene expression results can be produced that are similar to results obtained using gene models derived from a high quality genome, though biased towards conserved genes. Our results demonstrate the power and limitations of conducting RNA-Seq in non-model species.

SUBMITTER: Hornett EA

PROVIDER: S-EPMC3469347 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species.

Hornett Emily A EA Wheat Christopher W CW

BMC genomics 20120801

<h4>Background</h4>How well does RNA-Seq data perform for quantitative whole gene expression analysis in the absence of a genome? This is one unanswered question facing the rapidly growing number of researchers studying non-model species. Using Homo sapiens data and resources, we compared the direct mapping of sequencing reads to predicted genes from the genome with mapping to de novo transcriptomes assembled from RNA-Seq data. Gene coverage and expression analysis was further investigated in th ...[more]

PMID: 22853326

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Project description:BACKGROUND: RNA-seq is now widely used to quantitatively assess gene expression, expression differences and isoform switching, and promises to deliver results for the entire transcriptome. However, whether the transcriptional state of a gene can be captured accurately depends critically on library preparation, read alignment, expression estimation and the tests for differential expression and isoform switching. There are comparisons available for the individual steps but there is not yet a systematic investigation which specific genes are impacted by biases throughout the entire analysis workflow. It is especially unclear whether for a given gene, with current methods and protocols, expression changes and isoform switches can be detected. RESULTS: For the human genes, we report their detectability under various conditions using different approaches. Overall, we find that the input material has the biggest influence and may, depending on the protocol and RNA degradation, exhibit already strong length-dependent over- and underrepresentation of transcripts. The alignment step aligns for 50% of the isoforms up to 99% of the reads correctly; only in the presence of transcript modifications mainly short isoforms will have a low alignment rate. In our dataset, we found that, depending on the aligner and the input material used, the expression estimation of up to 93% of the genes being accurate within a factor of two; with the deviations being due to ambiguous alignments. Detection of differential expression using a negative-binomial count model works reliably for our simulated data but is dependent on the count accuracy. Interestingly, using the fold-change instead of the p-value as a score for differential expression yields the same performance in the situation of three replicates and the true change being two-fold. Isoform switching is harder to detect and for at least 109 genes the isoform differences evade detection independent of the method used. CONCLUSIONS: RNA-seq is a reliable tool but the repetitive nature of the human genome makes the origin of the reads ambiguous and limits the detectability for certain genes. RNA-seq does not equally well represent isoforms independent of their size which may range from ~200nt to ~100'000nt. Researchers are advised to verify that their target genes do not have extreme properties with respect to repeated regions, GC content, and isoform length and complexity.

Project description:BackgroundWhole genome resequencing projects may implement variant calling using draft reference genomes assembled de novo from short-read libraries. Despite lower quality of such assemblies, they allowed researchers to extend a wide range of population genetic and genome-wide association analyses to non-model species. As the variant calling pipelines are complex and involve many software packages, it is important to understand inherent biases and limitations at each step of the analysis.ResultsIn this article, we report a positional bias present in variant calling performed against draft reference assemblies constructed from de Bruijn or string overlap graphs. We assessed how frequently variants appeared at each position counted from ends of a contig or scaffold sequence, and discovered unexpectedly high number of variants at the positions related to the length of either k-mers or reads used for the assembly. We detected the bias in both publicly available draft assemblies from Assemblathon 2 competition as well as in the assemblies we generated from our simulated short-read data. Simulations confirmed that the bias causing variants are predominantly false positives induced by reads from spatially distant repeated sequences. The bias is particularly strong in contig assemblies. Scaffolding does not eliminate the bias but tends to mitigate it because of the changes in variants' relative positions and alterations in read alignments. The bias can be effectively reduced by filtering out the variants that reside in repetitive elements.ConclusionsDraft genome sequences generated by several popular assemblers appear to be susceptible to the positional bias potentially affecting many resequencing projects in non-model species. The bias is inherent to the assembly algorithms and arises from their particular handling of repeated sequences. It is recommended to reduce the bias by filtering especially if higher-quality genome assembly cannot be achieved. Our findings can help other researchers to improve the quality of their variant data sets and reduce artefactual findings in downstream analyses.

Dataset Information

Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species.

Publications

Quantitative RNA-Seq analysis in non-model species: assessing transcriptome assemblies as a scaffold and the utility of evolutionary divergent genomic reference species.

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