Pseudotsuga menziesii (Douglas-fir) is an ideal model system to study the effect of local adaptation and intraspecific variation in transcriptome responses to the environment. Nonetheless, the lack of genomic resources and standardized microarray platforms for gene expression profiling has been a limitation to test the hypothesis on transcriptome organization and variation. Only recently, deep mRNA sequencing has become a promising alternative to overcome the present limitations. However, information on the transcript abundance distribution is needed for unbiased gene expression profiling from mRNA sequencing data. Since this information is not available for adult conifer needle tissue, we inferred the transcript abundance distribution and tested the effect of sequencing depth on the reliable detection and quantification of transcripts from the needle tissue of 50-year-old Douglas-fir trees. We obtained a similar distribution of GO-slim categories in our mRNA-sequencing libraries and in previously published putative unique transcripts (PUTs) for Douglas-fir, that were used as alignment reference. However, the GO-slim distribution in the Douglas-fir libraries and the Douglas-fir PUTs differed from the GO-slim distributions reported from mRNA deep sequencing libraries obtained from Arabidopsis thaliana leaf tissue. Apparently, several highly abundant PUTs associated with proteins involved in photosynthesis were limiting the benefits of increased sequencing depth. Simulations and empirical data indicated that a 3-fold increase from 5 to 15 million aligned reads results in about twice the number of PUTs that surpass the 100 aligned reads threshold that was used for robust transcript quantification.
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coefficient of variation
expressed sequence tags
gene expression omnibus
kernel density estimate
Micro-Array Quality Control
putative unique transcript
quantitative polymerase chain reaction
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This project is part of the collaborative project "DougAdapt" with funding from the Deutsche Forschungsgemeinschaft to IE (DFG-project EN 829/4-1). The authors are grateful to Anita Kleiber and Anna-Maria Weisser for technical assistance with RNA extraction. The authors also thank Wolfgang Hess for valuable comments and discussion.
Conflict of interests
The authors declare that they have no competing interests.
All experiments comply with the current laws of the Federal Republic of Germany.
Data archiving statement
All sequence data has been submitted to the NCBI Sequence Read Archive (SRA, www.ncbi.nlm.nih.gov/sra). Accession numbers are SRR908308(COA1), SRR908309 (COA2), SRR868709 (INT1), SRR908307 (INT2). Accession number of the study: SRP026170.
Communicated by J. Wegrzyn
Electronic supplementary material
Below is the link to the electronic supplementary material.
Distribution of GO-slim categories in the namespace "Biological Process" within the 100 most abundant PUTs. Distribution of GO-slim categories within the 100 highest expressed PUTs in the deep sequencing libraries COA1, COA2, INT1 and INT2 that were detected when using the Müller or the Howe PUT set as alignment reference. Annotation and GO-slimming is described in Methods section (XLS 14 kb)
Annotation statistics for Howe PUT set. Summary of numbers of detected PUTs and detected PUTs with functional annotation. Functionally annotated PUTs have a hit in the Arabidopsis thaliana peptide database (ARA) or in the NCBI Plant RefSeq peptide database. "Unique annotations" are the set of all unique hits in the A. thaliana or the NCBI Plant RefSeq peptide database. GO annotations refer to the PUTs with GO slim annotation (details of functional and GO-slim annotation in Methods section). Blast2GO annotations refer to annotations inferred by Blast2GO. Relative numbers with respect to the PUT set are shown in parentheses, relative numbers with respect to the deep sequencing libraries are shown in square brackets (XLS 11 kb)
Distribution of GO-slim terms (Howe PUT set). The relative abundance of functional categories, represented by plant GO-slims in the four libraries and the Douglas-fir PUT set, compared with the relative abundance detected in deep mRNA-sequencing data generated from Arabidopsis thaliana whole seedlings (NCBI GEO [GSM762070]) and leaves (NCBI GEO [GSM881683]). The functional annotation of Douglas-fir PUTs was obtained by aligning the PUTs to the NCBI Plant RefSeq peptide database and feeding the alignment to the Blast2GO pipeline (for details, see Methods section). The distribution of the deviation of GO-slim abundances relative to the Howe PUT set or the A. thaliana samples in the namespace "molecular function" is shown as smoothed kernel density estimates (KDE) (a). The relative abundance of a GO-slim category in one of the four Douglas-fir libraries or the Douglas-fir PUT set is normalized by the relative abundance of this GO-slim category in an A. thaliana full seedling (b) or leaf (c) deep mRNA-sequencing library. A value of 0 plotted on the y-axis implies an equal distribution of GO-slim terms in the Douglas-fir libraries compared to A. thaliana deep mRNA sequencing libraries or the Douglas-fir PUT set (PDF 1702 kb)
Impact of sequencing depth on the number of reliably quantified PUTs when using the Howe PUT set. The number of PUTs with a hit in the NCBI Plant RefSeq peptide database detected with more than x number of aligned reads (value shown on the x-axis). To demonstrate the effect of sequencing depth, sub-samples of library COA2 are included (gradient: yellow to red). The number of aligned reads is printed in the legend. Estimates of expected binomial sampling error (as coefficient of variation [CV]), dependent on the number of aligned reads per PUT are shown for 10, 100 and 1,000 aligned reads per PUT (PDF 858 kb)
Shared annotations among Müller and Howe PUT sets, P. glauca transcript clusters and Arabidopsis peptides. Venn diagram which shows the overlap of the functional annotations inferred by Blast2GO of the Müller PUT set (Muller), the Howe PUT set (Howe), the P. glauca transcript cluster database (Picea) and the Arabidopsis thaliana peptide database (Ara). All sequence sets have been aligned to the NCBI Plant RefSeq peptides. This alignment was used for detecting annotations using Blast2GO (for details, see Methods section) (PDF 307 kb)
Top 1,000 most abundant PUTs in the deep sequencing libraries (alignment to Müller and Howe PUT set). Top 1,000 most abundant PUTs in the libraries COA1, COA2, INT1 and INT2 sorted by the number of aligned reads. For each PUT, the PUT name with the associated annotation inferred by Blast2GO and the number of aligned reads are printed in the form: PUT name-Blast2GO Annotation-counts (XLS 5787 kb)
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Hess, M., Wildhagen, H. & Ensminger, I. Suitability of Illumina deep mRNA sequencing for reliable gene expression profiling in a non-model conifer species (Pseudotsuga menziesii). Tree Genetics & Genomes 9, 1513–1527 (2013). https://doi.org/10.1007/s11295-013-0656-2
- Deep mRNA sequencing
- Sequencing depth
- Next-generation sequencing