Open Access Highly Accessed Methodology article

EXPRSS: an Illumina based high-throughput expression-profiling method to reveal transcriptional dynamics

Ghanasyam Rallapalli1, Eric M Kemen12, Alexandre Robert-Seilaniantz13, Cécile Segonzac14, Graham J Etherington1, Kee Hoon Sohn14, Daniel MacLean1 and Jonathan D G Jones1*

Author Affiliations

1 The Sainsbury Laboratory, Norwich Research Park, Colney, Norwich, UK NR4 7UH

2 Max-Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829 Köln, Germany

3 UMR INRA-Agrocampus Ouest-Université de Rennes 1, INRA, UMR1349, IGEPP, BP35327, 35653 Le Rheu Cedex, France

4 Institute of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand

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BMC Genomics 2014, 15:341  doi:10.1186/1471-2164-15-341

Published: 6 May 2014

Additional files

Additional file 1:

Supplemental Figures. Figure S1. Modified NlaIII-DGE Tag-seq protocol. Figure S2. Transcription at rRNA loci observed with EXPRSS and NlaIII-DGE tag sequencing. Figure S3. Correlation between sense and anti-sense transcript expression. Figure S4. Novel transcription detection using EXPRSS Tag-seq. Figure S5. Cumulative frequency distribution multi-matching reads. Figure S6. Pair-wise scatter plots of gene counts from flg22 treated replicates. Figure S7. Pair-wise correlation of fold changes between three methods tested. Figure S8. Pair-wise scatter plots of gene counts from 60 minutes flg22 treatment replicates from two independent experiments. Figure S9. Pair-wise scatter plots of gene counts from Col-0 flg22 time course replicates. Figure S10. Pair-wise scatter plots of gene counts from npr1-1 flg22 time course replicates. Figure S11. Pair-wise scatter plots of gene counts from jar1-1 flg22 time course replicates. Figure S12. Pair-wise scatter plots of gene counts from ein2-5 flg22 time course replicates. Figure S13. Hierarchical clustering of genes differentially expressed during flg22 time course of four genotypes. Figure S14. Heat maps of log2 fold changes from all data points of genes that are differentially expressed at least from one time point of flg22 time course. Figure S15. Hierarchical clustering of genes differentially expressed during flg22 time course of four genotypes compared to Col-0. Figure S16. Frequency distribution of Read1 and Read2 from paired end sequencing. Figure S17. Examples showing Read1 and Read2 from paired end sequencing. Figure S18. Length distribution of genes detected in EXPRSS. Figure S19. Cartoon depicting tag assignment to genes.

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Additional file 2:

Detailed protocols of EXPRSS and NlaIII-DGE and information of primers used.

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Additional file 3:

Zip archive of the analysis tools designed to import in to Galaxy web tool.

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Additional file 4:

Details of genes differentially expressed in EXPRSS Tag-seq.

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Additional file 5:

Details of genes differentially expressed in NlaIII-DGE Tag-seq.

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Additional file 6:

Details of genes differentially expressed in ATH1 array flg22 treatment of leaf discs.

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Additional file 7:

Details from EXPRSS Tag-seq, for genes used for qPCR verification.

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Additional file 8:

Details of genes differentially expressed in ATH1 array (leaf disc) but not in EXPRSS Tag-seq.

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Additional file 9:

Details of genes differentially expressed in ATH1 array flg22 treatment of leaf.

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Additional file 10:

Details of genes differentially expressed in ATH1 array flg22 treatment of seedlings.

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Additional file 11:

Overlap among three flg22 ATH1 array differential expression represented as pair-wise comparison.

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Additional file 12:

Locus details, log 2 fold change values and FDR of differentially expressed genes at 60 minutes of flg22 treatment from two independent experiments.

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Additional file 13:

Details of genes and log 2 fold change values of differentially expressed during flg22 time course in Col-0, npr1-1, jar1-1 and ein2-5.

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