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Open Access Highly Accessed Research article

Transcriptome changes during fruit development and ripening of sweet orange (Citrus sinensis)

Keqin Yu1, Qiang Xu12*, Xinlei Da1, Fei Guo1, Yuduan Ding1 and Xiuxin Deng12*

Author Affiliations

1 Key Laboratory of Horticultural Plant Biology of Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China

2 National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China

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BMC Genomics 2012, 13:10 doi:10.1186/1471-2164-13-10

Published: 10 January 2012

Additional files

Additional file 1:

The primer sequence information. This file listed the primers sequences used for real-time quantitative RT-PCR validation of RNA-seq data.

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Additional file 2:

The saturation evaluations of the eight libraries in this study. This file contained the information of the saturation evaluations of the RNA-seq tags in the eight libraries (MT and WT at four selected fruit developmental stages) against sequencing depth. The results revealed that with the increase of total sequence number (sequencing depth), the number of genes identified increased, but the number stabilized once the number of sequences reached 2.5 million, indicating enough information has been included in the RNA-seq data.

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Additional file 3:

Distribution of total tags number (A) and distinct tags number (B) in MT and WT at 120, 150, 190, and 220 DAF. This file showed the distribution of the number of total tags and distinct tags obtained in MT and WT at different developmental stages.

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Additional file 4:

Summary of tags mapped against a reference set of sweet orange unigenes. This file contained the summary result of tags mapping against a reference set of sweet orange unigenes.

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Additional file 5:

Number of stage-specific genes expressed in MT and WT. This file contained the summary result of stage-specific genes number in MT and WT.

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Additional file 6:

Transcriptome dynamics in MT during fruit development and ripening. This file contained the result of the hierarchical cluster analysis of genes expression profiles in MT. The log2 of transcripts per million (TPM) for each gene was used for the hierarchical clustering analysis at four developmental stages (120, 150, 190 and 220 DAF). In all, 19,440 genes were classified into 22 regulatory patterns, designated groups 1-22.

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Additional file 7:

List of differentially expressed genes between MT and WT. The table contained information of the differentially expressed genes with expression difference > 2, and genes differentially expressed at 0.05 significance level at each of the four fruit developmental stages.

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Additional file 8:

The ten most differentially expressed genes between MT and WT at each of the four selected fruit developmental stages. This file listed the ten most differentially expressed genes between MT and WT at different developmental stages, with their expression ratios between MT and WT, also containing simple annotation information.

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Additional file 9:

Dynamics patterns of gene expression of a set of genes differentially expressed between MT and WT at each of the four selected fruit developmental stages. This file contained the result of the hierarchical cluster analysis of expression profiles of differentially expressed genes between MT and WT at different developmental stages. The log2 of the ratio between the MT and the WT TPM for each gene was used to perform the cluster analysis.

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Additional file 10:

The five genes differentially expressed at all four selected developmental stages. This file contained the pattern of genes which were differentially expressed at all selected stages. At each stage (120, 150, 190 and 220 DAF), the log2 of the ratio between the MT and the WT TPM for each gene is represented.

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Additional file 11:

Functional categorization of genes differentially expressed between WT and MT. This file showed the distribution of GO categories of differentially expressed genes between WT and MT at the four selected stages of fruit development and ripening. The categorization was based on molecular activity of Gene Ontology items. Percentages are based on the proportion of the number of genes in each set.

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