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      Transcriptomic profiling of taproot growth and sucrose accumulation in sugar beet ( Beta vulgaris L.) at different developmental stages

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          Abstract

          In sugar beet ( Beta vulgaris L.), taproot weight and sucrose content are the important determinants of yield and quality. However, high yield and low sucrose content are two tightly bound agronomic traits. The advances in next-generation sequencing technology and the publication of sugar beet genome have provided a method for the study of molecular mechanism underlying the regulation of these two agronomic traits. In this work, we performed comparative transcriptomic analyses in the high taproot yield cultivar SD13829 and the high sucrose content cultivar BS02 at five developmental stages. More than 50,000,000 pair-end clean reads for each library were generated. When taproot turned into the rapid growth stage at the growth stage of 82 days after emergence (DAE), eighteen enriched gene ontology (GO) terms, including cell wall, cytoskeleton, and enzyme linked receptor protein signaling pathway, occurred in both cultivars. Differentially expressed genes (DEGs) of paired comparison in both cultivars were enriched in the cell wall GO term. For pathway enrichment analyses of DEGs that were respectively generated at 82 DAE compared to 59 DAE (the earlier developmental stage before taproot turning into the rapid growth stage), plant hormone signal transduction pathway was enriched. At 82 DAE, the rapid enlarging stage of taproot, several transcription factor family members were up-regulated in both cultivars. An antagonistic expression of brassinosteroid- and auxin-related genes was also detected. In SD13829, the growth strategy was relatively focused on cell enlargement promoted by brassinosteroid signaling, whereas in BS02, it was relatively focused on secondarily cambial cell division regulated by cytokinin, auxin and brassinosteroid signaling. Taken together, our data demonstrate that the weight and sucrose content of taproot rely on its growth strategy, which is controlled by brassinosteroid, auxin, cytokinin, and gibberellin.

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          Most cited references74

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          RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

          Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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            Cluster analysis and display of genome-wide expression patterns.

            A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
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              Analysis of microarray data using Z score transformation.

              High-throughput cDNA microarray technology allows for the simultaneous analysis of gene expression levels for thousands of genes and as such, rapid, relatively simple methods are needed to store, analyze, and cross-compare basic microarray data. The application of a classical method of data normalization, Z score transformation, provides a way of standardizing data across a wide range of experiments and allows the comparison of microarray data independent of the original hybridization intensities. Data normalized by Z score transformation can be used directly in the calculation of significant changes in gene expression between different samples and conditions. We used Z scores to compare several different methods for predicting significant changes in gene expression including fold changes, Z ratios, Z and t statistical tests. We conclude that the Z score transformation normalization method accompanied by either Z ratios or Z tests for significance estimates offers a useful method for the basic analysis of microarray data. The results provided by these methods can be as rigorous and are no more arbitrary than other test methods, and, in addition, they have the advantage that they can be easily adapted to standard spreadsheet programs.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 April 2017
                2017
                : 12
                : 4
                : e0175454
                Affiliations
                [001]Sugar Beet Physiological Research Institute, Inner Mongolia Agricultural University, Hohhot, China
                Universidade de Lisboa Instituto Superior de Agronomia, PORTUGAL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: SYZ YFZ.

                • Data curation: SYZ.

                • Formal analysis: YFZ.

                • Funding acquisition: SYZ.

                • Investigation: YFZ.

                • Methodology: YFZ.

                • Project administration: SYZ GLL YQS YFZ.

                • Resources: SYZ GLL.

                • Software: YFZ.

                • Supervision: SYZ GLL YQS.

                • Validation: YFZ XFW.

                • Visualization: YFZ.

                • Writing – original draft: YFZ.

                • Writing – review & editing: SYZ.

                Article
                PONE-D-16-45505
                10.1371/journal.pone.0175454
                5391080
                28406933
                6b658037-a395-4806-b283-2dd574325dd8
                © 2017 Zhang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 16 November 2016
                : 27 March 2017
                Page count
                Figures: 10, Tables: 4, Pages: 28
                Funding
                Funded by: the National Natural Science Foundation of China
                Award ID: 31260347
                Award Recipient :
                Funded by: China Agriculture Research System
                Award ID: ARS-210304
                Award Recipient :
                This work has been jointly supported by the following grants: the National Natural Science Foundation of China (31260347) and China Agriculture Research System (CARS-210304). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Disaccharides
                Sucrose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Disaccharides
                Sucrose
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Biology and Life Sciences
                Biochemistry
                Hormones
                Plant Hormones
                Auxins
                Biology and Life Sciences
                Biochemistry
                Plant Biochemistry
                Plant Hormones
                Auxins
                Biology and Life Sciences
                Plant Science
                Plant Biochemistry
                Plant Hormones
                Auxins
                Biology and Life Sciences
                Developmental Biology
                Plant Growth and Development
                Root Growth
                Biology and Life Sciences
                Plant Science
                Plant Growth and Development
                Root Growth
                Biology and Life Sciences
                Biochemistry
                Hormones
                Plant Hormones
                Biology and Life Sciences
                Biochemistry
                Plant Biochemistry
                Plant Hormones
                Biology and Life Sciences
                Plant Science
                Plant Biochemistry
                Plant Hormones
                Biology and Life Sciences
                Biochemistry
                Hormones
                Plant Hormones
                Cytokinins
                Biology and Life Sciences
                Biochemistry
                Plant Biochemistry
                Plant Hormones
                Cytokinins
                Biology and Life Sciences
                Plant Science
                Plant Biochemistry
                Plant Hormones
                Cytokinins
                Biology and Life Sciences
                Biochemistry
                Biosynthesis
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. All “clean reads” files are available from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) database (SRP090408).

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                Uncategorized

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