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      Global transcriptional profiling between inbred parents and hybrids provides comprehensive insights into ear-length heterosis of maize ( Zea mays)

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          Abstract

          Background

          Maize ( Zea mays) ear length, which is an important yield component, exhibits strong heterosis. Understanding the potential molecular mechanisms of ear-length heterosis is critical for efficient yield-related breeding.

          Results

          Here, a joint netted pattern, including six parent-hybrid triplets, was designed on the basis of two maize lines harboring long (T121 line) and short (T126 line) ears. Global transcriptional profiling of young ears (containing meristem) was performed. Multiple comparative analyses revealed that 874 differentially expressed genes are mainly responsible for the ear-length variation between T121 and T126 lines. Among them, four key genes, Zm00001d049958, Zm00001d027359, Zm00001d048502 and Zm00001d052138, were identified as being related to meristem development, which corroborated their roles in the superior additive genetic effects on ear length in T121 line. Non-additive expression patterns were used to identify candidate genes related to ear-length heterosis. A non-additively expressed gene ( Zm00001d050649) was associated with the timing of meristematic phase transition and was determined to be the homolog of tomato SELF PRUNING, which assists SINGLE FLOWER TRUSS in driving yield-related heterosis, indicating that Zm00001d050649 is a potential contributor to drive heterotic effect on ear length.

          Conclusion

          Our results suggest that inbred parents provide genetic and heterotic effects on the ear lengths of their corresponding F 1 hybrids through two independent pathways. These findings provide comprehensive insights into the transcriptional regulation of ear length and improve the understanding of ear-length heterosis in maize.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12870-021-02890-1.

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          Most cited references69

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                gzy@henau.edu.cn
                tangjihua1@163.com
                Journal
                BMC Plant Biol
                BMC Plant Biol
                BMC Plant Biology
                BioMed Central (London )
                1471-2229
                26 February 2021
                26 February 2021
                2021
                : 21
                : 118
                Affiliations
                [1 ]GRID grid.108266.b, ISNI 0000 0004 1803 0494, National Key Laboratory of Wheat and Maize Crops Science, , College of Agronomy, Henan Agricultural University, ; Zhengzhou, 450018 China
                [2 ]GRID grid.410654.2, ISNI 0000 0000 8880 6009, Hubei Collaborative Innovation Center for Grain Industry, , Yangtze University, ; Jingzhou, 433200 China
                Author information
                https://orcid.org/0000-0001-8082-1076
                Article
                2890
                10.1186/s12870-021-02890-1
                7908659
                d7e8997e-9ecd-4bfa-ab92-720c4a37d1a9
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 28 November 2020
                : 9 February 2021
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Plant science & Botany
                maize,heterosis,ear length,transcriptional regulation,non-additive expression patterns

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