5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comprehensive transcriptional variability analysis reveals gene networks regulating seed oil content of Brassica napus

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Regulation of gene expression plays an essential role in controlling the phenotypes of plants. Brassica napus ( B. napus) is an important source for the vegetable oil in the world, and the seed oil content is an important trait of B. napus.

          Results

          We perform a comprehensive analysis of the transcriptional variability in the seeds of B. napus at two developmental stages, 20 and 40 days after flowering (DAF). We detect 53,759 and 53,550 independent expression quantitative trait loci (eQTLs) for 79,605 and 76,713 expressed genes at 20 and 40 DAF, respectively. Among them, the local eQTLs are mapped to the adjacent genes more frequently. The adjacent gene pairs are regulated by local eQTLs with the same open chromatin state and show a stronger mode of expression piggybacking. Inter-subgenomic analysis indicates that there is a feedback regulation for the homoeologous gene pairs to maintain partial expression dosage. We also identify 141 eQTL hotspots and find that hotspot87-88 co-localizes with a QTL for the seed oil content. To further resolve the regulatory network of this eQTL hotspot, we construct the XGBoost model using 856 RNA-seq datasets and the Basenji model using 59 ATAC-seq datasets. Using these two models, we predict the mechanisms affecting the seed oil content regulated by hotspot87-88 and experimentally validate that the transcription factors, NAC13 and SCL31, positively regulate the seed oil content.

          Conclusions

          We comprehensively characterize the gene regulatory features in the seeds of B. napus and reveal the gene networks regulating the seed oil content of B. napus.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13059-022-02801-z.

          Related collections

          Most cited references100

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

              Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
                Bookmark

                Author and article information

                Contributors
                weibo.xie@mail.hzau.edu.cn
                guoliang@mail.hzau.edu.cn
                zhaohu@mail.hzau.edu.cn
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                7 November 2022
                7 November 2022
                2022
                : 23
                : 233
                Affiliations
                [1 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, National Key Laboratory of Crop Genetic Improvement, , Huazhong Agricultural University, ; Wuhan, China
                [2 ]Hubei Hongshan Laboratory, Wuhan, China
                [3 ]GRID grid.8664.c, ISNI 0000 0001 2165 8627, Department of Plant Breeding, , Justus Liebig University, ; Giessen, Germany
                [4 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, , Huazhong Agricultural University, ; Wuhan, China
                [5 ]GRID grid.35155.37, ISNI 0000 0004 1790 4137, Shenzhen Institute of Nutrition and Health, , Huazhong Agricultural University, ; Wuhan, China
                [6 ]GRID grid.488316.0, ISNI 0000 0004 4912 1102, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, , Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, ; Shenzhen, China
                Author information
                http://orcid.org/0000-0001-7191-5062
                Article
                2801
                10.1186/s13059-022-02801-z
                9639296
                36345039
                a1acff82-96c4-4bae-be3b-f7ccae73bcd4
                © The Author(s) 2022

                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
                : 16 May 2022
                : 22 October 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 32200501
                Award Recipient :
                Funded by: Hubei Hongshan Laboratory Fund
                Award ID: 2021HSZD004
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100014219, National Science Fund for Distinguished Young Scholars;
                Award ID: 32225037
                Award Recipient :
                Funded by: HZAU-AGIS Cooperation Fund
                Award ID: SZYJY2021004
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100013313, Overseas Expertise Introduction Project for Discipline Innovation;
                Award ID: B20051
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Genetics
                brassica napus,eqtl,subgenome,machine learning,regulatory network,seed oil content
                Genetics
                brassica napus, eqtl, subgenome, machine learning, regulatory network, seed oil content

                Comments

                Comment on this article