Comparative transcriptome analysis of dioecious floral development in <i>Trachycarpus fortunei</i> using Illumina and PacBio SMRT sequencing

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

Trachycarpus fortunei is a plant with significant economic and ornamental value. Both male and female flowers of T. fortunei originate as bisexual flowers, and selective abortion occurs during floral development. However, the regulatory mechanisms underlying this process remain unclear in T. fortunei. In this study, transcriptome sequencing with Illumina and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) platforms were used to investigate gene expression differences between male and female T. fortunei plants.

Results

A total of 833,137 full-length non-chimeric (FLNC) reads were obtained, and 726,846 high-quality full-length transcripts were identified. A total of 159 genes were differentially expressed between male and female flowers at all development stages. Some of the differentially expressed genes (DEGs) showed male bias, including serine/threonine-protein kinase ( STPK), THUMP1 homolog and other genes. Through single-nucleotide polymorphisms(SNPs) identification, 28 genes were considered as potential sex-associated SNPs. Time-Ordered Gene Co-expression Network (TO-GCN) analysis revealed that MADS2 and MADS26 may play important roles in the development of female and male flowers T. fortune plants, respectively.

Conclusions

These findings provide a genetic basis for flower development and differentiation in T. fortunei, and improve our understanding of the mechanisms underlying sexual differentiation in T. fortunei.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-023-04551-x.

Related collections

Most cited references 68

Record: found
Abstract: found
Article: found

Is Open Access

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Michael Love, Wolfgang Huber, Simon Anders (2014)

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.

0 comments Cited 25264 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2023)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

0 comments Cited 13734 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

fastp: an ultra-fast all-in-one FASTQ preprocessor

Shifu Chen, Yanqing Zhou, Yaru Chen … (2018)

Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

0 comments Cited 6192 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Yang Zhao: zhy737@126.com

Journal

Journal ID (nlm-ta): BMC Plant Biol

Journal ID (iso-abbrev): BMC Plant Biol

Title: BMC Plant Biology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2229

Publication date (Electronic): 3 November 2023

Publication date PMC-release: 3 November 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 536

Affiliations

[1 ]Institute for Forest Resources and Environment of Guizhou, Key Laboratory of Forest Cultivation in Plateau Mountain of Guizhou Province, College of Forestry, Guizhou University, ( https://ror.org/02wmsc916) Guiyang, 550025 Guizhou China

[2 ]School of Continuing Education, Yanbian University, ( https://ror.org/039xnh269) Yanji, 133002 Jilin China

Article

Publisher ID: 4551

DOI: 10.1186/s12870-023-04551-x

PMC ID: 10623883

PubMed ID: 37919651

SO-VID: 9ce83b86-7afb-4678-9169-0f1f64db9bf4

License:

Open Access This 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

Date received : 1 March 2023

Date accepted : 22 October 2023

Funding

Funded by: The characteristic forestry industry research project of Guizhou province

Award ID: GZMC-ZD20202098

Award Recipient : Feng Xiao Yang Zhao Xiurong Wang Yuexiong Mao

Custom metadata

ScienceOpen disciplines: Plant science & Botany

Keywords: trachycarpus fortunei,floral development,pacbio smrt,transcriptome,to-gcn

Data availability:

ScienceOpen disciplines: Plant science & Botany

Keywords: trachycarpus fortunei, floral development, pacbio smrt, transcriptome, to-gcn

Comparative transcriptome analysis of dioecious floral development in Trachycarpus fortunei using Illumina and PacBio SMRT sequencing

Read this article at

Abstract

Background

Results

Conclusions

Supplementary Information

Related collections

Plant MYBs

Most cited references 68

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Fast gapped-read alignment with Bowtie 2.

fastp: an ultra-fast all-in-one FASTQ preprocessor

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 199

Most referenced authors 1,181