Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat ( PPR) gene family in cultivated peanut ( Arachis hypogaea L.)

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

Peanut ( Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-023-09568-y.

Related collections

Most cited references 44

Record: found
Abstract: found
Article: found

Is Open Access

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

Bo Li, Colin Dewey (2011)

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.

0 comments Cited 5588 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

MEGA11: Molecular Evolutionary Genetics Analysis Version 11

Koichiro Tamura, Glen Stecher, Sudhir Kumar (2021)

The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor , and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net .

0 comments Cited 4237 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

TBtools - an integrative toolkit developed for interactive analyses of big biological data

Chengjie Chen, Hao Chen, Yi Zhang … (2020)

The rapid development of high-throughput sequencing techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data-handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools using a new plotting engine ("JIGplot") developed to maximize their interactive ability; this engine allows quick point-and-click modification of almost every graphic feature. TBtools is platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.

0 comments Cited 4097 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Bingyan Huang: huangbingyan@aliyun.com

Xinyou Zhang: haasz@126.com

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 28 August 2023

Publication date PMC-release: 28 August 2023

Publication date Collection: 2023

Volume: 24

Electronic Location Identifier: 495

Affiliations

[1 ]GRID grid.27871.3b, ISNI 0000 0000 9750 7019, College of Agriculture, , Nanjing Agricultural University, ; Nanjing, 210095 China

[2 ]GRID grid.495707.8, ISNI 0000 0001 0627 4537, Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, , Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, ; Zhengzhou, 450002 China

[3 ]Shangqiu Academy of Agriculture and Forestry, Shangqiu, 476002 China

Article

Publisher ID: 9568

DOI: 10.1186/s12864-023-09568-y

PMC ID: 10463326

PubMed ID: 37641021

SO-VID: 2adaf676-3ef4-472f-bf0b-65a7de382a4b

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 : 8 June 2023

Date accepted : 9 August 2023

Funding

Funded by: Henan Provincial R&D Projects of Interregional Cooperation for Local Scientific and Technological Development Guided by Central Government

Award ID: YDZX20214100004191

Funded by: National Key R &D Project

Award ID: 2022YFD1200400

Funded by: China Agriculture Research System

Award ID: CARS-13

Funded by: Major Science and Technology Projects of Henan Province

Award ID: 201300111000

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: peanut (arachis hypogaea l.),kernel weight,kernel size,qtls (quantitative trait loci),pentatricopeptide repeat (ppr)

Data availability:

ScienceOpen disciplines: Genetics

Keywords: peanut (arachis hypogaea l.), kernel weight, kernel size, qtls (quantitative trait loci), pentatricopeptide repeat (ppr)

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

Cited by 1

Co-localization of quantitative trait loci for pod and kernel traits and development of molecular marker for kernel weight on chromosome Arahy05 in peanut ( Arachis hypogaea L.)
Authors: Yuanjin Fang, Hua Liu, Ziqi Sun …

See all cited by

Most referenced authors 1,126

See all reference authors

Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat ( PPR) gene family in cultivated peanut ( Arachis hypogaea L.)

Read this article at

Abstract

Supplementary Information

Related collections

Genome Integrity

Most cited references 44

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

MEGA11: Molecular Evolutionary Genetics Analysis Version 11

TBtools - an integrative toolkit developed for interactive analyses of big biological data

Author and article information

Contributors

Journal

Affiliations

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 409

Cited by 1

Most referenced authors 1,126