CottonMD: a multi-omics database for cotton biological study

Yang, Zhiquan; Wang, Jing; Huang, Yiming; Wang, Shengbo; Wei, Lulu; Liu, Dongxu; Weng, Yonglin; Xiang, Jinhai; Zhu, Qiang; Yang, Zhaoen; Nie, Xiaoqin; Yu, Yu; Yang, Zuoren; Yang, Qingyong

doi:10.1093/nar/gkac863

Cited by 38 publications

(18 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The plant databases were created based on a variety of criteria, such as a focus on certain sequencing techniques [PlantExp ( Liu et al, 2022 ), PlantscRNAdb ( Chen et al, 2021 ), etc.] or a focus on individual plant species [Wildsoydb ( Xiao et al, 2022 ), CottonMD ( Yang et al, 2022 ), etc.]. These databases supplied adequate and pertinent data to facilitate machine learning despite information searches and differential analyses between constrained samples.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

et al. 2023

View full text Add to dashboard Cite

Introduction: With the advancement of RNA-seq technology and machine learning, training large-scale RNA-seq data from databases with machine learning models can generally identify genes with important regulatory roles that were previously missed by standard linear analytic methodologies. Finding tissue-specific genes could improve our comprehension of the relationship between tissues and genes. However, few machine learning models for transcriptome data have been deployed and compared to identify tissue-specific genes, particularly for plants.Methods: In this study, an expression matrix was processed with linear models (Limma), machine learning models (LightGBM), and deep learning models (CNN) with information gain and the SHAP strategy based on 1,548 maize multi-tissue RNA-seq data obtained from a public database to identify tissue-specific genes. In terms of validation, V-measure values were computed based on k-means clustering of the gene sets to evaluate their technical complementarity. Furthermore, GO analysis and literature retrieval were used to validate the functions and research status of these genes.Results: Based on clustering validation, the convolutional neural network outperformed others with higher V-measure values as 0.647, indicating that its gene set could cover as many specific properties of various tissues as possible, whereas LightGBM discovered key transcription factors. The combination of three gene sets produced 78 core tissue-specific genes that had previously been shown in the literature to be biologically significant.Discussion: Different tissue-specific gene sets were identified due to the distinct interpretation strategy for machine learning models and researchers may use multiple methodologies and strategies for tissue-specific gene sets based on their goals, types of data, and computational resources. This study provided comparative insight for large-scale data mining of transcriptome datasets, shedding light on resolving high dimensions and bias difficulties in bioinformatics data processing.

show abstract

Section: Discussionmentioning

confidence: 99%

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Genomic sequences, annotation files, and protein sequences for Gossypium species including G. herbaceum (A1), G. arboretum (A2), G. raimondii (D5), G. hirsutum (AD1), G. barbadense (AD2), G. tomentosum (AD3), G. mustelinum (AD4), and G. darwinii (AD5) were retrieved from Phytozome ( Goodstein et al., 2012 ) and CottonMD ( Yang et al., 2023 ). Protein sequences for the GST and MYB gene families were downloaded from the TAIR database ( https://www.arabidopsis.org/ ).…”

Section: Methodsmentioning

confidence: 99%

Anthocyanin gene enrichment in the distal region of cotton chromosome A07: mechanisms of reproductive organ coloration

Zheng,

Zhang,

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

IntroductionThe biosynthesis of secondary metabolites like anthocyanins is often governed by metabolic gene clusters (MGCs) in the plant ancestral genome. However, the existence of gene clusters specifically regulating anthocyanin accumulation in certain organs is not well understood.Methods and resultsIn this study, we identify MGCs linked to the coloration of cotton reproductive organs, such as petals, spots, and fibers. Through genetic analysis and map-based cloning, we pinpointed key genes on chromosome A07, such as PCC/GhTT19, which is involved in anthocyanin transport, and GbBM and GhTT2-3A, which are associated with the regulation of anthocyanin and proanthocyanidin biosynthesis. Our results demonstrate the coordinated control of anthocyanin and proanthocyanidin pathways, highlighting the evolutionary significance of MGCs in plant adaptation. The conservation of these clusters in cotton chromosome A07 across species underscores their importance in reproductive development and color variation. Our study sheds light on the complex biosynthesis and transport mechanisms for plant pigments, emphasizing the role of transcription factors and transport proteins in pigment accumulation.DiscussionThis research offers insights into the genetic basis of color variation in cotton reproductive organs and the potential of MGCs to enhance our comprehension of plant secondary metabolism.

show abstract

“…CuGenDB ( 92 ) covers cucurbits, such the important crops cucumber and melon, and contributes an update paper reporting a new focus on detection and annotation of genomic variants, as well as improved gene expression options. Also on crops, the new CottonMD ( 93 ) compiles multi-omics information including metabolomes facilitating the identification of trait-linked features and speeding future plan breeding efforts. Nod-like receptors (NLRs) are key proteins in plant disease resistance and the new NLRscape ( 94 ) offers resources and analyses of sequences, families and structures (from AF2).…”

Section: New and Updated Databasesmentioning

confidence: 99%

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Rigden

Fernández

2023

Nucleic Acids Research

View full text Add to dashboard Cite

The 2023 Nucleic Acids Research Database Issue contains 178 papers ranging across biology and related fields. There are 90 papers reporting on new databases and 82 updates from resources previously published in the Issue. Six more papers are updates from databases most recently published elsewhere. Major nucleic acid databases reporting updates include Genbank, ENA, ChIPBase, JASPAR, mirDIP and the Issue's first Breakthrough Article, NACDDB for Circular Dichroism data. Updates from BMRB and RCSB cover experimental protein structural data while AlphaFold 2 computational structure predictions feature widely. STRING and REBASE are stand-out updates in the signalling and enzymes section. Immunology-related databases include CEDAR, the second Breakthrough Article, for cancer epitopes and receptors alongside returning IPD-IMGT/HLA and the new PGG.MHC. Genomics-related resources include Ensembl, GWAS Central and UCSC Genome Browser. Major returning databases for drugs and their targets include Open Targets, DrugCentral, CTD and Pubchem. The EMPIAR image archive appears in the Issue for the first time. The entire database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, revisiting 463 entries, adding 92 new resources and eliminating 96 discontinued URLs so bringing the current total to 1764 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

show abstract

CottonMD: a multi-omics database for cotton biological study

Cited by 38 publications

References 70 publications

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Comparative analysis of tissue-specific genes in maize based on machine learning models: CNN performs technically best, LightGBM performs biologically soundest

Anthocyanin gene enrichment in the distal region of cotton chromosome A07: mechanisms of reproductive organ coloration

The 2023 Nucleic Acids Research Database Issue and the online molecular biology database collection

Contact Info

Product

Resources

About