SANe: The Seed Active Network for Discovering Transcriptional Regulatory Programs of Seed Development

Gupta, Chirag; Krishnan, Arjun; Collakova, Eva; Wolinski, Pawel; Pereira, Andy

doi:10.1101/165894

Cited by 3 publications

(3 citation statements)

References 97 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of these, 45 GO terms with 84 candidate genes were associated with seed development (Table 1 and Additional File 4). Among the GO terms, GO:0009793 (Embryo development ending in seed) and GO:0048316 (Seed development) were involved in the final stage of seed maturation in Arabidopsis and Medicago [96, 61]; GO:0032013 (Regulation of ARF protein signal transduction) was related to seed development and weight in rapeseed [63]; GO:0010154 (Fruit development) was involved in pod maturity in soybean and Medicago [59, 60]; and GO:0009790 (Embryo development) was related to seed development, size, and yield in many plant organisms [66–70]. Particularly, GO:0009793 was detected alongside 14 directly related GO terms (Supplementary Fig.…”

Section: Analysesmentioning

confidence: 99%

Retracted: Dissection of soybean populations according to selection signatures based on whole-genome sequences

et al. 2019

View full text Add to dashboard Cite

Background Domestication and improvement processes, accompanied by selections and adaptations, have generated genome-wide divergence and stratification in soybean populations. Simultaneously, soybean populations, which comprise diverse subpopulations, have developed their own adaptive characteristics enhancing fitness, resistance, agronomic traits, and morphological features. The genetic traits underlying these characteristics play a fundamental role in improving other soybean populations. Results This study focused on identifying the selection signatures and adaptive characteristics in soybean populations. A core set of 245 accessions (112 wild-type, 79 landrace, and 54 improvement soybeans) selected from 4,234 soybean accessions was re-sequenced. Their genomic architectures were examined according to the domestication and improvement, and accessions were then classified into 3 wild-type, 2 landrace, and 2 improvement subgroups based on various population analyses. Selection and gene set enrichment analyses revealed that the landrace subgroups have selection signals for soybean-cyst nematode HG type 0 and seed development with germination, and that the improvement subgroups have selection signals for plant development with viability and seed development with embryo development, respectively. The adaptive characteristic for soybean-cyst nematode was partially underpinned by multiple resistance accessions, and the characteristics related to seed development were supported by our phenotypic findings for seed weights. Furthermore, their adaptive characteristics were also confirmed as genome-based evidence, and unique genomic regions that exhibit distinct selection and selective sweep patterns were revealed for 13 candidate genes. Conclusions Although our findings require further biological validation, they provide valuable information about soybean breeding strategies and present new options for breeders seeking donor lines to improve soybean populations.

show abstract

Section: Analysesmentioning

confidence: 99%

Retracted: Dissection of soybean populations according to selection signatures based on whole-genome sequences

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Spatial specific genome data are obtained from different cells, tissues or organs (Liseron-Monfils et al, 2018). Temporal data are collected to study the gene expression during the different plant growth and developmental stages (Gupta et al, 2018;Van den Nishiyama et al, 2018). Perturbation data emphasizes the change in expression due to environmental change or mutation (Pal et al, 2017;Jiang et al, 2018).…”

Section: Genome Sequencing and Rna Sequencingmentioning

confidence: 99%

Recent approaches of Systems Biology and Omics in Plant Research

Rani,

Kumari,

Pati

et al. 2021

J. Current Opinion Crop Sci.,

View full text Add to dashboard Cite

The 2020 world population data sheet estimates that 30 years from now, the population is intended to outstretch approximately up to 9.9 billion. The exponential rise in population has led to the agitation of land and the environment. Abrupt changes in climatic conditions and increasing global population emphasize the propagation of novel crops and acclimatization of the available plants and crops to obtain sufficient food in the extended future. In addition to ‘Omics’, bioinformatics plays an essential role in understanding the underlying mechanism of molecular functional systems in various plants. Bioinformatics contributes towards multi-disciplinary interactions and helps to reshape agricultural tradition and production, providing knowledge for enhanced plant quality, and it also provides a plan of action for protection against adverse environmental conditions. This review examines recent approaches in systems biology for predicting the functionality of genes and their networks (GRNs) along with its influential effect in current research involving various disciplines like genomics, proteomics, transcriptomics, phenomics, interactomics, ionomics, epigenomics and metabolomics, which will be very useful to researchers in plant sciences.

show abstract

“…We believe that a vast majority of plant transcriptomes are still under-utilized in the retrieval of biologically relevant information. Secondary analyses of these datasets can reflect on new knowledge pertaining to the originally assayed biological processes, especially the role of regulatory genes 47, 48 , which is often overlooked in terms of the potential in typical GCN analysis.…”

Section: Coexpressed Gene Modules: a Treasure Trove For Plant Developmentioning

confidence: 99%

Recent advances in gene function prediction using context-specific coexpression networks in plants

Gupta

Pereira

2019

F1000Res

Self Cite

View full text Add to dashboard Cite

Predicting gene functions from genome sequence alone has been difficult, and the functions of a large fraction of plant genes remain unknown. However, leveraging the vast amount of currently available gene expression data has the potential to facilitate our understanding of plant gene functions, especially in determining complex traits. Gene coexpression networks—created by integrating multiple expression datasets—connect genes with similar patterns of expression across multiple conditions. Dense gene communities in such networks, commonly referred to as modules, often indicate that the member genes are functionally related. As such, these modules serve as tools for generating new testable hypotheses, including the prediction of gene function and importance. Recently, we have seen a paradigm shift from the traditional “global” to more defined, context-specific coexpression networks. Such coexpression networks imply genetic correlations in specific biological contexts such as during development or in response to a stress. In this short review, we highlight a few recent studies that attempt to fill the large gaps in our knowledge about cellular functions of plant genes using context-specific coexpression networks.

show abstract

SANe: The Seed Active Network for Discovering Transcriptional Regulatory Programs of Seed Development

Cited by 3 publications

References 97 publications

Retracted: Dissection of soybean populations according to selection signatures based on whole-genome sequences

Retracted: Dissection of soybean populations according to selection signatures based on whole-genome sequences

Recent approaches of Systems Biology and Omics in Plant Research

Recent advances in gene function prediction using context-specific coexpression networks in plants

Contact Info

Product

Resources

About