Blog
About
Search
Advanced search

8
views
43
references
 Top references
6
cited by
 Most cited
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      41
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      NetMiner-an ensemble pipeline for building genome-wide and high-quality gene co-expression network using massive-scale RNA-seq samples

      Read this article at

      ScienceOpenPublisherPMC
      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

          Accurately reconstructing gene co-expression network is of great importance for uncovering the genetic architecture underlying complex and various phenotypes. The recent availability of high-throughput RNA-seq sequencing has made genome-wide detecting and quantifying of the novel, rare and low-abundance transcripts practical. However, its potential merits in reconstructing gene co-expression network have still not been well explored. Using massive-scale RNA-seq samples, we have designed an ensemble pipeline, called NetMiner, for building genome-scale and high-quality Gene Co-expression Network (GCN) by integrating three frequently used inference algorithms. We constructed a RNA-seq-based GCN in one species of monocot rice. The quality of network obtained by our method was verified and evaluated by the curated gene functional association data sets, which obviously outperformed each single method. In addition, the powerful capability of network for associating genes with functions and agronomic traits was shown by enrichment analysis and case studies. In particular, we demonstrated the potential value of our proposed method to predict the biological roles of unknown protein-coding genes, long non-coding RNA (lncRNA) genes and circular RNA (circRNA) genes. Our results provided a valuable and highly reliable data source to select key candidate genes for subsequent experimental validation. To facilitate identification of novel genes regulating important biological processes and phenotypes in other plants or animals, we have published the source code of NetMiner, making it freely available at https://github.com/czllab/NetMiner.

          Related collections

          Most cited references 43

          • Record: found
          • Abstract: found
          • Article: not found

          Integration of biological networks and gene expression data using Cytoscape.

           Melissa Cline,  Michael Smoot,  Ethan Cerami … (2007)
          Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
          Article 0 comments Cited 780 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A gene-coexpression network for global discovery of conserved genetic modules.

             Eran Segal,  Jeffrey J. Stuart,  Daphne Koller … (2003)
            To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
            Article 0 comments Cited 638 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              PlantTFDB 3.0: a portal for the functional and evolutionary study of plant transcription factors

               Jinpu Jin,  Yi-He Zhang,  Lei Kong … (2013)
              With the aim to provide a resource for functional and evolutionary study of plant transcription factors (TFs), we updated the plant TF database PlantTFDB to version 3.0 (http://planttfdb.cbi.pku.edu.cn). After refining the TF classification pipeline, we systematically identified 129 288 TFs from 83 species, of which 67 species have genome sequences, covering main lineages of green plants. Besides the abundant annotation provided in the previous version, we generated more annotations for identified TFs, including expression, regulation, interaction, conserved elements, phenotype information, expert-curated descriptions derived from UniProt, TAIR and NCBI GeneRIF, as well as references to provide clues for functional studies of TFs. To help identify evolutionary relationship among identified TFs, we assigned 69 450 TFs into 3924 orthologous groups, and constructed 9217 phylogenetic trees for TFs within the same families or same orthologous groups, respectively. In addition, we set up a TF prediction server in this version for users to identify TFs from their own sequences.
              Article 0 comments Cited 277 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Hua Yu:
                ORCID: http://orcid.org/0000-0001-6691-1139
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Bingke Jiao: Role: Writing – review & editing
                Lu Lu: Role: Writing – review & editing
                Pengfei Wang: Role: Writing – review & editing
                Shuangcheng Chen: Role: Writing – review & editing
                Chengzhi Liang: Role: Funding acquisitionRole: Supervision
                Wei Liu: Role: Funding acquisitionRole: Writing – review & editing
                Yun Zheng: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 9 February 2018
                Publication date Collection: 2018
                Volume: 13
                Issue: 2
                Affiliations
                [1 ] Nantong Medical College and School of Pharmacy, Nantong University, Nantong, China
                [2 ] State Key Laboratory of Plant Genomics, Institute of Genetic and Developmental Biology, Chinese Academy of Sciences, Beijing, China
                [3 ] University of Chinese Academy of Sciences, Beijing, China
                [4 ] Nantong Polytechnic College, Nantong, China
                Kunming University of Science and Technology, CHINA
                Author notes

                Competing Interests: The authors declare no competing financial interests.

                [¤]

                Current address: Institute of Reproductive Medicine, Nantong Medical College and School of Pharmacy, Nantong University, Nantong, China

                Article
                Publisher ID: PONE-D-17-30744
                DOI: 10.1371/journal.pone.0192613
                PMC ID: 5806890
                PubMed ID: 29425247
                Copyright statement: © 2018 Yu et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 6, Tables: 1, Pages: 23
                Product
                Funding
                Funded by: The Strategic Priority Research Program of the Chinese Academy of Sciences
                Award ID: XDA08020302
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 21402101
                Award Recipient :
                Our work is financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA08020302) and National Natural Science Foundation of China (Grant No. 21402101). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Identification and Analysis
                Subject: Genetic Networks
                Subject: Computer and Information Sciences
                Subject: Network Analysis
                Subject: Genetic Networks
                Subject: Biology and Life Sciences
                Subject: Organisms
                Subject: Eukaryota
                Subject: Plants
                Subject: Grasses
                Subject: Rice
                Subject: Research and Analysis Methods
                Subject: Experimental Organism Systems
                Subject: Plant and Algal Models
                Subject: Rice
                Subject: Biology and Life Sciences
                Subject: Computational Biology
                Subject: Gene Regulatory Networks
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Regulatory Networks
                Subject: Biology and life sciences
                Subject: Biochemistry
                Subject: Nucleic acids
                Subject: RNA
                Subject: Non-coding RNA
                Subject: Long non-coding RNAs
                Subject: Research and Analysis Methods
                Subject: Experimental Organism Systems
                Subject: Model Organisms
                Subject: Arabidopsis Thaliana
                Subject: Research and Analysis Methods
                Subject: Model Organisms
                Subject: Arabidopsis Thaliana
                Subject: Biology and Life Sciences
                Subject: Organisms
                Subject: Eukaryota
                Subject: Plants
                Subject: Brassica
                Subject: Arabidopsis Thaliana
                Subject: Research and Analysis Methods
                Subject: Experimental Organism Systems
                Subject: Plant and Algal Models
                Subject: Arabidopsis Thaliana
                Subject: Biology and Life Sciences
                Subject: Genetics
                Subject: Gene Expression
                Subject: Biology and Life Sciences
                Subject: Agriculture
                Subject: Agronomy
                Subject: Biology and Life Sciences
                Subject: Cell Biology
                Subject: Cell Processes
                Subject: Cell Cycle and Cell Division
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

                Data availability:

                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                Similar content 41

                See all similar

                Cited by 6

                See all cited by

                Most referenced authors 1,396

                See all reference authors