Bioinformatics Analysis Of Gene Co-expression Networks And Pathway Enrichment – Bamboo is one of the fastest growing non-forest plants Moso bamboo (Phyllostachys edulis) is one of the most economically valuable bamboos in Asia, especially in China. With the release of the whole-genome sequence of Moso bamboo, there is a growing demand for accurate annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data from several developmental stages of the tissue. It is now possible for us to construct co-expression networks to improve bamboo gene annotation and reveal relationships between gene expression and growth traits. We assembled the genome sequence and 78 transcriptome data sets of mono bamboo to construct a genome-wide global and conditional co-expression network. We overlay gene expression results on networks with multiple dimensions (different developmental stages). By combining co-expression networks, module classification and function enrichment tools, we identified 1,896 functional modules involved in bamboo development, including functions such as photosynthesis, hormone biosynthesis, signal transduction and secondary cell wall biosynthesis. In addition, an online database (http://bioinformatics.cau.edu.cn/bamboo) was created to discover Moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set modification analysis, and other tools. In summary, we performed co-expression network analysis and functional module identification by integrating public and internal bamboo transcriptome data sets. Through data mining, we gained some new insights into growth and development regulation Our established online database can be convenient for the bamboo research community to identify functional genes or modules with important traits.
2.5 billion US dollars and about 2.5 billion people are financially dependent on it (Peng et al., 2013a, b; Zhao et al., 2017). Moso bamboo (Phyllostachys edulis, formerly Phyllostachys heterocycla) is one of the most economically valuable bamboos in Asia, particularly in China. With the release of the whole-genome sequence of Moso bamboo, there is an increasing demand for accurate description of bamboo genes at the whole-genome level. Considering the small proportion of annotated genes in the bamboo genome and the large collection of data, big data mining is essential and necessary to generate new insights into bamboo growth and development.
Bioinformatics Analysis Of Gene Co-expression Networks And Pathway Enrichment
In general, genes with coordinated expression under different experimental conditions indicate the existence of functional relationships. Thus, co-expression gene networks can link genes of unknown function to biological processes in an intuitive way. A growing number of studies have supported the versatility of co-expression analysis to predict and interpret gene functions (D’Heseleer et al., 2000; Aoki et al., 2007; Usadel et al., 2009; Morenorisueno et al., 2010). ); Li et al., 2015; Serin et al., 2016) through data mining tools and algorithms that describe complex co-expression patterns of multiple genes as a pair, global co-expression network analysis considers all samples (multiple data sources with independence) together and Integrates available information Based on correlations between genes (Bessel et al., 2011) In contrast to such a network, conditional co-expression networks aim to enhance our understanding of gene function from a subset of our transcriptome data set that has similarities and similar acquisition of raw materials and gene transcript expression. Regulatory mechanisms in the developmental process based on a series of selected relational patterns to hypothesize In co-expression analysis, gene expression visualization helps to visualize differential gene expression trends between samples. Consequently, co-expression networks with expression considerations can be used to link genes of unknown function to biological processes, understand gene transcription regulatory mechanisms in vivo, and prioritize candidate regulatory genes or modules of important traits.
Arabidopsis Gene Co Expression Network And Its Functional Modules
With full-length complementary DNA and RNA-seq data of Moso bamboo based on de novo sequencing data, Bamboo GDB has become the first genome database with comprehensive functionality for bamboo (Zhao et al., 2014). It is also an analytical platform consisting of comparative genomic analysis, protein-protein interaction networks, pathway analysis and visualization of genomic data. However, it only has 12 RNA-seq data sets in different tissues of Moso bamboo, which is far less than the existing RNA-seq data sets and does not meet the needs of researchers. In addition, BambooGDB has no analysis of co-expression networks, functional modules, cis-elements and gene set enrichment. ATTED-II (Aoki et al., 2016), a co-expression database for plant species, visualized multiple co-expression data sets for nine species (Arabidopsis, field mustard, soybean, barrel medicinal, poplar, tomato, grape). supplies, rice and corn). Of these, only two are members of the grass family (Poaceae), such as bamboo Identification of a co-expression network for bamboo is very important
Recently, large amounts of transcriptome data have become available in bamboo to establish co-expression gene networks related to plant growth and development. We used the NCBI SRA database (He et al., 2013; Peng et al., 2013a; Huang et al., 2013a). In addition, we identified 26 new in- generated the house transcriptome data set. To efficiently extract information from large data sets, we applied in silico methods to construct genome-wide global and conditional co-expression networks and to identify functional modules to annotate and predict bamboo gene functions. , we have created the BambooNET database
To assemble high-throughput transcriptome data, co-expression networks, functional modules, etc. BambooNET also includes co-expression network analysis, cis-component analysis and GSEA tools, which can be an online server for refining the annotation of bamboo gene functions.
Figure A Flowchart Presenting A Multistep Integrated Bioinformatics…
26 Moso bamboo ( Phyllostachys edulis ) samples of ICBR were collected during the spring of 2015 from six major bamboo growing areas in China, including (1) Yixing, Jiangsu Province (N: 31°15′08.41″, E: 119) ; is included °43′42.55″, 212 m); (2) Tiazmu Mountain, Zhejiang Province (N: 30°19′13.42″, E: 119°26′55.21″, 480 M); (3) Jianing, Hubei Province (N: 29°81′10.02″, E: 114°31′21.12″ 150 M); (4) Tajiang, Hunan Province (N: 28°28′39.74″, E: 112°11′18.62″, 320 M); . °59′41.43″, 120 m), which covers rhizomes, roots, shoots, leaves, pods and buds at different growth stages. Each composite sample was collected from the above six fields
The whole-genome sequence of Moso bamboo was accessed from 2013 public version 1 (Peng et al., 2013a) and corresponded to a genome size of ∼2 GB and 31,987 protein-coding genes. Reads from 78 RNA-seq samples were aligned to the bamboo genome (version 1.0) using Tophat v2.1.1 software ( Trapnell et al., 2009 ). Calculation of FPKM and identification of differentially expressed genes were performed using CuffDiff in Cufflinks v2.2.1 software ( Trapnell et al., 2010 ). Geo-enrichment analysis was performed using the Aggrego website ( Du et al., 2010 ).
To determine the minimum threshold of gene expression value (FPKM) in 78 bamboo samples, the FPKM value of the lowest 5% of genes in each RNA-seq sample and the SD of each experimental group were calculated. Then, the mathematical formula is “Threshold = Average (5% value) + 3
Enrichment Analysis: Gene Ontology And Kegg Pathways Analysis
SD” (You et al., 2016, 2017) was used to calculate the minimum expression value of each experimental group. The lowest limit of FPKM was 0.1474
PCC represents the co-expression relationship between two genes in 78 samples The closer the relationship between genes, the higher the PCC score MR, an algorithm for computing the PCC rank, takes a geometric average of the PCC from gene A to gene B and from gene B to gene A. Specifically, when gene A is the third most co-expressed gene, the PCC rank of gene A to gene B is 3. Thus, MR ensures that more reliable co-expression gene pairs will be excluded, so PCC and MR were used to construct a co-expression network.
A co-expression network has less than 3 and MR score less than 30 (Aoki et al., 2016), and these gene pairs were considered as positive co-expression correlations when their PCC values were greater than zero and negative co-expression. – expression – the relationship of expressions when their value was less than zero
Pdf) Genet: A Web Application To Explore And Share Gene Co Expression Network Analysis Data
All samples were used to construct the global network, while the ICBR sample was used for the conditional network. Following the same procedure, 65 data sets without stress treatment were selected to define tissue-preferentially expressed genes, and 10 data sets associated with dehydration and cold treatments were selected for stress-differentiated genes.
CPM ( Adamsek et al., 2006 ) was used to find modules with nodes more closely connected to each other than to out-group nodes in the bamboo co-expression network. Parameter selection was based on the number of modules, the coverage rate of genes, and the overlap rate of communities. Therefore, we chose a clique size of k = 6, which means that each node had co-expression interactions with at least five nodes in a module (Supplementary Figure S5). Gene set analysis (Yi et al., 2013) integrated annotations such as GO, gene families (transcriptional regulators, kinases, and carbohydrate-activated enzymes), and functions of modules were predicted by KEGG. TF family and kinase family classifications were collected from iTAK (Yi et al., 2016) and PlantTFDB (Jin et al., 2017). There were a total of 3,305 TFs and 1,598 kinases
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