知识中心 - 北京概普生物科技有限公司(GapTech)

带上你的数据，跟我一周“玩转”植物生物信息

生信干货 PlantTech ·2018年7月18日 22:03

PlantTech学院（小编老领导创办）专注于植物生物信息学培训，在这个夏天开创了生物信息培训全新教学和互动模式。每个学员都能在讲师的指导下，独立完成“特定基因家族的生物信息学分析”和“转录组数据深度挖掘分析”作为结业报告（基本上等同于两篇SCI的结果部分）。

费用及地点

培训费：4500元/人，3人以上团体报名每人优惠300元，食宿自理

培训地点：北京市海淀区学院路圣熙八号4层

培训时间：2018年8月1日-7日

支付方式：现金、支票、银行转账，可开发票

扫描二维码，了解课程安排、“结业报告模板2”及报名！

结业报告模板1-转录组数据分析与深度挖掘

1 表型分析和实验设计

In Order to reveal molecular mechanisms underlying seed development and seed size/weight, we selected two chickpea cultivars, Himchana 1 (small-seeded) and JGK 3 (large-seeded), which differ significantly in their seed size/weight. We performed RNA-seq of seven stages (S1-S7) of seed development and leave of mature plants from the two chickpea cultivars. All the tissue were done in three independent biological replicates.

Figure 1. Seed phenotype at different stages of development in the chickpea cultivars

2 转录组数据统计分析

A total of more than 1.5 billion high-quality reads (average ~63 million reads from each sample) were generated. The reads mapped to the chickpea genome, and the uniquely mapped reads of each sample were 28-68 million.

Table 1. Summary of read data generated, quality control and mapping on the chickpea genome for different samples

A total of more than 20 million genes were identified as expressed at least one sample. The number of different level of expression (FPKM ≥50, 10 ≤ FPKM ≥ 50, 2 ≤ FPKM ≥ 10, 0.1 ≤ FPKM ≥ 2) were analyzed. Generally, a slightly larger fraction of genes showed high expression in JGK 3 as compared with Himchana 1.

Figure 2. Gene expression in Himchana 1 and JGK 3.

3 转录组数据整体分析

To investigate the global differences in the transcriptome dynamics during seed development in two cultivars, we performed hierarchical clustering and principal component analysis (PCA) based on SCC analysis of average FPKM values for all the expressed genes in all samples. It showed that a higher correlation of similar tissue/developmental stage between the two cultivars.

Figure 3. Correlation between the transcriptomes of different stages of seed development in the two chickpea cultivars

4 差异基因整体分析

To investigate the transcriptional differences that characterize different stages of seed development in both the cultivars, we identified different expressed genes in each stage compared to the front stage (eg. S1_vs_S2, S2_vs_S3). And we identified gene sets showing significant differential expression between Himchana 1 and JGK 3 at each stage. In total, more than 8,000 genes exhibited significant higher expression, and about 9,000 genes exhibited significant lower expression in JGK 3 as compared with Himchana 1. The largest number of genes exhibited differential expression at S7 stage followed by S3 stage between the two cultivars.

Figure 4. Differential gene expression at different stages of seed development

5 两个品种差异基因分析

The GO enrichment analysis of differentially expressed genes in JGK 3 as compared to HC 1 identified several biological processes uniquely/commonly overrepresented at different stages of seed development. Various cell division-related terms, such as cell cycle, cell division, cell growth, were significantly enriched in the genes with higher expression, particularly at S3 stage.

To investigate the metabolic pathways responsible for the difference in seed size/weight of JGK 3 and HC 1, we use the MapMan tool to analyze the differentially expressed genes between the cultivars. The genes involved in starch metabolism and photosynthesis-related genes were more active in JGK 3.

Figure 5. GO enrichment and MapMan of differential gene expression in JGK 3 as compared with Himchana 1 at different stages of seed development

6.1 不同发育时期差异基因表达聚类分析

To identify the expression pattern of gene and investigate the biological process during seed development, all different expressed genes in each stage in both cultivars were performed hierarchical clustering, based on the expression of gene in all samples. The GO enrichment analysis of each cluster, which specific expressed at different stages, reveal the key biological process during seed development. We identified several key genes, and some involved in phytohormone biosynthesis and signal transduction, and some are transcription factors (TF).

Figure 6 Clustering of differential gene expression at different stages of seed development

6.2不同发育时期差异基因共表达网络分析（WGCNA）

To investigate the gene regulatory network during seed development, we identified co expressed gene sets via weighted gene co expression network analysis (WGCNA). We performed WGCNA of all different expression genes for both the chickpea cultivars separately. A total of 27 modules and 21 modules were identified in HC 1 and JGK 3. Further, we associated each of the co expression modules with stages of seed development via Pearson correlation coefficient analysis. And we analyzed the expression pattern of module by module epigengenes in all samples.

Figure 7 Co expression network during seed development in chickpea cultivars

Based on these results, we identified some modules which are specifically correlate with stages of development. We further investigate the gene regulatory network in these modules. GO enrichment analysis of module highlighted key biological processed. And we defined the regulatory network that associate the TFs with their co expressed genes harboring their binding sites (significantly enriched motifs).

Figure 8. Expression profile and transcriptional regulatory network associated with the modules

7.1 关键基因分析-激素相关基因

Genes involved in some phytohormone biosynthesis and signal transduction may play important roles in seed development based on above analysis and some published reports. We analyzed JA and SA synthesis genes and responsive gene expression in seed development at both cultivars by hierarchical clustering. We screened some key genes to construct protein interaction network based on String.

Figure 9. Expression and protein interaction network of phytohormone biosynthesis and signaling genes

7.2 关键基因分析-转录因子

It shows that TFs are important regulators in seed development based on above analysis and some published reports. We screened some TFs, such as NAC and WRKY, to identified regulatory network that associate the TFs with their co expressed genes harboring their binding sites (significantly enriched motifs).

Figure 10. Gene regulatory network that associate the TFs with their co expressed genes harboring their binding sites (significantly enriched motifs).

8 基因表达量验证

We performed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analyses for 18 genes showing stage-specific expression in all samples in HC 1 and/or JGK 3. The expression profiles and stage-specific expression of the tested genes revealed by RT-qPCR were similar to those observed in RNA-seq data, indicating accuracy of RNA-seq data to reflect the abundance of transcript levels.