METHODS: In this study, we performed a hybrid assembly of 454 and Illumina sequencing reads from Polygonum minus root and leaf tissues, respectively, to generate a combined transcriptome library as a reference.
RESULTS: A total of 34.37 million filtered and normalized reads were assembled into 188,735 transcripts with a total length of 136.67 Mbp. We performed a similarity search against all the publicly available genome sequences and found similarity matches for 163,200 (86.5%) of Polygonum minus transcripts, largely from Arabidopsis thaliana (58.9%). Transcript abundance in the leaf and root tissues were estimated and validated through RT-qPCR of seven selected transcripts involved in the biosynthesis of phenylpropanoids and flavonoids. All the transcripts were annotated against KEGG pathways to profile transcripts related to the biosynthesis of secondary metabolites.
DISCUSSION: This comprehensive transcriptome profile will serve as a useful sequence resource for molecular genetics and evolutionary research on secondary metabolite biosynthesis in Polygonaceae family. Transcriptome assembly of Polygonum minus can be accessed at http://prims.researchfrontier.org/index.php/dataset/transcriptome.
Methods: We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher's exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher's exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.
Results: The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.