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AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species.

Lee T, Yang S, Kim E, Ko Y, Hwang S, Shin J, Shim JE, Shim H, Kim H, Kim C, Lee I - Nucleic Acids Res. (2014)

Bottom Line: Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes.We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes.To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.

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(a) A box-and-whisker plot of AraNet v2 prediction power (measure by AUC) for maize UniProt GO-BP annotations and those by a randomized model. (b) A box-and-whisker plot that summarizes the expression levels of five genes that were highly ranked among maize leaf initiation candidates across 42 nonleaf tissues (N) and 18 leaf tissues (L). The expression levels were measured by the log base 2 of the intensity value of the hybridized spots. All five genes show significantly elevated expression levels in leaf tissues (by Wilcoxon rank-sum test: GRMZM2G013617, P = 1.67 × 10−5; GRMZM2G396114, P = 5.52 × 10−4; GRMZM2G458728, P = 8.45 × 10−8; GRMZM2G137046, P = 8.56 × 10−7; GRMZM2G099319, P = 2.75 × 10−7).
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Figure 3: (a) A box-and-whisker plot of AraNet v2 prediction power (measure by AUC) for maize UniProt GO-BP annotations and those by a randomized model. (b) A box-and-whisker plot that summarizes the expression levels of five genes that were highly ranked among maize leaf initiation candidates across 42 nonleaf tissues (N) and 18 leaf tissues (L). The expression levels were measured by the log base 2 of the intensity value of the hybridized spots. All five genes show significantly elevated expression levels in leaf tissues (by Wilcoxon rank-sum test: GRMZM2G013617, P = 1.67 × 10−5; GRMZM2G396114, P = 5.52 × 10−4; GRMZM2G458728, P = 8.45 × 10−8; GRMZM2G137046, P = 8.56 × 10−7; GRMZM2G099319, P = 2.75 × 10−7).

Mentions: Next, we tested whether AraNet v2 can predict genes for phenotypes in nonmodel plant species. To assess general prediction power of AraNet v2 for nonmodel plants, we performed the ‘Find new members of a pathway’ option for 85 maize GO-BP terms annotated by UniProt-GO Annotation database (22). We found that AraNet v2 achieved AUC > 0.7 for more than 25% of tested maize GO-BP terms (median AUC = 0.61), whereas randomized model showed no prediction power for most of the tested terms (Figure 3a). Particularly, we have searched for novel candidate genes involved in maize leaf initiations by running ‘Find new members of a pathway’ option with 17 maize genes known for the process (23). Among the top candidate genes, we found a known gene for maize leaf adaxial-abaxial patterning, GRMZM2G082264 (rank 16) (24), which is relevant to the regulation of leaf architecture. We also utilized the Maize Gene Expression Atlas (25), which provides maize gene expression data across 60 distinct tissues representing 11 major organs of inbred B73. We hypothesized that genes for leaf initiation are expressed significantly more in leaf tissues than in other tissues. To test this hypothesis, we collected gene expression data for the 60 distinct tissues from the Maize Genome DataBase (MaizeGDB) (26), and compared their distributions of expression levels between leaf and nonleaf tissues. We observed a significantly higher range of expression levels for five high-ranked genes [GRMZM2G013617 (rank 1), GRMZM2G458728 (rank 2), GRMZM2G396114 (rank 3), GRMZM2G137046 (rank 10) and GRMZM2G099319 (rank 12)] (Figure 3b) in 18 leaf tissues compared with 42 nonleaf tissues. Taken together, we conclude that AraNet v2 can also effectively predict candidate genes for nonmodel plant species.


AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species.

Lee T, Yang S, Kim E, Ko Y, Hwang S, Shin J, Shim JE, Shim H, Kim H, Kim C, Lee I - Nucleic Acids Res. (2014)

(a) A box-and-whisker plot of AraNet v2 prediction power (measure by AUC) for maize UniProt GO-BP annotations and those by a randomized model. (b) A box-and-whisker plot that summarizes the expression levels of five genes that were highly ranked among maize leaf initiation candidates across 42 nonleaf tissues (N) and 18 leaf tissues (L). The expression levels were measured by the log base 2 of the intensity value of the hybridized spots. All five genes show significantly elevated expression levels in leaf tissues (by Wilcoxon rank-sum test: GRMZM2G013617, P = 1.67 × 10−5; GRMZM2G396114, P = 5.52 × 10−4; GRMZM2G458728, P = 8.45 × 10−8; GRMZM2G137046, P = 8.56 × 10−7; GRMZM2G099319, P = 2.75 × 10−7).
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Figure 3: (a) A box-and-whisker plot of AraNet v2 prediction power (measure by AUC) for maize UniProt GO-BP annotations and those by a randomized model. (b) A box-and-whisker plot that summarizes the expression levels of five genes that were highly ranked among maize leaf initiation candidates across 42 nonleaf tissues (N) and 18 leaf tissues (L). The expression levels were measured by the log base 2 of the intensity value of the hybridized spots. All five genes show significantly elevated expression levels in leaf tissues (by Wilcoxon rank-sum test: GRMZM2G013617, P = 1.67 × 10−5; GRMZM2G396114, P = 5.52 × 10−4; GRMZM2G458728, P = 8.45 × 10−8; GRMZM2G137046, P = 8.56 × 10−7; GRMZM2G099319, P = 2.75 × 10−7).
Mentions: Next, we tested whether AraNet v2 can predict genes for phenotypes in nonmodel plant species. To assess general prediction power of AraNet v2 for nonmodel plants, we performed the ‘Find new members of a pathway’ option for 85 maize GO-BP terms annotated by UniProt-GO Annotation database (22). We found that AraNet v2 achieved AUC > 0.7 for more than 25% of tested maize GO-BP terms (median AUC = 0.61), whereas randomized model showed no prediction power for most of the tested terms (Figure 3a). Particularly, we have searched for novel candidate genes involved in maize leaf initiations by running ‘Find new members of a pathway’ option with 17 maize genes known for the process (23). Among the top candidate genes, we found a known gene for maize leaf adaxial-abaxial patterning, GRMZM2G082264 (rank 16) (24), which is relevant to the regulation of leaf architecture. We also utilized the Maize Gene Expression Atlas (25), which provides maize gene expression data across 60 distinct tissues representing 11 major organs of inbred B73. We hypothesized that genes for leaf initiation are expressed significantly more in leaf tissues than in other tissues. To test this hypothesis, we collected gene expression data for the 60 distinct tissues from the Maize Genome DataBase (MaizeGDB) (26), and compared their distributions of expression levels between leaf and nonleaf tissues. We observed a significantly higher range of expression levels for five high-ranked genes [GRMZM2G013617 (rank 1), GRMZM2G458728 (rank 2), GRMZM2G396114 (rank 3), GRMZM2G137046 (rank 10) and GRMZM2G099319 (rank 12)] (Figure 3b) in 18 leaf tissues compared with 42 nonleaf tissues. Taken together, we conclude that AraNet v2 can also effectively predict candidate genes for nonmodel plant species.

Bottom Line: Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes.We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes.To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

View Article: PubMed Central - PubMed

Affiliation: Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.

Show MeSH
Related in: MedlinePlus