高通量體學技術讓研究人員能輕鬆獲得一群或兩群有興趣研究的生物資訊，例如:因環境壓力而抑制以及誘導的兩群基因列表，可以藉由許多不同的特徵富集分析工具，來尋找哪些生物特徵與這兩群基因列表有顯著性的關係，並幫助研究人員了解生物的關聯性和潛在生物意義。
然而大多的網頁工具都是定性的特徵富集分析，定量特徵分析的工具卻是屈指可數，因此我們開發了YQFC:酵母菌定量特徵分析比較器網頁工具，並從酵母菌文獻和酵母菌資料庫中全面收集並處理了85種定量特徵。對於每個定量特徵提供了三個統計檢驗（t-test、U test和KS test），以檢驗此定量特徵在兩群酵母基因列表之間是否在統計學上有所差異。YQFC能鑑定出的獨特酵母菌定量特徵可能有助於研究人員找出研究的基因是否存在著不為人知生物機制，我們相信YQFC是個加快使用高通量體學技術之生物學研究的有用工具。
YQFC網址：http://cosbi2.ee.ncku.edu.tw/YQFC/

Based on high-throughput omics technologies, researchers can easily get two gene lists related to their biological issue. Researchers might apply enrichment analysis tools to identify different features between these two gene lists for deeper investigation. However, most enrichment analysis tools focus on identifying the enriched qualitative features. Therefore, we developed YQFC (Yeast Quantitative Features Comparator) which can directly compare 85 quantitative features between two yeast gene lists. For each quantitative feature, YQFC provides three statistical tests to test whether this quantitative feature is significantly different between the two input yeast gene lists. Finally, we compared the 585 repressed environmental stress response genes with 281 induced environmental stress response genes as case study,
including comprehensible graphical visualizations and present the results as table view and figure view, which allow insight into the functional relationships existing between the identified genes and the detail of quantitative data information. By using YQFC webtool may help researchers to know more the difference of quantitative features between the two input yeast gene lists. Therefore, we believe that YQFC is a useful tool to support the biologists to facilitate the analysis of yeast genes research.
Now, YQFC is available online at: http://cosbi2.ee.ncku.edu.tw/YQFC/

[1]Horgan,R. and Kenny,L. (2011) ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. The Obstetrician & Gynaecologist, 13(3), 189-195.
[2]Jing,L.S., Shah,F.F.M., Mohamad,M.S., Moorthy,K., Deris,S., Zakaria,Z., Napis,S. (2015) A review on bioinformatics enrichment analysis tools towards functional analysis of high throughput gene set data.
[3]Huang,D.W., Sherman,B.T. and Lempicki,R.A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucl. Acids Res., 37(1), 1-13.
[4]Jiao,X., Sherman,B.T., Huang,D.W., Stephens,R., Baseler,M.W., Lane,H.C., Lempicki,R.A. (2012) DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics, 28(13), 1805-1806.
[5]Balakrishnan,R., Park,J., Karra,K., Karra,K., Hitz,B.C., Binkley,G., Hong,E.L., Sullivan,J., Micklem,G., Cherry,J.M.(2012) YeastMine--an integrated data warehouse for Saccharomyces cerevisiae data as a multipurpose tool-kit. Database (Oxford), 2012, bar062.
[6]Weng,M.P. and Liao,B.Y. (2017) modPhEA: model organism phenotype enrichment analysis on eukaryotic gene sets. Bioinformatics, 33(21), 3505-3507.
[7]Chang,D.T., Li,W.S., Bai,Y.H., Wu,W.S. (2013) YGA: identifying distinct biological features between yeast gene sets. Gene, 518(1), 26-34.
[8]Wu,W.S., Tu,H.P., Chu,Y.H., Nordling, T.E.M, Tseng,Y.H., Liaw,H.J. (2018) YHMI: a web tool to identify histone modifications and histone/chromatin regulators from a gene list in yeast. Database (Oxford), 2018, bay116.
[9]Rathod,J., Tu,H.P., Chang,Y.I., Chu,Y.H., Tseng,Y.Y., Jean,J.S., Wu,W.S. (2018) YARG: A repository for arsenic-related genes in yeast. PLoS One, 13(7), e0201204.
[10]Teixeira,M.C., Monteiro,P.T., Palma,M., Costa,C., Godinho,C.P., Pais,P., Cavalheiro,M., Antunes,M., Lemos,A., Pedreira,T., Sá-Correia,I. (2018) YEASTRACT, an upgraded database for the analysis of transcription regulatory networks in Saccharomyces cerevisiae. Nucl. Acids Res., 46(D1), D348-D353.
[11]Wu,W.S., Hsieh,Y.C. and Lai,F.J. (2016) YCRD: Yeast Combinatorial Regulation Database. PLoS One, 11(7), e0159213.
[12]Holstege,F.C., Jennings,E.G., Wyrick,J.J., Lee,T.I., Hengartner,C.J., Green,M.R., Golub,T.R., Lander,E.S., Young,R.A. (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell, 95(5), 717-728.
[13]Nagalakshmi,U., Wang,Z., Waern,K. ,Chong,S., Raha,D., Gerstein,M., Snyder,M. (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science, 320(5881), 1344-1349.
[14]Lin,Z., Wu,W.S., Liang,H., Woo,Y., Li,W.H. (2010) The spatial distribution of cis regulatory elements in yeast promoters and its implications for transcriptional regulation. BMC Genomics, 11, 581.
[15]Pelechano,V., Wei,W. and Steinmetz,L.M. (2013) Extensive transcriptional heterogeneity revealed by isoform profiling. Nature, 497(7447), 127-131.
[16]Christiano,R., Nagaraj,N., Fröhlich,F., Walther,T. (2014) Global proteome turnover analyses of the Yeasts S. cerevisiae and S. pombe. Cell Rep., 9(5), 1959-1965.
[17]Csárdi,G., Franks,A., Choi,D.S., Airoldi,E., Drummond,D.A. (2015) Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet., 11(5), e1005206.
[18]Ho,B., Baryshnikova,A. and Brown,G.W. (2018) Unification of protein abundance datasets yields a quantitative Saccharomyces cerevisiae proteome. Cell Syst., 6(2), 192-205.
[19]Kim,H., Shin,J., Kim,E., Kim,H., Hwang,S., Shim,J.E., Lee,I. (2014) YeastNet v3: a public database of data-specific and integrated functional gene networks for Saccharomyces cerevisiae. Nucleic Acids Res., 42(D1), D731-D736.
[20]Wu,W.S., Wang,C.C., Jhou,M.J., Wang,Y.C. (2015) YAGM: a web tool for mining associated genes in yeast based on diverse biological associations. BMC Syst. Biol., 9(Suppl 6), S1.
[21]Lang,O.W., Nash,R.S., Hellerstedt,S.T., Engel,S.R. (2018) An introduction to the Saccharomyces Genome Database (SGD). Methods Mol. Biol., 1757, 21-30.
[22]Oughtred,R., Stark,C., Breitkreutz,B.J., Rust,J., Boucher,L., Chang,C., Kolas,N., O’Donnell,L., Leung,G., McAdam,R., Zhang,F., Dolma,S., Willems,A., Coulombe-Huntington,J., Chatr-aryamontri,A., Dolinski,K., Tyers,M., (2019) The BioGRID interaction database: 2019 update. Nucleic Acids Res., 47(D1), D529-D541.
[23]Hibbs,M.A., Hess,D.C., Myers,C.L., Huttenhower,K.L., Li,K., Troyanskaya,O.G. (2007) Exploring the functional landscape of gene expression: directed search of large microarray compendia. Bioinformatics, 23(20), 2692-2699.
[24]Kanji (2006) 100 Statistical Tests. Third Edition, SAGE Publications Ltd, London.
[25]Gasch,A.P., Spellman,P.T., Kao,C.M., Carmel-Harel,O., Eisen,M.B., Storz,G., Botstein,D., Brown,P.O. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell, 11(12), 4241-4257.
[26]Moore,J., Jacobs,H.T. and Kaiser,K. (1995) The relationship between mRNA half-life and gene function in the yeast Saccharomyces cerevisiae. Gene, 166(1), 145-149.
[27]Basehoar,A.D., Zanton,S.J. and Pugh,B.F. (2004) Identification and distinct regulation of yeast TATA box-containing genes. Cell, 116(5), 699-709.
[28]Gray,N.K. (1998) Translational control by repressor proteins binding to the 5'UTR of mRNAs. Methods Mol. Biol., 77, 379-397.