CrusTF: a comprehensive resource of transcriptomes for evolutionary and functional studies of crustacean transcription factors

• DOI: 10.1186/s12864-017-4305-2
• Publication Date: 2017-11-24T22:32:02Z
• Authors: Jing Qin; Yaohua Hu; Ka Yan Ma; Xiaosen Jiang; Ching Hei Ho; Ling Ming Tsang; Lefei Yi; Ricky Wai Tak Leung; Ka Hou Chu
• Keywords: 0303 health sciences; Evolution; QH426-470; 15. Life on land; Database; Transcriptional regulatory function; 03 medical and health sciences; Crustacea; Databases, Genetic; Genetics; Animals; 14. Life underwater; Transcription factor; Transcriptome; TP248.13-248.65; Phylogeny; Biotechnology; Transcription Factors

Crustacea, the second largest subphylum of Arthropoda, includes species major ecological and economic importance, such as crabs, lobsters, crayfishes, shrimps, barnacles. With rapid development crustacean aquaculture biodiversity loss, understanding gene regulatory mechanisms growth, reproduction, crustaceans is crucial to both conservation this group organisms. In these biological processes, transcription factors (TFs) play a vital role in regulating expression. However, are still largely unknown, because lack complete genome sequences most hampers studies on their transcriptional regulation system-wide scale. Thus, current TF databases derived from contain information for only few insufficient elucidate diversity large animal group. Our database CrusTF ( http://qinlab.sls.cuhk.edu.hk/CrusTF ) provides comprehensive evolutionary functional system. fills knowledge gap by exploring publicly available newly sequenced transcriptomes 170 identifying 131,941 TFs within 63 families. features three categories information: sequence, function, evolution TFs. The enables searching, browsing downloading sequences. infers DNA binding motifs TFs, thus facilitating users predict potential downstream targets. also presents analyses which improve our systems crustaceans. Given importance crustaceans, will constitute key resource research community biology biology. Moreover, serves model construction transcriptome data. A similar approach could be applied other groups organisms, more readily than genomes.