Zhang et al. BMC Genomics 2011, 12:63 http://www.biomedcentral.com/1471-2164/12/63 Page 6 of 20 Figure 3 Comparison of expression of the MPSS signatures between the mutant (black column) and wild-type (gray column). The expression ratios compare the abundance of each signature between the mutant and wild-type. Columns denote the number of signatures with an expression ratio within the stated range. molecular function, and biological process is shown in Figure 5. With regard to cellular component, the analy- sis revealed a high percentage of cell parts and orga- nelles. For categories based on molecular function, the genes were finally classified into eight categories, as shown in Figure 5B; the three most overrepresented GO terms were binding (nucleotide binding, protein binding, chromatin binding), catalytic, and transcription regula- tors. Differential expression genes were related to 17 biological processes, including cellular process, biologi- cal regulation, metabolic process, developmental process, response to stimulus, multicellular organismal process, and others (Figure 5C). The biological interpretation of the significant differential expression genes was further completed using KEGG pathway analyses (Additional File 5). A total of 200 different metabolic pathways were found in this study, with some being consistent with biological processes already revealed by GO analyses. The most represented pathways included metabolic pathways (168 enzymes represented), biosynthesis of secondary metabolites (100), plant-pathogen interaction (65), phenylpropanoid biosynthesis (45), spliceosome (38), and cell cycle (26). Of these, some were related to mutation trait formation based on previous knowledge, including biosynthesis of plant hormones, spliceosome, RNA degradation, ubiquitin-mediated proteolysis, and calcium signaling pathway. In addition, GO representa- tions from this study were compared with a GO repre- sentation based on all the unigenes from sweet orange in the TIGR gene index database, and no significant dif- ferences were seen between the two groups. Identification of flowering-related genes There are physical, chemical, and biological signals that lead to the onset of flowering. The four known pathways that respond to these signals have been characterized in Arabidopsis and some herbaceous model plants. In order to identify flowering-related differential expression genes in this study, putative functions of 36,523 nonre- dundant signatures were assessed by BLAST searches against the TAIR9, RAP-DB, and NCBI Nr protein data- sets. We identified 110 citrus unigenes representing putative homologs to flowering-related genes. Some of these genes are required for the day length response and some encode regulatory proteins specifically involved in the control of flowering, while others encode compo- nents of light signal transduction pathways or are involved in circadian clock function. A representation of the relationships among these processes is shown in Fig- ure 6 and the putative homologs of the key players in