A Trend Correlation (TC) Based New Method for Reverse Engineering Biological Network: We developed a new method to infer functional linkages and further biological network between genes from time-series expression data (He and Zeng, BMC Bioinformatics, 2006, 7:69). Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. We not only consider the qualitative information (i.e. the change trend) but also the quantitative information (the change level) in the original data. We seek to make the methods more noise-tolerant and at the same time to keep more useful information in the expression values. The main programs used in this method can be downloaded here. All these programs are written in Matlab. The first program TC_Linkage is used to calculate a maximal local alignment (sc) of change trend score for all the gene pairs in the array data. If you only want to infer functional linkages among some genes, the second program TC_Partial_Linkage is preferred, which is used to calculate a maximal local alignment (sc) of change trend score for the given gene pairs. Another program TC_CC is used to calculate cc (the correlated coefficient) between the maximal matched change levels of each gene pair.