Our three approaches use different sets of data features (although SSS uses of all of them).
The identification of SSS using RNA-seq is complicated by both experimental and analytical challenges.
DIC was computed from pCO2 measurements and TA estimates from SSS, using the carbonic acid constants of Mehrbach et al. [ 40] refitted by Dickson and Millero [ 41].
The identification of SSS using RNA-seq is complicated by both experimental and analytical challenges thus the following protocol was used to identify a set of high confidence SSS events within each strain.
SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex.
SSS uses a supervised maximum-likelihood naive-Bayes model to weight each edge with two separate probabilities: that of belonging to a small complex, and of belonging to a large complex.
Fourth, we performed second strand synthesis (SSS) using a biased dACG-TP/dU-TP mix (Fermentas), 10 units of E. coli DNA ligase (Invitrogen), 160 units of E. coli DNA polymerase (Invitrogen), and 2 units of E. coli RNase H (Invitrogen), followed by 1.8X AmpureXP SPRI bead purification (Agencourt).
SSS uses supervised learning to weight each edge with three scores: its posterior probability of being a small-co-complex edge (ie. of belonging to a small complex), of being a large-co-complex edge, and of not being a co-complex edge, given the features of the edge.
SSS uses supervised learning to weight each edge of the reliable PPI network with two posterior probabilities, that of being a small-co-complex edge (ie. of belonging to a small complex), and that of being a large-co-complex edge, given the edge's features.
