Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle

Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve AUPR. By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid

Additional file 1: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Table S1. Summary of samples. Sample names in this study are corresponded to samples names in our previous 16S rRNA gene study [9]. Genomic DNA (gDNA) concentrations obtained for each sample and total DNA retrieved are presented. (XLSX 10 kb

Additional file 8: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Table S5. Summary of microbial genomes. This table provides a summary of marker genes, completeness, contamination, and RPKM values for genomes investigated in this study. (XLSX 16 kb

Additional file 11: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Table S6. Summary of viral taxonomy. Taxonomic classification and vContact-based clustering for each viral contig are provided. (XLSX 81kb

Additional file 5: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Figure S2. Redundancy analyses (RDA) of microbial and viral populations. Each gene abundance (contig RPKM value) was used as input for RDA. The genes reductive dsrA and dsrD represent candidate sulfate-reducing populations, while mcrA, candidate methanogens. Forward selection provided the variables to constrain these populations, shown in the plots and stated below each plot with their associated RDA statistics. In all plots, P7 samples are indicated by gray circles, while P8 samples by white/empty circles. (PDF 518 kb

Additional file 3: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Table S3. RPKM values for viral, reductive dsrA-, dsrD-, and mcrA-containing contigs. These tables were used as inputs for many of the analyses indicated in the Methods session. (XLSX 376 kb

Additional file 4: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Figure S1. dsrD phylogenetic affiliation and abundance per sample. This is the expanded version of Fig. 1. The RAxML tree was constructed using 206 amino acid sequences. The gene affiliation was inferred from the best BLASTP hit. The 23 clusters in Fig. 1 are indicated here. Bolded names represent dsrD present in reconstructed genomes. The yellow, blue, and orange stars indicate dsrD in genomes represented in Fig. 2. For the heat map, dsrD-containing contig RPKM values were used as input. The statistical significance of hierarchical clustering branches is indicated by green stars (pvclust, approximately unbiased p < 0.05). (PDF 1128 kb

Additional file 12: of Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems

Figure S6. Principal component analysis (PCA) of geochemical variables. Pore water concentrations of sulfate, sulfide, ferrous iron (Fe II), and methane were retrieved from Dalcin Martins et al. [9] and used as input values for this analysis. P7 samples are represented by black circles, while P8 samples by gray circles. (PDF 102 kb