ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing.

Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators and co-option of cellular pathways.

Unlabelled: The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data now allows the design of secondary analyses that take advantage of this information to create new knowledge through specific computational approaches. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types.

Serial co-expression analysis of host factors from SARS-CoV viruses highly converges with former high-throughput screenings and proposes key regulators.

The current genomics era is bringing an unprecedented growth in the amount of gene expression data, only comparable to the exponential growth of sequences in databases during the last decades. This data allow the design of secondary analyses that take advantage of this information to create new knowledge. One of these feasible analyses is the evaluation of the expression level for a gene through a series of different conditions or cell types.

R Package imputeTestbench to Compare Imputation Methods for Univariate Time Series.

Missing observations are common in time series data and several methods are available to impute these values prior to analysis. Variation in statistical characteristics of univariate time series can have a profound effect on characteristics of missing observations and, therefore, the accuracy of different imputation methods. The package can be used to compare the prediction accuracy of different methods as related to the amount and type of missing data for a user-supplied dataset.

Predicting protein distance maps according to physicochemical properties.

The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures.