A method to reveal biological attributes from cell-free nucleic acids using a computing system comprising: obtaining, using a computing system, a frequency signal map having a plurality of signals, wherein each signal corresponds to the frequency of sequence read fragments at a particular genomic region, wherein sequences are of a cell-free nucleic acid sample, and wherein the cell-free nucleic acid sample is derived from at least one individual, a cohort, or a collection of nucleic acid sequences; constructing, using the computing system, a V-plot for each signal of the frequency signal map, wherein each V-plot is a measure of the frequency of sequence read fragments of the cell-free nucleic acid sample sequence; and revealing, using the computing system and a trained computational model, biological attributes of the at least one individual, the cohort, or the collection of nucleic acid sequences; wherein the biological attributes are revealed by entering the constructed V-plots derived from the cell-free nucleic acid sample sequences in the trained computational model; wherein the trained computational model has been trained to predict a feature by utilizing sequence signal V-plots derived from at least one collection of chromatin-derived nucleic acid sequences defined by the feature; and wherein the feature and the biological attributes both comprise at least one of a phenotype, a disorder, a tissue origin, a cell type, a physiological condition, or a transduction pathway activation. obtaining, using a computing system, a frequency signal map having a plurality of signals, wherein each signal corresponds to the frequency of sequence read fragments at a particular genomic region, wherein sequences are of a cell-free nucleic acid sample, and wherein the cell-free nucleic acid sample is derived from at least one individual, a cohort, or a collection of nucleic acid sequences; constructing, using the computing system, a V-plot for each signal of the frequency signal map, wherein each V-plot is a measure of the frequency of sequence read fragments of the cell-free nucleic acid sample sequence; and revealing, using the computing system and a trained computational model, biological attributes of the at least one individual, the cohort, or the collection of nucleic acid sequences; wherein the biological attributes are revealed by entering the constructed V-plots derived from the cell-free nucleic acid sample sequences in the trained computational model; wherein the trained computational model has been trained to predict a feature by utilizing sequence signal V-plots derived from at least one collection of chromatin-derived nucleic acid sequences defined by the feature; and wherein the feature and the biological attributes both comprise at least one of a phenotype, a disorder, a tissue origin, a cell type, a physiological condition, or a transduction pathway activation. wherein the biological attributes are revealed by entering the constructed V-plots derived from the cell-free nucleic acid sample sequences in the trained computational model; wherein the trained computational model has been trained to predict a feature by utilizing sequence signal V-plots derived from at least one collection of chromatin-derived nucleic acid sequences defined by the feature; and wherein the feature and the biological attributes both comprise at least one of a phenotype, a disorder, a tissue origin, a cell type, a physiological condition, or a transduction pathway activation.