The importance of forensic storage support: DNA quality from 11-year-old saliva on FTA cards.

Storage conditions influence the integrity of the recoverable DNA from forensic evidence in terms of yield and quality. FTA cards are widely used in the forensic practice as their chemically treated matrix provides protection from the moment of collection to the point of analysis with current STR typing technology. In this study, we assess the recoverability and the integrity of DNA from 11-year-old saliva on FTA cards using a forensic quantitative real-time polymerase chain reaction (qPCR) commercial assay. The quality after long-term storage was investigated in order to evaluate if the FTA device could assure enough stability over time, applying some internally validated quality criteria of the STR profile. Furthermore, we used a 3D interpolation model to combine the quantitative and qualitative data from qPCR to calculate the minimum optimal DNA input (MODI) to add to the downstream PCR reaction based on the quantitative and qualitative data of a sample. According to our results, when saliva sample is properly transferred onto FTA cards and then correctly stored according to the manufacturer's instructions, it is possible to recover sufficient amounts of DNA for human identification even after more than a decade of storage at ambient temperature. Degradation affected the quality of results especially when the Degradation Index exceeds the value of 2.12, requiring modifications of the standard internal workflow to improve the genotyping quality. Above this value, the application of a "corrective factor" to the PCR normalization process was necessary in order to adjust the recommended manufacturer's PCR DNA input taking into account the degradation level. Our results demonstrated the importance to consider in predictive terms the parameters obtained with the real-time quantification assay, both in terms of quantity (DNA concentration) and of quality (DI, inhibition). Informatics predictive tools including qPCR data together with the variables of storage duration and conditions should be developed in order to optimize the DNA analysis process.

GenoGAM 2.0: scalable and efficient implementation of genome-wide generalized additive models for gigabase-scale genomes.

BACKGROUND: GenoGAM (Genome-wide generalized additive models) is a powerful statistical modeling tool for the analysis of ChIP-Seq data with flexible factorial design experiments. However large runtime and memory requirements of its current implementation prohibit its application to gigabase-scale genomes such as mammalian genomes. RESULTS: Here we present GenoGAM 2.0, a scalable and efficient implementation that is 2 to 3 orders of magnitude faster than the previous version. This is achieved by exploiting the sparsity of the model using the SuperLU direct solver for parameter fitting, and sparse Cholesky factorization together with the sparse inverse subset algorithm for computing standard errors. Furthermore the HDF5 library is employed to store data efficiently on hard drive, reducing memory footprint while keeping I/O low. Whole-genome fits for human ChIP-seq datasets (ca. 300 million parameters) could be obtained in less than 9 hours on a standard 60-core server. GenoGAM 2.0 is implemented as an open source R package and currently available on GitHub. A Bioconductor release of the new version is in preparation. CONCLUSIONS: We have vastly improved the performance of the GenoGAM framework, opening up its application to all types of organisms. Moreover, our algorithmic improvements for fitting large GAMs could be of interest to the statistical community beyond the genomics field.