Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

Automation on the generation of genome scale metabolic models

Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table