CEESIt: A computational tool for the interpretation of STR mixtures.

In forensic DNA interpretation, the likelihood ratio (LR) is often used to convey the strength of a match. Expanding on binary and semi-continuous methods that do not use all of the quantitative data contained in an electropherogram, fully continuous methods to calculate the LR have been created. These fully continuous methods utilize all of the information captured in the electropherogram, including the peak heights.

Probabilistic characterisation of baseline noise in STR profiles.

There are three dominant contributing factors that distort short tandem repeat profile measurements, two of which, stutter and variations in the allelic peak heights, have been described extensively. Here we characterise the remaining component, baseline noise. A probabilistic characterisation of the non-allelic noise peaks is not only inherently useful for statistical inference but is also significant for establishing a detection threshold.

NOCIt: a computational method to infer the number of contributors to DNA samples analyzed by STR genotyping.

Repetitive sequences in the human genome called short tandem repeats (STRs) are used in human identification for forensic purposes. Interpretation of DNA profiles generated using STRs is often problematic because of uncertainty in the number of contributors to the sample. Existing methods to identify the number of contributors work on the number of peaks observed and/or allele frequencies.

Network deconvolution as a general method to distinguish direct dependencies in networks.

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums.

ClpP hydrolyzes a protein substrate processively in the absence of the ClpA ATPase: mechanistic studies of ATP-independent proteolysis.

ATP-dependent proteases are processive, meaning that they degrade full-length proteins into small peptide products without releasing large intermediates along the reaction pathway. In the case of the bacterial ATP-dependent protease ClpAP, ATP hydrolysis by the ClpA component has been proposed to be required for processive proteolysis of full-length protein substrates. We present here data showing that in the absence of the ATPase subunit ClpA, the protease subunit ClpP can degrade full-length protein substrates processively, albeit at a greatly reduced rate.

An information-based computational technique for estimation of chromatographic peak purity.

Assessment of the purity of chromatographic peaks is an important step in developing and validating purification procedures for complex mixtures. While curve-fitting techniques can be useful for determining the retention times and relative concentrations of the components of a chromatographic peak, their utility is limited by the lack of unambiguous criteria for determining the number of such components. In this work, we present a computational technique for analyzing chromatograms to estimate the number of components, their retention times, and their relative concentrations.