BioGranat-IG: a network analysis tool to suggest mechanisms of genetic heterogeneity from exome-sequencing data.

MOTIVATION: Recent exome-sequencing studies have successfully identified disease-causing sequence variants for several rare monogenic diseases by examining variants common to a group of patients. However, the current data analysis strategies are only insufficiently able to deal with confounding factors such as genetic heterogeneity, incomplete penetrance, individuals lacking data and involvement of several genes. RESULTS: We introduce BioGranat-IG, an analysis strategy that incorporates the information contained in biological networks to the analysis of exome-sequencing data. To identify genes that may have a disease-causing role, we label all nodes of the network according to the individuals that are carrying a sequence variant and subsequently identify small subnetworks linked to all or most individuals. Using simulated exome-sequencing data, we demonstrate that BioGranat-IG is able to recover the genes responsible for two diseases known to be caused by variants in an underlying complex. We also examine the performance of BioGranat-IG under various conditions likely to be faced by the user, and show that its network-based approach is more powerful than a set-cover-based approach.

Range of motion assessment of the shoulder and elbow joints using a motion sensing input device: a pilot study.

BACKGROUND: Motion sensing input devices could provide a practical and low-cost alternative method for repeated range of motion measurements. This study aimed to assess the reliability, accuracy and time requirements of a motion sensing input device (Microsoft Kinect) for ROM measurements comparing it with goniometer based measurements and subjective estimation. MATERIAL AND METHODS: Full ROM was measured in 14 shoulder and elbow joints using the different methods. The order was randomly selected and each movement was measured twice. The results were recorded in degrees and the time measured in seconds. RESULTS: In general, there was a poor to moderate agreement between the Kinect system compared to the goniometer. There was a good agreement between the goniometer-based and the subjective technique. The Kinect-based technique showed excellent test-retest reliability. CONCLUSION: The Kinect system showed good test-retest reliability, but lower accuracy compared to goniometer-based measurements. Improvements in patient positioning and measurement protocol standardization must be made before its implementation in clinical practice.

Genometa--a fast and accurate classifier for short metagenomic shotgun reads.

UNLABELLED: Metagenomic studies use high-throughput sequence data to investigate microbial communities in situ. However, considerable challenges remain in the analysis of these data, particularly with regard to speed and reliable analysis of microbial species as opposed to higher level taxa such as phyla. We here present Genometa, a computationally undemanding graphical user interface program that enables identification of bacterial species and gene content from datasets generated by inexpensive high-throughput short read sequencing technologies. Our approach was first verified on two simulated metagenomic short read datasets, detecting 100% and 94% of the bacterial species included with few false positives or false negatives. Subsequent comparative benchmarking analysis against three popular metagenomic algorithms on an Illumina human gut dataset revealed Genometa to attribute the most reads to bacteria at species level (i.e. including all strains of that species) and demonstrate similar or better accuracy than the other programs. Lastly, speed was demonstrated to be many times that of BLAST due to the use of modern short read aligners. Our method is highly accurate if bacteria in the sample are represented by genomes in the reference sequence but cannot find species absent from the reference. This method is one of the most user-friendly and resource efficient approaches and is thus feasible for rapidly analysing millions of short reads on a personal computer. AVAILABILITY: The Genometa program, a step by step tutorial and Java source code are freely available from http://genomics1.mh-hannover.de/genometa/ and on http://code.google.com/p/genometa/. This program has been tested on Ubuntu Linux and Windows XP/7.

Critical properties of the synchronization transition in space-time chaos.

We study two coupled spatially extended dynamical systems which exhibit space-time chaos. The transition to the synchronized state is treated as a nonequilibrium phase transition, where the average synchronization error is the order parameter. The transition in one-dimensional systems is found to be generically in the universality class of the Kardar-Parisi-Zhang equation with a growth-limiting term ("bounded KPZ"). For systems with very strong nonlinearities in the local dynamics, however, the transition is found to be in the universality class of directed percolation.