AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.

BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes). RESULTS: We develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS. CONCLUSIONS: Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

A Distributed Classifier for MicroRNA Target Prediction with Validation Through TCGA Expression Data.

BACKGROUND: MicroRNAs (miRNAs) are approximately 22-nucleotide long regulatory RNA that mediate RNA interference by binding to cognate mRNA target regions. Here, we present a distributed kernel SVM-based binary classification scheme to predict miRNA targets. It captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves. This is accomplished separately for various input features, such as thermodynamic and sequence-based features. Further, we use a principled approach to uniformly model both canonical and non-canonical seed matches, using a novel seed enrichment metric. Finally, we verify our miRNA-mRNA pairings using an Elastic Net-based regression model on TCGA expression data for four cancer types to estimate the miRNAs that together regulate any given mRNA. RESULTS: We present a suite of algorithms for miRNA target prediction, under the banner Avishkar, with superior prediction performance over the competition. Specifically, our final kernel SVM model, with an Apache Spark backend, achieves an average true positive rate (TPR) of more than 75 percent, when keeping the false positive rate of 20 percent, for non-canonical human miRNA target sites. This is an improvement of over 150 percent in the TPR for non-canonical sites, over the best-in-class algorithm. We are able to achieve such superior performance by representing the thermodynamic and sequence profiles of miRNA-mRNA interaction as curves, devising a novel seed enrichment metric, and learning an ensemble of miRNA family-specific kernel SVM classifiers. We provide an easy-to-use system for large-scale interactive analysis and prediction of miRNA targets. All operations in our system, namely candidate set generation, feature generation and transformation, training, prediction, and computing performance metrics are fully distributed and are scalable. CONCLUSIONS: We have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves for different species (human or mouse), or different target types (canonical or non-canonical). We analyzed the agreement between the target pairings using CLIP-seq data and using expression data from four cancer types. To the best of our knowledge, we provide the first distributed framework for miRNA target prediction based on Apache Hadoop and Spark. AVAILABILITY: All source code and sample data are publicly available at https://bitbucket.org/cellsandmachines/avishkar. Our scalable implementation of kernel SVM using Apache Spark, which can be used to solve large-scale non-linear binary classification problems, is available at https://bitbucket.org/cellsandmachines/kernelsvmspark.

Laparoscopic insertion with tip suturing, omentectomy, and ovariopexy improves lifespan of peritoneal dialysis catheters in children.

UNLABELLED: Over the past two decades, chronic peritoneal dialysis (PD) has emerged as the first choice pediatric dialysis modality. A recent study visually identified the cause of malfunction of PD catheters at the Red Cross Children's Hospital in Cape Town. The reasons that could be found, lead to changed Tenckhoff insertion-techniques from open to laparoscopic. This included suturing of the tip, omentectomy and ovarian-pexy by laparoscopy. In the present paper we prospectively analyzed, if changed insertion technique lead to an improved outcome. PATIENTS AND METHODS: 26 Patients required 36 laparoscopic Tenckhoff insertions during the period August of 2003 and July of 2006. Overall a total number of 222.5 catheter-months have been observed. Laparoscopic insertion technique required 3 port placements. The tip of the catheter was sutured to pelvic peritoneum, omentectomy performed through a port site and ovariopexy done when required. RESULTS: The mean lifespan of all Tenckhoff's was 6.4 +/- 6.3 months. The tip of the catheter was sutured 20 times, omentectomy done in 9 cases and 6 patients underwent ovarian pexy. In the group where the tip was sutured to the pelvic peritoneum catheter life was 8.4 months compared to the non-sutured group which was only 4.1. Omentectomy lead to an overall catheter survival of 8.0 months compared to the no omentectomy group, which had a survival of 5.8 months. The complication-rate concerning early problems and malfunctions in the sutured and omentectomy groups was also lower. Patients who underwent both, suturing of the tip and omentectomy had no malfunctions at all. CONCLUSION: Omentectomy and suturing the tip can lower the complication-rate and prolong catheter survival. Using these procedures could decrease costs and morbidity and prevent patients from having further operations.