Implementing a transcription factor interaction prediction system using the genometric query language

Novel technologies and growing interest have resulted in a large increase in the amount of data available for genomics and transcriptomics studies, both in terms of volume and contents. Biology is relying more and more on computational methods to process, investigate, and extract knowledge from this huge amount of data. In this work, we present the TICA web server (available at http://www.gmql.eu/tica/), a fast and compact tool developed to support data-driven knowledge discovery in the realm of transcription factor interaction prediction. TICA leverages both the GenoMetric Query Language, a novel query tool (based on the Apache Hadoop and Spark technologies) specialized in the integration and management of heterogeneous, large genomic datasets, and a statistical method for robust detection of co-locations across interval-based data, in order to infer physically interacting transcription factors. Notably, TICA allows investigators to upload and analyze their own ChIP-seq experiments datasets, comparing them both against ENCODE data or between themselves, achieving computation time which increases linearly with respect to dataset size and density. Using ENCODE data from three well-studied cell lines as reference, we show that TICA predictions are supported by existing biological knowledge, making the web server a reliable and efficient tool for interaction screening and data-driven hypothesis generation

Small-molecule inhibitor starting points learned from protein–protein interaction inhibitor structure

Motivation: Protein–protein interactions (PPIs) are a promising, but challenging target for pharmaceutical intervention. One approach for addressing these difficult targets is the rational design of small-molecule inhibitors that mimic the chemical and physical properties of small clusters of key residues at the protein–protein interface. The identification of appropriate clusters of interface residues provides starting points for inhibitor design and supports an overall assessment of the susceptibility of PPIs to small-molecule inhibition

RFMirTarget: A Random Forest Classifier for Human miRNA Target Gene Prediction

Abstract. MicroRNAs (miRNAs) are key regulators of eukaryotic gene expression whose fundamental role has been already identified in many cell pathways. The correct identification of miRNAs targets is a major challenge in bioinformatics. So far, machine learning-based methods for miRNA-target prediction have shown the best results in terms of specificity and sensitivity. However, despite its well-known efficiency in other classifying tasks, the random forest algorithm has not been employed in this problem. Therefore, in this work we present RFMirTarget, an efficient random forest miRNA-target prediction system. Our tool analyzes the alignment between a candidate miRNA-target pair and extracts a set of structural, thermodynamics, alignment and position-based features. Experiments have shown that RFMirTarget achieves a Matthew’s correlation coefficient nearly 48 % greater than the performance reported for the MultiMiTar, which was trained upon the same data set. In addition, tests performed with RFMirTarget reinforce the importance of the seed region for target prediction accuracy

Automatic Sleep Spindle Detection and Genetic Influence Estimation Using Continuous Wavelet Transform

Contains fulltext : 151960.pdf (publisher's version ) (Open Access)Mounting evidence for the role of sleep spindles in neuroplasticity has led to an increased interest in these non-rapid eye movement (NREM) sleep oscillations. It has been hypothesized that fast and slow spindles might play a different role in memory processing. Here, we present a new sleep spindle detection algorithm utilizing a continuous wavelet transform (CWT) and individual adjustment of slow and fast spindle frequency ranges. Eighteen nap recordings of ten subjects were used for algorithm validation. Our method was compared with both a human scorer and a commercially available SIESTA spindle detector. For the validation set, mean agreement between our detector and human scorer measured during sleep stage 2 using kappa coefficient was 0.45, whereas mean agreement between our detector and SIESTA algorithm was 0.62. Our algorithm was also applied to sleep-related memory consolidation data previously analyzed with a SIESTA detector and confirmed previous findings of significant correlation between spindle density and declarative memory consolidation. We then applied our method to a study in monozygotic (MZ) and dizygotic (DZ) twins, examining the genetic component of slow and fast sleep spindle parameters. Our analysis revealed strong genetic influence on variance of all slow spindle parameters, weaker genetic effect on fast spindles, and no effects on fast spindle density and number during stage 2 sleep

Improvement of aggregate packing model of interlocking concrete block pavement (ICBP) mixture using fly ash

Use of concrete paver blocks is becoming increasingly popular. They are used for the paving of approaches, paths and parking areas including their application in pre-engineered buildings and pavements. Interlocking Concrete Block Pavements (ICBP) have been extensively used in a number of countries for quite some time as a specialized problem-solving technique for providing pavements in areas where conventional types of construction are prove to be less durable due to many operational and environmental constraints. As it was observed that “Sri Lanka, Lak Vijaya Coal Power Station at Norocholai, Puttalam generates large amount of fly ash per day as a byproduct” which was considered as a waste & an environmental hazard ,leading to the limitation of its usage, this research focuses on utilizing the fly ash to improve the aggregate packing model of ICBP. Fly ash is used as a filler material in the paving block mixture to optimize the packing of the aggregate. Fly ash includes samples and control samples were tested for compressive strength, water absorption and were made to go through a Scanning Electron Microscope Analysis. Experimental results showed that 23 and 21 percent of cement can be replaced by Fly Ash in Grade 15 & 20 for OPC mixtures while 26 and 21 percent of cement can be replaced in Grade 15 & 20 for PLC mixtures. Optimization of the packing of aggregates is the process of determining the most suitable aggregate particle size and distribution to minimize the void content of an aggregate mix. An optimized aggregate mix will have a lesser amount of voids which needs to be filled with cement paste. Further, fly ash has improved the workability of the mixture due to the special nature of the particle. Better economy and durability also have been achieved as its utilization leads to the reduction of needed cement content and heat of hydration. To elaborate further, it will also help in safe-guarding the environment from ill effects of CO2 emissions from cement industry and contribute towards providing a solution for the disposal of fly ash produced by thermal power plants