An automated signal alignment algorithm based on dynamic time warping for capillary electrophoresis data

Correcting the retention time variation and measuring the similarity of time series is one of the most popular challenges in the area of analyzing capillary electrophoresis (CE) data. In this study, an automated signal alignment method is proposed by modifying the dynamic time warping (DTW) approach to align the time-series data. Preprocessing tools and further optimizations were developed to increase the performance of the algorithm. As a demonstrative case study, the developed algorithm is applied to the analysis of CE data from a selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) evaluation of the RNA secondary structure. The time-shift problem is one of the main components in the analysis of the SHAPE data. The accuracy and execution time of the algorithm are illustrated with experimental results obtained by applying to different types of data. The experimental results show that the signal alignment algorithm efficiently corrects the retention time variation. The developed tools can be readily adapted for the analysis of other biological datasets or time series

JTimeWarp: A software for Aligning Biological Signals using Warping Methods

It is a very common problem to align signals upon time-axis for analysis of datasets obtained from biological experiments. Since biological or chemical signals may be measured differently due to some factors such as temparature, pressure and others laboratory conditions, the signals may have different time scales. In this study, three commanly used signal alignment methods are implemented in a software named JTimeWarp.First, Dynamic Time Warping (DTW), which is the most popular method, is implemented. DTW method takes a look for an optimal warping path between two time series. DTW method has three basic steps: (1) generates cost matrix using a distance function; (2) computes accumulated cost matrix from the values contained in cost matrix; (3) finds warping path through the use of accumulated cost matrix. While building a warping path, DTW uses the elements of the accumulated cost matrix whose values are the smallest along the way [1]. Correlation Optimized Warping (COW) is another method derived from DTW to deliver better performance in finding an optimal alignment between two given time-dependent sequences under certain restrictions. COW applies piecewise linear stretching or compression of one signal, instead of pointwise warping like DTW. The dynamic programming optimization is used to determine the optimal positions of end points or nodes of the predetermined segments [1]. Parametric Time Warping (PTW) is unique with its approach to signal warping. PTW tries to fit a polynomial function defining the misalignment of signals. The polynomial functions generated by PTW include many terms in the parametric time warping. For these reasons, PTW approach is different amongst others warping methods [1]. In this study, a user friendly and interactive software called JTimeWarp is developed to align signals automatically. The software is implemented using java programming language and java swing library. User can load data and select a warping method for alignment. Since there is no perfect alignment methods, the software gives the users option of the manual correction. User can apply one of the warping methods and then correct the errors manually using interactive options. User also can apply all three methods at the same time and the select the best one for the signal alignment

Segmentation Algorithm via Cellular Neural/Nonlinear Network: Implementation on Bio-Inspired Hardware Platform

Abstract The Bio-inspired (Bi-i) Cellular Vision System is a computing platform consisting of sensing, array sensing-processing, and digital signal processing. The platform is based on the Cellular Neural/Nonlinear Network (CNN) paradigm. This article presents the implementation of a novel CNN-based segmentation algorithm onto the Bi-i system. Each part of the algorithm, along with the corresponding implementation on the hardware platform, is carefully described through the article. The experimental results, carried out for Foreman and Car-phone video sequences, highlight the feasibility of the approach, which provides a frame rate of about 26 frames/s. Comparisons with existing CNN-based methods show that the conceived approach is more accurate, thus representing a good trade-off between real-time requirements and accuracy

RNA SHAPE Analysis of Small RNAs and Riboswitches

We describe structural analysis of RNAs by SHAPE chemical probing. RNAs are treated with 1-methyl-7-nitroisatoic anhydride (1M7), a reagent that detects local nucleotides flexibility, and N-methylisatoic anhydride (NMIA) and 1-methyl-6-nitroisatoic anhydride (1M6), reagents which together detect higher-order and non-canonical interactions. Chemical adducts are detected as stops during reverse transcriptase-mediated primer extension. Probing information can be used to infer conformational changes and ligand binding, and to develop highly accurate models of RNA secondary structures

Principles for Understanding the Accuracy of SHAPE-Directed RNA Structure Modeling

Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of some apparent discrepancies between modeled and accepted structures. Recent studies by Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011) and Nat. Chem. 3:954 (2011)], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected based on prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work – tRNAPhe, the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected non-standard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNAPhe and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the Das and colleagues SHAPE data. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ~93% of individual base pairs and greater than 90% of helices in the four RNAs, essentially indistinguishable from the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally-directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward

Edge Detection Algorithms implemented on Bi-i Cellular Vision System

Bi-i (Bio-inspired) Cellular Vision system is built mainly on Cellular Neural /Nonlinear Networks (CNNs) type (ACE16k) and Digital Signal Processing (DSP) type microprocessors. CNN theory proposed by Chua has advanced properties for image processing applications. In this study, the edge detection algorithms are implemented on the Bi-i Cellular Vision System. Extracting the edge of an image to be processed correctly and fast is of crucial importance for image processing applications. Threshold Gradient based edge detection algorithm is implemented using ACE16k microprocessor. In addition, pre-processing operation is realized by using an image enhancement technique based on Laplacian operator. Finally, morphologic operations are performed as post processing operations. Sobel edge detection algorithm is performed by convolving sobel operators with the image in the DSP. The performances of the edge detection algorithms are compared using visual inspection and timing analysis. Experimental results show that the ACE16k has great computational power and Bi-i Cellular Vision System is very qualified to apply image processing algorithms in real time