SeqX: a tool to detect, analyze and visualize residue co-locations in protein and nucleic acid structures.

BACKGROUND: The interacting residues of protein and nucleic acid sequences are close to each other - they are co-located. Structure databases (like Protein Data Bank, PDB and Nucleic Acid Data Bank, NDB) contain all information about these co-locations; however it is not an easy task to penetrate this complex information. We developed a JAVA tool, called SeqX for this purpose. RESULTS: SeqX tool is useful to detect, analyze and visualize residue co-locations in protein and nucleic acid structures. The user: a. selects a structure from PDB; b. chooses an atom that is commonly present in every residues of the nucleic acid and/or protein structure(s). c. defines a distance from these atoms (3-15 A). The SeqX tool detects every residue that is located within the defined distances from the defined "backbone" atom(s); provides a DotPlot-like visualization (Residues Contact Map), and calculates the frequency of every possible residue pairs (Residue Contact Table) in the observed structure. It is possible to exclude +/- 1 to 10 neighbor residues in the same polymeric chain from detection, which greatly improves the specificity of detections (up to 60% when tested on dsDNA). Results obtained on protein structures showed highly significant correlations with results obtained from literature (p < 0.0001, n = 210, four different subsets). The co-location frequency of physico-chemically compatible amino acids is significantly higher than is calculated and expected in random protein sequences (p < 0.0001, n = 80). CONCLUSION: The tool is simple and easy to use and provides a quick and reliable visualization and analyses of residue co-locations in protein and nucleic acid structures. AVAILABILITY AND REQUIREMENTS: http://janbiro.com/Downloads.html SeqX, Java J2SE Runtime Environment 5.0 (available from [see Additional file 1] http://www.sun.com) and at least a 1 GHz processor and with a minimum 256 Mb RAM. Source codes are available from the authors.

A novel ECG telemetry and monitoring system based on Z-Wave communication.

This paper presents a novel ECG telemetry system based on Z-Wave communication protocol. The proposed system consists of small portable devices that acquire, compress and transmit the ECG to a RF-USB interface connected to a central monitoring computer. The received signals are filtered, QRS complexes and P and T waves are localized, and different waveforms are classified in order to be able to provide diagnosis tools like heart rate variability and turbulence analysis. Due to the limitation of communication bandwidth, the maximum number of measuring devices connected to a central monitor is four. The proposed system composed of inexpensive components can serve as flexible alternative to current ECG monitoring systems.

Quick ECG analysis for on-line holter monitoring systems.

Computer-aided bedside patient monitoring requires real-time analysis of vital functions. On-line Holter monitors need reliable and quick algorithms to perform all the necessary signal processing tasks. This paper presents the methods that were conceptualized and implemented at the development of such a monitoring system at Medical Clinic No. 4 of Targu-Mures. The system performs the following ECG signal processing steps: (1) Decomposition of the ECG signals using multi-resolution wavelet transform, which also eliminates most of the high and low frequency noises. These components will serve as input for wave classification algorithms; (2) Identification of QRS complexes, P and T waves using two different algorithms: a sequential clustering and a neural-network-based classification. This latter also distinguishes normal R waves from abnormal cases; (3) Localization of several kinds of arrhythmia using a spectral method. An autoregressive model is applied to estimate the series of R-R intervals. The coefficients of the AR model are predicted using the Kalman filter, and these coefficients will determine a local spectrum for each QRS complex. By analyzing this spectrum, different arrhythmia cases are identified. The algorithms were tested using the MIT-BIH signal database and own multichannel ECG registrations. The QRS complex detection ratio is over 99.5%