Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data

Background: As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results: Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. Conclusions: The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary

A novel Hexa data encoding method for 2D image crypto-compression

Abstract: We proposed a novel method for 2D image compression-encryption whose quality is demonstrated through accurate 2D image reconstruction at higher compression ratios. The method is based on the DWT-Discrete Wavelet Transform where high frequency sub-bands are connected with a novel Hexadata crypto-compression algorithm at compression stage and a new fast matching search algorithm at decoding stage. The novel crypto-compression method consists of four main steps: 1) A five-level DWT is applied to an image to zoom out the low frequency sub-band and increase the number of high frequency sub-bands to facilitate the compression process; 2) The Hexa data compression algorithm is applied to each high frequency sub-band independently by using five different keys to reduce each sub-band to1/6of its original size; 3) Build a look up table of probability data to enable decoding of the original high frequency sub-bands, and 4) Apply arithmetic coding to the outputs of steps (2) and (3). At decompression stage a fast matching search algorithm is used to reconstruct all high frequency sub-bands. We have tested the technique on 2D images including streaming from videos (YouTube). Results show that the proposed crypto-compression method yields high compression ratios up to 99% with high perceptual quality images

Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

Objective: The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class separability value. Features are not only the most important, but also the most difficult task from the classification process as they define input data and classification quality. An ideal set of features would make the classification problem trivial. This article presents novel methods of feature extraction processing and automatic epilepsy seizure classification combining machine learning methods with genetic evolution algorithms. Methods: Classification is performed on EEG data that represent electric brain activity. At first, the signal is preprocessed with digital filtration and adaptive segmentation using fractal dimensions as the only segmentation measure. In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into lower dimension space and classify the final result into ictal or interictal epochs. Results: The final application of GP SVM method improves the discriminatory performance of a classifier by reducing feature dimensionality at the same time. Members of the GP tree structure represent the features themselves and their number is automatically decided by the compression function introduced in this paper. This novel method improves the overall performance of the SVM classification by dramatically reducing the size of input feature vector. Conclusion: According to results, the accuracy of this algorithm is very high and comparable, or even superior to other automatic detection algorithms. In combination with the great efficiency, this algorithm can be used in real-time epilepsy detection applications. From the results of the algorithm's classification, we can observe high sensitivity, specificity results, except for the Generalized Tonic Clonic Seizure (GTCS). As the next step, the optimization of the compression stage and final SVM evaluation stage is in place. More data need to be obtained on GTCS to improve the overall classification score for GTCS.Web of Science142449243

Approximation and Compression Techniques to Enhance Performance of Graphics Processing Units

A key challenge in modern computing systems is to access data fast enough to fully utilize the computing elements in the chip. In Graphics Processing Units (GPUs), the performance is often constrained by register file size, memory bandwidth, and the capacity of the main memory. One important technique towards alleviating this challenge is data compression. By reducing the amount of data that needs to be communicated or stored, memory resources crucial for performance can be efficiently utilized.This thesis provides a set of approximation and compression techniques for GPUs, with the goal of efficiently utilizing the computational fabric, and thereby increase performance. The thesis shows that these techniques can substantially lower the amount of information the system has to process, and are thus important tools in the process of meeting challenges in memory utilization.This thesis makes contributions within three areas: controlled floating-point precision reduction, lossless and lossy memory compression, and distributed training of neural networks. In the first area, the thesis shows that through automated and controlled floating-point approximation, the register file can be more efficiently utilized. This is achieved through a framework which establishes a cross-layer connection between the application and the microarchitecture layer, and a novel register file organization capable of leveraging low-precision floating-point values and narrow integers for increased capacity and performance.Within the area of compression, this thesis aims at increasing the effective bandwidth of GPUs by presenting a lossless and lossy memory compression algorithm to reduce the amount of transferred data. In contrast to state-of-the-art compression techniques such as Base-Delta-Immediate and Bitplane Compression, which uses intra-block bases for compression, the proposed algorithm leverages multiple global base values to reach a higher compression ratio. The algorithm includes an optional approximation step for floating-point values which offers higher compression ratio at a given, low, error rate.Finally, within the area of distributed training of neural networks, this thesis proposes a subgraph approximation scheme for graph data which mitigates accuracy loss in a distributed setting. The scheme allows neural network models that use graphs as inputs to converge at single-machine accuracy, while minimizing synchronization overhead between the machines

Efficient Storage of Genomic Sequences in High Performance Computing Systems

ABSTRACT: In this dissertation, we address the challenges of genomic data storage in high performance computing systems. In particular, we focus on developing a referential compression approach for Next Generation Sequence data stored in FASTQ format files. The amount of genomic data available for researchers to process has increased exponentially, bringing enormous challenges for its efficient storage and transmission. General-purpose compressors can only offer limited performance for genomic data, thus the need for specialized compression solutions. Two trends have emerged as alternatives to harness the particular properties of genomic data: non-referential and referential compression. Non-referential compressors offer higher compression rations than general purpose compressors, but still below of what a referential compressor could theoretically achieve. However, the effectiveness of referential compression depends on selecting a good reference and on having enough computing resources available. This thesis presents one of the first referential compressors for FASTQ files. We first present a comprehensive analytical and experimental evaluation of the most relevant tools for genomic raw data compression, which led us to identify the main needs and opportunities in this field. As a consequence, we propose a novel compression workflow that aims at improving the usability of referential compressors. Subsequently, we discuss the implementation and performance evaluation for the core of the proposed workflow: a referential compressor for reads in FASTQ format that combines local read-to-reference alignments with a specialized binary-encoding strategy. The compression algorithm, named UdeACompress, achieved very competitive compression ratios when compared to the best compressors in the current state of the art, while showing reasonable execution times and memory use. In particular, UdeACompress outperformed all competitors when compressing long reads, typical of the newest sequencing technologies. Finally, we study the main aspects of the data-level parallelism in the Intel AVX-512 architecture, in order to develop a parallel version of the UdeACompress algorithms to reduce the runtime. Through the use of SIMD programming, we managed to significantly accelerate the main bottleneck found in UdeACompress, the Suffix Array Construction

Image compression based on 2D Discrete Fourier Transform and matrix minimization algorithm

In the present era of the internet and multimedia, image compression techniques are essential to improve image and video performance in terms of storage space, network bandwidth usage, and secure transmission. A number of image compression methods are available with largely differing compression ratios and coding complexity. In this paper we propose a new method for compressing high-resolution images based on the Discrete Fourier Transform (DFT) and Matrix Minimization (MM) algorithm. The method consists of transforming an image by DFT yielding the real and imaginary components. A quantization process is applied to both components independently aiming at increasing the number of high frequency coefficients. The real component matrix is separated into Low Frequency Coefficients (LFC) and High Frequency Coefficients (HFC). Finally, the MM algorithm followed by arithmetic coding is applied to the LFC and HFC matrices. The decompression algorithm decodes the data in reverse order. A sequential search algorithm is used to decode the data from the MM matrix. Thereafter, all decoded LFC and HFC values are combined into one matrix followed by the inverse DFT. Results demonstrate that the proposed method yields high compression ratios over 98% for structured light images with good image reconstruction. Moreover, it is shown that the proposed method compares favorably with the JPEG technique based on compression ratios and image quality

A novel steganography approach for audio files

We present a novel robust and secure steganography technique to hide images into audio files aiming at increasing the carrier medium capacity. The audio files are in the standard WAV format, which is based on the LSB algorithm while images are compressed by the GMPR technique which is based on the Discrete Cosine Transform (DCT) and high frequency minimization encoding algorithm. The method involves compression-encryption of an image file by the GMPR technique followed by hiding it into audio data by appropriate bit substitution. The maximum number of bits without significant effect on audio signal for LSB audio steganography is 6 LSBs. The encrypted image bits are hidden into variable and multiple LSB layers in the proposed method. Experimental results from observed listening tests show that there is no significant difference between the stego audio reconstructed from the novel technique and the original signal. A performance evaluation has been carried out according to quality measurement criteria of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR)