Burrows-Wheeler Research Articles

Fuzzy Power, Converter for Color Sorting System

Intelligent robot technology is currently widely applied to all facets of human production and existence. Future development must focus on using intelligent robots rather than humans to achieve automatic sorting. In the field of sorting robots, color sorting robots are a significant research object. This paper examines the color recognition principle of the TAOS Company’s TCS3200 color sensor, based on the introduction of the theory of three primary colors. It goes into detail about how important white balance is. An ATmega16 single-chip microcomputer serves as the robot's brain, color and grey sensors serve as its sense organs, and steering gears are used as its moving parts in the design of a color block sorting robot. It is capable of recognizing, organizing, and manipulating various color blocks. The technique of multiple detections and high probability results are used in color recognition, which greatly increases detection accuracy. There is a specific practical benefit to the system.

Learning and Executing Re-Usable Behaviour Trees From Natural Language Instruction

Domestic and service robots have the potential to transform industries such as health care and small-scale manufacturing, as well as the homes in which we live. However, due to the overwhelming variety of tasks these robots will be expected to complete, providing generic out-of-the-box solutions that meet the needs of every possible user is clearly intractable. To address this problem, robots must therefore not only be capable of learning how to complete novel tasks at run-time, but the solutions to these tasks must also be informed by the needs of the user. In this letter we demonstrate how behaviour trees, a well established control architecture in the fields of gaming and robotics, can be used in conjunction with natural language instruction to provide a robust and modular control architecture for instructing autonomous agents to learn and perform novel complex tasks. We also show how behaviour trees generated using our approach can be generalised to novel scenarios, and can be re-used in future learning episodes to create increasingly complex behaviours. We validate this work against an existing corpus of natural language instructions, demonstrate the application of our approach on both a simulated robot solving a toy problem, as well as two distinct real-world robot platforms which, respectively, complete a block sorting scenario, and a patrol scenario.

Parallel and private generalized suffix tree construction and query on genomic data

BackgroundSeveral technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery.MethodsIn this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework.ResultsThe experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup.

Reversible data hiding in JPEG images based on coefficient-first selection

Joint photographic experts group (JPEG) is the most commonly used image format on the Internet, and using reversible data hiding (RDH) technology to verify its authenticity and integrity is of great significance. So far, many RDH algorithms for JPEG image have been proposed, which improve the performance of the algorithm via block sorting, frequency selection and other adaptive strategies. In this paper, a new JPEG RDH method based on coefficient-first selection strategy is proposed. In our new method, the distribution models of alternating current (AC) coefficients belonging to different discrete cosine transform (DCT) coefficient blocks are constructed first, and then the distortion value of each AC coefficient can be calculated through the estimated probability density function of the constructed coefficient model. In the embedding process, the coefficients with low distortion values will be preferentially selected for data hiding. Various experimental results demonstrate that compared with the state-of-the-art JPEG RDH methods, our proposed may have better visual quality and less increase of file storage size.

A fast algorithm for constructing suffix arrays for DNA alphabets

The continuous improvement of sequencing technologies has been paralleled by the development of efficient algorithms and data structures for sequencing data analysis and processing. Suffix array is one of data structures that are used to construct the Burrows-Wheeler transform (BWT) for long length genomes. Building a suffix array itself is an expensive-resource process since the computations are dominant by sorting suffixes in a lexical order. Most of the suffix array construction algorithms consider the general and integer alphabets without utilizing special cases for fixed-size ones such as DNA alphabets. In this paper, we exploit the nature of four-sized DNA alphabets and utilize their predefined lexicographical ordering in order to construct suffix arrays for genomic data correctly and efficiently. The suffix array construction algorithm for DNA alphabets is evaluated using three real data sets with different lengths ranging from small E-coli genome to long length Homo sapiens GRCh38.p13 chromosomes. For long length genomes, their corresponding sequence is divided into parts (i.e. reads) with a minimum overlap length, the suffix array is computed for each part separately, and finally all partially computed arrays are merged together into a single one. We studied the effects of varying the reads/overlap lengths on the running time of the proposed suffix array construction algorithm and conclude that the minimum overlap length should be equal to the average length of the longest common prefix between the adjacent parts.

Finding Maximal Exact Matches Using the r-Index.

Efficiently finding maximal exact matches (MEMs) between a sequence read and a database of genomes is a key first step in read alignment. But until recently, it was unknown how to build a data structure in [Formula: see text] space that supports efficient MEM finding, where r is the number of runs in the Burrows-Wheeler Transform. In 2021, Rossi et al. showed how to build a small auxiliary data structure called thresholds in addition to the r-index in [Formula: see text] space. This addition enables efficient MEM finding using the r-index. In this article, we present the tool that implements this solution, which we call MONI. Namely, we give a high-level view of the main components of the data structure and show how the source code can be downloaded, compiled, and used to find MEMs between a set of sequence reads and a set of genomes.

P-smoother: efficient PBWT smoothing of large haplotype panels.

MotivationAs large haplotype panels become increasingly available, efficient string matching algorithms such as positional Burrows-Wheeler transformation (PBWT) are promising for identifying shared haplotypes. However, recent mutations and genotyping errors create occasional mismatches, presenting challenges for exact haplotype matching. Previous solutions are based on probabilistic models or seed-and-extension algorithms that passively tolerate mismatches.ResultsHere, we propose a PBWT-based smoothing algorithm, P-smoother, to actively ‘correct’ these mismatches and thus ‘smooth’ the panel. P-smoother runs a bidirectional PBWT-based panel scanning that flips mismatching alleles based on the overall haplotype matching context, which we call the IBD (identical-by-descent) prior. In a simulated panel with 4000 haplotypes and a 0.2% error rate, we show it can reliably correct 85% of errors. As a result, PBWT algorithms running over the smoothed panel can identify more pairwise IBD segments than that over the unsmoothed panel. Most strikingly, a PBWT-cluster algorithm running over the smoothed panel, which we call PS-cluster, achieves state-of-the-art performance for identifying multiway IBD segments, a challenging problem in the computational community for years. We also showed that PS-cluster is adequately efficient for UK Biobank data. Therefore, P-smoother opens up new possibilities for efficient error-tolerating algorithms for biobank-scale haplotype panels.Availability and implementationSource code is available at github.com/ZhiGroup/P-smoother.

Low Complexity Encoder with Multilabel Classification and Image Captioning燤odel

Due to the advanced development in the multimedia-on-demand traffic in different forms of audio, video, and images, has extremely moved on the vision of the Internet of Things (IoT) from scalar to Internet of Multimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT, which are commonly employed in diverse application areas, especially for capturing remote sensing (RS) images. At the same time, the interpretation of the captured RS image also plays a crucial issue, which can be addressed by the multi-label classification and Computational Linguistics based image captioning techniques. To achieve this, this paper presents an efficient low complexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves the low complexity encoder using the Neighborhood Correlation Sequence (NCS) with a burrows wheeler transform (BWT) technique called LCE-BWT for encoding the RS images captured by the UAV. The application of NCS greatly reduces the computation complexity and requires fewer resources for image transmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presented to determine the multiple class labels of the RS image, shows the novelty of the work. Finally, the Computational Linguistics based Bidirectional Encoder Representations from Transformers (BERT) technique is applied for image captioning, to provide a proficient textual description of the RS image. The performance of the presented technique is tested using the UCM dataset. The simulation outcome implied that the presented model has obtained effective compression performance, reconstructed image quality, classification results, and image captioning outcome.

Heart Disease Diagnosis Using Electrocardiography (ECG) Signals

Electrocardiogram (ECG) monitoring models are commonly employed for diagnosing heart diseases. Since ECG signals are normally acquired for a longer time duration with high resolution, there is a need to compress the ECG signals for transmission and storage. So, a novel compression technique is essential in transmitting the signals to the telemedicine center to monitor and analyse the data. In addition, the protection of ECG signals poses a challenging issue, which encryption techniques can resolve. The existing Encryption-Then-Compression (ETC) models for multimedia data fail to properly maintain the trade-off between compression performance and signal quality. In this view, this study presents a new ETC with a diagnosis model for ECG data, called the ETC-ECG model. The proposed model involves four major processes, namely, pre-processing, encryption, compression, and classification. Once the ECG data of the patient are gathered, Discrete Wavelet Transform (DWT) with a Thresholding mechanism is used for noise removal. In addition, the chaotic map-based encryption technique is applied to encrypt the data. Moreover, the Burrows-Wheeler Transform (BWT) approach is employed for the compression of the encrypted data. Finally, a Deep Neural Network (DNN) is applied to the decrypted data to diagnose heart disease. The detailed experimental analysis takes place to ensure the effective performance of the presented model to assure data security, compression, and classification performance for ECG data.

$\mathit {O(N)}$ Memory-Free Hardware Architecture for Burrows-Wheeler Transform

A novel hardware architecture for Burrows-Wheeler Transform (BWT) scheme is presented. The core idea is to have a memory-free strategy that does not involve any software overhead during BWT operation. This is achieved by introducing a register-file concept and utilizing basic digital logic circuits to perform the entire BWT operation. Additionally, this is a kind of transformation scheme that does not utilize any kind of matrix during transformation, and thereby, it is free from run-time memory consumption. It efficiently handles the string terminating mechanism in the proposed design without involving any extra terminating symbol. This string terminator-free architecture eventually reduces additional operation and storage space to maintain the string, and thereby, the architecture does not necessitate any register read and write operations. This architecture exhibits efficient transformation without involving any indexing method or sorting mechanism during an inverse transformation operation. This architecture achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N)$</tex-math></inline-formula> time complexity compared to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N^{2})$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(N \log N)$</tex-math></inline-formula> as experienced by the existing state-of-the-art approaches.

An effective image compression technique based on burrows wheeler transform with set partitioning in hierarchical trees

AbstractIn this article, the efficient image compression which consists of Burrows–Wheeler transform (BWT) with set partitioning in hierarchical trees (SPIHT). The main phases of the proposed system are: partitioning, compression of non‐ROI areas, Fusion and compression of ROI areas. To enhance the propose of the proposed methodology, the morphological functions are updated by dividing two types of images with the consideration of dilation and erosion control. After that, the convolution and correlation in the deformation provide good accuracy of the segmentation at the fastest speed. In this proposed methodology, SPIHT encryption is a lossy compression technique aimed at understanding the non‐ROI area, while the BWT is a lossless compression technique performed to achieve a summary of the ROI area. Finally, separating these two parts of the image merges the image and reconstructs it to the desired quality. The test sends a variety of images and analyzes the performance of the proposed system using compression ratio and PSNR measurements.

Wheeler languages

The recently introduced class of Wheeler graphs, inspired by the Burrows-Wheeler Transform (BWT) of a given string, admits an efficient index data structure for searching for subpaths with a given path label, and lifts the applicability of the Burrows-Wheeler Transform from a single string to an entire language. In this paper we study the regular languages accepted by automata having a Wheeler graph as transition function and prove results on determinization, Myhill-Nerode characterization, decidability, and closure properties for this class of languages.

Computation of the suffix array, Burrows-Wheeler transform and FM-index in V-order

V-order is a total order on strings that determines an instance of Unique Maximal Factorization Families (UMFFs), a generalization of Lyndon words. The fundamental V-comparison of strings can be done in linear time and constant space. V-order has been proposed as an alternative to lexicographic order (lexorder) in the computation of suffix arrays and in the suffix-sorting induced by the Burrows-Wheeler transform (BWT). In line with the recent interest in the connection between suffix arrays and Lyndon factorization, in this paper we obtain similar results for the V-order factorization. Indeed, we show that the results describing the connection between suffix arrays and Lyndon factorization are matched by analogous V-order processing. We also describe a methodology for efficiently computing the FM-Index in V-order, as well as V-order substring pattern matching using backward search.

Simplitigs as an efficient and scalable representation of de Bruijn graphs

de Bruijn graphs play an essential role in bioinformatics, yet they lack a universal scalable representation. Here, we introduce simplitigs as a compact, efficient, and scalable representation, and ProphAsm, a fast algorithm for their computation. For the example of assemblies of model organisms and two bacterial pan-genomes, we compare simplitigs to unitigs, the best existing representation, and demonstrate that simplitigs provide a substantial improvement in the cumulative sequence length and their number. When combined with the commonly used Burrows-Wheeler Transform index, simplitigs reduce memory, and index loading and query times, as demonstrated with large-scale examples of GenBank bacterial pan-genomes.

Blockchain-assisted secure image transmission and diagnosis model on Internet of Medical Things Environment

In recent days, the Internet of Medical Things (IoMT) is commonly employed in different aspects of healthcare applications. Owing to the increasing necessitates of IoT, a huge amount of sensing data is collected from distinct IoT gadgets. To investigate the generated data, artificial intelligence (AI) models plays an important role to achieve scalability and accurate examination in real-time environment. However, the characteristics of IoMT result in certain design challenges, namely, security and privacy, resource limitation, and inadequate training data. At the same time, blockchain, an upcoming technology, has offered a decentralized architecture, which gives secured data transmission and resources to distinct nodes of the IoT environment and is stimulated for eliminating centralized management and eliminates the challenges involved in it. This paper designs deep learning (DL) with blockchain-assisted secure image transmission and diagnosis model for the IoMT environment. The presented model comprises a few processes namely data collection, secure transaction, hash value encryption, and data classification. Primarily, elliptic curve cryptography (ECC) is applied, and the optimal key generation of ECC takes place using hybridization of grasshopper with fruit fly optimization (GO-FFO) algorithm. Then, the neighborhood indexing sequence (NIS) with burrow wheeler transform (BWT), called NIS-BWT, is employed to encrypt the hash values. At last, a deep belief network (DBN) is utilized for the classification process to diagnose the existence of disease. An extensive experimental validation takes place to determine the analysis of the optimal results of the presented model, and the results are investigated under diverse aspects.

Association Between Collagenous and Lymphocytic Colitis and Risk of Severe Coronavirus Disease 2019

Association Between Collagenous and Lymphocytic Colitis and Risk of Severe Coronavirus Disease 2019

Evaluation of analytical protocols of alignment mapping tools using high throughput next-generation genome sequencing data

Background: Ever since the development of first next-generation genome sequencer (NGS) in 2005, there are rapid developments of high throughput next-generation genome sequencing (HT-NGS) techniques and tools used in genetics and genomics has become much more comfortable and cheaper. The result is the generation of a massive amount of data sets, requiring detailed analysis, which becomes impossible without the use of appropriate bioinformatics tools. One of the crucial steps in the analysis of NGS data is to map readings to a reference sequence. Although the dominance of Illumina synthesis by sequencing (SBS) technology has been noticeable in recent years, the choice of the tools is hampered and the variety of input data and reference genomes. Moreover, the tools used are crucial for result files and further analysis.Methods: The subject of this paper is the three most frequently used alignment mapping programs, which have functions to allow working with many platforms: BWA, Bowtie2 and SMALT. The task of the tested aligners is to match short sequences coming from NGS with reference sequences. The most popular: BWA and Bowtie2 use for this purpose the Burrows-Wheeler transformation and SMALT maps the sequences using hashing and dynamic programming. The presented paper aimed to compare the quality and efficiency of the alignment mapping programs under examination, due to three criteria: i) the quality of the compared sequences of different lengths and from different platforms; ii) coefficient of wrongly compared sequences; iii) the computational resources used.Results: By comparing the results of the mapping analyses for all the programs used, the least popular SMALT is the best. Obtaining the highest percentage of mapped readings for each platform and maintaining the lowest computational memory usage, turns out to be the most optimal choice.Conclusions: The results presented in this paper can be used to verify and rebuild data analysis pipelines from NGS based so far on other tools. We conclude that by using the tools under appropriate conditions, it is possible to improve the quality of the analyses, speed them up and reduce their cost.

Burrows-Wheeler transform acceleration based on CUDA

Burrows-Wheeler transform acceleration based on CUDA

When a dollar makes a BWT

The Burrows-Wheeler-Transform (BWT) is a reversible string transformation which plays a central role in text compression and is fundamental in many modern bioinformatics applications. The BWT is a permutation of the characters, which is in general better compressible and allows to answer several different query types more efficiently than the original string.It is easy to see that not every string is a BWT image, and exact characterizations of BWT images are known. We investigate a related combinatorial question. In many applications, a sentinel character $ is added to mark the end of the string, and thus the BWT of a string ending with $ contains exactly one $-character. Given a string w, we ask in which positions, if any, the $-character can be inserted to turn w into the BWT image of a word ending with $. We show that this depends only on the standard permutation of w and present a O(nlog⁡n)-time algorithm for identifying all such positions, improving on the naive quadratic time algorithm. We also give a combinatorial characterization of such positions and develop bounds on their number and value. This is an extended version of [Giuliani et al. ICTCS 2019].

Computing the original eBWT faster, simpler, and with less memory.

Given an input string, the Burrows-Wheeler Transform (BWT) can be seen as a reversible permutation of it that allows efficient compression and fast substring queries. Due to these properties, it has been widely applied in the analysis of genomic sequence data, enabling important tasks such as read alignment. Mantaci et al. [TCS2007] extended the notion of the BWT to a collection of strings by defining the extended Burrows-Wheeler Transform (eBWT). This definition requires no modification of the input collection, and has the property that the output is independent of the order of the strings in the collection. However, over the years, the term eBWT has been used more generally to describe any BWT of a collection of strings. The fundamental property of the original definition (i.e., the independence from the input order) is frequently disregarded. In this paper, we propose a simple linear-time algorithm for the construction of the original eBWT, which does not require the preprocessing of Bannai et al. [CPM 2021]. As a byproduct, we obtain the first linear-time algorithm for computing the BWT of a single string that uses neither an end-of-string symbol nor Lyndon rotations. We also combine our new eBWT construction with a variation of prefix-free parsing (PFP) [WABI 2019] to allow for construction of the eBWT on large collections of genomic sequences. We implement this combined algorithm (pfpebwt) and evaluate it on a collection of human chromosomes 19 from the 1,000 Genomes Project, on a collection of Salmonella genomes from GenomeTrakr, and on a collection of SARS-CoV2 genomes from EBI's COVID-19 data portal. We demonstrate that pfpebwt is the fastest method for all collections, with a maximum speedup of 7.6x on the second best method. The peak memory is at most 2x larger than the second best method. Comparing with methods that are also, as our algorithm, able to report suffix array samples, we obtain a 57.1x improvement in peak memory. The source code is publicly available at https://github.com/davidecenzato/PFP-eBWT.