FPGA acceleration of reference-based compression for genomic data

One of the key challenges facing genomics today is efficiently storing the massive amounts of data generated by next-generation sequencing platforms. Reference-based compression is a popular strategy for reducing the size of genomic data, whereby sequence information is encoded as a mapping to a known reference sequence. Determining the mapping is a computationally intensive problem, and is the bottleneck of most reference-based compression tools currently available. This paper presents the first FPGA acceleration of reference-based compression for genomic data. We develop a new mapping algorithm based on the FM-index search operation which includes optimisations targeting the compression ratio and speed. Our hardware design is implemented on a Maxeler MPC-X2000 node comprising 8 Altera Stratix V FPGAs. When evaluated against compression tools currently available, our tool achieves a superior compression ratio, compression time, and energy consumption for both FASTA and FASTQ formats. For example, our tool achieves a 30% higher compression ratio and is 71.9 times faster than the fastqz tool

A Reference-Free Lossless Compression Algorithm for DNA Sequences Using a Competitive Prediction of Two Classes of Weighted Models

The development of efficient data compressors for DNA sequences is crucial not only for reducing the storage and the bandwidth for transmission, but also for analysis purposes. In particular, the development of improved compression models directly influences the outcome of anthropological and biomedical compression-based methods. In this paper, we describe a new lossless compressor with improved compression capabilities for DNA sequences representing different domains and kingdoms. The reference-free method uses a competitive prediction model to estimate, for each symbol, the best class of models to be used before applying arithmetic encoding. There are two classes of models: weighted context models (including substitutional tolerant context models) and weighted stochastic repeat models. Both classes of models use specific sub-programs to handle inverted repeats efficiently. The results show that the proposed method attains a higher compression ratio than state-of-the-art approaches, on a balanced and diverse benchmark, using a competitive level of computational resources. An efficient implementation of the method is publicly available, under the GPLv3 license.Peer reviewe

Lempel-Ziv-like Parsing in Small Space

Lempel-Ziv (LZ77 or, briefly, LZ) is one of the most effective and widely-used compressors for repetitive texts. However, the existing efficient methods computing the exact LZ parsing have to use linear or close to linear space to index the input text during the construction of the parsing, which is prohibitive for long inputs. An alternative is Relative Lempel-Ziv (RLZ), which indexes only a fixed reference sequence, whose size can be controlled. Deriving the reference sequence by sampling the text yields reasonable compression ratios for RLZ, but performance is not always competitive with that of LZ and depends heavily on the similarity of the reference to the text. In this paper we introduce ReLZ, a technique that uses RLZ as a preprocessor to approximate the LZ parsing using little memory. RLZ is first used to produce a sequence of phrases, and these are regarded as metasymbols that are input to LZ for a second-level parsing on a (most often) drastically shorter sequence. This parsing is finally translated into one on the original sequence. We analyze the new scheme and prove that, like LZ, it achieves the $k$th order empirical entropy compression $n H_k + o(n\log\sigma)$ with $k = o(\log_\sigma n)$, where $n$ is the input length and $\sigma$ is the alphabet size. In fact, we prove this entropy bound not only for ReLZ but for a wide class of LZ-like encodings. Then, we establish a lower bound on ReLZ approximation ratio showing that the number of phrases in it can be $\Omega(\log n)$ times larger than the number of phrases in LZ. Our experiments show that ReLZ is faster than existing alternatives to compute the (exact or approximate) LZ parsing, at the reasonable price of an approximation factor below $2.0$ in all tested scenarios, and sometimes below $1.05$, to the size of LZ.Comment: 21 pages, 6 figures, 2 table

FPGA Acceleration of Reference-Based Compression for Genomic Data

Abstract-One of the key challenges facing genomics today is efficiently storing the massive amounts of data generated by nextgeneration sequencing platforms. Reference-based compression is a popular strategy for reducing the size of genomic data, whereby sequence information is encoded as a mapping to a known reference sequence. Determining the mapping is a computationally intensive problem, and is the bottleneck of most referencebased compression tools currently available. This paper presents the first FPGA acceleration of reference-based compression for genomic data. We develop a new mapping algorithm based on the FM-index search operation which includes optimisations targeting the compression ratio and speed. Our hardware design is implemented on a Maxeler MPC-X2000 node comprising 8 Altera Stratix V FPGAs. When evaluated against compression tools currently available, our tool achieves a superior compression ratio, compression time, and energy consumption for both FASTA and FASTQ formats. For example, our tool achieves a 30% higher compression ratio and is 71.9 times faster than the fastqz tool

Compressing Genome Resequencing Data

Recent improvements in high-throughput next generation sequencing (NGS) technologies have led to an exponential increase in the number, size and diversity of available complete genome sequences. This poses major problems in storage, transmission and analysis of such genomic sequence data. Thus, a substantial effort has been made to develop effective data compression techniques to reduce the storage requirements, improve the transmission speed, and analyze the compressed sequences for possible information about genomic structure or determine relationships between genomes from multiple organisms.;In this thesis, we study the problem of lossless compression of genome resequencing data using a reference-based approach. The thesis is divided in two major parts. In the first part, we perform a detailed empirical analysis of a recently proposed compression scheme called MLCX (Maximal Longest Common Substring/Subsequence). This led to a novel decomposition technique that resulted in an enhanced compression using MLCX. In the second part, we propose SMLCX, a new reference-based lossless compression scheme that builds on the MLCX. This scheme performs compression by encoding common substrings based on a sorted order, which significantly improved compression performance over the original MLCX method. Using SMLCX, we compressed the Homo sapiens genome with original size of 3,080,436,051 bytes to 6,332,488 bytes, for an overall compression ratio of 486. This can be compared to the performance of current state-of-the-art compression methods, with compression ratios of 157 (Wang et.al, Nucleic Acid Research, 2011), 171 (Pinho et.al, Nucleic Acid Research, 2011) and 360 (Beal et.al, BMC Genomics, 2016)

Indexing Highly Repetitive String Collections

Two decades ago, a breakthrough in indexing string collections made it possible to represent them within their compressed space while at the same time offering indexed search functionalities. As this new technology permeated through applications like bioinformatics, the string collections experienced a growth that outperforms Moore's Law and challenges our ability of handling them even in compressed form. It turns out, fortunately, that many of these rapidly growing string collections are highly repetitive, so that their information content is orders of magnitude lower than their plain size. The statistical compression methods used for classical collections, however, are blind to this repetitiveness, and therefore a new set of techniques has been developed in order to properly exploit it. The resulting indexes form a new generation of data structures able to handle the huge repetitive string collections that we are facing. In this survey we cover the algorithmic developments that have led to these data structures. We describe the distinct compression paradigms that have been used to exploit repetitiveness, the fundamental algorithmic ideas that form the base of all the existing indexes, and the various structures that have been proposed, comparing them both in theoretical and practical aspects. We conclude with the current challenges in this fascinating field

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

Modelos de compressão e ferramentas para dados ómicos

The ever-increasing growth of the development of high-throughput sequencing technologies and as a consequence, generation of a huge volume of data, has revolutionized biological research and discovery. Motivated by that, we investigate in this thesis the methods which are capable of providing an efficient representation of omics data in compressed or encrypted manner, and then, we employ them to analyze omics data. First and foremost, we describe a number of measures for the purpose of quantifying information in and between omics sequences. Then, we present finite-context models (FCMs), substitution-tolerant Markov models (STMMs) and a combination of the two, which are specialized in modeling biological data, in order for data compression and analysis. To ease the storage of the aforementioned data deluge, we design two lossless data compressors for genomic and one for proteomic data. The methods work on the basis of (a) a combination of FCMs and STMMs or (b) the mentioned combination along with repeat models and a competitive prediction model. Tested on various synthetic and real data showed their outperformance over the previously proposed methods in terms of compression ratio. Privacy of genomic data is a topic that has been recently focused by developments in the field of personalized medicine. We propose a tool that is able to represent genomic data in a securely encrypted fashion, and at the same time, is able to compact FASTA and FASTQ sequences by a factor of three. It employs AES encryption accompanied by a shuffling mechanism for improving the data security. The results show it is faster than general-purpose and special-purpose algorithms. Compression techniques can be employed for analysis of omics data. Having this in mind, we investigate the identification of unique regions in a species with respect to close species, that can give us an insight into evolutionary traits. For this purpose, we design two alignment-free tools that can accurately find and visualize distinct regions among two collections of DNA or protein sequences. Tested on modern humans with respect to Neanderthals, we found a number of absent regions in Neanderthals that may express new functionalities associated with evolution of modern humans. Finally, we investigate the identification of genomic rearrangements, that have important roles in genetic disorders and cancer, by employing a compression technique. For this purpose, we design a tool that is able to accurately localize and visualize small- and large-scale rearrangements between two genomic sequences. The results of applying the proposed tool on several synthetic and real data conformed to the results partially reported by wet laboratory approaches, e.g., FISH analysis.O crescente crescimento do desenvolvimento de tecnologias de sequenciamento de alto rendimento e, como consequência, a geração de um enorme volume de dados, revolucionou a pesquisa e descoberta biológica. Motivados por isso, nesta tese investigamos os métodos que fornecem uma representação eficiente de dados ómicros de maneira compactada ou criptografada e, posteriormente, os usamos para análise. Em primeiro lugar, descrevemos uma série de medidas com o objetivo de quantificar informação em e entre sequencias ómicas. Em seguida, apresentamos modelos de contexto finito (FCMs), modelos de Markov tolerantes a substituição (STMMs) e uma combinação dos dois, especializados na modelagem de dados biológicos, para compactação e análise de dados. Para facilitar o armazenamento do dilúvio de dados acima mencionado, desenvolvemos dois compressores de dados sem perda para dados genómicos e um para dados proteómicos. Os métodos funcionam com base em (a) uma combinação de FCMs e STMMs ou (b) na combinação mencionada, juntamente com modelos de repetição e um modelo de previsão competitiva. Testados em vários dados sintéticos e reais mostraram a sua eficiência sobre os métodos do estado-de-arte em termos de taxa de compressão. A privacidade dos dados genómicos é um tópico recentemente focado nos desenvolvimentos do campo da medicina personalizada. Propomos uma ferramenta capaz de representar dados genómicos de maneira criptografada com segurança e, ao mesmo tempo, compactando as sequencias FASTA e FASTQ para um fator de três. Emprega criptografia AES acompanhada de um mecanismo de embaralhamento para melhorar a segurança dos dados. Os resultados mostram que ´e mais rápido que os algoritmos de uso geral e específico. As técnicas de compressão podem ser exploradas para análise de dados ómicos. Tendo isso em mente, investigamos a identificação de regiões únicas em uma espécie em relação a espécies próximas, que nos podem dar uma visão das características evolutivas. Para esse fim, desenvolvemos duas ferramentas livres de alinhamento que podem encontrar e visualizar com precisão regiões distintas entre duas coleções de sequências de DNA ou proteínas. Testados em humanos modernos em relação a neandertais, encontrámos várias regiões ausentes nos neandertais que podem expressar novas funcionalidades associadas à evolução dos humanos modernos. Por último, investigamos a identificação de rearranjos genómicos, que têm papéis importantes em desordens genéticas e cancro, empregando uma técnica de compressão. Para esse fim, desenvolvemos uma ferramenta capaz de localizar e visualizar com precisão os rearranjos em pequena e grande escala entre duas sequências genómicas. Os resultados da aplicação da ferramenta proposta, em vários dados sintéticos e reais, estão em conformidade com os resultados parcialmente relatados por abordagens laboratoriais, por exemplo, análise FISH.Programa Doutoral em Engenharia Informátic