2,345 research outputs found
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
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
Compression of next-generation sequencing reads aided by highly efficient de novo assembly
We present Quip, a lossless compression algorithm for next-generation
sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing
reference-based compression, we have developed, to our knowledge, the first
assembly-based compressor, using a novel de novo assembly algorithm. A
probabilistic data structure is used to dramatically reduce the memory required
by traditional de Bruijn graph assemblers, allowing millions of reads to be
assembled very efficiently. Read sequences are then stored as positions within
the assembled contigs. This is combined with statistical compression of read
identifiers, quality scores, alignment information, and sequences, effectively
collapsing very large datasets to less than 15% of their original size with no
loss of information.
Availability: Quip is freely available under the BSD license from
http://cs.washington.edu/homes/dcjones/quip
A Reference-Free Algorithm for Computational Normalization of Shotgun Sequencing Data
Deep shotgun sequencing and analysis of genomes, transcriptomes, amplified
single-cell genomes, and metagenomes has enabled investigation of a wide range
of organisms and ecosystems. However, sampling variation in short-read data
sets and high sequencing error rates of modern sequencers present many new
computational challenges in data interpretation. These challenges have led to
the development of new classes of mapping tools and {\em de novo} assemblers.
These algorithms are challenged by the continued improvement in sequencing
throughput. We here describe digital normalization, a single-pass computational
algorithm that systematizes coverage in shotgun sequencing data sets, thereby
decreasing sampling variation, discarding redundant data, and removing the
majority of errors. Digital normalization substantially reduces the size of
shotgun data sets and decreases the memory and time requirements for {\em de
novo} sequence assembly, all without significantly impacting content of the
generated contigs. We apply digital normalization to the assembly of microbial
genomic data, amplified single-cell genomic data, and transcriptomic data. Our
implementation is freely available for use and modification
MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression
Background: Metagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and hence tend to result in large file sizes, typically ranging between 1–10 GB. This leads to challenges in analyzing, transferring and storing metagenomic data. In order to overcome these data processing issues, we introduce MetaCRAM, the first de novo, parallelized software suite specialized for FASTA and FASTQ format metagenomic read processing and lossless compression. Results: MetaCRAM integrates algorithms for taxonomy identification and assembly, and introduces parallel execution methods; furthermore, it enables genome reference selection and CRAM based compression. MetaCRAM also uses novel reference-based compression methods designed through extensive studies of integer compression techniques and through fitting of empirical distributions of metagenomic read-reference positions. MetaCRAM is a lossless method compatible with standard CRAM formats, and it allows for fast selection of relevant files in the compressed domain via maintenance of taxonomy information. The performance of MetaCRAM as a stand-alone compression platform was evaluated on various metagenomic samples from the NCBI Sequence Read Archive, suggesting 2- to 4-fold compression ratio improvements compared to gzip. On average, the compressed file sizes were 2-13 percent of the original raw metagenomic file sizes. Conclusions: We described the first architecture for reference-based, lossless compression of metagenomic data. The compression scheme proposed offers significantly improved compression ratios as compared to off-the-shelf methods such as zip programs. Furthermore, it enables running different components in parallel and it provides the user with taxonomic and assembly information generated during execution of the compression pipeline. Availability: The MetaCRAM software is freely available at http://web.engr.illinois.edu/~mkim158/metacram.html. The website also contains a README file and other relevant instructions for running the code. Note that to run the code one needs a minimum of 16 GB of RAM. In addition, virtual box is set up on a 4GB RAM machine for users to run a simple demonstration
Compressão eficiente de sequências biológicas usando uma rede neuronal
Background: The increasing production of genomic data has led to
an intensified need for models that can cope efficiently with the lossless
compression of biosequences. Important applications include long-term
storage and compression-based data analysis. In the literature, only a
few recent articles propose the use of neural networks for biosequence
compression. However, they fall short when compared with specific
DNA compression tools, such as GeCo2. This limitation is due to the
absence of models specifically designed for DNA sequences. In this
work, we combine the power of neural networks with specific DNA and
amino acids models. For this purpose, we created GeCo3 and AC2, two
new biosequence compressors. Both use a neural network for mixing
the opinions of multiple specific models.
Findings: We benchmark GeCo3 as a reference-free DNA compressor
in five datasets, including a balanced and comprehensive dataset
of DNA sequences, the Y-chromosome and human mitogenome, two
compilations of archaeal and virus genomes, four whole genomes, and
two collections of FASTQ data of a human virome and ancient DNA.
GeCo3 achieves a solid improvement in compression over the previous
version (GeCo2) of 2:4%, 7:1%, 6:1%, 5:8%, and 6:0%, respectively.
As a reference-based DNA compressor, we benchmark GeCo3 in four
datasets constituted by the pairwise compression of the chromosomes
of the genomes of several primates. GeCo3 improves the compression in
12:4%, 11:7%, 10:8% and 10:1% over the state-of-the-art. The cost of
this compression improvement is some additional computational time
(1:7_ to 3:0_ slower than GeCo2). The RAM is constant, and the tool
scales efficiently, independently from the sequence size. Overall, these
values outperform the state-of-the-art. For AC2 the improvements and
costs over AC are similar, which allows the tool to also outperform the
state-of-the-art.
Conclusions: The GeCo3 and AC2 are biosequence compressors with
a neural network mixing approach, that provides additional gains over
top specific biocompressors. The proposed mixing method is portable,
requiring only the probabilities of the models as inputs, providing easy
adaptation to other data compressors or compression-based data analysis
tools. GeCo3 and AC2 are released under GPLv3 and are available
for free download at https://github.com/cobilab/geco3 and
https://github.com/cobilab/ac2.Contexto: O aumento da produção de dados genómicos levou a uma
maior necessidade de modelos que possam lidar de forma eficiente com
a compressão sem perdas de biosequências. Aplicações importantes
incluem armazenamento de longo prazo e análise de dados baseada em
compressão. Na literatura, apenas alguns artigos recentes propõem o
uso de uma rede neuronal para compressão de biosequências. No entanto,
os resultados ficam aquém quando comparados com ferramentas
de compressão de ADN específicas, como o GeCo2. Essa limitação
deve-se à ausência de modelos específicos para sequências de ADN.
Neste trabalho, combinamos o poder de uma rede neuronal com modelos
específicos de ADN e aminoácidos. Para isso, criámos o GeCo3 e
o AC2, dois novos compressores de biosequências. Ambos usam uma
rede neuronal para combinar as opiniões de vários modelos específicos.
Resultados: Comparamos o GeCo3 como um compressor de ADN
sem referência em cinco conjuntos de dados, incluindo um conjunto
de dados balanceado de sequências de ADN, o cromossoma Y e o mitogenoma
humano, duas compilações de genomas de arqueas e vírus,
quatro genomas inteiros e duas coleções de dados FASTQ de um viroma
humano e ADN antigo. O GeCo3 atinge uma melhoria sólida
na compressão em relação à versão anterior (GeCo2) de 2,4%, 7,1%,
6,1%, 5,8% e 6,0%, respectivamente. Como um compressor de ADN
baseado em referência, comparamos o GeCo3 em quatro conjuntos
de dados constituídos pela compressão aos pares dos cromossomas
dos genomas de vários primatas. O GeCo3 melhora a compressão em
12,4%, 11,7%, 10,8% e 10,1% em relação ao estado da arte. O custo
desta melhoria de compressão é algum tempo computacional adicional
(1,7 _ a 3,0 _ mais lento do que GeCo2). A RAM é constante e a
ferramenta escala de forma eficiente, independentemente do tamanho
da sequência. De forma geral, os rácios de compressão superam o estado
da arte. Para o AC2, as melhorias e custos em relação ao AC são
semelhantes, o que permite que a ferramenta também supere o estado
da arte.
Conclusões: O GeCo3 e o AC2 são compressores de sequências biológicas
com uma abordagem de mistura baseada numa rede neuronal,
que fornece ganhos adicionais em relação aos biocompressores específicos
de topo. O método de mistura proposto é portátil, exigindo apenas
as probabilidades dos modelos como entradas, proporcionando uma fácil
adaptação a outros compressores de dados ou ferramentas de análise
baseadas em compressão. O GeCo3 e o AC2 são distribuídos sob GPLv3
e estão disponíveis para download gratuito em https://github.com/
cobilab/geco3 e https://github.com/cobilab/ac2.Mestrado em Engenharia de Computadores e Telemátic
Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment
Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.Peer reviewe
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