BINOS4DNA: Bitmap Indexes and NoSQL for Identifying Species with DNA Signatures through Metagenomics Samples

Karimi, Ramin; Bellatreche, Ladjel; Girard, Patrick; Boukorca, Ahcene; Hajdú, András

doi:10.1007/978-3-319-10265-8_1

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…• Number of processed blocks are odd (1,3,5,7,9,11,13) • Number of processed blocks are even (2,4,6,8,10,12,14) Figure 8: CPU usage of N2…”

Section: Scenario Bmentioning

confidence: 99%

“…Metagenomics, a high-throughput culture-independent technique has provided the ability to investigate the entire community of microorganisms of an environment with analyzing their genetic content obtained directly from their natural residence [9,15].…”

Section: Introductionmentioning

confidence: 99%

“…Reads generated by high-throughput sequencing technology are short in length and large in volume, very noisy and partial, with too many missing parts [9,19]. They contain sequencing errors caused by the sequencer machines.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of hadoop cluster foranalyzing large-scale sequence data inbioinformatics

Tóth¹,

Karimi²

2019

AMI

Self Cite

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Unexpected growth of high-throughput sequencing platforms in recent years impacted virtually all areas of modern biology. However, the ability to produce data continues to outpace the ability to analyze them. Therefore, continuous efforts are also needed to improve bioinformatics applications for a better use of these research opportunities. Due to the complexity and diversity of metagenomics data, it has been a major challenging field of bioinformatics. Sequence-based identification methods such as using DNA signature (unique k-mer) are the most recent popular methods of real-time analysis of raw sequencing data. DNA signature discovery is compute-intensive and time-consuming. Hadoop, the application of parallel and distributed computing is one of the popular applications for the analysis of large scale data in bioinformatics. Optimization of the time-consumption and computational resource usages such as CPU consumption and memory usage are the main goals of this paper, along with the management of the Hadoop cluster nodes.

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“…• Number of processed blocks are odd (1,3,5,7,9,11,13) • Number of processed blocks are even (2,4,6,8,10,12,14) Figure 8: CPU usage of N2…”

Section: Scenario Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimization of hadoop cluster foranalyzing large-scale sequence data inbioinformatics

Tóth¹,

Karimi²

2019

AMI

Self Cite

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“…Re l a t e d w o r k s Various methods have attempted so far for storing huge amounts of data into the NoSQL databases. Karimi et al [11] proposed a scalable technique that can match large amounts of DNA signatures data at a rapid rate using various DBMS like Hbase, Hive, Hadoop, and NoSQL DBMS. They retrieved bacterial genomes from the NCBI dataset.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison for Data Retrieval from NoSQL and SQL Databases: A Case Study for COVID-19 Genome Sequence Dataset

Chakraborty

Paul

Hasan

2021

2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST)

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NoSQL database management system is introduced to tackle different sorts of challenges, including performing op erations on unstructured, semi-structured, and structured data. NoSQL databases gained popularity because of the improved performance than the SQL databases. We aim to investigate the NoSQL system's performance, namely MongoDB and Cassandra and SQL database, namely MySQL for DNA sequences data from the COVID-19 dataset. Studies of the DNA sequences are essential for medical diagnosis and biotechnology. However, it is quite challenging to store these genomics data in a traditional RDMS because of their unstructured nature. NoSQL is an efficient solution for textual characters like genomics data. We used around 3GB of human genome data from the COVID-19 dataset provided by NCBI. The original data was in the FASTA format, and we process these data into JSON format. Also, we have analyzed the different query syntax, data load time, and query performance time for the genomics data.

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“…O MongoDB apresentou melhor desempenho do que o MySQL. O trabalho de [Karimi et al 2014] utilizou índices bitmap para melhorar a velocidade de busca de genomas de bactérias em um ambiente com Hadoop MapReduce, Hive e Hbase.…”

Section: Introductionunclassified

Avaliação de Desempenho para Banco de Dados com Genoma em Nuvem Privada

Dopke¹,

Rockenbach²,

Griebler³

2021

Anais Da XXI Escola Regional De Alto Desempenho Da Região Sul (ERAD RS 2021)

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Os bancos de dados são ferramentas particularmente interessantes para a manipulação de dados gerados através do sequenciamento de DNA. Este artigo tem como objetivo avaliar o desempenho de três bancos de dados com cargas relacionadas ao sequenciamento de DNA: PostgreSQL e MySQL como bancos de dados relacionais e MongoDB como banco de dados NoSQL. Os resultados demonstram que o PostgreSQL se sobressai aos demais.

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BINOS4DNA: Bitmap Indexes and NoSQL for Identifying Species with DNA Signatures through Metagenomics Samples

Cited by 5 publications

References 20 publications

Optimization of hadoop cluster foranalyzing large-scale sequence data inbioinformatics

Optimization of hadoop cluster foranalyzing large-scale sequence data inbioinformatics

Performance Comparison for Data Retrieval from NoSQL and SQL Databases: A Case Study for COVID-19 Genome Sequence Dataset

Avaliação de Desempenho para Banco de Dados com Genoma em Nuvem Privada

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