Benchmark Dataset for Whole Genome Sequence Compression

Biji, C. L.; Nair, Achuthsankar S.

doi:10.1109/tcbb.2016.2568186

Cited by 11 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The life sciences are becoming "big data companies," and that is setting the standard for addressing that storage problem in the scientific community [43]. Scientists have over the past decade needed the storage space provided by genomic data, yet in the future brain data that is equivalent to world digital information would be difficult to manage [44], [45]. Therefore, there is a need for a modern, efficient approach that will resolve all the challenges of genomic data such as storage space, fast processing, and system throughput [15], [45].…”

Section: Fig 2 Compression Of Input Data Using Cartesian Producmentioning

confidence: 99%

“…Scientists have over the past decade needed the storage space provided by genomic data, yet in the future brain data that is equivalent to world digital information would be difficult to manage [44], [45]. Therefore, there is a need for a modern, efficient approach that will resolve all the challenges of genomic data such as storage space, fast processing, and system throughput [15], [45]. Figure 4 depicts the decryption of the encrypted data which is reverse process of encryption.…”

Section: Fig 2 Compression Of Input Data Using Cartesian Producmentioning

confidence: 99%

See 1 more Smart Citation

Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

Gudodagi

Reddy

2021

International Journal of Biology and Biomedical Engineering

View full text Add to dashboard Cite

Compression of genomic data has gained enormous momentum in recent years because of advances in technology, exponentially growing health concerns, and government funding for research. Such advances have driven us to personalize public health and medical care. These pose a considerable challenge for ubiquitous computing in data storage. One of the main issues faced by genomic laboratories is the 'cost of storage' due to the large data file of the human genome (ranging from 30 GB to 200 GB). Data preservation is a set of actions meant to protect data from unauthorized access or changes. There are several methods used to protect data, and encryption is one of them. Protecting genomic data is a critical concern in genomics as it includes personal data. We suggest a secure encryption and decryption technique for diverse genomic data (FASTA / FASTQ format) in this article. Since we know the sequenced data is massive in bulk, the raw sequenced file is broken into sections and compressed. The Advanced Encryption Standard (AES) algorithm is used for encryption, and the Galois / Counter Mode (GCM) algorithm, is used to decode the encrypted data. This approach reduces the amount of storage space used for the data disc while preserving the data. This condition necessitates the use of a modern data compression strategy. That not only reduces storage but also improves process efficiency by using a k-th order Markov chain. In this regard, no efforts have been made to address this problem separately, from both the hardware and software realms. In this analysis, we support the need for a tailor-made hardware and software ecosystem that will take full advantage of the current stand-alone solutions. The paper discusses sequenced DNA, which may take the form of raw data obtained from sequencing. Inappropriate use of genomic data presents unique risks because it can be used to classify any individual; thus, the study focuses on the security provisioning and compression of diverse genomic data using the Advanced Encryption Standard (AES) Algorithm.

show abstract

Section: Fig 2 Compression Of Input Data Using Cartesian Producmentioning

confidence: 99%

Section: Fig 2 Compression Of Input Data Using Cartesian Producmentioning

confidence: 99%

Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

Gudodagi

Reddy

2021

International Journal of Biology and Biomedical Engineering

View full text Add to dashboard Cite

show abstract

“…Dataset: Standardizing the dataset to be used in the compression tests is one of the main points to ensure that the results achieved by compression tools are comparable. We found in the literature two authors who published datasets for genome compression benchmarking [110,111].…”

Section: Criteria Suggestion For Evaluating Genome Compression Toolsmentioning

confidence: 99%

“…The dataset proposed by [111] consists of sequences from several different organisms, with 1105 prokaryotes, 200 plasmids, 164 viruses, and 65 eukaryotes. Furthermore, the author of this work found a scientific way to select samples for compiling the dataset for the benchmark, using multi-stage sampling strategies.…”

Section: Criteria Suggestion For Evaluating Genome Compression Toolsmentioning

confidence: 99%

Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review

et al. 2020

View full text Add to dashboard Cite

The recent decrease in cost and time to sequence and assemble of complete genomes created an increased demand for data storage. As a consequence, several strategies for assembled biological data compression were created. Vertical compression tools implement strategies that take advantage of the high level of similarity between multiple assembled genomic sequences for better compression results. However, current reviews on vertical compression do not compare the execution flow of each tool, which is constituted by phases of preprocessing, transformation, and data encoding. We performed a systematic literature review to identify and compare existing tools for vertical compression of assembled genomic sequences. The review was centered on PubMed and Scopus, in which 45726 distinct papers were considered. Next, 32 papers were selected according to the following criteria: to present a lossless vertical compression tool; to use the information contained in other sequences for the compression; to be able to manipulate genomic sequences in FASTA format; and no need prior knowledge. Although we extracted performance compression results, they were not compared as the tools did not use a standardized evaluation protocol. Thus, we conclude that there's a lack of definition of an evaluation protocol that must be applied by each tool.

show abstract

“…And will the resulting gains be even worth the trouble of switching? Previous attempts at answering these questions (Zhu et al, 2013;Hosseini et al, 2016;Sardaraz and Tahir, 2016;Biji and Achuthsankar, 2017) are limited by testing too few compressors and by using restricted test data. Therefore we set out to benchmark a broader selection of available compressors on a variety of relevant test data.…”

mentioning

confidence: 99%

Sequence Compression Benchmark (SCB) database — a comprehensive evaluation of reference-free compressors for FASTA-formatted sequences

Kryukov

Ueda

Nakagawa

et al. 2019

Preprint

View full text Add to dashboard Cite

Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for more efficient compression tool. We systematically benchmarked the available compressors on representative DNA, RNA and Protein datasets. We tested specialized sequence compressors 2bit, BLAST, DNA-COMPACT, DELIMINATE, Leon, MFCompress, NAF, UHT and XM, and general-purpose compressors brotli, bzip2, gzip, lz4, lzop, lzturbo, pbzip2, pigz, snzip, xz, zpaq and zstd. Overall, NAF and zstd performed well in terms of transfer/decompression speed. However, checking benchmark results is necessary when choosing compressor for specific data type and application. Benchmark results database is available at: http://kirr.dyndns.org/sequence-compression-benchmark/.

show abstract

Benchmark Dataset for Whole Genome Sequence Compression

Abstract: The sample dataset and the respective links are available @ https://sourceforge.net/projects/benchmarkdnacompressiondataset/.

Cited by 11 publications

References 25 publications

Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

Security Provisioning and Compression of Diverse Genomic Data based on Advanced Encryption Standard (AES) Algorithm

Vertical lossless genomic data compression tools for assembled genomes: A systematic literature review

Sequence Compression Benchmark (SCB) database — a comprehensive evaluation of reference-free compressors for FASTA-formatted sequences

Contact Info

Product

Resources

About