Data compression with Python: application of different algorithms with the use of threads in genome files
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data. In this article we analyze the potential value and benefits of these solutions as well as their risks
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cost, time, accuracy and clarity versus other traditional approaches or format. On the other hand,
there are still important limitations that restrict the broad applicability of these solutions, most
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development is sufficient for the application of these solution across many domains and use cases and
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The Value and Benefits of Data-to-Text Technologies
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Data compression with Python: application of different algorithms with the use of threads in genome files
- 1. Data compression with Python: application of differentData compression with Python: application of different algorithms with the use of threads in genome filesalgorithms with the use of threads in genome files Vinicius Seus¹ ([email protected]) Alex Camargo¹ ([email protected]) Diego Mengarda² ([email protected]) ¹FURG ²UNIPAMPA Brazil MOL2NET International Conference Series on Multidisciplinary Sciences http://sciforum.net/conference/mol2net-03
- 2. 2 Introduction This work proposes and evaluates an implementation of different algorithms of data compression using the Python programming language allied to the use of threads. As a case study it was used genomic data available from the NCBI (National Center for Biotechnology Information) public database. MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition
- 3. 3 Introduction MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition Figure 1. Graphical Abstract
- 4. 4 Materials and Methods For the experimental environment it was used a simplified structure composed of a computer with: 3.07GHz processor (12 cores), 12 GB RAM and 500 GB hard disk. Table 1 shows the results of the experiments for the file "ref_ASM45574v1_gnomon_scaffolds.txt" with a total size of 122 MB, referring to an excerpt from the genome identified by "Aligator sinensis", belonging to the family Alligatoridae. MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition
- 5. 5 Conclusions The main contribution of this work was to present an algorithm option for data compression based on the Python programming language. By default, the algorithms were not designed to work in parallel, however, with the use of the Python Threading library this was achieved. With the experimental environment implemented, it was possible to analyze the performance of both the compression rate and the compression time for each algorithm. As future works, we intend to extend the range of algorithms to be studied as well as the application and analysis of decompression metrics with emphasis on public genomic data. MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition
- 6. 6 References BURROWS, Michael; WHEELER, David J. A block-sorting lossless data compression algorithm. 1994. VERLI, Hugo et al. Bioinformática da Biologia à flexibilidade molecular. Porto Alegre, Brasil, v. 1, 2014. WELCH, Terry A. A technique for high-performance data compression. Computer, v. 6, n. 17, p. 8-19, 1984. MOL2NET 2017, International Conference on Multidisciplinary Sciences, 3rd edition