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HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data
المؤلفون المشاركون
Yao, Haichang
Ji, Yimu
Li, Kui
Liu, Shangdong
He, Jing
Wang, Ru-chuan
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-11-16
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating.
With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges.
FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read.
Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement.
For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc.
Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology.
In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed.
HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences.
It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding.
A large number of experiments fully evaluated the performance of HRCM.
Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression.
Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yao, Haichang& Ji, Yimu& Li, Kui& Liu, Shangdong& He, Jing& Wang, Ru-chuan. 2019. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yao, Haichang…[et al.]. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yao, Haichang& Ji, Yimu& Li, Kui& Liu, Shangdong& He, Jing& Wang, Ru-chuan. HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data. BioMed Research International. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1124366
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1124366
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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