Details
| Original language | English |
|---|---|
| Article number | 121 |
| Journal | BMC BIOINFORMATICS |
| Volume | 24 |
| Publication status | Published - 28 Mar 2023 |
Abstract
Background: In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.
Keywords
- Compression, Random access, Variants, VCF
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Structural Biology
- Biochemistry, Genetics and Molecular Biology(all)
- Biochemistry
- Biochemistry, Genetics and Molecular Biology(all)
- Molecular Biology
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Applied Mathematics
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In: BMC BIOINFORMATICS, Vol. 24, 121, 28.03.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - GVC
T2 - efficient random access compression for gene sequence variations
AU - Adhisantoso, Yeremia Gunawan
AU - Voges, Jan
AU - Rohlfing, Christian
AU - Tunev, Viktor
AU - Ohm, Jens Rainer
AU - Ostermann, Jörn
N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. This work is supported by Leibniz University Hannover, L3S Research Center, and RWTH Aachen University.
PY - 2023/3/28
Y1 - 2023/3/28
N2 - Background: In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.
AB - Background: In recent years, advances in high-throughput sequencing technologies have enabled the use of genomic information in many fields, such as precision medicine, oncology, and food quality control. The amount of genomic data being generated is growing rapidly and is expected to soon surpass the amount of video data. The majority of sequencing experiments, such as genome-wide association studies, have the goal of identifying variations in the gene sequence to better understand phenotypic variations. We present a novel approach for compressing gene sequence variations with random access capability: the Genomic Variant Codec (GVC). We use techniques such as binarization, joint row- and column-wise sorting of blocks of variations, as well as the image compression standard JBIG for efficient entropy coding. Results: Our results show that GVC provides the best trade-off between compression and random access compared to the state of the art: it reduces the genotype information size from 758 GiB down to 890 MiB on the publicly available 1000 Genomes Project (phase 3) data, which is 21% less than the state of the art in random-access capable methods. Conclusions: By providing the best results in terms of combined random access and compression, GVC facilitates the efficient storage of large collections of gene sequence variations. In particular, the random access capability of GVC enables seamless remote data access and application integration. The software is open source and available at https://github.com/sXperfect/gvc/.
KW - Compression
KW - Random access
KW - Variants
KW - VCF
UR - http://www.scopus.com/inward/record.url?scp=85151108637&partnerID=8YFLogxK
U2 - 10.1186/s12859-023-05240-0
DO - 10.1186/s12859-023-05240-0
M3 - Article
C2 - 36978010
AN - SCOPUS:85151108637
VL - 24
JO - BMC BIOINFORMATICS
JF - BMC BIOINFORMATICS
SN - 1471-2105
M1 - 121
ER -