Evidence map›Paper›PMID 39028587›Full record

ArticleGigaScience2024

GSC: efficient lossless compression of VCF files with fast query.

Xiaolong Luo, Yuxin Chen, Ling Liu, Lulu Ding, Yuxiang Li, Shengkang Li, Yong Zhang, Zexuan Zhu

Abstract read
In one paragraph

Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Analysis-ready VCF at Biobank scale using Zarr.bioRxiv : the preprint server for biology · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xiaolong LuoCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.ORCID 0009-0007-9672-6728
Yuxin ChenBGI Research, Wuhan 430074, China.ORCID 0000-0002-9246-1829
Ling LiuGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, China.ORCID 0009-0009-0954-6986
Lulu DingNational Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0002-1165-1226
Yuxiang LiBGI Research, Wuhan 430074, China.ORCID 0000-0002-1575-3692
Shengkang LiBGI Research, Wuhan 430074, China.ORCID 0000-0002-6864-5644
Yong ZhangBGI Research, Wuhan 430074, China.ORCID 0000-0001-9950-1793
Zexuan ZhuNational Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060, China.ORCID 0000-0001-8479-6904

Funding

National Key Research and Development Program of China 2022YFF1202104National Natural Science Foundation of China 61871272
6 · The paper itself

Abstract

backgroundWith the rise of large-scale genome sequencing projects, genotyping of thousands of samples has produced immense variant call format (VCF) files. It is becoming increasingly challenging to store, transfer, and analyze these voluminous files. Compression methods have been used to tackle these issues, aiming for both high compression ratio and fast random access. However, existing methods have not yet achieved a satisfactory compromise between these 2 objectives.

findingsTo address the aforementioned issue, we introduce GSC (Genotype Sparse Compression), a specialized and refined lossless compression tool for VCF files. In benchmark tests conducted across various open-source datasets, GSC showcased exceptional performance in genotype data compression. Compared with the industry's most advanced tools (namely, GBC and GTC), GSC achieved compression ratios that were higher by 26.9% to 82.4% over GBC and GTC on the datasets, respectively. In lossless compression scenarios, GSC also demonstrated robust performance, with compression ratios 1.5× to 6.5× greater than general-purpose tools like gzip, zstd, and BCFtools-a mode not supported by either GBC or GTC. Achieving such high compression ratios did require some reasonable trade-offs, including longer decompression times, with GSC being 1.2× to 2× slower than GBC, yet 1.1× to 1.4× faster than GTC. Moreover, GSC maintained decompression query speeds that were equivalent to its competitors. In terms of RAM usage, GSC outperformed both counterparts. Overall, GSC's comprehensive performance surpasses that of the most advanced technologies.

conclusionGSC balances high compression ratios with rapid data access, enhancing genomic data management. It supports seamless PLINK binary format conversion, simplifying downstream analysis.

Indexed as

Data CompressionSoftwareAlgorithmsComputational BiologyGenotypeHigh-Throughput Nucleotide SequencingHumanslossless compressionrapid random accessVCF/BCF files

Identifiers

PMID39028587
PMCPMC11258903

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.