Evidence map›Paper›PMID 35870880›Full record

ArticleBMC bioinformatics2022

CMIC: an efficient quality score compressor with random access functionality.

Hansen Chen, Jianhua Chen, Zhiwen Lu, Rongshu Wang

Abstract read
In one paragraph

Article in BMC bioinformatics, 2022. 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.

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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

4 authors.

Hansen ChenSchool of Information, Yunnan University, Chenggong Campus, Kunming, Yunnan, China.
Jianhua ChenSchool of Information, Yunnan University, Chenggong Campus, Kunming, Yunnan, China. chenjh@ynu.edu.cn.ORCID http://orcid.org/0000-0002-3637-2565
Zhiwen LuSchool of Information, Yunnan University, Chenggong Campus, Kunming, Yunnan, China.
Rongshu WangSchool of Information, Yunnan University, Chenggong Campus, Kunming, Yunnan, China.

Funding

National Natural Science Foundation of China 61861045
6 · The paper itself

Abstract

backgroundOver the past few decades, the emergence and maturation of new technologies have substantially reduced the cost of genome sequencing. As a result, the amount of genomic data that needs to be stored and transmitted has grown exponentially. For the standard sequencing data format, FASTQ, compression of the quality score is a key and difficult aspect of FASTQ file compression. Throughout the literature, we found that the majority of the current quality score compression methods do not support random access. Based on the above consideration, it is reasonable to investigate a lossless quality score compressor with a high compression rate, a fast compression and decompression speed, and support for random access.

resultsIn this paper, we propose CMIC, an adaptive and random access supported compressor for lossless compression of quality score sequences. CMIC is an acronym of the four steps (classification, mapping, indexing and compression) in the paper. Its framework consists of the following four parts: classification, mapping, indexing, and compression. The experimental results show that our compressor has good performance in terms of compression rates on all the tested datasets. The file sizes are reduced by up to 21.91% when compared with LCQS. In terms of compression speed, CMIC is better than all other compressors on most of the tested cases. In terms of random access speed, the CMIC is faster than the LCQS, which provides a random access function for compressed quality scores.

conclusionsCMIC is a compressor that is especially designed for quality score sequences, which has good performance in terms of compression rate, compression speed, decompression speed, and random access speed. The CMIC can be obtained in the following way: https://github.com/Humonex/Cmic .

Indexed as

Data CompressionHigh-Throughput Nucleotide SequencingAlgorithmsSoftwareFASTQLossless compressorQuality scoreRandom access

Identifiers

PMID35870880
PMCPMC9308261

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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.