Evidence map›Paper›PMID 35354909›Full record

ArticleScientific reports2022

StackZDPD: a novel encoding scheme for mass spectrometry data optimized for speed and compression ratio.

Jinyin Wang, Miaoshan Lu, Ruimin Wang, Shaowei An, Cong Xie, Changbin Yu

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Jinyin WangZhejiang University, Hangzhou, 310058, China.
Miaoshan LuZhejiang University, Hangzhou, 310058, China.
Ruimin WangSchool of Engineering, Westlake University, Hangzhou, 310023, China.
Shaowei AnSchool of Life Science, Westlake University, Hangzhou, 310023, China.
Cong XieCollege of Artificial Intelligence and Big Data for Medical Science, Shandong First Medical University, Jinan, 250117, China.
Changbin YuCollege of Artificial Intelligence and Big Data for Medical Science, Shandong First Medical University, Jinan, 250117, China. Yucb@sdfmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the pervasive, standardized format for interchange and deposition of raw mass spectrometry (MS) proteomics and metabolomics data, text-based mzML is inefficiently utilized on various analysis platforms due to its sheer volume of samples and limited read/write speed. Most research on compression algorithms rarely provides flexible random file reading scheme. Database-developed solution guarantees the efficiency of random file reading, but nevertheless the efforts in compression and third-party software support are insufficient. Under the premise of ensuring the efficiency of decompression, we propose an encoding scheme "Stack-ZDPD" that is optimized for storage of raw MS data, designed for the format "Aird", a computation-oriented format with fast accessing and decoding time, where the core compression algorithm is "ZDPD". Stack-ZDPD reduces the volume of data stored in mzML format by around 80% or more, depending on the data acquisition pattern, and the compression ratio is approximately 30% compared to ZDPD for data generated using Time of Flight technology. Our approach is available on AirdPro, for file conversion and the Java-API Aird-SDK, for data parsing.

Indexed as

Data CompressionAlgorithmsMass SpectrometryProteomicsSoftware

Identifiers

PMID35354909
PMCPMC8967824

What OpenQuestion holds

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Registered trials

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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.