Evidence map›Paper›PMID 41290940›Full record

ArticleScientific reports2025

Fastq-dupaway: a fast and memory-efficient tool for deduplication of single- and paired-end NGS data.

A I Sigorskikh, M A Kompaniets, I S Ilnitskiy, G K Ryabykh, A A Mironov

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

A I Sigorskikh *Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia.
M A Kompaniets *Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia. mariakoma12@gmail.com.
I S IlnitskiyFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia.
G K RyabykhFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia.
A A MironovFaculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119234, Russia.

Funding

Russian Science Foundation 23-14-00136VIGG RAS FFRW-2024-0004
6 · The paper itself

Abstract

The rapid emergence of large-scale next-generation sequencing (NGS) data has created a growing demand for efficient preprocessing tools. Removal of polymerase chain reaction (PCR) duplicates is a critical step in many NGS data processing pipelines to reduce amplification bias. Currently, numerous de novo-based PCR duplicate removal tools are available, which cluster identical or highly similar reads without reference genome alignment. Practical application of such programs to large-scale NGS data (100 GB and more) is hampered by significant computational requirements, particularly high computer short-term memory usage comparable to the original file sizes. Processing large datasets generated by modern high-throughput techniques such as Hi-C or RNA-chromatin interaction sequencing can require hundreds of gigabytes of RAM, posing an exceptionally high computational demand. Here, we present Fastq-dupaway as a new tool for efficient PCR duplicate removal from both single-end and paired-end sequencing data ( https://github.com/AndrewSigorskih/fastq-dupaway ). Its key innovation lies in its primary operational modes, which are designed to use a small, parameterizable amount of RAM (2-10 GB) independent of input data size, at the cost of requiring approximately 2× the input file size in disk space. This enables the processing of very large datasets even on standard personal computers. Fastq-dupaway matches or exceeds (by up to threefold) the processing speed of major de novo deduplication tools, while maintaining the same level of duplicate removal.

Indexed as

Computational BiologyHigh-Throughput Nucleotide SequencingSoftwareHumansPolymerase Chain ReactionSequence Analysis, DNABioinformatics’ toolComputational efficiencyNGS data processingPCR duplicates removal

Identifiers

PMID41290940
PMCPMC12749013

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