Evidence map›Paper›PMID 40563921›Full record

ArticleBiology2025

Analysis of Software Read Cross-Contamination in DNBSEQ Data.

Dmitry N Konanov, Vera Y Tereshchuk, Ignat V Sonets, Elena V Korneenko, Aleksandra V Lukina-Gronskaya, Anna S Speranskaya, Elena N Ilina

Abstract read
In one paragraph

Article in Biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Dmitry N KonanovResearch Institute for System Biology and Medicine, Moscow 117246, Russia.
Vera Y TereshchukPhystech School of Biological and Medical Physics of MIPT, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia.ORCID 0009-0009-3917-4482
Ignat V SonetsResearch Institute for System Biology and Medicine, Moscow 117246, Russia.ORCID 0009-0009-7266-6657
Elena V KorneenkoResearch Institute for System Biology and Medicine, Moscow 117246, Russia.
Aleksandra V Lukina-GronskayaResearch Institute for System Biology and Medicine, Moscow 117246, Russia.
Anna S SperanskayaResearch Institute for System Biology and Medicine, Moscow 117246, Russia.ORCID 0000-0001-6326-1249
Elena N IlinaResearch Institute for System Biology and Medicine, Moscow 117246, Russia.ORCID 0000-0003-0130-5079

Funding

Russian Science Foundation 24-15-00419
6 · The paper itself

Abstract

DNA nanoball sequencing (DNBSEQ) is one of the most rapidly developing sequencing technologies and is widely applied in genomic and transcriptomic investigations. Recently, a new PE300 sequencing option primarily recommended for amplicon analysis was released for DNBSEQ-G99 and G400 devices. Given their unprecedentedly high data yield per flow cell, the new PE300 kits could be a great choice for various sequencing tasks, but we found that combining different types of DNA libraries in a single run could lead to undesired artifacts in the data. In this study, we investigate the occasional read cross-contamination that we first observed in our DNBSEQ PE300 run. The phenomenon, which we refer to as "software contamination", is not actual contamination but primarily manifests as improper forward/reverse read pairing, improper demultiplexing, or as "digital chimeric" reads. Although rare, these artifacts were found in all runs we have analyzed, including several MGI demo datasets (both PE100 and PE150). In this study, we demonstrate that these artifacts arise primarily from the incorrect resolution of sequencing signals produced by neighboring DNA nanoballs, leading to mixing out forward and reverse reads or improper demultiplexing. The artifacts occur most frequently with read pairs where the length of insert sequence is shorter than the read length. Based on a few external NA12878 human exome sequencing data, we conclude that the total improper pairing rate in DNBSEQ data is comparable to Illumina ones. Overall, the problem only affects the analysis results when simultaneously sequenced libraries have markedly different insert size distribution or flow cell loading. Additionally, we demonstrate here that raw DNBSEQ data might contain ~2% optical duplicates, resulting from the same effect of close neighboring of DNB-sites in the flow cell.

Indexed as

data filteringDNBSEQread duplicatessequencing artifacts

Identifiers

PMID40563921
PMCPMC12189395

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.