Evidence map›Paper›PMID 38187611›Full record

ArticlebioRxiv : the preprint server for biology2023

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection.

Ziyuan Wang, Yinshan Fang, Ziyang Liu, Ning Hao, Hao Helen Zhang, Xiaoxiao Sun, Jianwen Que, Hongxu Ding

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Ziyuan WangDepartment of Pharmacy Practice and Science, University of Arizona, Tucson, Arizona, USA.
Yinshan FangColumbia Center for Human Development, Department of Medicine, Columbia University Medical Center, New York, New York, USA.
Ziyang LiuDepartment of Pharmacy Practice and Science, University of Arizona, Tucson, Arizona, USA.
Ning HaoStatistics and Data Science GIDP, University of Arizona, Tucson, Arizona, USA.
Hao Helen ZhangStatistics and Data Science GIDP, University of Arizona, Tucson, Arizona, USA.
Xiaoxiao SunStatistics and Data Science GIDP, University of Arizona, Tucson, Arizona, USA.
Jianwen QueColumbia Center for Human Development, Department of Medicine, Columbia University Medical Center, New York, New York, USA.
Hongxu DingDepartment of Pharmacy Practice and Science, University of Arizona, Tucson, Arizona, USA.
The University of Texas MD Anderson Cancer Center · USUniversity of North Carolina at Chapel Hill · USHospital Del Mar · ES

Funding

Improve Lung Regeneration Through Targeting Tuft Cells Following Viral InfectionR01HL159675 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI QUE, JIANWEN · 2021 to 2024
$2.4M
Cell Fate Regulation in Esophageal Progenitor CellsR01DK132251 · NIDDK · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LU, CHAO, QUE, JIANWEN · 2022 to 2025
$2.3M
VEGF/KDR Signaling in Airway Epithelial Regeneration and DiseaseR01HL152293 · NHLBI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI QUE, JIANWEN · 2021 to 2024
$2.0M
Tuft Cells Modulate Macrophage Response Following Lung Viral InfectionR21AI163753 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI QUE, JIANWEN · 2022 to 2023
$446k
NHLBI NIH HHS R01 HL152293NHLBI NIH HHS R01 HL159675NIAID NIH HHS R21 AI163753NIDDK NIH HHS R01 DK132251
6 · The paper itself

Abstract

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and

Identifiers

PMID38187611
PMCPMC10769248
OpenAlexW4389989968

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.