Evidence map›Paper›PMID 41273075›Full record

ArticleNucleic acids research2026

SeekPCMdb: knowledge on disease-associated protein-coding mutations.

Peng Yang, Ziyang Wang, Tuo Xiong, Zhanjie Fang, Xiuli Zhu, Lilin Yao, Ying Cui, Rongxing Wei, Zhiming Qian, Jian Wang and 2 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2026. 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

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

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

12 authors.

Peng YangChina National Center for Bioinformation, Beijing100101, China.ORCID 0009-0001-8724-1506
Ziyang WangChina National Center for Bioinformation, Beijing100101, China.ORCID 0009-0003-6161-0224
Tuo XiongChina National Center for Bioinformation, Beijing100101, China.
Zhanjie FangChina National Center for Bioinformation, Beijing100101, China.
Xiuli ZhuChina National Center for Bioinformation, Beijing100101, China.
Lilin YaoChina National Center for Bioinformation, Beijing100101, China.
Ying CuiChina National Center for Bioinformation, Beijing100101, China.
Rongxing WeiChina National Center for Bioinformation, Beijing100101, China.
Zhiming QianChina National Center for Bioinformation, Beijing100101, China.
Jian WangChina National Center for Bioinformation, Beijing100101, China.
Yin-Ying WangChina National Center for Bioinformation, Beijing100101, China.
Peilin JiaChina National Center for Bioinformation, Beijing100101, China.ORCID 0000-0003-4523-4153

Funding

Chinese Academy of Sciences XDA0460202Chinese Academy of Sciences XDA0460204National Natural Science Foundation of China 32270706National Natural Science Foundation of China 62473355National Natural Science Foundation of China 92374103National Science and Technology 2024ZD0533502
6 · The paper itself

Abstract

Protein-coding mutations (PCMs) play crucial roles in the onset and progression of human diseases. With the widespread application of next-generation sequencing, an increasing number of disease-associated PCMs have been reported. However, these mutations are scattered across a vast body of literature, limiting their accessibility for systematic investigation. To address this challenge, we present SeekPCMdb (https://ngdc.cncb.ac.cn/seekpcmdb/), a literature-derived knowledge base that systematically catalogs human disease-related PCMs. SeekPCMdb employs a semi-automated workflow that integrates manual curation of high-quality publications with large language model (LLM)-assisted information extraction, capturing gene-mutation-disease relationships from >12 000 PubMed abstracts. The current release contains 11 395 curated entries, covering 3516 genes, 4505 diseases, and 23 mutation types. Each entry is richly annotated at multiple levels, including gene function, mutation consequence, and disease classification, enabling thorough biological interpretation. Comparative analysis shows that 63.7% of PCMs in SeekPCMdb are not cataloged in ClinVar, underscoring its unique contribution. SeekPCMdb features a user-friendly web interface that supports interactive searching, visualization, and download, along with multi-dimensional filtering based on gene, mutation type, or disease. By bridging unstructured literature and structured mutation knowledge, SeekPCMdb provides an invaluable resource for investigating genotype-phenotype correlations, assessing mutation pathogenicity, and identifying cross-disease mutational patterns.

Indexed as

Databases, GeneticDiseaseGenetic Predisposition to DiseaseKnowledge BasesMutationOpen Reading FramesProteinsSoftwareHumansInternetUser-Computer InterfaceProteins

Identifiers

PMID41273075
PMCPMC12807789

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.