Evidence map›Paper›PMID 41376169›Full record

ArticlePlant communications2026

panHiTE: A comprehensive and accurate pipeline for TE detection in large-scale population genomes.

Kang Hu, Minghua Xu, Liqing Ding, You Zou, Xin Gao, Jianxin Wang

Abstract read
In one paragraph

Article in Plant communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. 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

6 authors.

Kang HuSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Minghua XuSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Liqing DingSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
You ZouSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China; High Performance Computing Center, Central South University, Changsha 410083, China.
Xin GaoComputer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; Center of Excellence on Generative AI, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Jianxin WangSchool of Computer Science and Engineering, Central South University, Changsha 410083, China; Xiangjiang Laboratory, Changsha 410205, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China. Electronic address: jxwang@mail.csu.edu.cn.

Funding

Non-US Government Research Support type
6 · The paper itself

Abstract

Transposable elements (TEs) are key drivers of genomic variation and species evolution. Although advances in high-throughput sequencing have enabled population-scale identification of TE insertions, accurate detection across large and complex genomes remains challenging. Existing tools often struggle to efficiently process large genomes, recover low-copy elements, or accurately reconstruct full-length TEs, limiting comprehensive TE analyses. Here, we present panHiTE, a population-scale TE detection framework that introduces several methodological innovations. First, panHiTE employs a dynamically updated global TE library to avoid redundant detection of previously identified elements, improving computational efficiency and enabling application to extremely large genomes, such as the 15-Gb wheat genome. Second, to recover low-copy TEs that are frequently missed in individual genomes, panHiTE realigns candidate elements across population-scale genomes, enabling accurate reconstruction of full-length TEs across accessions. Third, because long terminal repeat retrotransposons constitute a major fraction of plant genomes, panHiTE integrates a deep-learning-based detection algorithm developed in this study, achieving higher sensitivity and precision than the state-of-the-art tool panEDTA in population-scale analyses. In addition, a fault-tolerant redundancy-removal algorithm efficiently groups divergent family members, generating TE libraries with more than 50% fewer sequences while doubling the number of Perfect TEs across 26 maize genomes. These advances enable panHiTE to deliver high-resolution TE annotations and accurately resolve TE-gene positional relationships, thereby facilitating the systematic identification of TE-induced differential expression loci (TIDELs). In 32 Arabidopsis accessions, panHiTE identifies 85 TIDELs associated with diverse biological functions and metabolic pathways. Overall, panHiTE provides a robust and scalable solution for population-scale TE discovery and functional characterization in complex plant genomes.

Indexed as

DNA Transposable ElementsGenome, PlantGenomicsHigh-Throughput Nucleotide SequencingRetroelementsTriticumZea maysDNA Transposable ElementsRetroelementslarge-scale genomic analysislong terminal repeat retrotransposonspan-TE detectionpopulation-specific variation

Identifiers

PMID41376169
PMCPMC12983257

What Socratic 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 Socratic graph.