Evidence map›Paper›PMID 39427206›Full record

ArticleMobile DNA2024

Accelerating de novo SINE annotation in plant and animal genomes.

Herui Liao, Yanni Sun, Shujun Ou

Abstract read
In one paragraph

Article in Mobile DNA, 2024. 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

3 authors.

Herui LiaoDepartment of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.
Yanni SunDepartment of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China. yannisun@cityu.edu.hk.
Shujun OuDepartment of Molecular Genetics, The Ohio State University, Columbus, OH, 43210, USA. ou.195@osu.edu.

Funding

OSU Enterprise for Research, Innovation and Knowledge GR130542
6 · The paper itself

Abstract

Genome annotation is an important but challenging task. Accurate identification of short interspersed nuclear elements (SINEs) is particularly difficult due to their lack of highly conserved sequences. AnnoSINE is state-of-the-art software for annotating SINEs in plant genomes, but it is computationally inefficient for large genomes. Moreover, its applicability to animals is limited due to the absence of animal pHMMs in its HMM library. Therefore, we propose AnnoSINE_v2, which extends accurate SINE annotation for animal genomes with greatly optimized computational efficiency. Our results show that AnnoSINE_v2's annotation of SINEs has over 20% higher F1-score compared to the existing tools on animal genomes and enables the processing of complicated genomes, like human and zebrafish, which were beyond the capabilities of AnnoSINE_v1. AnnoSINE_v2 is freely available on Conda and GitHub: https://github.com/liaoherui/AnnoSINE_v2 .

Indexed as

Genome annotationSINE identificationTransposable element

Identifiers

PMID39427206
PMCPMC11490119

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.