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PLncFire: A Scalable Pipeline for Transcriptome-wide Discovery of Plant lncRNAs

Mistry, S. D.; Saxena, S.; Patel, D.; Rizvi, A. Z.

2025-10-15 bioinformatics
10.1101/2025.10.13.682253 bioRxiv
Show abstract

Long non-coding RNAs (lncRNAs) are key regulators of plant biology, yet their discovery is hindered by low sequence conservation and a lack of comprehensive annotations. To overcome these challenges, we developed PLncFire, a modular computational pipeline that automates the genome-wide identification and annotation of lncRNAs from standard RNA-seq data. PLncFire integrates quality control, transcript assembly, and a robust consensus coding-potential assessment using CPC2, PlantLncPipe, and FEELnc to generate high-confidence predictions. It classifies lncRNAs as known or novel, facilitates their prioritisation through differential expression analysis, and is designed for scalability and reproducibility across diverse plant species. PLncFire provides a standardised framework to empower large-scale lncRNA discovery and advance comparative functional genomics. The source code is available at https://github.com/ahsan-rizvi/PLncFire.git.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Plant Physiology
◐ 238 papers in training set
Top 0.4%
12.5%
Similar papers in this journal
2
Plant Communications
○ 36 papers in training set
Top 0.1%
7.1%
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3
The Plant Journal
○ 215 papers in training set
Top 0.8%
7.1%
Similar papers in this journal
4
Applications in Plant Sciences
○ 23 papers in training set
Top 0.1%
6.5%
Similar papers in this journal
5
Bioinformatics
◐ 1204 papers in training set
Top 4%
6.5%
Similar papers in this journal
6
Plant Direct
○ 95 papers in training set
Top 0.5%
5.3%
7
The Plant Cell
◐ 161 papers in training set
Top 0.8%
5.3%
50% of probability mass above
8
New Phytologist
○ 346 papers in training set
Top 2%
5.3%
9
Frontiers in Plant Science
○ 256 papers in training set
Top 2%
4.7%
10
The Plant Genome
○ 57 papers in training set
Top 0.3%
4.2%
11
Plant Biotechnology Journal
○ 64 papers in training set
Top 0.4%
3.1%
12
in silico Plants
◐ 27 papers in training set
Top 0.1%
2.1%
13
Plant Methods
○ 42 papers in training set
Top 0.4%
1.7%
14
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 3%
1.7%
15
BMC Bioinformatics
○ 457 papers in training set
Top 4%
1.6%
16
Physiologia Plantarum
○ 39 papers in training set
Top 0.8%
1.6%
17
GigaScience
◐ 212 papers in training set
Top 2%
1.6%
18
Nature Plants
○ 94 papers in training set
Top 1%
1.3%
19
BMC Genomics
○ 406 papers in training set
Top 6%
1.1%
20
Horticulture Research
◐ 47 papers in training set
Top 0.7%
1.1%
21
Genome Biology
○ 637 papers in training set
Top 8%
1.1%
22
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
1.1%
23
Plant Molecular Biology
○ 20 papers in training set
Top 0.7%
1.0%
24
Molecular Plant
○ 39 papers in training set
Top 0.9%
1.0%
25
Molecular Ecology Resources
○ 171 papers in training set
Top 2%
1.0%
26
Plant and Cell Physiology
◐ 52 papers in training set
Top 1%
1.0%
27
Briefings in Bioinformatics
◐ 354 papers in training set
Top 7%
0.8%
28
G3: Genes, Genomes, Genetics
◐ 252 papers in training set
Top 5%
0.8%
29
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%
30
PeerJ
◐ 308 papers in training set
Top 13%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.