Back

LncPlankton V1.0: a comprehensive collection of plankton long non-coding RNAs

Debit, A.; Vincens, P.; Bowler, C.; Cruz de Carvalho, H.

2023-11-05 plant biology
10.1101/2023.11.03.565479 bioRxiv
Show abstract

Long considered as transcriptional noise, long non-coding RNAs (lncRNAs) are emerging as central, regulatory molecules in a multitude of eukaryotic species, from plants to animals to fungi. Yet, our knowledge about the occurrence of these molecules in the marine environment, namely in planktonic protists, is still elusive. To fill this gap of knowledge we developed LncPlankton v1.0, which is the first comprehensive database of marine plankton lncRNAs. By integrating the predictions derived from ten distinctive coding potential prediction tools in a majority voting setting, we identified 2,210,359 lncRNAs distributed across 414 marine plankton species from over nine different phyla. A user-friendly, open-access web interface for the exploration of the database was implemented (https://www.lncplankton.bio.ens.psl.eu/). We believe LncPlankton v1.0 will serve as a rich resource for studies of lncRNAs that will contribute to small- and large-scale analyses in a wide range of marine plankton species and allow comparative analysis well beyond the marine environment.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.1%
18.5%
Similar papers in this journal
2
Nucleic Acids Research
◐ 1281 papers in training set
Top 0.9%
17.0%
Similar papers in this journal
3
BMC Genomics
○ 406 papers in training set
Top 0.6%
7.3%
Similar papers in this journal
4
RNA Biology
○ 78 papers in training set
Top 0.2%
5.5%
5
Genome Biology
○ 637 papers in training set
Top 2%
5.5%
Similar papers in this journal
50% of probability mass above
6
BMC Biology
○ 265 papers in training set
Top 0.3%
5.5%
Similar papers in this journal
7
F1000Research
○ 88 papers in training set
Top 0.3%
4.0%
8
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.2%
9
PLOS ONE
● 5266 papers in training set
Top 36%
3.2%
10
Scientific Reports
○ 3612 papers in training set
Top 32%
3.2%
11
Database
◐ 61 papers in training set
Top 0.2%
2.7%
12
PeerJ
◐ 308 papers in training set
Top 5%
1.7%
13
Nature Communications
○ 5641 papers in training set
Top 46%
1.7%
14
GigaScience
◐ 212 papers in training set
Top 3%
1.1%
15
Biology Methods and Protocols
◐ 61 papers in training set
Top 1%
1.1%
16
eLife
● 5828 papers in training set
Top 59%
1.1%
17
Frontiers in Genetics
○ 230 papers in training set
Top 4%
1.0%
18
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.0%
19
Communications Biology
○ 993 papers in training set
Top 26%
1.0%
20
Genome Research
● 468 papers in training set
Top 6%
1.0%
21
Life Science Alliance
● 285 papers in training set
Top 8%
0.8%
22
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 44%
0.6%
23
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 8%
0.6%
24
Genes
○ 144 papers in training set
Top 5%
0.6%
25
RNA
● 189 papers in training set
Top 2%
0.6%
26
Journal of Genetics and Genomics
○ 38 papers in training set
Top 0.9%
0.6%
27
Biology
○ 45 papers in training set
Top 1%
0.6%
28
PROTEOMICS
○ 43 papers in training set
Top 0.9%
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.