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ChIP-Hub: an Integrative Platform for Exploring Plant Regulome

Chen, D.; Fu, L.-Y.; Zhang, P.; Chen, M.; Kaufmann, K.

2019-09-14 bioinformatics
10.1101/768903 bioRxiv
Show abstract

Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles publicly available datasets reanalyzed from >40 plant species, allowing visualization and meta-analysis.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 1%
37.7%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 1%
8.5%
Similar papers in this journal
3
Nature Plants
○ 94 papers in training set
Top 0.4%
6.4%
Similar papers in this journal
50% of probability mass above
4
Genome Biology
○ 637 papers in training set
Top 2%
6.0%
Similar papers in this journal
5
Molecular Plant
○ 39 papers in training set
Top 0.2%
6.0%
Similar papers in this journal
6
Nature Genetics
○ 286 papers in training set
Top 2%
4.6%
7
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.5%
8
Nature Biotechnology
○ 172 papers in training set
Top 2%
2.5%
9
Genome Research
● 468 papers in training set
Top 3%
2.3%
10
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.3%
11
Scientific Reports
○ 3612 papers in training set
Top 55%
1.6%
12
Communications Biology
○ 993 papers in training set
Top 18%
1.4%
13
Science Advances
● 1243 papers in training set
Top 23%
1.4%
14
Bioinformatics
◐ 1204 papers in training set
Top 7%
1.3%
15
Nature
○ 645 papers in training set
Top 8%
1.3%
16
Cell Genomics
○ 172 papers in training set
Top 4%
1.1%
17
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 39%
0.9%
18
eLife
● 5828 papers in training set
Top 68%
0.8%
19
Current Biology
○ 665 papers in training set
Top 12%
0.6%
20
GigaScience
◐ 212 papers in training set
Top 5%
0.6%
21
Scientific Data
○ 209 papers in training set
Top 3%
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.