Back

Computational methods for the discovery and annotation of viral integrations

Palatini, U.; Pischedda, E.; Bonizzoni, M.

2021-09-01 bioinformatics
10.1101/2021.08.28.458009 bioRxiv
Show abstract

The transfer of genetic material between viruses and eukaryotic cells is pervasive. Somatic integrations of DNA viruses and retroviruses have been linked to persistent viral infection and genotoxic effects. Integrations into germline cells, referred to as Endogenous Viral Elements (EVEs), can be co-opted for host functions. Besides DNA viruses and retroviruses, EVEs can also derive from nonretroviral RNA viruses, which have often been observed in piRNA clusters. Here, we describe a bioinformatic framework to annotate EVEs in a genome assembly, study their widespread occurrence and polymorphism and identify sample-specific viral integrations using whole-genome sequencing data.

Matching journals

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

1
BMC Bioinformatics
○ 457 papers in training set
Top 0.1%
29.8%
Similar papers in this journal
2
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
7.6%
Similar papers in this journal
3
BMC Genomics
○ 406 papers in training set
Top 0.8%
6.4%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 4%
5.2%
Similar papers in this journal
5
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.8%
5.2%
Similar papers in this journal
50% of probability mass above
6
GigaScience
◐ 212 papers in training set
Top 0.6%
5.2%
7
PLOS Computational Biology
● 1863 papers in training set
Top 8%
4.6%
8
Briefings in Bioinformatics
◐ 354 papers in training set
Top 2%
4.1%
9
PLOS ONE
● 5266 papers in training set
Top 34%
3.9%
10
Genome Biology
○ 637 papers in training set
Top 4%
3.1%
11
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.3%
12
Viruses
○ 332 papers in training set
Top 3%
1.8%
13
Virus Evolution
◐ 155 papers in training set
Top 0.8%
1.8%
14
F1000Research
○ 88 papers in training set
Top 2%
1.6%
15
Genes
○ 144 papers in training set
Top 3%
1.1%
16
Gigabyte
● 62 papers in training set
Top 0.9%
1.1%
17
Nature Protocols
○ 33 papers in training set
Top 0.4%
1.1%
18
Journal of Bioinformatics and Systems Biology
○ 15 papers in training set
Top 0.2%
1.0%
19
International Journal of Molecular Sciences
○ 494 papers in training set
Top 16%
0.8%
20
PeerJ
◐ 308 papers in training set
Top 11%
0.8%
21
Scientific Reports
○ 3612 papers in training set
Top 75%
0.8%
22
Molecular Biology and Evolution
◐ 542 papers in training set
Top 6%
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.