Back

SVants: A long-read based method for structural variation detection in bacterial genomes

Hanson, B. M.; Johnson, J. S.; Leopold, S. R.; Sodergren, E.; Weinstock, G. M.

2019-11-01 bioinformatics
10.1101/822312 bioRxiv
Show abstract

MotivationMobile genetic elements (MGEs) are genetic material that can transfer between bacterial cells and move to new locations within a single bacterial genome. These elements range from several hundred to tens of thousands of bases, and are often bordered by repeat regions, which makes resolving these elements difficult with short-read sequencing data. The development and availability of long-read sequencing technologies has opened up new opportunities in the study of structural variation but there is a lack of bioinformatics tools designed to take advantage of these longer reads.\n\nResultsWe present an assembly-free method for identifying the location of these MGEs when compared to any reference genome (including draft genomes). Using an artificially constructed Escherichia coli genome containing single and tandem-repeats of a Tn9 transposon, we demonstrate the ability of SVants to accurately identify multiple insertion sites as well as count the number of repeats of this MGE. Additionally, we show that SVants accurately identifies the transposon of interest, Tn9, but does not erroneously identify existing IS1 regions present within the chromosome of the E. coli artificial reference.\n\nAvailability and ImplementationSVants is available as open-source software at https://github.com/EpiBlake/SVants

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 3%
7.7%
Similar papers in this journal
2
Microbial Genomics
● 225 papers in training set
Top 0.5%
7.7%
Similar papers in this journal
3
BMC Genomics
○ 406 papers in training set
Top 0.8%
6.6%
Similar papers in this journal
4
BMC Bioinformatics
○ 457 papers in training set
Top 1%
6.6%
Similar papers in this journal
5
GigaScience
◐ 212 papers in training set
Top 0.4%
6.1%
Similar papers in this journal
6
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
5.4%
7
PLOS ONE
● 5266 papers in training set
Top 28%
5.4%
8
Access Microbiology
● 25 papers in training set
Top 0.1%
4.0%
9
Scientific Reports
○ 3612 papers in training set
Top 26%
4.0%
50% of probability mass above
10
PLOS Computational Biology
● 1863 papers in training set
Top 9%
3.4%
11
BMC Research Notes
○ 33 papers in training set
Top 0.1%
3.2%
12
Genome Biology
○ 637 papers in training set
Top 5%
2.3%
13
Genes
○ 144 papers in training set
Top 1%
2.1%
14
Gigabyte
● 62 papers in training set
Top 0.5%
2.1%
15
mSystems
● 394 papers in training set
Top 4%
2.1%
16
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.1%
17
Journal of Bioinformatics and Systems Biology
○ 15 papers in training set
Top 0.1%
1.5%
18
mSphere
● 302 papers in training set
Top 5%
1.3%
19
F1000Research
○ 88 papers in training set
Top 2%
1.3%
20
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.3%
21
G3 Genes|Genomes|Genetics
◐ 351 papers in training set
Top 3%
1.1%
22
BMC Microbiology
○ 49 papers in training set
Top 1%
1.0%
23
Nucleic Acids Research
◐ 1281 papers in training set
Top 12%
1.0%
24
Clinical Infectious Diseases
◐ 235 papers in training set
Top 2%
1.0%
25
PeerJ
◐ 308 papers in training set
Top 11%
0.8%
26
Philosophical Transactions of the Royal Society B
● 51 papers in training set
Top 0.9%
0.8%
27
International Journal of Molecular Sciences
○ 494 papers in training set
Top 16%
0.8%
28
Microbiology Spectrum
● 469 papers in training set
Top 10%
0.8%
29
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 7%
0.8%
30
Microorganisms
○ 106 papers in training set
Top 4%
0.8%

"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.