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Vizitig: context-rich exploration of sequencing datasets

Degardins, B.; Paperman, C.; MARCHET, C.

2025-04-22 bioinformatics
10.1101/2025.04.19.649656 bioRxiv
Show abstract

Vizitig is the first platform for real-time exploration and querying of DNA and RNA sequence graphs across many samples, unifying visualization, metadata, and flexible search. It integrates raw and reference-based data, handles complex variation, and provides a human-readable query language with scalable graph loading. Vizitig enables fast, reproducible analysis in both pangenomics and pantranscriptomics.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Nature Methods
○ 385 papers in training set
Top 0.6%
14.9%
Similar papers in this journal
2
Bioinformatics
◐ 1204 papers in training set
Top 2%
14.9%
Similar papers in this journal
3
Genome Biology
○ 637 papers in training set
Top 0.5%
14.9%
Similar papers in this journal
4
Nucleic Acids Research
◐ 1281 papers in training set
Top 2%
9.6%
Similar papers in this journal
50% of probability mass above
5
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 0.3%
7.8%
Similar papers in this journal
6
Nature Communications
○ 5641 papers in training set
Top 24%
6.7%
7
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
5.4%
8
GigaScience
◐ 212 papers in training set
Top 0.5%
5.4%
9
Genome Research
● 468 papers in training set
Top 2%
2.7%
10
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.1%
11
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.7%
12
Nature Biotechnology
○ 172 papers in training set
Top 3%
1.7%
13
Nature Computational Science
○ 55 papers in training set
Top 1%
1.1%
14
PLOS Computational Biology
● 1863 papers in training set
Top 17%
1.1%
15
PeerJ
◐ 308 papers in training set
Top 8%
1.1%
16
Genomics, Proteomics & Bioinformatics
188 papers in training set
Top 2%
0.8%
17
iScience
○ 1154 papers in training set
Top 42%
0.6%
18
BMC Genomics
○ 406 papers in training set
Top 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.