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nf-cavalier: A Nextflow Pipeline for Rare Disease Variant Prioritization and Reporting

Munro, J. E.; Reid, J.; Bahlo, M. E.; Bennett, M. F.

2026-08-10 bioinformatics
10.64898/2026.08.06.743410 bioRxiv
Show abstract

nf-cavalier is a Nextflow pipeline that automates genomic variant annotation, filtering, and reporting for individuals with rare Mendelian diseases. The pipeline takes as input variant callsets for an individual, family, or rare disease cohort, together with a target gene panel or a phenotype of interest. Variants are then filtered using various customisable criteria, including predicted gene consequence, computational pathogenicity predictions, population frequency, and familial segregation. The sequencing data for candidate variants is then visualised for human review. Candidate variant results are returned in user-friendly output formats, including interactive HTML reports and PowerPoint slide decks, with embedded links to external resources that enable rapid review by clinical research teams. nf-cavalier is maintained on GitHub (bahlolab/nf-cavalier) and licensed under the permissive MIT open-source licence.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Nucleic Acids Research
◐ 1281 papers in training set
Top 1%
14.8%
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2
Bioinformatics
◐ 1204 papers in training set
Top 2%
11.7%
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3
Nature Communications
○ 5641 papers in training set
Top 24%
6.6%
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4
Genetics in Medicine
○ 78 papers in training set
Top 0.2%
6.6%
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5
The American Journal of Human Genetics
○ 234 papers in training set
Top 0.9%
5.4%
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6
Genome Medicine
○ 183 papers in training set
Top 0.9%
4.2%
7
PLOS ONE
● 5266 papers in training set
Top 33%
4.0%
50% of probability mass above
8
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 1%
3.5%
9
Genome Research
● 468 papers in training set
Top 2%
3.2%
10
BMC Bioinformatics
○ 457 papers in training set
Top 3%
3.2%
11
Genome Biology
○ 637 papers in training set
Top 4%
2.6%
12
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
2.4%
13
Human Mutation
○ 34 papers in training set
Top 0.2%
2.3%
14
Database
◐ 61 papers in training set
Top 0.3%
2.3%
15
BMC Medical Genomics
○ 50 papers in training set
Top 0.4%
2.1%
16
Nature Biotechnology
○ 172 papers in training set
Top 2%
1.7%
17
Nature Genetics
○ 286 papers in training set
Top 3%
1.7%
18
Scientific Data
○ 209 papers in training set
Top 2%
1.4%
19
Nature Methods
○ 385 papers in training set
Top 5%
1.3%
20
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.3%
21
Nature Computational Science
○ 55 papers in training set
Top 1%
1.1%
22
Genetic Epidemiology
○ 55 papers in training set
Top 0.6%
1.0%
23
PLOS Genetics
● 862 papers in training set
Top 11%
1.0%
24
Scientific Reports
○ 3612 papers in training set
Top 78%
0.6%
25
GENETICS
◐ 483 papers in training set
Top 5%
0.6%
26
Molecular Systems Biology
○ 162 papers in training set
Top 4%
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.