bioRxivBioinformaticsPreprint
acorn: an R package for de novo variant analysis
Abstract
BackgroundThe study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs also exist to test the burden of these variants in genomic regions; however, I am unaware of a program that fits in between these two aspects of de novo variant assessment. This intermediate space is important for assessing the quality of de novo variants and to understand the characteristics of the callsets. For this reason, I developed the R package acorn. Resultsacorn is an R package that examines various features of de novo variants including subsetting the data by individual(s), variant type, or genomic region; calculating features including variant change counts, variant lengths, and presence/absence at CpG sites; and characteristics of parental age in relation to de novo variant counts. Conclusionsacorn is an R package that fills a critical gap in assessing de novo variants and will be of benefit to many investigators studying de novo variation.
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- approach: experimental assay
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Arnav: Site specific error models to identify variants in RNA
Avinash Ramu, Donald F. Conrad·bioRxiv·Aug 22, 2018
- VcfR: an R package to manipulate and visualize VCF format data
Brian J. Knaus, Niklaus J. Grünwald·bioRxiv·Feb 26, 2016
- HAT: de novo variant calling for highly accurate short-read and long-read sequencing data
Jeffrey K. Ng, Tychele N. Turner·BioinformaticsPeer-reviewed·Jan 1, 2024
- Canvas SPW: Calling De Novo Copy Number Variants In Pedigrees
Sergii Ivakhno, Eric E. Roller, … Anthony J. Cox·BioinformaticsPeer-reviewed·Sep 26, 2017
- Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants
Zheng Wang, Guihu Zhao, … Jinchen Li·Genomics Proteomics & BioinformaticsPeer-reviewed·Mar 7, 2022
- Comparing Variant Call Files for Performance Benchmarking of Next-Generation Sequencing Variant Calling Pipelines
John G. Cleary, Ross Braithwaite, … Francisco M. De La Vega·bioRxiv·Aug 3, 2015