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HaploCart:Human mtDNA haplogroup classification using a pangenomic reference graph

Authors :
Rubin, Joshua Daniel
Vogel, Nicola Alexandra
Gopalakrishnan, Shyam
Sackett, Peter Wad
Renaud, Gabriel
Rubin, Joshua Daniel
Vogel, Nicola Alexandra
Gopalakrishnan, Shyam
Sackett, Peter Wad
Renaud, Gabriel
Source :
Rubin , J D , Vogel , N A , Gopalakrishnan , S , Sackett , P W & Renaud , G 2023 , ' HaploCart : Human mtDNA haplogroup classification using a pangenomic reference graph ' , PLOS Computational Biology , vol. 19 , no. 6 , e1011148 .
Publication Year :
2023

Abstract

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

Details

Database :
OAIster
Journal :
Rubin , J D , Vogel , N A , Gopalakrishnan , S , Sackett , P W & Renaud , G 2023 , ' HaploCart : Human mtDNA haplogroup classification using a pangenomic reference graph ' , PLOS Computational Biology , vol. 19 , no. 6 , e1011148 .
Notes :
application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1426749155
Document Type :
Electronic Resource