scholarly journals On the Minimum Error Correction Problem for Haplotype Assembly in Diploid and Polyploid Genomes

2016 ◽  
Vol 23 (9) ◽  
pp. 718-736 ◽  
Author(s):  
Paola Bonizzoni ◽  
Riccardo Dondi ◽  
Gunnar W. Klau ◽  
Yuri Pirola ◽  
Nadia Pisanti ◽  
...  
PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234470
Author(s):  
Sina Majidian ◽  
Mohammad Hossein Kahaei ◽  
Dick de Ridder

2016 ◽  
Author(s):  
Sarah O. Fischer ◽  
Tobias Marschall

AbstractHaplotype assembly or read-based phasing is the problem of reconstructing both haplotypes of a diploid genome from next-generation sequencing data. This problem is formalized as the Minimum Error Correction (MEC) problem and can be solved using algorithms such as WhatsHap. The runtime of WhatsHap is exponential in the maximum coverage, which is hence controlled in a pre-processing step that selects reads to be used for phasing. Here, we report on a heuristic algorithm designed to choose beneficial reads for phasing, in particular to increase the connectivity of the phased blocks and the number of correctly phased variants compared to the random selection previously employed in by WhatsHap. The algorithm we describe has been integrated into the WhatsHap software, which is available under MIT licence from https://bitbucket.org/whatshap/whatshap.


Author(s):  
Axel Goblet ◽  
Steven Kelk ◽  
Matúš Mihalák ◽  
Georgios Stamoulis

2020 ◽  
Author(s):  
Jim Shaw ◽  
Yun William Yu

AbstractResolving haplotypes in polyploid genomes using phase information from sequencing reads is an important and challenging problem. We introduce two new mathematical formulations of polyploid haplotype phasing: (1) the min-sum max tree partition (MSMTP) problem, which is a more flexible graphical metric compared to the standard minimum error correction (MEC) model in the polyploid setting, and (2) the uniform probabilistic error minimization (UPEM) model, which is a probabilistic analogue of the MEC model. We incorporate both formulations into a long-read based polyploid haplotype phasing method called flopp. We show that flopp compares favorably to state-of-the-art algorithms—up to 30 times faster with 2 times fewer switch errors on 6x ploidy simulated data.


Sign in / Sign up

Export Citation Format

Share Document