Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

Abstract

A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.

Robust inference of population size histories from genomic sequencing data

“…In addition to being able to analyze unphased data, our method can also take a form of pseudo-haploid data as input. Generating pseudo-haploid data is a strategy often applied to low-coverage sequencing data, where reliable diploid genotype calls are not feasible, and may introduce unwanted biases, for example in ancient human DNA studies [ 42 ]. In pseudo-haploid data, at each SNP, one sequencing read covering the respective SNP is chosen uniformly at random, and the allele on this read is then reported as the haploid genotype for the individual.…”

Integrative Pathogenicity Assay and Operational Taxonomy-Based Detection of New Forma Specialis of Fusarium oxysporum Causing Datepalm Wilt

“…We did not obtain the sequencing of Pg1, Pg5, and Pgx1, which might be due to sequencing errors. The sequencing errors lead to a low sequencing rate and/or no sequencing that could be due to high heterozygosity in genetic regions, homopolymeric sequences, runs of G or C in the regions, stops in the regions, and polymerase slippage during the Sanger sequencing method [ 26 , 27 , 28 , 29 ]. The sequences deposited in GenBank were assigned the accession numbers given in Supplementary Table S3 .…”

Blue Turns to Gray: Paleogenomic Insights into the Evolutionary History and Extinction of the Blue Antelope (Hippotragus leucophaeus)

“…As input fasta files for D statistics ( Green et al 2010 ; Durand et al 2011 ), the nuclear diversity estimates and Bayesian molecular dating, we generated pseudohaploid consensus sequences for all specimens using Consensify ( Barlow et al 2020 ). We generated the base count input file using ANGSD v0.923 ( Korneliussen et al 2014 ; -minQ 30, -minMapQ 30, -uniqueonly 1, -remove_bads 1, -baq 2, -dumpCounts 3, -C 0, -only_proper_pairs 0, -doCounts 1, -trim 0).…”