Tackling reference bias in genotyping by using founder sequences with PanVC 3.

Summary: Overcoming reference bias and calling insertions and deletions are major challenges in genotyping. We present , a set of software that can be utilized as part of various variant calling workflows. We show that, by incorporating known genetic variants to a set of founder sequences to which reads are aligned, reference bias is reduced and precision of calling insertions and deletions is improved.

Founder reconstruction enables scalable and seamless pangenomic analysis.

Motivation: Variant calling workflows that utilize a single reference sequence are the de facto standard elementary genomic analysis routine for resequencing projects. Various ways to enhance the reference with pangenomic information have been proposed, but scalability combined with seamless integration to existing workflows remains a challenge.

Results: We present PanVC with founder sequences, a scalable and accurate variant calling workflow based on a multiple alignment of reference sequences.

Linear time minimum segmentation enables scalable founder reconstruction.

Background:  We study a preprocessing routine relevant in pan-genomic analyses: consider a set of aligned haplotype sequences of complete human chromosomes. Due to the enormous size of such data, one would like to represent this input set with a few sequences that retain as well as possible the contiguities of the original sequences. Such a smaller set gives a scalable way to exploit pan-genomic information in further analyses (e.

Towards pan-genome read alignment to improve variation calling.

Background: Typical human genome differs from the reference genome at 4-5 million sites. This diversity is increasingly catalogued in repositories such as ExAC/gnomAD, consisting of >15,000 whole-genomes and >126,000 exome sequences from different individuals. Despite this enormous diversity, resequencing data workflows are still based on a single human reference genome.

Evaluating approaches to find exon chains based on long reads.

Unlabelled: Transcript prediction can be modeled as a graph problem where exons are modeled as nodes and reads spanning two or more exons are modeled as exon chains. Pacific Biosciences third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task.