Is it time to change the reference genome?

Mapping Intimacies ◽

10.1101/533166 ◽

2019 ◽

Author(s):

Sara Ballouz ◽

Alexander Dobin ◽

Jesse Gillis

Keyword(s):

Reference Genome ◽

Variant Calling ◽

Type Specimen ◽

Human Reference Genome ◽

Current Reference

AbstractThe use of the human reference genome has shaped methods and data across modern genomics. This has offered many benefits while creating a few constraints. In the following piece, we outline the history, properties, and pitfalls of the current human reference genome. In a few illustrative analyses, we focus on its use for variant-calling, highlighting its nearness to a “type specimen”. We suggest that switching to a consensus reference offers important advantages over the current reference with few disadvantages.

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Genome Graphs and the Evolution of Genome Inference

10.1101/101816 ◽

2017 ◽

Cited By ~ 2

Author(s):

Benedict Paten ◽

Adam M. Novak ◽

Jordan M. Eizenga ◽

Garrison Erik

Keyword(s):

Reference Genome ◽

Variant Calling ◽

Modern Human ◽

Human Biology ◽

Scientific Achievement ◽

Read Mapping ◽

Human Genomics ◽

Human Reference Genome ◽

Human Genomes ◽

Reference Bias

AbstractThe human reference genome is part of the foundation of modern human biology, and a monumental scientific achievement. However, because it excludes a great deal of common human variation, it introduces a pervasive reference bias into the field of human genomics. To reduce this bias, it makes sense to draw on representative collections of human genomes, brought together into reference cohorts. There are a number of techniques to represent and organize data gleaned from these cohorts, many using ideas implicitly or explicitly borrowed from graph based models. Here, we survey various projects underway to build and apply these graph based structures—which we collectively refer to as genome graphs—and discuss the improvements in read mapping, variant calling, and haplotype determination that genome graphs are expected to produce.

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Genome Graphs

10.1101/101378 ◽

2017 ◽

Cited By ~ 34

Author(s):

Adam M. Novak ◽

Glenn Hickey ◽

Erik Garrison ◽

Sean Blum ◽

Abram Connelly ◽

...

Keyword(s):

Reference Genome ◽

Human Genetics ◽

De Novo ◽

Variant Calling ◽

Reference Structure ◽

Read Mapping ◽

Human Reference Genome ◽

A Genome ◽

Universal Reference ◽

Genome Graph

AbstractThere is increasing recognition that a single, monoploid reference genome is a poor universal reference structure for human genetics, because it represents only a tiny fraction of human variation. Adding this missing variation results in a structure that can be described as a mathematical graph: a genome graph. We demonstrate that, in comparison to the existing reference genome (GRCh38), genome graphs can substantially improve the fractions of reads that map uniquely and perfectly. Furthermore, we show that this fundamental simplification of read mapping transforms the variant calling problem from one in which many non-reference variants must be discovered de-novo to one in which the vast majority of variants are simply re-identified within the graph. Using standard benchmarks as well as a novel reference-free evaluation, we show that a simplistic variant calling procedure on a genome graph can already call variants at least as well as, and in many cases better than, a state-of-the-art method on the linear human reference genome. We anticipate that graph-based references will supplant linear references in humans and in other applications where cohorts of sequenced individuals are available.

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Performance Assessment of Variant Calling Pipelines using Human Whole Exome Sequencing and Simulated data

10.1101/359109 ◽

2018 ◽

Author(s):

Manojkumar Kumaran ◽

Umadevi Subramanian ◽

Bharanidharan Devarajan

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Reference Genome ◽

Variant Calling ◽

Simulated Data ◽

Variant Call ◽

Human Reference Genome ◽

Indel Detection ◽

Whole Exome ◽

Clinical Variants

AbstractThe whole exome sequencing (WES) is a time-consuming technology in the identification of clinical variants and it demands the accurate variant caller tools. The currently available tools compromise accuracy in predicting the specific types of variants. Thus, it is important to find out the possible combination of best aligner-variant caller tools for detecting SNVs and InDels separately. Moreover, many important aspects of InDel detection are not overlooked while comparing the performance of tools. One such aspect is the detection of InDels with respect to base pair length. To assess the performance of variant (especially InDels) caller in combination with different aligners, 20 automated pipelines were developed and evaluated using gold reference variant dataset (NA12878) from Genome in a Bottle (GiaB) consortium of human whole exome sequencing. Additionally, the simulated exome data from two human reference genome sequences (GRCh37 and GRCh38) were used to compare the performance of the pipelines. By analyzing various performance metrices, we observed that BWA and Novoalign aligners performed better with DeepVariant and SAMtools callers for detecting SNVs, and with DeepVariant and GATK for Indels. Altogether, DeepVariant with BWA and Novoalign performed best. Further, we showed that merging the top performing pipelines improved the accurate variant call set. Collectively, this study would help the investigators to effectively improve the sensitivity and accuracy in detecting specific variants.

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Enabling multiscale variation analysis with genome graphs

10.1101/2021.02.03.429603 ◽

2021 ◽

Author(s):

Brice Letcher ◽

Martin Hunt ◽

Zamin Iqbal

Keyword(s):

Genetic Variation ◽

Directed Acyclic Graph ◽

Structural Variation ◽

Reference Genome ◽

Multiple Scales ◽

State Of The Art ◽

Variant Calling ◽

Variation Analysis ◽

New Algorithms ◽

Genome Graph

AbstractBackgroundStandard approaches to characterising genetic variation revolve around mapping reads to a reference genome and describing variants in terms of differences from the reference; this is based on the assumption that these differences will be small and provides a simple coordinate system. However this fails, and the coordinates break down, when there are diverged haplotypes at a locus (e.g. one haplotype contains a multi-kilobase deletion, a second contains a few SNPs, and a third is highly diverged with hundreds of SNPs). To handle these, we need to model genetic variation that occurs at different length-scales (SNPs to large structural variants) and that occurs on alternate backgrounds. We refer to these together as multiscale variation.ResultsWe model the genome as a directed acyclic graph consisting of successive hierarchical subgraphs (“sites”) that naturally incorporate multiscale variation, and introduce an algorithm for genotyping, implemented in the software gramtools. This enables variant calling on different sequence backgrounds. In addition to producing regular VCF files, we introduce a JSON file format based on VCF, which records variant site relationships and alternate sequence backgrounds.We show two applications. First, we benchmark gramtools against existing state-of-the-art methods in joint-genotyping 17 M. tuberculosis samples at long deletions and the overlapping small variants that segregate in a cohort of 1,017 genomes. Second, in 706 African and SE Asian P. falciparum genomes, we analyse a dimorphic surface antigen gene which possesses variation on two diverged backgrounds which appeared to not recombine. This generates the first map of variation on both backgrounds, revealing patterns of recombination that were previously unknown.ConclusionsWe need new approaches to be able to jointly analyse SNP and structural variation in cohorts, and even more to handle variants on different genetic backgrounds. We have demonstrated that by modelling with a directed, acyclic and locally hierarchical genome graph, we can apply new algorithms to accurately genotype dense variation at multiple scales. We also propose a generalisation of VCF for accessing multiscale variation in genome graphs, which we hope will be of wide utility.

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The design and construction of reference pangenome graphs with minigraph

Genome Biology ◽

10.1186/s13059-020-02168-z ◽

2020 ◽

Vol 21 (1) ◽

Cited By ~ 7

Author(s):

Heng Li ◽

Xiaowen Feng ◽

Chong Chu

Keyword(s):

Data Model ◽

Reference Genome ◽

Structural Variants ◽

Current Reference ◽

Sequencing Technologies ◽

Recent Advances ◽

Multiple Genomes ◽

Design And Construction

Abstract The recent advances in sequencing technologies enable the assembly of individual genomes to the quality of the reference genome. How to integrate multiple genomes from the same species and make the integrated representation accessible to biologists remains an open challenge. Here, we propose a graph-based data model and associated formats to represent multiple genomes while preserving the coordinate of the linear reference genome. We implement our ideas in the minigraph toolkit and demonstrate that we can efficiently construct a pangenome graph and compactly encode tens of thousands of structural variants missing from the current reference genome.

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Similarities and differences between variants called with human reference genome HG19 or HG38

BMC Bioinformatics ◽

10.1186/s12859-019-2620-0 ◽

2019 ◽

Vol 20 (S2) ◽

Cited By ~ 4

Author(s):

Bohu Pan ◽

Rebecca Kusko ◽

Wenming Xiao ◽

Yuanting Zheng ◽

Zhichao Liu ◽

...

Keyword(s):

Reference Genome ◽

Human Reference Genome ◽

Similarities And Differences

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Recovery of non-reference sequences missing from the human reference genome

BMC Genomics ◽

10.1186/s12864-019-6107-1 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 3

Author(s):

Ran Li ◽

Xiaomeng Tian ◽

Peng Yang ◽

Yingzhi Fan ◽

Ming Li ◽

...

Keyword(s):

Human Genome ◽

Tandem Repeats ◽

Reference Genome ◽

De Novo ◽

Precise Location ◽

Protein Coding ◽

Human Reference Genome ◽

Mhc Haplotype ◽

Reference Sequences ◽

Flanking Regions

Abstract Background The non-reference sequences (NRS) represent structure variations in human genome with potential functional significance. However, besides the known insertions, it is currently unknown whether other types of structure variations with NRS exist. Results Here, we compared 31 human de novo assemblies with the current reference genome to identify the NRS and their location. We resolved the precise location of 6113 NRS adding up to 12.8 Mb. Besides 1571 insertions, we detected 3041 alternate alleles, which were defined as having less than 90% (or none) identity with the reference alleles. These alternate alleles overlapped with 1143 protein-coding genes including a putative novel MHC haplotype. Further, we demonstrated that the alternate alleles and their flanking regions had high content of tandem repeats, indicating that their origin was associated with tandem repeats. Conclusions Our study detected a large number of NRS including many alternate alleles which are previously uncharacterized. We suggested that the origin of alternate alleles was associated with tandem repeats. Our results enriched the spectrum of genetic variations in human genome.

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Closing Human Reference Genome Gaps: Identifying and Characterizing Gap-Closing Sequences

G3 Genes|Genome|Genetics ◽

10.1534/g3.120.401280 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2801-2809 ◽

Cited By ~ 1

Author(s):

Tingting Zhao ◽

Zhongqu Duan ◽

Georgi Z. Genchev ◽

Hui Lu

Keyword(s):

Reference Genome ◽

De Novo ◽

Sequence Length ◽

Sequencing Data ◽

Human Reference Genome ◽

Satellite Sequences ◽

Long Read ◽

Data Gap ◽

Simple Repeats ◽

Gap Closing

Despite continuous updates of the human reference genome, there are still hundreds of unresolved gaps which account for about 5% of the total sequence length. Given the availability of whole genome de novo assemblies, especially those derived from long-read sequencing data, gap-closing sequences can be determined. By comparing 17 de novo long-read sequencing assemblies with the human reference genome, we identified a total of 1,125 gap-closing sequences for 132 (16.9% of 783) gaps and added up to 2.2 Mb novel sequences to the human reference genome. More than 90% of the non-redundant sequences could be verified by unmapped reads from the Simons Genome Diversity Project dataset. In addition, 15.6% of the non-reference sequences were found in at least one of four non-human primate genomes. We further demonstrated that the non-redundant sequences had high content of simple repeats and satellite sequences. Moreover, 43 (32.6%) of the 132 closed gaps were shown to be polymorphic; such sequences may play an important biological role and can be useful in the investigation of human genetic diversity.

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