Challenges of ancient genomics and pan-genomics Kay Nieselt Center - - PowerPoint PPT Presentation

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Challenges of ancient genomics and pan-genomics Kay Nieselt Center - - PowerPoint PPT Presentation

Sep 15, 2022 268 likes •564 views

Challenges of ancient genomics and pan-genomics Kay Nieselt Center for Bioinformatics Tbingen University of Tbingen Overview Introduction: ancient DNA (microbial) paleogenomics Challenges of computational paleogenomics

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Challenges of ancient genomics and pan-genomics

Kay Nieselt Center for Bioinformatics Tübingen University of Tübingen

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Overview

  • Introduction:
  • ancient DNA
  • (microbial) paleogenomics
  • Challenges of computational paleogenomics

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Ancient DNA - Paleogenomics

With advent of NGS studying extinct organisms has become possible Most prominent extinct organisms are:

  • Neandertals
  • Other humans, e.g. denisovans
  • Mammoth
  • Lately also: bacteria and viruses

Field of Paleogenomics: reconstruction and analysis

  • f ancient genomes

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Microbial Paleogenomics

Motivation:

  • Emergence of human diseases
  • Evolution of Bacteria and Viruses
  • Co-evolution with host

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Computational Paleogenomics

  • 1. Genome assembly:
  • reference-based (reconstruction of genome

via mapping)

  • de novo
  • 2. SNP calling
  • 3. Genome comparison
  • 4. Phylogeny reconstruction

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Ancient DNA - Workflow

6 | Source of Figure: Janet Kelso, Keynote BioVis 2016

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Ancient DNA - Issues

  • Short fragments:

mean fragment length between 60 and 150 bp

  • Damaged:

aDNA chemically damages -> C->T (and G->A) conversion at 5’ (3’) end of fragment

  • Metagenomes:

sample contains complex background (mixture

  • f ancient and modern DNA)
  • Contamination:

nature of complex background can also be due to contamination

  • Low amount of endogenous DNA

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Ancient DNA - Approaches

  • Short fragments:

Produce paired-end reads longer than mean fragment length, after sequencing these are merged into a common single read

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Forward read Reverse read Overlap Merged read

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Ancient DNA - Approaches

  • Damage:

used for authentication, afterwards often treated with UDG

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Ancient DNA - Issues

  • Metagenomes:

sample contains complex background (mixture

  • f ancient and modern DNA)
  • Possible approach: Use Malt 1 to characterize

taxonomic content

  • Low amount of endogenous DNA:

Approach: specific enrichment for target species Problems: modern reference, no de novo assembly,…

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1Herbig et al, bioRxiv 2016

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EAGER1 – a fully automated (ancient) genome

reconstruction pipeline

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Preprocessing

F a s t Q

Clip&Merge FastQC Read Mapping FastQ BWA, BWA-mem … Samtools DeDup QualiMap MapDamage Preseq CircularMapper SAM BAM Genotyping GATK: Preprocessing HaplotypeCaller UnifiedGenotyper VariantFiltration VCF2Genome FastA VCF Schmutzi ANGSD Genot. Likelih.

1Peltzer et al, Genome Biol. 2016

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Challenge 1: Genome Reconstruction

Question: how to reconstruct the genome of the DNA of interest from the raw reads

  • by mapping against a reference (only one?), or
  • by de novo assembly, or
  • by a hybrid approach?

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Challenge 1: Genome Reconstruction

Approaches:

  • by mapping against a reference:

Problems: 1) single-reference mapping, 2) for aDNA only modern references used

  • by de novo assembly:

Problems: low coverage, almost never mate pairs, ... Possible approach: MADAM 1 – improved ancient DNA

genome assembly

  • by hybrid of both: not aware how

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1Seitz and Nieselt, PeerJ Preprints 2016

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Challenge 2: Calling SNPs

To call SNPs: After mapping typical (best practice) approach:

  • Apply tools such as GATK or freebayes or ANGSD

to mapping assemblies (i.e, bam files) Challenge: how to efficiently call SNPs from de novo assemblies when input is low coverage (ancient) DNA?

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Challenge 3: Genome Comparison

Comparison of ancient and modern genomes:

  • Large-scale variations: genomic rearrangements

(translocations, inversions)

  • Small-scale variations: gene content, insertions,

deletions, SNPs

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Challenge 3: Genome Comparison

Ancient genomes: mostly built from single common reference Modern genomes: assembled Challenge: how to compare?

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Challenge 3: Genome Comparison

Comparative analyses based on genomic positions is challenging due to different coordinate systems across genomes. Possible approaches:

  • compare genomes via one specific reference

But: genomic regions that cannot be aligned to the reference are lost.

  • r

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Build metareference that contains the superset

  • f all genomes

SuperGenome1 (other call this pan-genome)

1Herbig et al, Bioinformatics 2012

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SuperGenome

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SuperGenome: Common coordinate system of all aligned genomes, independent of a prechosen reference genome, together with injective mapping of each genome onto SuperGenome Input: WGA of all known genomes of species of interest

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The SuperGenome and Pan-Genome

Nice „side effect“ of SuperGenome: it allows

  • consistent assignment of coordinates to genomic

annotations (e.g. genes)

  • straightforward determination of pan-genome*

by determining the orthologs from overlapping coordinates in the SuperGenome

(* For us the pan-genome (aka supra-genome) is the full complement of all genes of a clade, e.g. species)

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Challenge 3A: WGA

Sheer size of available genomes of a single species: currently up to thousands Challenge: How to compute a WGA of thousands of genomes?

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Challenge 4: Phylogenetics

Phylogenetic trees from aDNA and modern genomes are reconstructed

  • 1. to assess history of evolution,
  • 2. to date `root’ and/or emergence of specific

clades.

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Challenge 4: Phylogenetics

How reliable are phylogenetic trees built from genomic data reconstructed from short reads? Typical workflow:

  • 1. map short reads to a single reference sequence,
  • 2. extract single nucleotide polymorphisms (SNPs),
  • 3. infer phylogenetic tree from the aligned SNP

positions.

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Biased phylogenies? - I

  • 1. Source of common alignment?

Reads mapped to one reference -> bias? Possible answer: REALPHY1 (Reference sequence Alignment-based Phylogeny builder)

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1Bertels et al, Mol. Biol. Evol. 2014

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Biased phylogenies? - II

  • 2. SNPs versus whole genome-based phylogeny?

Bias of ML trees reconstructed from SNP-only positions shown by Bertels1 Possible approach to further investigate: TreeToReads2 - a pipeline for simulating raw reads from phylogenies

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1Bertels et al, Mol. Biol. Evol. 2014 2McTavish et al, bioRxiv 2016

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Biased phylogenies? - III

  • 3. Influence of `Ns´on phylogeny?

Many aDNA samples suffer from low amount of endogenous DNA -> low coverage genomes -> missing positions (`Ns´) Question / Challenges:

  • impact of `complete column deletion´versus

`partial column deletion´

  • threshold of coverage for `good´phylogeny

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Biased phylogenies? - IV

  • 4. SNP-based phylogenies without alignment

and/or genome reconstruction? Possible approach: kSNP3.0 3

  • 5. General challenge: Alignment-free versus

Alignment-based phylogenies? 4

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3Gardner et al, Bioinformatics 2015 4Haubold, Brief. Bioinformatics 2013

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Summary:

Challenges of ancient (pan-)genomics:

  • 1. Reconstruction of ancient genomes from

bacteria (or viruses) via mapping or assembly or hybrid approach

  • 2. SNP calling of low coverage genomes
  • 3. WGA of ancient and modern genomes
  • 4. Phylogenetic tree reconstruction from ancient

and modern genomes

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References

Bertels F, Silander OK, Pachkov M, Rainey PB & van Nimwegen E (2014). Automated reconstruction of whole-genome phylogenies from short- sequence reads. Molecular biology and evolution, 31, 1077-1088. Gardner SN, Slezak T & Hall BG (2015). kSNP3. 0: SNP detection and phylogenetic analysis of genomes without genome alignment or reference

  • genome. Bioinformatics (Oxford, England), 31, 2877.

Haubold B (2014). Alignment-free phylogenetics and population genetics. Briefings in bioinformatics, 15, 407-418. Herbig A, Jäger G, Battke F & Nieselt K (2012). GenomeRing: alignment visualization based on SuperGenome coordinates. Bioinformatics 28: i7–i15 Herbig A, Maixner F, Bos KI, Zink A, Krause J & Huson DH (2016). MALT: Fast alignment and analysis of metagenomic DNA sequence data applied to the Tyrolean Iceman. bioRxiv, 050559. McTavish EJ, Pettengill J, Davis S, Rand H, Strain E, Allard M & Timme, RE (2016). TreeToReads-a pipeline for simulating raw reads from phylogenies. bioRxiv, 037655. Peltzer A, Jäger G, Herbig A, Seitz A, Kniep C, Krause J & Nieselt, K (2016). EAGER: efficient ancient genome reconstruction. Genome biology, 17, 1. Seitz A & Nieselt K, Improving ancient genome assembly. PeerJ Preprints 2016.

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