C Appendix 3: Literature

This appendix keeps literature review up-to-date.

C.0.1 Bergeron2023

  • difficulty of com-paring GMR estimates derived using different methodologies
  • alternative bioinformatic pipelines used in different studies can yield GMR estimates that vary by a factor of two
  • a stronger effect of paternal than maternal age on the mutation rate seems to be universal for birds and mammals due to more germline mutations accumulating throughout the life of the male
  • male-driven evolution hypothesis
  • hominoid slowdown hypothesis

C.1 Genome assembly

C.1.1 Johnson et al. (2020)

Draft genome assemblies using sequencing reads from Oxford Nanopore Technology and Illumina platforms for four species of North American Fundulus killifish

de novo assembly of high-quality genomes combining short-read Illumina data to polish assemblies from long-read ONT data.

C.1.2 Plomion et al. (2018)

Oak genome reveals facets of long lifespan

C.1.3 Msc

  • also mutation detection
  • Use Cibulskis et al. (2013) to detect somatic mutations
  • orthogroups expanded in tandem duplicated genes are enriched in gene ontology terms relating to biotic interactions (disease-resistance R genes)
  • expanded orthorgroups are more degenerated (higher pi0/pi4) than stable orhogroups
  • rapide selection on leucine-rich repeat receptor like kinase (LRR-RLK) for oak immunity
  • compared genome size from Kmer (Jellyfish) and flux cytometry
  • assembly, TE detection and annotation, gene prediction and annotation, mutation detection
  • consider mutation rate estimation meaningless
  • mutation detection: BWA-MEM + MuTect

C.1.4 SI

C.2 Mutation detection

C.2.1 Orr et al. (2019)

A phylogenomic approach reveals a low somatic mutation rate in a long-lived plant

  • Methodology
    • 8 samples with Illumina HiSeq 2k5
    • de novo reference aligned on E. grandis and iteratively corrected using bcftools consensus
    • variant calling for positive control mapping with NGM and variant detection with GATK4 keeping variants present
      1. in all three replicates of each branch types
      2. at least one branch tip had a different genotype than the other branch tips
      3. the site is biallelic (multiple somatic mutations are improbable)
      4. total depth is less or equal to ~2 times the expected depth (avoid alignment issues)
      5. excessive heterozygosity is less or equal 40 (avoid genotyping errors)
      6. the site is not in a repetitive region of E. grandis
    • positive control used to compute phylogenetic trees, comparing the distance to architecture to identify the most pasimonious tree
    • somatic mutations detected using GEAR's dng-call method (Ramu et al. 2013) with expectations bades on the physical tree architecture

C.2.2 Ramu et al. (2013)

DeNovoGear: De novo indel and point mutation discovery and phasing

Tools to detect ponit mutations used in Orr et al. 2019 and compared with SAMtools, polyMutt, and GATK.

C.2.3 Hanlon et al. (2019)

Somatic mutations substantially increase the per‐generation mutation rate in the conifer Picea sitchensis

  • Methodology
    • gene capture 2 leaf samples and 2 bark samples
    • aligned on natural hybrid NextGenMap
    • GATK haplotype call with -ERC and vcf with invariant and varying sites
    • filter poor quality site
    • filter weakly supported sample (leaf or bark) genotype (MAF<0.05) probably due to sequencing error
    • filtering site and genotype level for high confidence and low confidence mutation pools
    • keep site were all four sample passed
    • bark are the same, leaves are the same, but bark and leaves differ
    • visual check on IGV (alignment errors):
      • All reads that showed the candidate mutant allele also showed a unique allele at a nearby site in the heterozygous samples, and at least one of the homozygous samples contained reads supporting the two alleles
      • At the candidate site and at a nearby polymorphic site, there were three haplotypes in the heterozygous samples, each sup- ported by multiple reads
      • All four samples contained at least one read supporting the candidate mutant allele
    • validated mutations by resequencing with primers
  • Results
    • somatic base substitution rate of 2.7 × 10−8 per base pair within a generation
  • Discussion
    • although somatic mutations raise genetic load in conifers, they generate important genetic variation and enable selection both among cell lineages within individual trees and among offspring

C.2.4 Cibulskis et al. (2013)

Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

  • MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1
  • The sensitivity and specificity of any somatic mutation–calling method varies along the genome and depends on several factors:
    • the depth of sequence coverage in the tumor and a patient-matched normal sample
    • the local sequencing error rate
    • the allelic fraction of the mutation
    • the evidence thresholds used to declare a mutation
  • Methodolgy
    • Somatic mutation detection:
      • alignment of normal and tumor reads
      • marking duplicated reads
      • recalibration of base quality scores
      • local realignement
      • removal of low quality sequence data
      • variant detcion using a bayesian classifier
      • filtering to remove false positive resulting from correlated sequencing artifacts that are not captured by the error model
      • designation of the variants as somatic or germline by a second bayesian classifier
    • Variant detection
      • compare M0 assuming no variant at the site and non-reference bases due to random sequencing errors
      • to M(m,f) assuming true variant allele m at allele fraction f
      • compute log odds score (LOD) and use a threshold depending on expected mutation frequency and desired false positive rate
    • Variant filtering
      • STD standard = no filters vs HC high confidence applying 6 filters
      • Filter proximal gap
      • Filter strand bias
      • Filter poor mapping
      • Filter triallelic site
      • Filter clustered position
      • Filter observed in control
    • Specificity
      • Overcalling events for the tumor data
        • sequencing errors
        • innacurate read placements
      • undercalling germline events in the matched normal data
        • low sequencing depth

C.2.5 Nicholson et al. (2018)

Fixation and Spread of Somatic Mutations in Adult Human Colonic Epithelium

Variant fixation requires all wild-type stem cells to be displaced defining a process of monoclonal conversion of crypts that takes many years in human colonic epithelium. But biased behaviors are confirmed to subvert these processes to achieve variant over representation.

C.2.6 Alioto et al. (2015)

A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

  • PCR-free library preparation
  • Mutation coverage > 100X
  • Control coverage close to mutation coverage (\(\pm10\%\))
  • Optimize aligner/variant caller combination (BWA for SSM with next step)
  • Combine several mutations callers (Strelka and MuTect for SSM)
  • Allow mutations in or near repeats
  • Filter by mapping quality, strand bias, positional bias, presence of soft-clipping to minimize mapping artefacts

C.3 Unclassified

C.3.1 Michael & VanBuren (2020)

Building near-complete plant genomes

C.4 To read

C.4.1 Belser et al. (2018)

C.4.2 Edwards et al. (1990)

C.4.3 Leebens-Mack et al. (2019)

  • Schmid-Siegert et al. 2017

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