An introduction to get started in genome assembly and annotation

Contributors

Author(s)	Laura Leroi
Editor(s)	Anthony Bretaudeau Alexandre Cormier Stéphanie Robin Erwan Corre

Questions

• What are the guidelines before starting a Genome Assembly and Annotation project?

Steps before starting a genome project

.left[

• Step 1: Build a wide community for the project if possible

• Step 2: Gather information about the target genome

• Step 3: Select the best possible DNA source and extraction

• Step 5: Choose an appropriate sequencing technology

• Step 6: Check the computational resources and requirements ]

Build a wide community for the project (if it’s possible)

.left[ The aim of a genome project is to sequence the entire target genome for a wide range of genomics applications. ]

.left[ Analyses, reanalyses and integration of genomic and other phenotype information require: ]

• Facilities: Wet lab, sequencing, bioinformatics,…
• Personnel: Skill intensive
• Software: Knowledge intensive

.left[ warning Data storage, maintenance, transfer, and analysis costs will also likely remain substantial and represent an increasing proportion of overall sequencing costs in the future. ]

Genome information: Genome size

.pull-left[ How to collect informations?

• Flux cytometry
• Databases:
  • Fungi: http://www.zbi.ee/fungalgenomesize
  • Animals: http://www.genomesize.com
  • Plants: http://data.kew.org/cvalues
• Bibliography ]

.pull-right[ .image-100[ ]]

.footnote[https://commons.wikimedia.org/w/index.php?curid=19537795]

Genome information: GC content

.pull-left[ Why?

.left[ Sequencing is not random! GC and AT rich regions are under-represented. ]

How to solve?

• Chemistry quirks
• Increase the sequencing depth
• Technologies combination (both long and short reads) ]

.pull-right[ .image-100[ ]]

.footnote[Chaisson et al. Genetic variation and the de novo assembly of human genomes. Nat Rev Genet 16, 627–640 (2015).]

Genome information: Ploidy level

.pull-right[ .image-55[ ]]

.pull-left[

Ploidy (N):

Number of sets of chromosomes in a cell

]

Higher ploidy -> harder to assemble => Increase of sequencing depth

.footnote[Daniel Hartl. Essential Genetics: A Genomics Perspective. Jones & Bartlett Learning. p. 177. ISBN 978-0-7637-7364-9. (2011).]

Genome information: Heterozygosity level

.pull-left[ .left[ Heterozygous: Locus-specific, diploid (2N) organism has two different alleles of a particular gene at the same locus ]]

.pull-right[ .image-100[ ]]

.left[ Heterozygosity is a metric used to denote the probability an individual will be heterozygous for a particular allele ]

Higher heterozygosity -> harder to assemble => Increase of sequencing depth

.footnote[https://www.genome.gov/genetics-glossary/heterozygous]

Genome information: Heterozygosity level

.image-125[ ]

.footnote[Heng Li’s blog: lh3.github.io/2021/04/17/concepts-in-phased-assemblies]

Genome information: Complexity aka repeats elements

.left[ It is impossible to resolve repeats of length L unless you have reads longer than L ]

Most common source of assembly errors:

.pull-left[ .image-65[ ]]

.pull-right[ .image-65[ ]]

Genome information: Others

• Karyotype: chromosome number
• Sex chromosome system: None, XY, ZW, UV,…
• Purity: possible presence of contaminants and/or symbionts?
• Is there any other useful data (NCBI, SRA, ENA, etc) that could improve my assembly?

Genome information: Tips

.pull-left[

• Flux cytometry : genome size and ploidy level
• k-mer frequency from Illumina reads : genome size, ploidy level, GC content, heterozygosity and even repeats composition! ]

.pull-right[ .image-20[ ]]

The best possible DNA

.left[ Select the best possible DNA source and extraction. The extraction of high-quality DNA is the most important aspect of a successful genome project

The lack of a good starting material will limit the choice of sequencing technology and will affect the quality of obtained data ]

The best possible DNA: Chemical purity of DNA

.left[ Sample-related contaminants:

• Polysaccharides
• Proteoglycans
• Proteins
• Secondary metabolites
• Polyphenols
• Humic acids
• Pigments
• Etc,…

All these contaminants can impair the efficacy of library preparation in any technology, especially true for PCR-free libraries (both PacBio and ONT) ]

The best possible DNA: Quantity of DNA

.left[ Different technologies require different amount of DNA:

• Illumina and 10x > 3 ng
• BioNano > 200 ng
• ONT > 1 μg
• Dovetail (Hic) > 5 μg
• PacBio > 15 μg ]

The best possible DNA: Structural integrity of DNA

.left[ High Molecular Weight (HMW) for Nanopore/PacBio (obtained mainly from fresh material) ]

The best possible DNA: Tips

.left[

• Many DNA extraction protocol available for a lot of species/taxa (VGP, Darwin Tree of Life, Nanopore, PacBio, etc)
• Keep DNA samples from the same individual in case of library preparation/sequencing fail, need more coverage, new sequencing technology, etc
• Use a single individual and sequence a haploid, highly inbred diploid organism, or isogenic individual ]

Appropriate sequencing technology

.left[ Mostly dependant on the quantity and quality of DNA and the cost but many parameters need to be considered prior to running an NGS experiment:

• Short versus long reads or both
• Read length
• Read quality/error rate
• Genome read coverage/depth
• Library preparation
• Downstream applications ]

Appropriate sequencing technology: Primary assembly

.left[

• Illumina: short reads (up to 2x250bp) with high quality reads. Sequencing bias with AT/GC rich regions
• IonTorrent: short reads (up to 500bp) with medium quality reads
• Nanopore: long reads (average ~15kbp) with low quality reads. Errors are not randomly distributed!
• PacBio:
  • CLR: long reads (average ~20kbp) with low quality reads
  • HiFi: long reads (average ~15kbp) with high quality reads ]

Appropriate sequencing technology: Scaffolding

.left[

• HiC: single or double restriction enzyme fragmentation. Need huge amount of coverage.
• Omni-C: sequence-independent endonuclease ]

Appropriate sequencing technology: Short vs long reads

.pull-left[ Depending on the sequencing technology reads length and sequencing depth are different.

• More sequencing depth but shorter reads
• Longer reads but less sequencing depth ]

.pull-rigth[ .image-40[ ]]

.footnote[https://github.com/alexcorm/developments-in-next-generation-sequencing]

Appropriate sequencing technology: Short vs long reads

.pull-left[ Reads accuracy differs depending on the sequencing technology:

• Illumina and PacBio HiFi: more accurate
• ONT and PacBio CLR: less accurate (but longer) ]

.pull-rigth[ .image-40[ ]]

Appropriate sequencing technology

.image-100[ ]

.footnote[Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat Rev Genetics 20, 631–656 (2019).]

Appropriate sequencing technology: Coverage versus depth

.left[ Coverage: fraction of the genome sequenced at least by one read

Depth: average number of reads that cover any given region

Intuitively, more reads should increase coverage and depth. ]

.image-40[ ]

.footnote[Chaisson et al. Genetic variation and the de novo assembly of human genomes. Nat Rev Genet 16, 627–640 (2015).]

Computational resources and requirements

.left[ To succeed you need to have sufficient compute resources (CPUS, RAM, walltime and storage).

• The resource are different between:
  • Assembly
  • Annotation
  • Other analysis tools
• For genome assembly:
  • Running times and RAM increase with the type and amount of data
  • More data for large genomes, increase running time/RAM/Storage
  • Most of tools running on a single node: parallelized but not distributed
• For genome annotation:
  • Mapping/alignment of external data (RNA-seq, proteins) can be parallelized and distributed
  • Annotation process can be parallelized and distributed ]

Typical sequencing strategies: Bacterial genomes

.left[

• PacBio CLR or Oxford Nanopore reads at 40-50x coverage, self-correction and/or hybrid correction (using Illumina data)
• PacBio HiFi reads at 20-30x coverage
• Illumina 2x250bp paired-end reads from MiSeq ]

Typical sequencing strategies: Larger genomes

.left[

• PacBio CLR or Oxford Nanopore reads at 100x coverage, hybrid correction using Illumina data and scaffolding using HiC or Omni-C
• PacBio HiFi reads at 30x coverage and scaffolding using HiC or Omni-C
• PacBio HiFi reads at 30x coverage, 120x Oxford Nanopore ultra long reads ]

Thank you!