---
# System prepended metadata

title: Complex Genomes

---

# Complex Genomes

### Basic Assembly Flow

Raw reads > evaluate quality > trimming / cleanup > assembly with parameter set > is your assembly okay? > draft assembly

***Main differences of bacterial vs complex genome:***
choice of k-mer, depth of coverage, typically going to use a lot more data

Many wants to do this

#### 1st:Assemble

- SPAdes (short reads) and Abyss (short reads)
- Flye (vs CANU) (Long reads)
- Merging

#### 2nd: Assembly of assemblies

EXAMPLES
### SPAdes assembly (of Drosophila data)
Spades picks the right k-mer for us!! :) 

- Input paired-end reads 
- You can pull pacbio and nanopore data directly into SPAdes
- trusted-contigs: if there is a discrepency between my data and other, they are right (untrusted means they are right)
- cov-cutoff

(see code in slides)


### Flye assembly 


## How much coverage do you need?

### Illumina Data

If you have a great reference genome and only need a bit of genetic variation data - only 6-8x

You need more around 16x to call two different haplotypes (8x per haplotype)

Draft assembly: 30x 

Generally improving quality on Illumina up to 50-60x and then you plateau out, you don't get much more out of higher coverage Illumina data

### Oxford Nanopore, long read

If you're going to combine it with Illumina data you can squeeze by with 5-8x, but when you start getting above ***10x*** and and add Illumina, it starts to add up very nicely

30x - 50x - great data IF you have great high quality data/high molecular weight DNA (>20kb is long and >200kb is good and long, over a megabase is reallyyyy long)for long data.

Long reads provide that spatial data over a really long area - good for haplotypes

How to find coverage:

Genome size = 500,000,000
Sequencing = 50 GB of data

Genome size/sequencing = approximate coverage

Use: SAMTOOLS

### Challenges of complex genomes

SIZE, repeat regions, gene families, heterochromatin, polyploidy

Polyploids pose a complication for de Bruijn graphs (Tool: WhatsHap), you need VERY high depth of sequencing data for polyploids

### Heterozygosity

- related to polyploid, how do you distinguish alleles from gene families?
- Assembly errors in some programs: but PLATANUS handles heterozygosity really well 
    - http://platanus.bio.titech.ac.jp/


### How do we get a better assembly?

Scaffolding: use linkage data 
- genetic maps (recommends Chromonomer)
- long reads [bionano]
    - bionano gives you a "pattern of distances" 
    - "nicks" the dna at specific places and you know where the nicks SHOULD occur in my data and compare it
    - use RUNbng
- Hi-C
    - Dovetail Genomics
    - Hi-C assembly tools: https://github.com/marbl/SALSA


## Nanopore

- can usually run 3x through 1 flow cell (1000 pores, 300 pores, 100 pores)
- 10 micrograms of DNA for a flowcell
- 
