4. NGS: Assembly

Question 1

online databases:

uniprot - sprot vs trembl?

Answer 1

• sprot: manually curated
• trembl: computer annotated

Question 2

online databases:
uniprot?

Answer 2

uniprot: functionally annotated protein sequences

Question 3

online databases:

Genbank

eg Arabidopsis thaliana?

Answer 3

sequence data obtained and submitted by scientists

2,700,417 Arabidopsis thaliana sequence entries
(full length and partial protein-coding genes, non-coding genes, nuclear fragments/chromosomes/scaffolds, organellar genes/genomic regions,…)

Question 4

online databases:

genome resource?

Answer 4

complete set of protein-coding genes for one genome (nt, aa)

several exist (species specific, NCBI, ENSEMBL, UniProt, JGI …)

Question 5

.fastq file?

4 rows?

Answer 5

• Raw and pre-processed data
• text-based format
• storing both a biological sequence and its corresponding quality scores.
• Both encoded with a single ASCII character for brevity.

row 1 - @ sequence identifier - location on flow cell
row 2 - raw sequence letters
row 3 - a plus sing ‘+’ (optionally more)
row 4 - quality of the read (=same length as sequence row)

Question 6

Sequence data: coverage?

Answer 6

each genome position is sequenced multiple times

coverage: average number of times each base is covered by independent reads

Question 7

Sequence data - optimal coverage? Sanger? Illumina?

Answer 7

optimal coverage depends on objective, sample, sequencing technology, …
* Sanger: a coverage of 8x is sufficient
* Illumina: 50-100x coverage is typical

Question 8

Principle of assembly

overlaps?

Answer 8

overlaps of identical sequence regions are assumed to originate from the same genomic location

this assumption is often not met!
- same sequence, different origins
- different sequence, same origin

Question 9

What are the challenges of assembly?

Answer 9

• untrimmed poor-quality reads
• errors
  
  • base calling errors
  • deletion
  • insertion
• low coverage, bad linkage
• unknown orientation
• contamination
• high heterozygosity
• polyploidy
• repeats !!!

Question 10

What problem do repeats present in assembly?

Answer 10

if repeats < fragments: ok, can be assembled correctly

if:
repeats > fragments or
repeats&raquo_space; fragments
reads can’t be matched correctly

and some repeats in eukaryotes are much larger than the size of reads! (eg transposable elements!)

Question 11

What types of repeats?

Answer 11

• simple repeats
• tandem or dispersed gene families
• segmental duplications
• interspersed repeats (transposable elements)
  
  • DNA transposons (corn Ac element)
  • viral retrotransposons (yeast Ty, fly Copia elements)
  • non-viral retrotransposons (SINEs, LINEs)
• polyploids!!

Question 12

Assembly: general steps ?

Answer 12

• identify read overlaps, assemble into contigs
• determine order and orientation of contigs: scaffolds
• finish into a chromosome-level assembly

Question 13

Assembly: different approaches for contigs?

Answer 13

greedy

OLC (overlap layout consensus)

de bruijn

Question 14

Assembly: greedy approach

what is generated?

what next?

what may this result in?

when to terminate?

Answer 14

generate a look-up table with the prefixes and suffixes of all sequence reads

Then, given a starting read, extend it with another read, every extension: based on greatest overlap

–> however may result in misassembly

terminate when conflicting information is found
* e.g., two or more reads could extend but do not overlap each other
* result >=1 contigs

Question 15

Assembly: greedy approach

usage?

results? problems?

Example?

Answer 15

still used to assemble organellar genomes from short-read shotgun data

• smaller size than nuclear genome
• single (circular) genome
• several hundred copies per cell
• generally good results (but problems with repeats!)

computer lab: NOVOPlasty

Question 16

OLC algorithm

what is it? what type of data structure used and what do elements represent?

Answer 16

OLC algorithm: overlap graph

overlap (string) graph
* nodes represent sequence reads
* edge weights: prefix and suffix overlap

Question 17

OLC algo

goal and approach?

Answer 17

goal & approach
* find genome / contigs by traversing the graph

Question 18

OLC

what can you say about computing the overlap graph? how is it done?

how is it updated?

Answer 18

compute overlap graph (huge! messy!)
find overlaps with suffix trees and/or dynamic programming

layout
* remove redundancy in the graph where possible
* compute contigs from parts of the overlap graph consensus
* pick for each contig position one nucleotide

Question 19

OLC approach is suitable/not suitable for …?

Answer 19

computationally not feasible for NGS short-reads
* shorter reads & higher coverage
➡ too many pairwise comparisons
➡ too few unique overlaps
* higher error rates than Sanger, different error patterns
* repeats!!

Question 20

OLC - problems?

Answer 20

multiple possible paths = multiple possible genomes
* contigs: set of longest contigous segments which can unambiguously be identified - usually hundreds of thousands!

repeats!
* repeats that can’t be resolved are often left out

Question 21

What was OLC first developed for and what does that mean for us?

Answer 21

• first developed and used for Sanger data
• updated for new long-read technologies! (Pacific Biosciences, Oxford Nanopore)

not so good for short reads of NGS

Question 22

Best approach for NGS ?

Answer 22

many short reads –>
de Bruijn graph

Question 23

De Bruijn graph - method?

Answer 23

• sequence reads
• break reads into k-mers (e.g., 4-mers)
• use these to construct a DBG - (drop k-mers that are too (in) frequent)
• target sequence: path that visits each edge once…at least in theory!
  = eulerian path (cycle)

(output: also contigs!)

Question 24

What could be the reasons for a bubble in a genome assembly graph?

Answer 24

biological:
- SNP
- heterozygosity

technological:
- sequencing errors

Question 25

DBG - What are some more complex topological features?

Answer 25

- spur - bubble - frayed rope

Question 26

Shotgun sequencing definition

Answer 26

Shotgun sequencing is a laboratory technique for determining the DNA sequence of an organism’s genome. The method involves randomly breaking up the genome into small DNA fragments that are sequenced individually. A computer program looks for overlaps in the DNA sequences, using them to reassemble the fragments in their correct order to reconstitute the genome.

Question 27

How to determin the best k-mer size

Answer 27

break reads into k-mers, plot their abundances, repeat for multiple values for k * choose k with optimal separation of signal from noise * choose k large enough to reduce the number of (redundant) nodes * choose k small enough to reduce the number of subgraphs (gaps)

Question 28

Software using the de Bruijn approach - data structures? - errors? - implementations? - which to choose?

Answer 28

* auxiliary data structures store information about reads, k-mers, indices, positions, paired reads, etc * handling of sequencing errors, repeats, etc * incorporation of quality information * different implementations (for genomes) - Velvet, AbySS, AllPaths, Meraculous, SOAPdenovo, ... - different data structures! - hash table, Bloom filter, FM index, etc no single optimal implementation & parameter settings ➜ try different software/parameters

Question 29

Def contif

Answer 29

set of DNA segments or sequences that overlap in a way that provides a contiguous representation of a genomic region

Question 30

Def linkage

Answer 30

- close location of genes or other DNA markers to each other on chromosomes. - The closer the genes are to each other on a chromosome, the more likely they are linked or inherited together from parents to offspring

Question 31

Def consensus in OLC

Answer 31

* pick for each contig position one nucleotide

Question 32

Def haplotype

Answer 32

- A haplotype is a physical grouping of genomic variants (or polymorphisms) that tend to be inherited together. - A specific haplotype typically reflects a unique combination of variants that reside near each other on a chromosome.

Question 33

Def meta-data

Answer 33

* data about the data! * from the same gene, individual, population, species - more than one sequence - more than one data type

Question 34

What is scaffolding in genome assembly?

Answer 34

* infer order, orientation, distance of contigs * link contigs with Ns into scaffolds

Question 35

How can scaffolding be achieved?

Answer 35

- with mate pairs - with long reads - with long-range linkage information

Question 36

Scaffolding with long-range linkage information example? originally designed for? what does it identify? what not known?

Answer 36

example: HiC, chromosome conformation capture * originally designed to study 3D genome structure * identify genomic regions physically adjacent in 3D → closer in 1D * can be used to scaffold a draft genome assembly * exact distance & orientation of contigs not known

Question 37

Scaffolding with long reads how? 2 ways problem?

Answer 37

- long reads can link ≥2 contigs - align contigs and long reads OR - use hybrid assemblers that use both short and long reads as input - problem: long reads generally have much higher error rates!

Question 38

What issues are there in finishing a genome assembly? How can this be solved?

Answer 38

Finishing a genome assembly * most short read assemblies remain as scaffolds - no chromosomal assignment of scaffolds - large gaps * filling gaps and chromosomal-level assembly is expensive and time-consuming Can be resolved with long reads: now more chromosome-level assemblies

Question 39

How are assemblies evaluated?

Answer 39

* placement of paired reads (orientation, distance) * computation of quality metrics - numbers & lengths of contigs and scaffolds - N50 value * quantify expected gene content for a given lineage - BUSCO

Question 40

What is the N50 value

Answer 40

used to evaluate assemblies N50: 50% of the entire assembly is contained in contigs/scaffolds of at least this length

Question 41

Nuclear genome: how is it really, and how is this data represented ?

Answer 41

nuclear genome * diploid * homozygous & heterozygous positions * haplotypes * multiple linear chromosomes currently available data * collapsed sequence data, pseudohaploid * information about heterozygous positions* * fully resolved haplotypes* *requires special software and/or long or linked reads!

Question 42

what does scaffold length and quality depend on?

Answer 42

scaffold length & quality depends on repeat content

Question 43

What are some analysis challanges for NGS data?

Answer 43

data management (storage) algorithms - always need new analysis pipelines - need more expertise multiple genome assemblies from the same species * intraspecific diversity! * inventory vs. working with pan-genomes missing & fragmented & erroneous genome regions

Question 44

What is a Hamiltonian path ?

Answer 44

passes through every VERTEX exactly once

Question 45

What is an Eulerian path?

Answer 45

passes through every EDGE exactly once

Question 46

EXAM QUESTION what is a scaffold? Why do most genome assemblies consist of scaffolds? Give 2 reasons (2022)

Answer 46

* infer order, orientation, distance of contigs * link contigs with Ns into scaffolds

Question 47

EXAM QUESTION How to use Hamiltonian path in sequence assembly, two problems (2020)

Answer 47

DBG * break reads into k-mers (e.g., 4-mers) --> these become the edges * break these into k-1-mers --> these become the nodes * use these to construct a DBG - (drop k-mers that are too (in) frequent) * target sequence: path that visits each edge once…at least in theory! = eulerian path or cycle - not hamiltonian! nodes can be reused.

Question 48

How do OLC and de Bruijn assembly methods deal with repeats? Why?

Answer 48

Both handle unresolvable repeats by essentially leaving them out. Unresolvable repeats break the assembly into fragments. Fragments are contigs (short for contiguous)