Modern sequencing technologies generate millions to billions of short reads (i.e., DNA fragments) from a DNA Sample

Reads are typically 100–300 base pairs long for short-read technologies and up to tens of kilobases for long-read technologies

DNA sample of unkown sequence

Reads

Sequencing

Reads overlap where they represent the same genomic region

Assembled DNA sequence (i.e., contig)

Reads

Assembly

Overlap information is used to merge reads into contiguous DNA sequences called contigs

Sequencing reads are matched to a reference genome to determine their correct positions

Alignment relies on identifying overlaps and shared sequences between the reads and the reference

RefSeq provides high-quality reference genomes, transcriptomes, and proteins

Variations occur when a read differs from the reference genome

Single-nucleotide polymorphisms (SNPs) are single base changes between the read and the reference

​Indels are small insertions or deletions that alter the alignment pattern

Reduces time and cost for studies focused on variant detection or evolutionary comparisons.

Reads corresponding to regions missing in the reference cannot be mapped, leaving unassembled gaps.

Reads

Inaccurate assembly

These gaps can affect downstream analysis, especially for novel genes or functional elements.

Gaps can occur due to incomplete reference sequences or highly divergent regions in the sample genome.

Variations like insertions, deletions, inversions, or translocations may not align correctly to the reference.

Failure to account for structural variations can skew results and mask important genomic differences.

Assemblers may interpret these variations as mismatches or sequencing errors.

It does not rely on pre-existing data, allowing for unbiased genome reconstruction. Essential for novel organisms or those with no reference genome.

Instead of mapping to a reference, reads are assembled by finding overlaps between reads and merging them

High computational requirements due to complex algorithms

Most methods use graph-based methods (more on this in the next lecture).

Struggles with repeats, sequencing errors, and low-coverage regions (more on this later).

Biological factors: Repetitive sequences, structural variations, and genome size.

These challenges complicate the process of accurately reconstructing a genome.

Technical issues: Sequencing errors, low coverage, and short read lengths.

Overcoming these challenges requires balancing biological and technical factor

Advances in sequencing technology (e.g., long-read sequencing)

Careful experimental design (e.g., choosing read length and depth)

Repeats are sequences of DNA that occur multiple times in the genome

AGCTGATC

TTAGCCGA

CGAT CGAT CGAT CGAT

Note: Repeats are especially abundant in eukaryotic genomes, comprising up to 50% of human DNA.

Common types of repeats 

Tandem repeats: Consecutive copies of the same sequence. 

Interspersed repeats: Similar sequences scattered throughout the genome

AGCTGATC

TTAGCCGA

CGAT CGAT

CGAT CGAT

How will the assembler know the difference between these two options? Maybe it has high coverage instead of more repeats?

Fragmentation: Assemblers may break contigs at repetitive regions, resulting in gaps.

versus

​Collapsing repeats: Similar repeats may be merged into a single copy, leading to incorrect assemblies.

Short reads: Often shorter than repeat regions, making it difficult to span and resolve repeats.

Long reads: Can span entire repetitive regions, reducing ambiguity and improving assembly accuracy.

If I have two reads at the ends of a repeat, and I know the distance between the reads, I know the length of repeat

Forward read

Reverse read

Known read gap

(This is why having paired-end reads that do not overlap is helpful.)

Redundant data (high coverage) helps correct errors by identifying the most likely base (i.e., consensus)

TACGATCGGATTACGCGTAGGCTAGCTTACGGACTCGATGTACGATCGGATTACGCGTAGG

Contigs are the first level of assembly, where reads are merged based on overlaps. In other words, they represent reconstructed DNA without gaps (i.e., continuous).

What do contigs indicate?

• Longer contigs suggest better assembly quality.
• Fragmented contigs indicate challenges such as repeats or low coverage.

What is a contig FASTA file?

• Contains the sequence of each contig in FASTA format.
• Used for downstream analysis like annotation and comparison.

Header format:

NODE_1 is the number of the contig

length_251580 is the sequence length

cov_96.965763 is the k-mer coverage of the largest k used in assembly (will be discussed on Thursday)

Scaffolds are higher-order assemblies formed by ordering and orienting contigs

​What do scaffolds indicate?

• Larger scaffolds suggest fewer gaps and better assembly resolution.
• Remaining gaps in scaffolds are represented as "N" regions.

Paired-end reads provide distance and orientation information to connect contigs.

What is a scaffold FASTA file includes contigs linked by paired-end reads with "N"s as the base

Provides a higher-level view of genome assembly, bridging contigs to form scaffolds.

Contigs

Scaffold

We almost always use this file for downstream processes.

Genome size (e.g., length of E. coli genome)

Higher N50 values indicate more contiguous assemblies

Largest contigs that make up the first 50

Remaining contigs

N50 = 8

Lower L50 values indicate fewer, larger contigs, which is better for assembly quality

L50 = 4

For L50, count the number of contigs used in the N50 calculation

We can visualize contigs and how they connect with a Bandage graph

Each colored line is a contig/scaffold

Highly branched assemblies are not ideal

Leads to fragmented graphs and helps reduce repeat collapsing

Collapsed, tangled graphs great for low-coverage regions

Large k

Small k

Potential bulge

Removal of a bulge will quickly deteriorate the graph and lose read information

If P needs to be removed, we "project" the information (e.g., coverage) onto Q

P's edges are then removed in the process

Potential tips

Removes P (shortest) and projects information onto Q

We can visualize this using an assembly graph from a tool called Bandage