MCB Lecture #9 Sept 23/14 Illumina library preparation, de novo genome assembly

Illumina sequencing ube.com/watch? v=womKfikWlxMhttps:// ube.com/watch? v=womKfikWlxM

Illumina sequencing - summary 1.Template consists of DNA fragments amplified by bridge clustering 2."Sequencing by synthesis" used to generate DNA sequences 3.DNA sequence read as unique fluorescent signatures following base incorporation

Illumina sequencing - summary 4.Adapters at each end of the template molecule bind the flowcell adaptors and facilitate bridge amplification 5."Dual indexing" allows multiple samples to be sequenced on the same flowcell, each having a unique set of indices 6.Paired-end sequencing extends the regular sequencing protocol to read each template molecule in both directions

Paired-end sequencing Objective: allows repetitive regions to be sequenced more precisely

Paired-end sequencing Be careful to distinguish terms! Do not confuse adapters with the read or template fragment

Paired-end sequencing "Insert" is even more confusing Refers to entire fragment, including both the reads and the unsequenced "inner mate" region between them Term stems from long-dead plasmid sequencing approaches

Paired-end sequencing It is possible to have paired end reads that overlap each other Can assemble to create long, highly accurate contiguous reads

Paired-end sequencing If the template fragment is too short, it is possible to read past the end of the fragment Results in adapter region being included in read Needs to be removed computationally.

Library preparation How exactly are template fragments generated? Lots of methods, I only present two: TruSeq and Nextera Most common Illumina methods (specific kits available from Illumina) Think about: where might biases arise?

TruSeq library preparation Step #1: Fragment DNA Typically via shearing Produces uniformly sized fragments

TruSeq library preparation Step #2: Create blunt ends using a polymerase to remove 3' overhangs and fill in 5' overhangs Use bead purification to remove smallest fragments, blunt ending reagents

TruSeq library preparation Step #3: Adenylate 3' ends to prevent self- ligation while adding adapters

TruSeq library preparation Step #4: Ligate adapters containing sequencing primer, indices, flowcell capture site

Nextera library preparation Nextera uses engineered transposases to fragment genomic DNA and add sequencing adaptors at the same time Low DNA input requirement "Transposome" = transposon + DNA for attachment

Nextera library preparation Step #1: Use "tagmentation" to simultaineously fragment template DNA and add sequencing adapters 300bp insert size reflects minimum needed by transposases to cut and add adapters

Nextera library preparation Step 2: Purify fragments from transposome (part of Nextera kit) Result: fragment contains both 5' and 3' sequencing adapters

Nextera library preparation Step #3: Use PCR to add indices and flowcell capture sites to the fragment Non-template fragments excluded during bead clean-up following this step

Nextera library preparation Final result: Template fragment Sequencing adapters Dual indices Flowcell capture sites (same structure as TruSeq)

Library prep is not error-free

Library prep is not error-free

Library prep is not error-free Regions with lower coverage are GC-rich No method is perfect Also note: Nextera uses low cycle PCR, has potential for bias

Mate pairs Paired end sequencing actually binds each fragment to the flowcell and sequences from each end Size limitations: large fragments are too floppy to sequence well Mate pairs: maintain same philosophy of adding inserts of known sizes, but facilitating larger insert sizes

Nextera mate pair library preparation Step #1: Use Nextera tagmentation to fragment template and add adapters Adaptors are biotinylated for later steps

Nextera mate pair library preparation Step #2: Fragment is circularized using a "biotin junction adapter"

Nextera mate pair library preparation Step #3: Circular molecules fragmented, biotin tags used to enrich fragments having junction Recall: junction contains original fragment ends

Nextera mate pair library preparation Step #4: Use TruSeq protocol to end repair, A- tail, and ligate flowcell capture sequences and barcodes Final product has all the normal parts of an Illumina template library but also junction region mid-fragment

Questions?

Digging deeper into the guts de novo genome assembly Important to know to be able to tune assembly software appropriately! Two paradigms: 1.Overlap/layout/consensus 2.De Bruijn graphs Both find overlaps between sequences, create a network representation, and find the best path through that network to represent the final assembly

Overlap/layout/consensus genome assembly Step #1: Compare all reads to each other to find those that overlap Let's do it together! Reads (5'->3'): TGGCA CAATT ATTTGAC GCATTGCAA TGCAAT

Overlap/layout/consensus genome assembly Step #2: Create overlap graph arranging reads according to their overlaps Step #3: Find unique path through the graph Step #4: Assemble overlapping reads by aligning the reads and deriving consensus

Overlap/layout/consensus genome assembly Requires all-vs-all comparison of reads becomes computationally intensive as the number of reads increases Developed and applied for Sanger and 454 sequencing Not dead yet! Has reemerged for PacBio and other long-read techniques

But consider errors Our network was for perfectly accurate reads What happens when you have both the correct TGGCA read and a TGCCA read containing a substitution sequencing error?

De Bruijn graph assembly Instead of comparing all reads with each other, split reads up into kmers i.e., subsets of each read of a given length Much more computationally efficient than all- vs-all comparison in overlap/layout/consensus

De Bruijn graph assembly Step #1: Tally kmers Let's find all kmers where k=4 for our set of reads from before TGGCA CAATT ATTTGAC GCATTGCAA TGCAAT

De Bruijn graph assembly Step #2: Create graph of kmer overlap, where kmers are nodes and overlap between them are edges More complex than overlap graph Step #3: Find unique path through the graph Can leverage kmers adjacent to each other in reads to reduce complexity Step #4: Synthesize path into a consensus sequence

De Bruijn graph assembly Doesn’t need all-vs-all comparison so is much faster Can handle large numbers of reads, e.g., as generated by Illumina technology Graph is much more complicated, RAM intensive More sensitive to errors

De Bruijn graph assembly Consider errors: make the graph even more complicated with bubbles, dead ends Consider repeats: parts of the graph with no unique path through it Graph broken on each side, forming contigs

Next class Quality control of Illumina data Adapter trimming Error correction Next week: de novo genome assembly