Robert Arthur Kevin Lee Xing Liu Pushkar Pande Gena Tang Racchit Thapliyal Tianjun Ye

 Sequencing Methods  Experimental comparison of De Bruijn graph and Overlay graph assemblers  Preliminary Results  Lab Exercise

 Sanger Sequencing ◦ Cycle sequencing rxn ◦ ddNTP-terminated dye- labeled products ◦ High-resolution electrophoretic separation ◦ Parallelized in 96 or 384 capillaries ◦ Read lengths up to 1kBp ◦ Raw accuracy up to % ◦ Costs 50 ¢ per kB Sequencing Methods

 Second Gen. Sequencing ◦ Cyclical array methods  454  Illumina  AB SOLiD  Polonator  HeliScope ◦ Platforms vary in biochemistry and array generation yet conceptually similar in workflow Sequencing Methods

Illumina

Illumina continued

AB SOLiD

 Create a DNA library ◦ Ligate adaptors to fragments  Emulsion PCR ◦ Agarose beads ◦ Oil, water, PCR reagents ◦ Results in 1 mill copies / fragment for each bead 454 Pyrosequencing

 Beads arrayed into picotiter plate ◦ Immobilized via addition of enzyme containing beads ◦ Each cell contains exactly 1 bead  Bst polymerase, luciferase, apyrase, ATP sulferylase used More 454

Even more 454 Example of Output Flow Order TACGTACG 1-mer 2-mer 3-mer 4-mer KEY (TCAG) Measures the presence or absence of each nucleotide at any given position

Videos (454 Workflow)

Videos (Pyrosequencing) note: we did not choose the music

Comparison of 2 nd Gen Platforms

 Sequencing Methods  Experimental comparison of De Bruijn graph and Overlay graph assemblers  Preliminary Results  Lab Exercise

De Bruijn Graph assemblers and Overlay Graph assemblers  De Bruijn Graph assemblers ◦ Velvet, Abyss, Euler  Overlay Graph assemblers ◦ Newbler, Edena, SSAKE, VCAKE

 Write a C program to simulate reads from reference genome with specific read length, coverage and base error rate ◦ Human chr 22, ~33.5M bases ◦ Streptococcus Suis, NC_ , ~2M bases ◦ Helicobacter acinonychis Sheeba, ~ 1.5M bases  Write anther C program to measure the quality of assemblers ◦ N50 length ◦ No. of contigs ◦ Max contig length ◦ No. of mis-assembled contigs Synthetic Data used for Experiments

 De Bruijn graph assemblers are only suitable for short reads data  K limitation ◦ Use Hash table or Sorting to index K-mers  Need use a unique key(value) to represent each K-mer  K= = bit integer (unsigned int)  K= = bit integer (unsigned long long)  K>32? multiple integer to represent the hash table key Read Length

 Simulate reads from Streptococcus Suis  300 read length, 50X coverage, error rate 0.1%  Velvet default: K <= 31, so we use 31 # of contigs (total length) N50 length# of misassembled contigs (total length) Velvet46515 ( bp)115 bp5 (1346 bp)  Recompile velvet, K = 99 # of contigs (total length) N50 length# of misassembled contigs (total length) Velvet441( bp)15328 bp1 (34 bp)

 It is stated in some literatures that “De Bruijn based approach prone to false positives”, “Overlap graph has better quality” Quality and Accuracy

Assembl ers # of contigs (total length) N50 length# of misassembled contigs (total length) Velvet336 ( bp)10.4 kbp17 ( bp) Edena340 ( bp)9,8 kbp0 (0 bp)  Simulate reads from Helicobacter acinonychis Sheeba  35 read length, 50X coverage, error rate 0.1%

Assembl ers # of contigs (total length) N50 length# of misassembled contigs (total length) Velvet1106 ( bp)5266 bp12 ( bp) Edena1003 ( bp)6416 bp0 (0 bp)  Simulate reads from Streptococcus Suis  35 read length, 50X coverage, error rate 0.1%

 Overlap graph based assemblers are computing-expensive and use more memory ◦ All-to-all alignment of reads, O(n 2 ) ◦ Use more memory to store overlap graph  Typically, number of reads is weigh larger than the number of K-mers ◦ Especially for short reads data  With the same coverage and genome length, shorter reads means more reads ◦ It is stated that De Bruijn graph are more suitable for NGS data  Shorter reads, and high throughput Runtime and Memory Usage

AssemblersTimeMemory Velvet33 secs~220 M SSAKE26 mins~900 M VCAKE107 mins~1.1 G  Simulate reads from Streptococcus Suis  reads  50 read length, 20X coverage, error rate 0.1%  Xeon E GHz

 Recent advance of pattern matching algorithms and technical enable the use of overlap graph ◦ Suffix tree, Suffix array, Prefix array, compressed suffix array  Suffix array ◦ Be able to find overlap between reads in linear time ◦ Usage of compressed suffix array can significantly reduce the memory requirements of overlap graph assemblers  Examples ◦ D. Hernandez, P. François, L. Farinelli, M. Osteras, and J. Schrenzel, De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Research. 18: , ◦ Jared T. Simpson and Richard Durbin Efficient construction of an assembly string graph using the FM-index, Bioinformatics (2010) 26 (12):i367-i373. ◦ Pasqual  Pushkar and I have developed a parallel sequence assembler based on overlap graph in our research project However!

AssemblersTimeMemory Velvet292 mins~17 GB Edena37 mins~7 GB Pasqual43 mins~8 GB Parallel Pasqual9 mins~8 GB  Simulate reads from Human chr22  reads  50 read length, 20X coverage, error rate 0.1%  Xeon E GHz with 4 cores/8 threads

 H. influenzae ◦ 30 ~ 300 length  Velvet does not work ◦ K is fixed ◦ If we use big K, the reads shorter than K can not be assembled. ◦ If we use small K, it is difficult to assemble the long reads  Overlap graph assemblers do not have this issue ◦ Newbler Mixed Length Reads

 Controversial ◦ It is still unclear about the relation between De Bruijn graph and Overlap graph  We can still conclude from the experiments ◦ Regarding quality and accuracy, Overlap graph assemblers are thought to be better than De Bruijn graph assembler ◦ De Bruijn graph assemblers does not work for long reads ◦ De Bruijn graph assemblers does not work for mixed length reads (K is fixed) ◦ Traditional overlap graph assemblers are slower and use more memory, but latest assemblers are better than De Bruijn graph assemblers Conclusion

 Sequencing Methods  Experimental comparison of De Bruijn graph and Overlay graph assemblers  Preliminary Results  Lab Exercise

Quality score and length distribution Mean lengthMedian lengthStd dev M

Quality score and length distribution Mean lengthMedian lengthStd dev M

Quality score and length distribution Mean lengthMedian lengthStd dev M

Quality score and length distribution Mean lengthMedian lengthStd dev M

Quality score and length distribution Mean lengthMedian lengthStd dev M

Quality score and length distribution Mean lengthMedian lengthStd dev M

Velvet IdKNo. of contigsN50Max lengthTotal length% reads used M M M M M M $> velveth -fasta -long $> velvetg Input: Fasta/Fastq Output: Fasta

WGS assembler (Celera) IdNo.of ContigsN50Max lengthTotal length% reads used M M M M M M $> sffToCA –trim soft –libraryname ${Id}-trimsoft –output ${Id}-trimsoft ${Id}.sff $> runCA –p ${Id} –d ${Id} ovlConcurrency=4 ${id}-trimsoft.frg Input: frg format Output: Fasta >50 separate programs make up the Celera Assembler pipeline runCA script helps manage them all

Newbler De Novo Assembly IdNo.of ContigsN50Max lengthTotal length M M M M M M Reference Assembly – (Haemophilus-influenzae-refseq.fasta ) IdNo.of ContigsN50Max lengthTotal length M M M M M M Input:.sff Output: Fasta $> runAssembly // de novo assembly

MIRA IdNo.of ContigsN50Max lengthTotal length% reads used M M M M M M MIRA stands for Mimicking Intelligent Read Assembly $> sff_extract –s ${Id}_in.454.fasta -q ${Id}_in.454.fasta.qual -x ${Id}_traceinfo_in.454.xml ${Id}.sff $> mira --project=${Id} --job=denovo,genome,normal,454 -GE:not=4 >& ${Id}_assembly.log Input: Fasta + qual + trace info Output: Fasta, Ace

Eagle view - M19107.ace

Eagle view - M19501.ace

 “Next-generation DNA sequencing” Shendure et. al, NatureBiotechnology-2008.pdf NatureBiotechnology-2008.pdf  “Next-generation DNA sequencing methods” Mardis et. al, AnnuRevGenet-2008.pdf AnnuRevGenet-2008.pdf Works Cited

 Sequencing Methods  Experimental comparison of De Bruijn graph and Overlay graph assemblers  Preliminary Results  Lab Exercise

 Download the Lab Exercise file from the Genome Assembly wiki page Lab Exercise