Kelley Bullard, Henry Dewhurst, Kizee Etienne, Esha Jain, VivekSagar KR, Benjamin Metcalf, Raghav Sharma, Charles Wigington, Juliette Zerick Genome Assembly

Outline  Input Data  Sequence read data  Pipeline Review  Un-processed data  Assemblers  Preliminary data – assembler comparison  Visualization  Future

Input Data V. navarrensisV. vulnificus V Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio navarrensis- 454 SequenceID Min. Read Length 21 bp25 bp19 bp28 bp Max. Read Length 738 bp573 bp704 bp Avg. Read Length (± bp) (± bp) (± bp) (± bp) Total Reads160,56013,854303,434218,021 Coverage15x1.23x28.06x20.51x Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio vulnificus- 454 SequenceID2009V Min. Read Length 26 bp21 bp23 bp22 bp18 bp Max. Read Length 593 bp597 bp723 bp594 bp736 bp Avg. Read Length (± bp) (± bp) (± bp) (± bp) (± bp) Total Reads191,280786,944352,726173,538777,228 Coverage17x65x32x16x63x Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio navarrensis- Illumina SequenceID Min. Read Length 76 bp Max. Read Length 76 bp Avg. Read Length 76 bp Total Reads19,316,65929,414,237126,298,69192,338,634 Coverage326x496x250x237x Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio vulnificus- Illumina SequenceID2009V Min. Read Length 76 bp Max. Read Length 76 bp Avg. Read Length 76 bp Total Reads15,764,32914,562,25215,343,64816,007,89515,495,709 Coverage~250x Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

454 raw reads PRE-PROCESSING Illumina raw reads Pre-processing 454 reads Illumina reads Statistical analysis Read stats Published Genomes from public databases V. vulnificus YJ016 V. vulnificus CMCP6 V. vulnificus MO6-24/O Align Illumina against the reference Fastqc Prinseq NGS QC Compare mapping statistics Reference genome samstats bwa REFERENCE SELECTION Hybrid DeNovo Ray MIRA Illumina/ 454/ Hybrid DeNovo assembly 454 DeNovo Newbler CABOG SUTTA Illumina DeNovo Allpaths LG SOAP DeNovo Velvet Taipan SUTTA contigs * 3 Align illumina reads against 454 contigs Unmapped reads Mac vector CLC wb contigs Unmapped reads Evaluation GAGE Hawk-eye Illumina/(454?) reference based assembly AMOScmp contigs Unmapped reads DENOVO ASSEMBLY REFERENCE BASED ASSEMBLY Draft/ Finished genome Reference evaluation DNA Diff MUMmer Parameter optimization CONTIG MERGING All possible combinations of the best 3 Mimimus MAIA PAGIT Mauve Finished genome Scaffolds GAGE GENOME FINISHING Gap filling Nulceotide identity MUMmer GRASS Built-in Process 454 Illumina Info. Chosen Ref. Assemblers Illumina 454 LEGEND hybrid

Vibrio vulnificus- 454 Metric Per Base Seq. Quality Per Seq. Quality Score Per Base Seq. Content Per Base GC Content Per Seq. GC Content Per Base N Content Seq. Length Dist. Seq. Dup. Levels Overrepresented Seqs. Kmer Content

Vibrio navarrensis- 454; unprocessed data Metric Per Base Seq. Quality Per Seq. Quality Score Per Base Seq. Content Per Base GC Content Per Seq. GC Content Per Base N Content Seq. Length Dist. Seq. Dup. Levels Overrepresented Seqs. Kmer Content Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio vulnificus- Illumina; unprocessed data Metric2009V Per Base Seq. Quality Per Seq. Quality Score Per Base Seq. Content Per Base GC Content Per Seq. GC Content Per Base N Content Seq. Length Dist. Seq. Dup. Levels Overrepresented Seqs. Kmer Content Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Vibrio navarrensis- Illumina; unprocessed data Metric Per Base Seq. Quality Per Seq. Quality Score Per Base Seq. Content Per Base GC Content Per Seq. GC Content Per Base N Content Seq. Length Dist. Seq. Dup. Levels Overrepresented Seqs. Kmer Content Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Per base sequence quality vul_454_ nav_454_ vul_ill_ nav_ill_ Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Per base sequence content vul_454_ vul_ill_ nav_ill_ nav_454_ Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Seq. duplicate levels vul_454_ nav_454_ vul_ill_ nav_ill_ Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Pre-processing stats ParameterValue Total sequences15,343,648 Good sequences9,775,116 Bad sequences5,568,532 Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

454 raw reads PRE-PROCESSING Illumina raw reads Pre-processing 454 reads Illumina reads Statistical analysis Read stats Published Genomes from public databases V. vulnificus YJ016 V. vulnificus CMCP6 V. vulnificus MO6-24/O Align Illumina against the reference Fastqc Prinseq NGS QC Compare mapping statistics Reference genome samstats bwa REFERENCE SELECTION Hybrid DeNovo Ray MIRA Illumina/ 454/ Hybrid DeNovo assembly 454 DeNovo Newbler CABOG SUTTA Illumina DeNovo Allpaths LG SOAP DeNovo Velvet Taipan SUTTA contigs * 3 Align illumina reads against 454 contigs Unmapped reads Mac vector CLC wb contigs Unmapped reads Evaluation GAGE Hawk-eye Illumina/(454?) reference based assembly AMOScmp contigs Unmapped reads DENOVO ASSEMBLY REFERENCE BASED ASSEMBLY Draft/ Finished genome Reference evaluation DNA Diff MUMmer Parameter optimization CONTIG MERGING All possible combinations of the best 3 Mimimus MAIA PAGIT Mauve Finished genome Scaffolds GAGE GENOME FINISHING Gap filling Nulceotide identity MUMmer GRASS Built-in Process 454 Illumina Info. Chosen Ref. Assemblers Illumina 454 LEGEND hybrid

Assemblers NamePlatformSource file InstallationUsage Allpaths LGIllumina SOAP DeNovoIllumina VelvetIllumina SUTTAHybrid RAYHybrid CLC genomics workbenchHybrid Newbler454 CABOG454 Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

CLC Genomics  Word Size: Automatic Word Size  CLC bio's de novo assembly algorithm works by using de Bruijn graphs. It makes a table of all sub-sequences of a certain length (called words) found in the reads.  Bubble Size: Automatic Bubble Size  A bubble is defined as a bifurcation in the graph where a path furcates into two nodes and then merge back into one.  Minimum Contig Length: 200  Mismatch cost : 2  The cost of a mismatch between the read and the reference sequence.  Insertion cost: 3  The cost of an insertion in the read (causing a gap in the reference sequence)  Deletion cost: 3  The cost of having a gap in the read. The score for a match is always 1.  Length fraction: 0.5  Set minimum length fraction of a read that must match the reference sequence. Setting a value at 0.5 means that at least half the read needs to match the reference sequence for the read to be included in the final mapping.  Similarity: 0.8  Set minimum fraction of identity between the read and the reference sequence. If you want the reads to have e.g. at least 90% identity with the reference sequence in order to be included in the final mapping, set this value to 0.9.  Update contigs based on mapped reads  This means that the original contig sequences produced from the de novo assembly will be updated to reflect the mapping of the reads Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Velvet  De brujin assembler  Max kmer length-31, default 29  Commands  velveth directory -k-mer -readtype –file format filename  velvetg VAssemILL -exp_cov auto -cov_cutoff auto  exp_cov – allow the sytem to infer expected coverage of unique regions  Cov_cutoff - Allow the system to infer the removal of low coverage nodes  Designed for very short reads (25-50bp) Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Newbler  De Novo OLC assembler  Uses k-mer based hashing  Command – runAssembly [ filename ]  Designed for longer reads (454) Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

SOAP DeNovo2  Short reads DeNovo assembler  Designed to study Illumina GAII contigs  Command - SOAPdenovo-127mer all -s test.config -K 30 -R -p 4 -N o test_OP 1>ass.log 2>ass.err  Parameters specified:  Insert_size: 0, single end reads  Kmer_size: 23, default  asm_flag: both contigs and scaffold Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Assembler comparison- 454 ToolN50No. of contigs Avg. contig length No. of large contigs Largest contigRead usage % CLC Genomics wb. 93, ,107NA Newbler194, , , ToolN50No. of contigs Avg. contig length No. of large contigs Largest contig Read usage % CLC Genomics wb. 84, ,828NA Newbler111, , , nav_454_ vul_454_ Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Assembler comparison- Illumina ToolN50No. of contigsAvg. contig length Read usage %Largest contig Median coverage depth SOAP DeNovo1,07728,760184NA Velvet17,4081,4023, , CLC Genomics wb56, , ,565NA ToolN50No. of contigsAvg. contig length Read usage %Largest contig Median coverage depth SOAP DeNovo1,09426,773207NA Velvet15,6991,2533, , CLC Genomics wb87, , ,510NA nav_ill_ vul_ill_ Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

454 raw reads PRE-PROCESSING Illumina raw reads Pre-processing 454 reads Illumina reads Statistical analysis Read stats Published Genomes from public databases V. vulnificus YJ016 V. vulnificus CMCP6 V. vulnificus MO6-24/O Align Illumina against the reference Fastqc Prinseq NGS QC Compare mapping statistics Reference genome samstats bwa REFERENCE SELECTION Hybrid DeNovo Ray Illumina/ 454/ Hybrid DeNovo assembly 454 DeNovo Newbler CABOG SUTTA Illumina DeNovo Allpaths LG SOAP DeNovo Velvet SUTTA contigs * 3 Align illumina reads against 454 contigs Unmapped reads Mac vector CLC wb contigs Unmapped reads Evaluation GAGE Hawk-eye Illumina/454? reference based assembly AMOScmp contigs Unmapped reads DENOVO ASSEMBLY REFERENCE BASED ASSEMBLY Draft/ Finished genome Reference evaluation DNA Diff Parameter optimization CONTIG MERGING All possible combinations of the best 3 Mimimus MAIA PAGIT Mauve Finished genome Scaffolds GAGE GENOME FINISHING Gap filling Nulceotide identity MUMmer GRASS Built-in Process 454 Illumina Info. Chosen Ref. Assemblers Illumina 454 LEGEND hybrid

Reference Genomes  V. vulnificus MO6-24/O  V. vulnificus YJ016  V. vulnificus CMCP6

Reference vs. all contigs- 454 Tool/ReferenceCMCP6YJ016MO6-24/O Aligned contigs% Aligned bases% Aligned contigs% Aligned bases% Aligned contigs% Aligned bases% CLC Genomics wb. (n = 313) Newbler (n = 347) nav_454_ vul_454_ Tool/ReferenceCMCP6YJ016MO6-24/O Aligned contigs%- Aligned bases% Aligned contigs% Aligned bases% Aligned contigs% Aligned bases% CLC Genomics wb.NA Newbler (n = 142)

Reference vs. all contigs- Illumina Tool/ReferenceCMCP6YJ016MO6-24/O Aligned contigs% Aligned bases% Aligned contigs% Aligned bases% Aligned contigs% Aligned bases% SOAP DeNovo (n = 28,760) Velvet (n = 1402) nav_ill_ vul_ill_ Tool/ReferenceCMCP6YJ016MO6-24/O Aligned contigs% Aligned bases% Aligned contigs% Aligned bases%- Aligned contigs% Aligned bases% SOAP DeNovo (n = 26,773) Velvet(n = 1,253)

Visualization Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Road ahead…..  Get all the tools working  Optimize tool parameters  Use Illumina reads to finish 454 contigs  Performance considerations for the tool Input Data / Sequence Read Data / Pipeline Review / Un-processed data / Assemblers / Preliminary Data / Visualization / Future

Questions???