Genome Assembly. Sequencing Output. High Throughput Sequencing
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1 Genome High Throughput Sequencing Sequencing Output Example applications: Sequencing a genome (DNA) Sequencing a transcriptome and gene expression studies (RNA) ChIP (chromatin immunoprecipitation) Example platforms: 454 Illumina SOLiD F-3 Hundreds of millions of sequencing reads, each ~100 nts in length We need to re-assemble the genome from these sequencing reads - ACGTCGTCGATTCGTATGCGTACGTGTCGTGTA - CCGCGCGTACTCTCGATGTACGTGTTTACGATGTACTGTTGT - TTTGATCGTGTCACTGAAACACCATTACTTATCTTGGACATC - TGGATCGATTTTATCGAGTGTCGATTATGCGATCGAA - TCATACGCGTATCGATTATGCCACGTCGTCGAT - AGCGCTCTGGACGACGTCATCGTCGATCGATCGATCGTAAA - TCGGTCTCGATCGAGTGTCTGCGCGTCTCTGAGAAGAG - CTCCTCCGAGCGTCGCGATACGTATCGGATCTGACGTATTCGAT - CCGTATGCGTCGCGATCGATCGTGATTTGAGTCGATTACGT - TACGACGTTACGCGTCATTTGATGCGTATGCG - GAAGCGTACATGTCGTCTCTTCGTATGCGATCGTATGACG - ACCGATCGATTTCGATGTCGTATGTTGCGAT - ACGTCGTCGATTCGTATGCGTACGTGTCGTGTA - CCGCGCGTACTCTCGATGTACGTGTTTACGATGTACTGTTGT - TTTGATCGTGTCACTGAAACACCATTACTTATCTTGGACATC - TGGATCGATTTTATCGAGTGTCGATTATGCGATCGAA - TCATACGCGTATCGATTATGCCACGTCGTCGAT - AGCGCTCTGGACGACGTCATCGTCGATCGATCGATCGTAAA - TCGGTCTCGATCGAGTGTCTGCGCGTCTCTGAGAAGAG - CTCCTCCGAGCGTCGCGATACGTATCGGATCTGACGTATTCGAT - CCGTATGCGTCGCGATCGATCGTGATTTGAGTCGATTACGT - TACGACGTTACGCGTCATTTGATGCGTATGCG - GAAGCGTACATGTCGTCTCTTCGTATGCGATCGTATGACG - ACCGATCGATTTCGATGTCGTATGTTGCGAT - GTGCGTCGTATATGCCATCGTTCGTCGTACGTAC - ATCATCGATCGTTATATCGCGCCCATTCTA - CGAGAGTCGTCGTCGATTCGAGTCTGCGATCG - GATGCGGACGTATGTATATTACGTACGTTGACGTA - CAACTTTCTCTTCCGGTTCTCTCTAGACTTACGATCG - TCCGCTCGTCGCGCGCGATGCGCGACACACGTTACC - AGACTGAGTATATCGCGTATGCGACTGTATATACTGCGTC - GGATCGAGTCATCGTCGATCGAGTCGTATCGTAATCG - GCGATCGTTTCGAGTCATGCGATCTGATGCGAGTACGTCGTA - TTATTATCGTCGTACGTCGTCATCGATGCGTATATACT - AGTACGTATCTGAGCGTCTCTTCGGACGATCGATCGTACACGTGA - TTTATTACGACGATACGTGCTTGCGTATTGTTTCG - GTGCGTCGTATATGCCATCGTTCGTCGTACGTAC - ATCATCGATCGTTATATCGCGCCCATTCTA - CGAGAGTCGTCGTCGATTCGAGTCTGCGATCG - GATGCGGACGTATGTATATTACGTACGTTGACGTA - CAACTTTCTCTTCCGGTTCTCTCTAGACTTACGATCG - TCCGCTCGTCGCGCGCGATGCGCGACACACGTTACC - AGACTGAGTATATCGCGTATGCGACTGTATATACTGCGTC - GGATCGAGTCATCGTCGATCGAGTCGTATCGTAATCG - GCGATCGTTTCGAGTCATGCGATCTGATGCGAGTACGTCGTA - TTATTATCGTCGTACGTCGTCATCGATGCGTATATACT - AGTACGTATCTGAGCGTCTCTTCGGACGATCGATCGTACACGTGA - TTTATTACGACGATACGTGCTTGCGTATTGTTTCG F-4
2 DNA Sequencing DNA Sequencing CGTCATCGTATATTCATACGTACGGACTCCATCGTA F-5 F-6 DNA Sequencing >CGTCATCGTATATTCATACGTACGGACTCCATCG >GCGTATTATACGACTTATCCGGTCAATATATTTATATG >CCGTTATATCGATACTCGTCATGAGCGAGATATGACGAGCGACGATC... >CGATATCCATCATTACGAGACTCTCTAAGCGAT >CACTATTATTATATTACGATTATCTCTTAC >TTTCTAAGTCTTCGTCTTATGCGACGATTATACCGTATATTTTTCATTT >GTATGGATATCGCGTCACGCCGATTAACGCCGATTCG >CGCGATCGCGCGCGATCGCGTTCGTCGTATCGCCG >TGAAATAAGAGTATCTGAGTATCATTATCGCGATGCGCGATAACG >GCGTATTATACGACTTATCCGGTCAATATATTTATAT >CGCGATCGCGCGCGATCGCGTTCGTCGTATCGCCGATCGC >ATACACGTGAATATCGTTCGATCATATCTGCGTCTGGTCA >TGAAATAAGAGTATCTGAGTATCATTATCGCGATGCGCGA >CCGTTATATCGATACTCGTCATGAGCGAGATATGACGAGCGACGATCC >CGTCATCGTATATTCATACGTACGGACTCCATCG >TTTCTAAGTCTTCGTCTTATGCGACGATTATACCGTATATTTTTCATTT >GTATGGATATCGCGTCACGCCGATTAACGCCGATTCGTCGAGTA > > >TAA TAA A F-7 F-8
3 F-9 > >TAA > F-10 > >TAA > F-11 > >TAA > F-12 > >TAA >
4 F-13 > >TAA > AG F-14 > >TAA > F-15 > >TAA > AG F-16 > >TAA > A
5 Challenges debruijn graph can be implemented with a hash table Repeats Sequencing errors Each entry in hash table corresponds to an edge in the graph (each key is a k-mer and each value is the number of occurrences of the k-mer). Nodes are stored implicitly. F-17 F-18 C T C T corresponds to Euler path through graph Genome sequence starts with any k-mer (edge in the graph) Repeatedly extend genome sequence forward, one nucleotide at a time, until no further extensions are possible o The genome sequence is extended and a nucleotide added to its end if there exists a nucleotide (A, C, G, or T) that can be added to the end of the k-1 final nucleotides of the genome sequence to form a k-mer that is an edge in the graph. o If there are multiple individual nucleotides that can be added to the final k-1 nucleotides in the genome sequence to form k-mer edges in the graph, then the nucleotide resulting in the k-mer edge with the largest number of occurrences is chosen. o Each time the genome sequence is extended by a nucleotide, the corresponding k-mer edge is removed from the graph. F-19 corresponds to Euler path through graph Genome sequence starts with any k-mer (edge in the graph) Repeatedly extend genome sequence backward, one nucleotide at a time, until no further extensions are possible o The genome sequence is extended and a nucleotide added to its front if there exists a nucleotide (A, C, G, or T) that can be added to the front of the k-1 first nucleotides of the genome sequence to form a k-mer that is an edge in the graph. o If there are multiple individual nucleotides that can be prepended to the first k-1 nucleotides in the genome sequence to form k-mer edges in the graph, then the nucleotide resulting in the k-mer edge with the largest number of occurrences is chosen. o Each time the genome sequence is extended by a nucleotide, the corresponding k-mer edge is removed from the graph. F-20
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