DNA Sequencing • Shear DNA into millions of small fragments • Read 500 – 700 nucleotides at a time from the small fragments (Sanger method)
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Fragment Assembly • Assembles the individual overlapping short fragments (reads) into a genomic sequence • Shortest Superstring problem from last time is an overly simplified abstraction • Problems: – DNA read error rate of 1% to 3% – Can’t separate coding and template strands – DNA is full of repeats
• Let’s take a closer look
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Construction of Repeat Graph • Construction of repeat graph from k – mers: emulates an SBH experiment with a huge (virtual) DNA chip. • Breaking reads into k – mers: Transform sequencing data into virtual DNA chip data.
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Construction of Repeat Graph (cont’d) • Error correction in reads: “consensus first” approach to fragment assembly. Makes reads (almost) error-free BEFORE the assembly even starts. • Using reads and mate-pairs to simplify the repeat graph (Eulerian Superpath Problem).
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Approaches to Fragment Assembly Find a path visiting every VERTEX exactly once in the OVERLAP graph: Hamiltonian path problem
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Approaches to Fragment Assembly (cont’d)
Find a path visiting every EDGE exactly once in the REPEAT graph: Eulerian path problem
Linear time algorithms are known
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Making Repeat Graph Without DNA • Problem: Construct the repeat graph from a collection of reads.
? • Solution: Break the reads into smaller pieces. 10/18/2013
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Repeat Sequences: Emulating a DNA Chip • Virtual DNA chip allows the biological problem to be solved within the technological constraints.
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Repeat Sequences: Emulating a DNA Chip (cont’d) • Reads are constructed from an original sequence in lengths that allow biologists a high level of certainty. • They are then broken again to allow the technology to sequence each within a reasonable array.
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Minimizing Errors • If an error exists in one of the 20-mer reads, the error will be perpetuated among all of the smaller pieces broken from that read.
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Minimizing Errors (cont’d) • However, that error will not be present in the other instances of the 20-mer read. • So it is possible to eliminate most point mutation errors before reconstructing the original sequence.
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Conclusion from Previous Lecture • Graph theory is a vital tool for solving biological problems • Wide range of applications, including sequencing, motif finding, protein networks, and many more
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DNA Sequencing Timeline
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Generations of Sequences
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High-Throughput Sequencing • Also referred to as Next-Generation Sequencing • Parallelize the sequencing process, producing thousands or millions of sequences concurrently • Lower the cost of DNA sequencing beyond what is possible with standard dye-terminator methods. • In ultra-high-throughput sequencing as many as 500,000 sequencing-by-synthesis operations may be run in parallel
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Next Generation Sequencing: Amplified Single Molecule Sequencing
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Next Generation Sequencing: Amplified Single Molecule Sequencing
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454 Sequencing
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454 Sequencing
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454 Sequencing / Pyrosequencing
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454 Sequencing / Pyrosequencing
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454 Sequencing / Pyrosequencing
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SOLiD
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SOLiD
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Sequencing By Ligation
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Illumina
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Illumina
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Illumina
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Which Next-Gen Sequencer to Choose for your Project?
Human Genome Project • In Dec. 1, 1999, researchers in the Human Genome Project announced the complete sequencing of the DNA making up human chromosome 22. • In 2000, the completion of a “working draft” DNA sequence of the human genome was announced. • Special issues of Nature and Science came out in February of 2001 with the complete working draft human genome.
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Human Genome Project • International HapMap Project began in 2002. • Special issue of Nature Human Genome Collection (2006) • On June 13, 2013, The U.S. Supreme Court ruled that naturally occurring DNA cannot be patented, but that synthetically created cDNA is patent-eligible.
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References
• Simons, Robert W. Advanced Molecular Genetics Course, UCLA (2002). • Batzoglou, S. Computational Genomics Course, Stanford University (2006). http://ai.stanford.edu/~serafim/CS262_2006/ • Vierstraete, Andy. Next Generation Sequencing, University of Ghent. http://users.ugent.be/~avierstr/nextgen/nextgen.html
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Next Time • Protein Sequencing • Sections 8.10-8.15
