Last lecture summary • recombinant DNA technology • DNA polymerase (copy DNA), restriction endonucleases (cut DNA), ligases (join DNA) • DNA cloning – vector (plasmid, BAC), PCR • genome mapping relative locations of genes are established by following inheritance patterns visual appearance of a chromosome when stained and examined under a microscope the order and spacing of the genes, measured in base pairs sequence map • genetic markers • polymorphic (alternative alleles) • restriction fragment length polymorphisms (RFLPs) • some restriction sites exist as two alleles • simple sequence length polymorphisms (SSLPs) • repeat sequences, minisatellites (repeat unit up to 25 bp), microsatellites (repeat unit of 2-4 bp) • single nucleotide polymorphisms (SNPs, pron.: “snips”) • Positions in a genome where some individuals have one nucleotide and others have a different nucleotide RFLP SSLP New stuff DNA sequencing • Sanger method, chain-termination method, developed 1974, Nobel prize in chemistry 1980 • The key principle: use of dideoxynucleotide triphosphates (ddNTPs) as DNA chain terminators. dNTP ddNTP source: http://openwetware.org/wiki/BE.109:Bio-material_engineering/Sequence_analysis source: wikipedia Shotgun sequencing • Current technology can only reliably sequence a short stretch – • • • • a ‘read’ is typically ~1000 bp. However genomes are large. The sequence of a long DNA molecule has to be constructed from a series of shorter sequences. This is done by breaking (cleaving by restriction endonuclease) the molecule into fragments, determining their sequences, and using a computer to search for overlaps and build up the master sequence This shotgun sequencing is the standard approach for sequencing small prokaryotic genomes. But is much more difficult with larger eukaryotic genomes, as it can lead to errors when repeats are analyzed. • human genome is repeat-rich, >50% repeats (50-500 kpb duplicated regions with >98% identity) Target Copies Shotgun Sequence each short piece Sequence assembly Consensus Finalizing (directed read) source: slides by Martin Farach-Colton source: Brown T. A. , Genomes. 2nd ed. http://www.ncbi.nlm.nih.gov/books/NBK21129/ Human genome project (HGP) • Determine the sequence of haploid human genome • Govermentally funded (DOE) • Began in 1990, working draft published in 2001, complete in 2003, last chromosome finished in 2006 • Cost: $3 billion • Whose genome was sequenced? • The “reference genome” is a composite from several people who donated blood samples. Celera - competition begins • In 1998, a similar, privately • • • • funded quest was launched by the American researcher Craig Venter, and his company Celera Genomics. The $300,000,000 Celera effort was intended to proceed at a faster pace and at a fraction of the cost. Celera wanted to patent identified genes. Celera promised to release data annually (while the HGP daily). However, Celera would, unlike HGP, not permit free redistribution or scientific use of the data. HGP was compelled to release (7.7. 2000) the first draft of the human genome before Celera for this reason. How did it end? • March 2000 – president Clinton announced that the • • • • genome sequence could not be patented, and should be made freely available to all researchers. The statement sent Celera's stock plummeting and dragged down the biotechnology-heavy Nasdaq. The biotechnology sector lost about $50 billion in two days. Celera and HGP annouced jointly the draft sequence in 2000. The drafts covered about 83% of the genome. Improved drafts were announced in 2003 and 2005, filling in to ≈92% of the sequence currently. Human genome • 3 billions bps, ~20 000 – 25 000 genes • Only 1.1 – 1.4 % of the genome sequence codes for proteins. • State of completion: • best estimate – 92.3% is complete • problematic unfinished regions: centromeres, telomeres (both contain highly repetitive sequences), some unclosed gaps • It is likely that the centromeres and telomeres will remain unsequenced until new technology is developed • Genome is stored in databases • Primary database – Genebank (http://www.ncbi.nlm.nih.gov/sites/entrez?db=nucleotide) • Additional data and annotation, tools for visualizing and searching • UCSCS (http://genome.ucsc.edu) • Ensembl (http://www.ensembl.org) Hierarchical genome shotgun – HGS • Hierarchical genome shotgun, hierarchical shotgun sequencing, clone-by-clone sequencing, map-based shotgun sequencing, clone contig sequencing • Adopted by HGP • Strategy “map first, sequence second” • Create physical map • Divide chromosomes to smaller fragments. • Order (map) them to correspond to their respective locations on the chromosomes. • Determine the base sequence of each of the mapped fragments. Hierarchical genome shotgun – HGS 1. Map genome • As genetic markers (landmarks), short tagged sites (STS) were used (200 to 500 base pair DNA sequence that has a single occurrence in the genome) 2. Copy target DNA 3. Make BAC library The sequenced sub-clones • cleave (partial cleavage by restriction endonuclease) all target DNA are copies randomly, insert these sub-clones into BACs linked up to produce the DNA 4. Physically map all BACs contig, which is the de-coded 5. Find a subset of BACs that cover target DNA version of the original source • minimal tiling path DNA. As this method progresses, 6. Shotgun sequence only BACs at minimal tailinglarger path and larger • Divide BACs into fragments (ultrasoundcontigs or pressure), plasmid until a will bedo produced, cloning, reconstruct BAC sequence single ordered contig of the 7. Fill in gaps between BACS genome is achieved. 8. Merge into consensus sequence http://www.nature.com/scitable/content/idealized-representation-of-the-hierarchical-shotgun-sequencing-48221 Minimal tiling path A collection of overlapping bacterial artificial chromosome (BAC) clones. The clones outlined in red, which provide a minimal tiling path across the corresponding genomic region, are selected for sequencing. Coverage • As it was shown, individual nucleotides are represented • • • • • • more than with one read. Coverage is the average number of reads representing a given nucleotide in the reconstructed sequence. Let’s say that for a source strand of length G = 100 Kbp we sequence R = 1 500 reads of average legth L = 500. Thus, we collect N = RL = 750 Kbps of data. So we have sequenced on average every bp in the source N/G = 7.5 times. The coverage is 7.5X Coverage in HGS adopted by HGP was 8X. Whole genome shotgun – WGS • Adopted by Celera. • De facto application of shotgun to large genome. Never • • • • done before on such a large scale. Expensive and time consuming mapping is not performed. Each piece of DNA is cut into smaller fragments. Each fragment is sequenced first, and then overlapping sequences are joined together to create the contig. To achieve enough of accuracy, higher coverage (20X) had to be used. Crucial for the assembly was development of new algorithms. Genome assembly • Aligning and merging short fragments of DNA sequence in order to reconstruct the original (loger) sequence. • reads – typically 500-1000 bp, merge them into contigs, arrange/merge contigs into scaffolds • scaffold – a series of contigs that are in the right order but are not necessarily connected in one continuous stretch of sequence source: Xiong, Essential Bioinformatics Genome assembly • Can be very computationally intensive when dealt at the whole genome level. • Major challenges: • sequence errors – can be corrected by drawing consensus sequence from an alignment of multiple overlapped sequences • contamination by bacterial vectors – can be removed using filtering programs prior to assembly • repeats – RepeatMasker (http://www.repeatmasker.org/) can be used to detect and mask repeats Forward-reverse constraint • Common constraint to avoid errors due to the repeats: forward-reverse constraint • Sequence is generated from both ends of a clone → distance between the two opposing fragments of a clone is fixed to a certain range (defined by a clone length). When the constraint is applied, even when one of the fragments has a perfect match with a repetitive element outside the range, it is not able to be moved to that location to cause missassembly. red fragment is misassembled no constraint constraint source: Xiong, Essential Bioinformatics Sequence assemblers • base calling – convert raw/processed data from a sequencing instrument into sequences and scores • individual bases have scores, reflect the likelihood the base is correct/incorrect • in capillary sequencing, identify the sequence from chromatogram source: Lee SH, Vigliotti VS, Pappu S., J Clin Pathol. 2010, 63(3) 235-9 PMID: 19858529 PHRED • Base caller • Reads DNA sequence chromatogram files and analyzes the peaks to call bases. • base quality score – PHRED examines peaks around each base call to assign a score q to each base call that is logarithmically linked to the error propability P: = − 10 log10(), typically taken q = 20 corresponding to 1% error probability (99% correct base calling) • Phred is a two-step process: • Training: Given a set of reads, labels as to which bases are correct, and a set of quality statistics for each base, produce a model that can predict error rates for unseen bases • Application: Given new reads and quality statistics, predict the quality for each of the bases. PHRAP • Sequence assembly • Takes PHRED base-call files with quality scores as input. • Aligns individual fragments in a pairwise fashion. The base quality information is taken into account during the pairwise alignment. Personal human genomes • Personal genomes had not been sequenced in the Human Genome Project to protect the identity of volunteers who provided DNA samples. • Following personal genomes are available by July 2011: • Japanese male (2010, PMID: 20972442) • Korean male (2009, PMID: 19470904) • Chinese male (2008, PMID: 18987735) • Nigerian male (2008, PMID: 18987734) • J. D. Watson (2008, PMID: 18421352) • J. C. Venter (2007, PMID: 17803354) • HGP sequence is haploid, however, the sequence maps for Venter and Watson for example are diploid, representing both sets of chromosomes.