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Last lecture summary

Last lecture summary. Sequencing strategies. Hierarchical genome shotgun HGS – Human Genome Project “map first, sequence second ” clone-by-clone … cloning is performed twice (BAC, plasmid). Sequencing strategies. Whole genome shotgun WGS – Celera shotgun, no mapping

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Last lecture summary

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  1. Last lecture summary

  2. Sequencing strategies • Hierarchical genome shotgun HGS – Human Genome Project • “map first, sequence second” • clone-by-clone … cloning is performed twice (BAC, plasmid)

  3. Sequencing strategies • Whole genome shotgun WGS – Celera • shotgun, no mapping • Coverage- the average number of reads representing a given nucleotide in the reconstructed sequence. HGS: 8, WGS: 20

  4. Genome assembly • reads, contigs, scaffolds • base calling, sequence assembly, PHRED/PHRAP

  5. 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)

  6. New stuff

  7. New generation sequencing (NGS) • The completion of human genome was just a start of modern DNA sequencing era – “high-throughput next generation sequencing” (NGS). • New approaches, reduce time and cost. • Holly Grail of sequencing – complete human genome below $ 1000. • Archon X Prize • http://genomics.xprize.org/ • $10 million prize is to be awarded to the private company that is able to sequence 100 human genomes within 10 days at cost of no more than $10 000 per genome

  8. 1st and 2nd generation of sequencers • 1st generation – ABI Prism 3700 (Sanger, fluorescence, 96 capillaries), used in HGP and in Celera • Sanger method overcomes NGS by the read length (600 bps) • 2nd generation - birth of HT-NGS in 2005. 454 Life Sciences developed GS 20 sequencer. Combines PCR with pyrosequencing. • Pyrosequencing – sequencing-by-synthesis • Relies on detection of pyrophosphate release on nucleotide incorporation rather than chain termination with ddNTs. • The release of pyrophosphate is detected by flash of light (chemiluminiscence). • Average read length: 400 bp • Roche GS-FLX 454 (successor of GS 20) used for J. Watson’s genome sequencing.

  9. 3rd generation • 2nd generation still uses PCR amplification which may introduce base sequence errors or favor certain sequences over others. • To overcome this, emerging 3rd generation of seqeuencers performs the single molecule sequencing (i.e. sequence is determined directly from one DNA molecule, no amplification or cloning). • Compared to 2nd generation these instruments offer higher throughput, longer read lengths (~1000 bps), higher accuracy, small amount of starting material, lower cost

  10. source: http://www.genome.gov/27541954 NHGRI Costs transition to 2nd generation $0.19 National Human Genome Research Institute (NHGRI) tracks the costs associated with sequencing.

  11. Which genomes were sequenced? • http://www.ncbi.nlm.nih.gov/sites/genome • GOLD – Genomes online database(http://www.genomesonline.org/) • information regarding complete and ongoing genome projects

  12. Important genomics projects • The analysis of personal genomes has demonstrated, how difficult is to draw medically or biologically relevant conclusions from individual sequences. • More genomes need to be sequenced to learn how genotype correlates with phenotype. • 1000 Genomesproject (http://www.1000genomes.org/) started in 2009. Sequence the genomes of at least a 1000 people from around the world to create the detailed and medically useful picture of human genetic variation. • 2nd generation of sequencers is used in 1000 Genomes. • 10 000 Genomes will start soon.

  13. Important genomics projects • ENCODE project (ENCyclopedia Of DNA Elements, http://www.genome.gov/ENCODE/) • by NHGRI • identify all functional elements in the human genome sequence • Defined regions of the human genome corresponding to 30Mb (1%) have been selected. • These regions serve as the foundation on which to test and evaluate the effectiveness and efficiency of a diverse set of methods and technologies for finding various functional elements in human DNA.

  14. Rapid Evolution of Next Generation Sequencing Technologies • 2000: Human genome working drafts • Data unit of approximately 10x coverage of human • 10 years and cost about $3 billion • 2008: Major genome centers can sequence the same number of base pairs every 4 days • 1000 Genome project launched • World-wide capacity dramatically increasing • 2009: Every 4 hours ($25,000) • 2010: Every 14 minutes ($5,000) • Illumina HiSeq2000 machine produces 200 gigabases per 8 day run

  15. cDNA • isolate mRNA from suitable cells • convert it to complementary DNA (cDNA) using the enzyme reverse transcriptase (+ DNA poymerase) • cDNA contains only expressed genes, no intergenic regions, no introns (just exons). • Because usually the desired gene sequences still represent only a tiny proportion of the total cDNA population, the cDNA fragments are amplified by cloning/PCR. • cDNA library – a library is defined simply as a collection of different DNA sequences that have been incorporated into a vector.

  16. ESTs • Expressed Sequence Tag • Their use was promoted by Craig Venter. At that time (1991) it was a revolutionary way for gene identification. • EST is a short subsequence (200-800 bps) of cDNA sequence. They are unedited, randomly selected single-pass sequence reads derived from cDNA libraries. • They can be generated either from 5’ or from 3’ end. mRNA cDNA 5’ ESTs 3’ ESTs

  17. ESTs • ESTs and cDNA sequences provide direct evidence for all sampled transcripts and they are currently the most important resources for transcriptome exploration. • ESTs/cDNA sequences cover the genes expressed in the given tissue of the given organism under the given conditions. • housekeeping genes – gene products required by the cell under all growth conditions (genes for DNA polymerase, RNA polymerase, rRNA, tRNA, …) • tissue specific genes – different genes are expressed in the brain and in the liver, enzymes responding to a specific environmental condition such as DNA damage, …

  18. ESTs vs. whole genome • Whole genome sequencing is still impractical and expensive for organisms with large genome size. • Genome expansion, as a result of retrotransposon repeats, makes whole genome sequencing less attractive for plants such as maize. • Transposons - sequences of DNA that can move (transpose) themselves to new positions within the genome. • Retrotransposons – subclass of transposons, they can amplify themselves. Ubiquitous in eukaryotic organisms (45%-48% in mammals, 42% in human). Particularly abundant in plants (maize – 49-78%, wheat – 68%) • Genome expansion – increase in genome size, one of the elements of genome evolution

  19. EST properties • Individual raw EST has negligible biological information, it is just a very short copy of mRNA . • It is highly error prone, especially at the ends. The overall sequence quality is usually significantly better in the middle. Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker's guide to expressed sequence tag (EST) analysis. Brief Bioinform. 2007 8(1):6-21. PMID: 16772268.

  20. Problems in ESTs • Redundancy • Under-representation and over-representation of selected host transcripts (i.e. sequence bias) • Base calling errors (as high as 5%) • Contamination from vector sequences • Repeats may pose problems • Natural sequence variations (e.g. SNPs) – how to distinguish them and sequencing artifacts?

  21. ESTs on the web • Largest repository: dbEST (http://www.ncbi.nlm.nih.gov/dbEST/) • 1.7. 2011 – 69 992 536 ESTs from more than 1 000 organisms • UniGene (http://www.ncbi.nlm.nih.gov/unigene) stores unique genes and represents a nonredundant set of gene-oriented clusters generated from ESTs.

  22. EST analysis generic steps involved in EST analysis The aim of the analysis: augment weak signals, make consensus, when a multitude of ESTs are analysed reconstruct transcriptome of the organism. Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker's guide to expressed sequence tag (EST) analysis. Brief Bioinform. 2007 8(1):6-21. PMID: 16772268.

  23. EST preprocessing • Reduces the overall noise in EST data to improve the efficacy of subsequent analyses. • Remove vector contaminating fragments. • Compare ESTs with non-redundant vector databases (UniVec - http://www.ncbi.nlm.nih.gov/VecScreen/UniVec.html, EMVEC – http://www.ebi.ac.uk/Tools/sss/ncbiblast/vectors.html) • Repeats must be detected and masked using RepeatMasker (http://www.repeatmasker.org/). • Resources for EST pre-processing: page 12 in Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker's guide to expressed sequence tag (EST) analysis. Brief Bioinform. 2007 8(1):6-21. PMID: 16772268.

  24. EST clustering • Collect overlapping ESTs from the same transcript of a single gene into a unique cluster to reduce redundancy. • Clustering is based on the sequence similarity. • Different steps for EST clustering are described in detail in Ptitsyn A, Hide W. CLU: a new algorithm for EST clustering. BMC Bioinformatics. 2005; 6 Suppl 2:S3. PubMed PMID: 16026600 • The maximum informative consensus sequence is generated by ‘assembling’ these clusters, each of which could represent a putative gene. This step serves to elongate the sequence length by culling information from several short EST sequences simultaneously. • Sequence clustering and assembly: CAP3

  25. Functional annotations • Database similarity searches (BLAST) are subsequently performed against relevant DNA databases and possible functionality is assigned for each query sequence if significant database matches are found. • Additionally, a consensus sequence can be conceptually translated to a putative peptide and then compared with protein sequence databases. Protein centric functional annotation, including domain and motif analysis, can be carried out using protein analysis tools.

  26. EST analysis pipelines • Large-scale sequencing projects (thousands of ESTs generated daily) – store, organize and annotate EST data in an automatic pipeline. • Database of raw chromatograms → clean, cluster, assemble, generate consensus, translate, assign putative function based on various DNA/protein similarity searches • examples: • TGI Clustering tools (TGICL) http://compbio.dfci.harvard.edu/tgi/software/ • PartiGene http://nebc.nerc.ac.uk/tools/other-tools/est

  27. Sequence Alignment

  28. What is sequence alignment ? CTTTTCAAGGCTTA GGCTTATTATTGC Fragments overlaps CTTTTCAAGGCTTA GGCTATTATTGC CTTTTCAAGGCTTA GGCT-ATTATTGC

  29. What is sequence alignment ? CCCCATGGTGGCGGCAGGTGACAG CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT TGGCGGCTCGGGACAGTCGCGCATAAT CCATGGTGGTGGCTGGGGATAGTA TGAGGCAGTCGCGCATAATTCCG “EST clustering” CCCCATGGTGGCGGCAGGTGACAG CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT TGGCGGCTCGGGACAGTCGCGCATAAT CCATGGTGGTGGCTGGGGATAGTA TGAGGCAGTCGCGCATAATTCCG consensus TTACCCCATGGTGGCGGCTGGGGACAGTCGCGCATAATTCCG

  30. Sequence alphabet

  31. Sequence alignment • Procedure of comparing sequences • Point mutations – easy • More difficult example • However, gaps can be inserted to get something like this ACGTCTGATACGCCGTATAGTCTATCTACGTCTGATTCGCCCTATCGTCTATCT gapless alignment ACGTCTGATACGCCGTATAGTCTATCTCTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT----CTGATTCGC---ATCGTCTATCT gapped alignment insertion × deletion indel

  32. Why align sequences – continuation • The draft human genome is available • Automated gene finding is possible • Gene: AGTACGTATCGTATAGCGTAA • What does it do? • One approach: Is there a similar gene in another species? • Align sequences with known genes • Find the gene with the “best” match

  33. Flavors of sequence alignment pair-wise alignment × multiple sequence alignment

  34. Flavors of sequence alignment global alignment × local alignment global align entire sequence stretches of sequence with the highest density of matches are aligned, generating islands of matches or subalignments in the aligned sequences local

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