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 (
Additional data and annotation, tools for visualizing and searching
UCSCS (
Ensembl (

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
$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 $ 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 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.
PYROSEQUENCING
- "Sequencing by synthesis" involves taking a single strand of the DNA to be sequenced and then synthesizing its complementary strand enzymatically. The pyrosequencing method is based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemiluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step. The template DNA is immobile, and solutions of A, C, G, and T nucleotides are sequentially added and removed from the reaction. Light is produced only when the nucleotide solution complements the first unpaired base of the template. The sequence of solutions which produce chemiluminescent signals allows the determination of the sequence of the template.
- Based on excellent and up-to-date review Pareek CS, Smoczynski R, Tretyn A. Sequencing technologies and genome sequencing. J Appl Genet Jun 23. PubMed PMID:

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. NHGRI Costs transition to 2nd generation $0.19
source:
NHGRI Costs
transition to 2nd generation
Moore's Law - doubling of 'compute power' every two years
Moore’s Law curve here is hypothetical
In both graphs, note: (1) the use a logarithmic scale on the Y axis; and (2) the sudden and profound out-pacing of Moore's Law beginning in January The latter represents the time when the sequencing centers transitioned from Sanger-based (dideoxy chain termination sequencing) to 'second generation' (or 'next-generation') DNA sequencing technologies.
$0.19
National Human Genome Research Institute (NHGRI) tracks the costs associated with sequencing.

11. Which genomes were sequenced?
GOLD – Genomes online database (
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 Genomes project ( started in 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.
Genomes will start soon.

13. Important genomics projects
ENCODE project (ENCyclopedia Of DNA Elements,
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 ( 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.
- cerpano z Nagaraj SH, Gasser RB, Ranganathan S. A hitchhiker's guide to expressed sequence tag (EST) analysis. Brief Bioinform Jan;8(1):6-21. PubMed PMID:
mRNA
cDNA
5’ ESTs
3’ ESTs

17. ESTs
ESTs and cDNA sequences provide direct evidence for all the 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 sizes.
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
- ubiquitous - vsudypritomny

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 (1):6-21. PMID:

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 (
– ESTs from more than organisms
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 (1):6-21. PMID:

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 - EMVEC –
Repeats must be detected and masked using RepeatMasker (
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 (1):6-21. PMID:

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:
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)
PartiGene

27. Sequence Alignment

28. What is sequence alignment ?
CTTTTCAAGGCTTA
GGCTTATTATTGC
Fragments overlaps
CTTTTCAAGGCTTA
GGCTATTATTGC
navozeni squence alignmentu na prikladech, kdy se tento objevuje v predchozim vykladu
nahore je presny overlap
dole je priblizny overlap, jsou ukazana dve zarovnani, zarovnani s vlozenou mezerou je vice optimalni
CTTTTCAAGGCTTA
GGCT-ATTATTGC

29. What is sequence alignment ?
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
“EST clustering”
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
toto je pouze demonstracni zarovnani
nahore jsou EST sekvence
clustering vede ke konsensualni sekvenci
ale ty sekvence musime nejak takhle setridit
samozrejme jsou tam chyby, caste na koncich ESTU, dve jsou ukazany (ale mam jich tam vic)
TTACCCCATGGTGGCGGCTGGGGACAGTCGCGCATAATTCCG
consensus

30. Sequence alphabet Adenine A Thymine T Cytosine G Guanine C Name
side chain charge at physiological pH 7.4
Name
3 letters
1 letter
Positively charged side chains
Arginine
Arg R
Histidine
His H
Lysine
Lys K
Negatively charged side chains
Aspartic Acid
Asp D
Glutamic Acid
Glu E
Polar uncharged side chains
Serine
Ser S
Threonine
Thr T
Asparagine
Asn N
Glutamine
Gln Q
Special
Cysteine
Cys C
Selenocysteine
Sec U
Glycine
Gly G
Proline\
Pro P
Hydrophobic side chains
Alanine
Ala A
Leucine
Leu L
Isoleucine
Ile I
Methionine
Met M
Phenylalanine
Phe F
Tryptophan
Trp W
Tyrosine
Tyr Y
Valine
Val V
Adenine A
Thymine T
Cytosine G
Guanine C

31. Sequence alignment Procedure of comparing sequences
Point mutations – easy
More difficult example
However, gaps can be inserted to get something like this
ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT
gapless alignment
ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT
Gaps correspond to inserion in one sequnce, or deletion in another. (indel)
Comparing two genes it is generally impossible to tell if an indel is an insertion in one gene, or a deletion in another, unless ancestry is known: ACGTCTGATACGCCGTATCGTCTATCT ACGTCTGAT---CCGTATCGTCTATCT
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
- párové/násobné zarovnání
- Sequences that are quite similar and approximately the same length are suitable candidates for global alignment.
- Local alignments are more suitable for aligning sequences that are similar along some of their lengths but dissimilar in others, sequences that differ in length, or sequences that share a conserved region or domain.

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
- párové/násobné zarovnání
- Sequences that are quite similar and approximately the same length are suitable candidates for global alignment.
- Local alignments are more suitable for aligning sequences that are similar along some of their lengths but dissimilar in others, sequences that differ in length, or sequences that share a conserved region or domain.
local

35. Protein vs. DNA sequences
Given the choice of aligning DNA or protein, it is often more informative to compare protein sequences.
There are several reasons for this:
Many changes in DNA do not change the amino acid that is specified.
Many amino acids share related biophysical properties. Though these amino acids are not identical, they can be more easily substituted each with other. These relationships can be accounted for using scoring systems.
When is it appropriate to compare nucleic sequences?
confirming the identity of DNA sequence in database search, searching for polymorphisms, confirming identity of cloned cDNA
When nucleotide sequence is analyzed, it is usually preferable to study the protein sequences.
Particularly 3rd position in codon does not change the coded amino acid.

36. Evolution of sequences
The sequences are the products of molecular evolution.
When sequences share a common ancestor, they tend to exhibit similarity in their sequences, structures and biological functions.
DNA1
DNA2
Protein1
Protein2
- similar sequences produce similar proteins – this is probably the most powerful idea of bioinformatics because it enables us to make predictions. Often little is known about the function of new sequence from a genome sequencing program, but if similar sequences can be found in a database for which functional or structural information is available, then this can be used as the basis of a prediction of function or structure for the new sequence.
Sequence similarity
Similar 3D structure
Similar function
Similar sequences produce similar proteins
However, this statement is not a rule. See Gerlt JA, Babbitt PC. Can sequence determine function? Genome Biol. 2000;1(5) PMID: