Download Sequencing genomes

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
• Coverage - the average number of reads representing a given
nucleotide in the reconstructed sequence. HGS: 8, WGS: 20
Genome assembly
• reads, contigs, scaffolds
• base calling, sequence assembly, PHRED/PHRAP
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)
New stuff
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
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.
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
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.
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
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 (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.
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.
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
cDNA
1. isolate mRNA from suitable cells
2. 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.
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 singlepass sequence reads derived from cDNA libraries.
• They can be generated either from 5’ or from 3’ end.
mRNA
cDNA
5’ ESTs
3’ ESTs
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, …
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
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.
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?
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.
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.
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 –
http://www.ebi.ac.uk/Tools/sss/ncbiblast/vectors.html)
http://www.ncbi.nlm.nih.gov/VecScreen/UniVec.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.
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
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.
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
Sequence Alignment
What is sequence alignment ?
CTTTTCAAGGCTTA
GGCTTATTATTGC
Fragments overlaps
CTTTTCAAGGCTTA
GGCTATTATTGC
CTTTTCAAGGCTTA
GGCT-ATTATTGC
What is sequence alignment ?
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG
TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
“EST clustering”
CCCCATGGTGGCGGCAGGTGACAG
CATGGGGGAGGATGGGGACAGTCCGG
TTACCCCATGGTGGCGGCTTGGGAAACTT
TGGCGGCTCGGGACAGTCGCGCATAAT
CCATGGTGGTGGCTGGGGATAGTA
TGAGGCAGTCGCGCATAATTCCG
TTACCCCATGGTGGCGGCTGGGGACAGTCGCGCATAATTCCG
consensus
Sequence alphabet
side chain charge at physiological
pH 7.4
Positively charged
side chains
Negatively charged
side chains
Polar uncharged side
chains
Special
Hydrophobic side
chains
Name
Arginine
Histidine
Lysine
Aspartic Acid
Glutamic Acid
Serine
Threonine
Asparagine
Glutamine
Cysteine
Selenocysteine
Glycine
Proline\
Alanine
Leucine
Isoleucine
Methionine
Phenylalanine
Tryptophan
Tyrosine
Valine
3 letters
Arg
His
Lys
Asp
Glu
Ser
Thr
Asn
Gln
Cys
Sec
Gly
Pro
Ala
Leu
Ile
Met
Phe
Trp
Tyr
Val
1 letter
R
H
K
D
E
S
T
N
Q
C
U
G
P
A
L
I
M
F
W
Y
V
Adenine
A
Thymine
T
Cytosine
G
Guanine
C
Sequence alignment
• Procedure of comparing sequences
• Point mutations – easy
ACGTCTGATACGCCGTATAGTCTATCT
ACGTCTGATTCGCCCTATCGTCTATCT
gapless alignment
• More difficult example
ACGTCTGATACGCCGTATAGTCTATCT
CTGATTCGCATCGTCTATCT
• However, gaps can be inserted to get something like this
insertion × deletion
indel
ACGTCTGATACGCCGTATAGTCTATCT
----CTGATTCGC---ATCGTCTATCT
gapped alignment
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
Flavors of sequence alignment
pair-wise alignment × multiple sequence alignment
Flavors of sequence alignment
global alignment × local alignment
global
local
align entire sequence
stretches of sequence with
the highest density of
matches are aligned,
generating islands of
matches or subalignments in
the aligned sequences
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
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
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: 11178260