Download What is a genome?

Bioinformatics for Genomics
It has not escaped our notice that the specific pairing we have postulated immediately suggests a
possible copying mechanism for the genetic material.
When I was young my Father used to tell me that the two most worthwhile pursuits in life were
the pursuit of truth and of beauty and I believe that Alfred Nobel must have felt much the same
when he gave these prizes for literature and the sciences.
Sometime in the future, I am a hundred percent certain
scientists will sit down at a computer terminal, design what
they want the organism to do, and build it.
What is a genome?
What is a genome?
The entirety of the inheritable
information of an organism
Being able to sequence the
genome allows us to 'read' all this
information of an organism
(and hopefully understand it)
The steps of the sequencing of the
human genome
1953. Watson and Crick propose the double helix model for DNA
1977. Sanger proposes the sequencing method with terminators
1986. Dulbecco auspicates in Science sequencing of the human genome
1988. Watson becomes director of the project at the NIH
1991. Craig Venter (then NIH) publishes the first EST sequencing
1992. Watson leaves the direction of the genome project at NIH, enter Collins
1992. Venter leaves NIH and founds TIGR
1995. TIGR sequences from the first bacterial genome, with the shotgun method
1998. Venter founds Celera Genomics and PE; He announces the genome for 2001
The race for the human genome starts (Venter VS Collins)
26 June 2000. Bill Clinton announces the completion of the human genome
sequencing (actually two 'proofs', published in early 2001)
to sequence the genome we must first be
able to sequence a gene
How long is a gene?
Bacterial gene
Eukaryotic gene
How long is a gene?
1000 bp for a bacterial gene
3000 bp for a human gene (much
more variable)
How long is a Sanger sequence?
Ok, but how long is a genome?
And how many genes does it contain?
Ok, but how long is a genome?
•
•
•
•
•
Human genome: 3,12 billion bases, 3120 Megabases (Mb)
Drosophila: 175 Megabases
Small eukaryote (Saccharomyces cerevisiae): 12 Mb
Big bacterial genome (Pseudomonas aeuriginosa): 6,4 Mb
Small bacterial genome (Haemophilus inflenzae): 1,8 Mb
And how many genes does it contain?
• Human genome: 3120 Mb, 25.000-40.000 genes
• Pseudomonas aeuriginosa: 6,4 Mb, 5570 genes
• Haemophilus inflenzae: 1,8 Mb, 1727 genes
So how do we sequence an entire genome?
The strategy of the public consortium for the
human genome
Cut a chromosome into pieces with
restriction enzymes, clone one piece in
a vector, subclone into smaller vectors,
and into smaller vectors
YACs (yeast artificial chromosome),
inserts from 0.5-1 Mb
BACs (bacterial artificial chromosomes),
inserts of 0.1-0.2 Mb
Cosmids, inserts under 50 Kb
Plasmid vectors, few kb
Merging the fragments of the genome
cloned in the different vectors
Here comes Venter, with his rebellion against NIH
Just 1% of the human genome translated into proteins
Why not start to sequence just mRNA?
Venter generates cDNA from RNA (reverse transcription) → cDNA cloning →
random sequencing of clones → production of EST
EST = Expressed Sequence Tags
1991: Venter publishes 337 ESTs corresponding to genes expressed in the
brain
The publication in Science was preceded by the presentation of a patent
application for the sequenced ESTs
Patent rejected, he leaves NIH to give birth to the TIGR, financed by
Healthcare Investment, with the goal to compete with the public
consortium to sequence the human genome
1993 – A novel idea for the first genome
End of 1993. Hamilton Smith (Nobel in 1978 for the
description of the first restriction enzyme) enters
the Scientific Committee of the TIGR.
Smith proposes the idea of the shotgun sequencing
Chosen organism: Haemophilus influenzae
Why shotgun?
NOT A NOVEL TECHNOLOGY
IT IS A NOVEL EXPERIMENTAL METHOD
It was invented to work with Sanger technology...
… but the principle is still used with nextgen
1. Random fragmentation of many copies of genomic DNA
Sonication, hydroshearing...
2. Cloning of the genomic fragments in a plasmid vector
3. Sequencing of the inserts based on PCR primers
located on the plasmid (known sequence)
Each sequence we obtain will be called a sequencing READ
3. Sequencing of the inserts based on PCR primers
located on the plasmid (known sequence)
An excess of sequences is generated
If the genome is x long, at least 5x bases are sequenced
The obtained sequences will cover the genome,
randomly, with different 'depth', or 'coverage'
Genome coverage: The average depth of sequencing
coverage is theoretically LN/G
L is the read length
N is the number of reads
G is the genome length
LN= total number of bases generated
Genome coverage: The average depth of sequencing
coverage is theoretically LN/G
L is the read length
N is the number of reads
G is the genome length
LN= total number of bases generated
EXAMPLE: 1 Megabase genome
100 nt reads
200,000 reads
The coverage is 200,000x100/1,000,000=20
Genome coverage: The average depth of sequencing
coverage is theoretically LN/G
HOWEVER: genome coverage of 1 does not mean that each
base in my genome has been sequenced, because I am
sequencing randomly
P=1-e-m
The probability that a base has been sequenced (P) is equal
to 1 minus e (Euler's number = 2.71828) elevated to -m,
where m is the coverage
Genome coverage
A coverage of 5x leads
to sequencing of
99.33% of the bases
IN THEORY
Possible problems?
Genome coverage
Uneven sequencing
Can be due to
Technical biases
Difficult stretches
So in the times of Sanger sequencing 10x was
considered good
Now with NextGen you want to go 30x, better if 50x
4. use bioinformatic techniques to ASSEMBLE the genome
Most bases will be covered on both strands, a few gaps will be
present
Example: I want to sequence the
following genome
This lecture is very very
interesting
I fragment, clone and sequence
This l
is lect
ure is
e is v
ry ver
teres
ecture
s very v
ry inte
esting
But I do not know the order of the fragments
Thanks to the partial overlapping
I can reconstruct the sequence
This l
is lect
ecture
ure is
e is v
s very v
ry ver
ry inte
teres
esting
Advantages of shotgun sequencing
-No Need for genomic mapping prior to sequencing
-High level of automation
-rapidità the sequencing phase
-reducing costs
Issues of shot-gun sequencing
- Highly purified genomic DNA
- Bionformatic support
- Difficulty in presence of repeated sequences in
the genome
Let's try to sequence the
sentence below
It's a fair bet that if it's fair
tomorrow, then my fair wife and I
will head to the Spring Fair, held in
a fair sized park, in this fair city to
win a prize, if everyone plays fair
It's a fair bet that if it's fair
tomorrow, then my fair wife and I
will head to the Spring Fair, held in
a fair sized park, in this fair city to
win a prize, if everyone plays fair
We will have a number of sequences
that will read 'fair' and we would not
know how to assemble them
So repeats, and other issues, can lead to gaps in the
assembly
Our assembly will not consist of 1 or more
chromosome, but multiple contigs
CONTIG: a contiguous sequence generated by the
overlap of sequencing reads
5. Finishing
This is the step in which we try to close the gaps
5. Finishing
This is the step in which we try to close the gaps
Molecular biology methods can help:
Cloning in vectors that can receive long inserts
PCR
Inverse PCR
Bioinformatics can also help, with a number of
approaches, but mainly with scaffolding
PAIRED END READS
These are reads that are generated when both ends of a
fragment are sequenced
We will thus have 2
reads, that represent
2 fragments of the
genome which are
distinct, but near
PAIRED END READS
Many technologies can generate paired end reads
(Sanger, 454, Illumina)
We can use the information from pairs to order contigs
into a scaffold
SCAFFOLD
A sequence of a genome that is composed of contigs and
gaps
Contigs are ordered based on the information from
paired ends
The unknown bases are indicated with N
SHOTGUN SEQUENCING PIPELINE
1. Fragmentation
2. Cloning
3. Random sequencing at high coverage
4. Bioinformatic assembly
5. Finishing
Final result: a complete assembly (sometimes)
So, does shotgun sequencing work?
Sequencing of Haemophilus influenzae (1995) proved that
it does
Automatization of the preparation steps
Improvement of Sanger sequencing technologies
Evolution of powerful assembly bioinformatics algorithms
Sequencing of D. melanogaster (2000)
Sequencing of the human genome (1998-2003)
"When we started to sequence the Drosophila, we had already halved the
timing and sequencing of a genome assembly such as Haemophilus, which
only three years ago represented a whole year of work. Today it would only
take eight hours to sequence it and fifteen minutes to assemble it. "
Craig Venter
5 November 2003
And the human genome
In 1998, the public project,
which has already costed1.9 billion dollars, is going slowly ...
In January 1998, Mike Hunkapiller (PE)
Shows Venter the PRISM 3700 (96 capillaries)
Celera Genomics is founded (PE funding, direction by Venter): 300 PRISM
3700, 100 robots, and computers for 80 million dollars
Celera announcees the sequencing of the human genome in two years, with a
shotgun approach to cost 200-500 million dollars
The race continues, with strong ethical implications
26 June 2000. Bill Clinton announces the completion of the human genome
sequencing (actually two 'proofs', published in early 2001).
And now the bioinformatics!
The assembly starts when we generate the
output of the sequencing machines
This output can differ, but most of the time it
will be a big FASTQ file
maybe you know what a FASTA file is?
a FASTA file
>PoolPRRSnew_1/1
CCCCGGGTCAAGGGCTGTTGTTTTATTGTTCACCTTCATTATGACAGTGCGAGGTGGTTT
ATACGGGATTTGCTGCAACAC
>PoolPRRSnew_2/1
CTTTCATGTGGTACGCAAGGGCGGCCAGGATCCGGTCACGATTGGGAACTAACTGCCGC
CCTGCTTCAATTTTGCAGCCGA
Starts with a
>
Followed by the name of the sequence
On the second line we have the nucleotides
The FASTQ FORMAT
Specific file format for DNA sequences
@PoolPRRSnew_1/1
CCCCGGGTCAAGGGCTGTTGTTTTATTGTTCACCTTCATTATGACAGTGCGAGGTGGT
+
1D1>CDF1AAAEABFGGHHGDGFAGC2EEAG11FBF2GHFHHHBBEGCEFFE/AEEEDGGE
@PoolPRRSnew_2/1
CTTTCATGTGGTACGCAAGGGCGGCCAGGATCCGGTCACGATTGGGAACTAACTGCC
+
ABBBBFFFFFBCFGGGGGGGGGGGGGGHHAHHHFGGGGHGEGHFHGHHGHHHHHHH
It is a plain text file that contains all the information regarding
the reads and their quality
THE FASTQ FORMAT
Each sequencing read is described in 4 line
FIRST LINE: @ followed by the unique name of the sequence
SECOND ROW: nucleotide sequence
THIRD ROW: +
FOURTH ROW: the quality of each base, in ASCII code
@PoolPRRSnew_1/1
CCCCGGGTCAAGGGCTGTTGTTTTATTGTTCACCTTCATTATGACAGTGCGAGGTGGTTTATACGG
+
1AAA?1D1>CDF1AAAEABFGGHHGDGFAGC2EEAG11FBF2GHFHHHBBEGCEFFE/AEEEDGGE?E?
A BASE QUALITY
Every sequence can contain errors
Base quality: given a probability of error P, the quality is:
Q = -10 log10P
So the probability of descresce error in logarithmic scale with
increasing quality
Bases with a quality <20 are usually discarded: QUALITY CHECK
THE ASSEMBLY
The ASSEMBLER is a software that uses algorithms to combine
the short sequences obtained in the process of sequencing in
longer fragments (contigs)
The goal is to find the shortest string (the genome) that
includes all of the input strings (the reads)
As the number of input strings grows, the difficulty of the
problem grows exponentially (NP complete problem)
We must use approximation algorithms
ASSEMBLY ALGORITHMS
An example of algorithm 'greedy':
1. pairwise comparison of the sequences
2. Choose the pair that aligns best
3. repeat steps 1 and 2
Greedy algorithms have issues with repeats
ASSEMBLY ALGORITHMS
Other methods are more refined and based on graphs
– Overlap-layout-consensus
– EULER assembler
ASSEMBLY ALGORITHMS
The difficulties that an assembler has to face are:
amount of data (gigabytes of sequences)
presence of repeated sequences
presence of sequencing errors
Tens of Assemblers have been developed, how to choose
Assemblathon 2: evaluating de novo methods of genome assembly in three
vertebrate species - GigaScience 2013, 2:10 doi:10.1186/2047-217X-2-10
Read the literature
• Consider your specific biological system
• See what others have done
• Read the manuals
Try different assemblers and evaluate your results
ASSEMBLY RESULTS
Your assembler will output a set of contigs, usually in a
fasta file
>contig19.1|size257_bb
gctcatttataacgttcctcaaagcctaaatgacaggttcaggtgggttgccttgctccc
gtcacctacccttcaaaatcaataatttcacccgctcctctatcaactgcaatcaatata
aaggtgtactcccacttccattctttggtcttttttttatatatgtgcacagctcctcaa
tctctaggacctcgatatccttaagtgactttgtgctgttgcagctcttatctaactttt
cttgcacatagccgccc
>contig20.1|size182_bb
ccaaaaagtataaacttttcaacgcctttttgaaacaatttccaagcttataaaacttga
taaggaaccggtcttccagccgcatcacgcactaaatcaccgttatcaaatttagctgct
acataattgccatgcccaatttttccgcccttatacattacaggtttcacttcttttgca
cc
How good is my assembly? What parameters should we check?
ASSEMBLY RESULTS
Your assembler will output a set of contigs, usually in a
fasta file
>contig19.1|size257_bb
gctcatttataacgttcctcaaagcctaaatgacaggttcaggtgggttgccttgctccc
gtcacctacccttcaaaatcaataatttcacccgctcctctatcaactgcaatcaatata
aaggtgtactcccacttccattctttggtcttttttttatatatgtgcacagctcctcaa
tctctaggacctcgatatccttaagtgactttgtgctgttgcagctcttatctaactttt
cttgcacatagccgccc
>contig20.1|size182_bb
ccaaaaagtataaacttttcaacgcctttttgaaacaatttccaagcttataaaacttga
taaggaaccggtcttccagccgcatcacgcactaaatcaccgttatcaaatttagctgct
acataattgccatgcccaatttttccgcccttatacattacaggtttcacttcttttgca
cc
To evaluate the assembly we use multiple parameters:
the number of contigs
the genome size
N50: the length of the shortest contig that added to those of higher
dimensions comprises at least 50% of the genome
N50: the length of the shortest contig that added to those of
higher dimensions comprises at least 50% of the genome
EXAMPLE OF ASSEMBLY RESULTS
Sequencing of 16 genomes of Klebsiella
pneumoniae
ASSEMBLY RESULTS
Genome size: GOOD, always as expected
N50: not bad, always above 100000nt
Contigs number: acceptable, maybe not for KPCVA3
However, since we know our biological system, we know these
genomes contain plasmids, which make the assembly quality lower
ASSEMBLY RESULTS
What is GOOD depends on what we aim for
If we are sequencing a novel genome, we
should try to CLOSE it
If we want to compare similar isolates we can
accept the above results