1. Introduction to next generation sequencing
Pimlapas Leekitcharoenphon (Shinny)

2. History
1953
Watson & Crick
The first DNA sequences (24 bp of lac operator in 1973 ) were obtained in the early 1970s using laborious methods based on two-dimensional chromatography. Several notable advancements in DNA sequencing were made during the 1970s. Frederick Sanger developed rapid DNA sequencing methods at the MRC Centre, Cambridge, UK and published a method for "DNA sequencing with chain-terminating inhibitors" in 1977.[6] Walter Gilbert and Allan Maxam at Harvard also developed sequencing methods, including one for "DNA sequencing by chemical degradation".[7][8] In 1973, Gilbert and Maxam reported the sequence of 24 basepairs using a method known as wandering-spot analysis.[9].The first full DNA genome to be sequenced was that of bacteriophage φX174 in 1977.[10.Leroy E. Hood's laboratory at the California Institute of Technology and Smith announced the first semi-automated DNA sequencing machine in 1986.[citation needed] This was followed by Applied Biosystems' marketing of the first fully automated sequencing machine, the ABI 370, in 1987 In 1995, Venter, Hamilton Smith, and colleagues at The Institute for Genomic Research (TIGR) published the first complete genome of a free-living organism, the bacterium Haemophilus influenzae. The circular chromosome contains 1,830,137 bases and its publication in the journal Science[12] marked the first published use of whole-genome shotgun sequencing, eliminating the need for initial mapping efforts. By 2001, shotgun sequencing methods had been used to produce a draft sequence of the human genome.[13][14]Several new methods for DNA sequencing were developed in the mid to late 1990s. These techniques comprise the first of the "next-generation" sequencing methods. In 1996,
First delivered to the United States Government in 1950, the UNIVAC 1101 or ERA 1101 is considered to be the first computer that was capable of storing and running a program from memory.

3. History
The first DNA sequences (24 bp of lac operator in 1973 ) were obtained in the early 1970s using laborious methods based on two-dimensional chromatography. Several notable advancements in DNA sequencing were made during the 1970s. Frederick Sanger developed rapid DNA sequencing methods at the MRC Centre, Cambridge, UK and published a method for "DNA sequencing with chain-terminating inhibitors" in 1977.[6] Walter Gilbert and Allan Maxam at Harvard also developed sequencing methods, including one for "DNA sequencing by chemical degradation".[7][8] In 1973, Gilbert and Maxam reported the sequence of 24 basepairs using a method known as wandering-spot analysis.[9].The first full DNA genome to be sequenced was that of bacteriophage φX174 in 1977.[10.Leroy E. Hood's laboratory at the California Institute of Technology and Smith announced the first semi-automated DNA sequencing machine in 1986.[citation needed] This was followed by Applied Biosystems' marketing of the first fully automated sequencing machine, the ABI 370, in 1987 In 1995, Venter, Hamilton Smith, and colleagues at The Institute for Genomic Research (TIGR) published the first complete genome of a free-living organism, the bacterium Haemophilus influenzae. The circular chromosome contains 1,830,137 bases and its publication in the journal Science[12] marked the first published use of whole-genome shotgun sequencing, eliminating the need for initial mapping efforts. By 2001, shotgun sequencing methods had been used to produce a draft sequence of the human genome.[13][14]Several new methods for DNA sequencing were developed in the mid to late 1990s. These techniques comprise the first of the "next-generation" sequencing methods. In 1996,
First delivered to the United States Government in 1950, the UNIVAC 1101 or ERA 1101 is considered to be the first computer that was capable of storing and running a program from memory.

4. History
1980
3 billion $
human genome project

5. 2001: Draft 2003: Complete 1990-2003 Human genome project History
1998 The private owned company Celera sets out to sequence the human genome. Founded by Craig Venter.
2001 both genome projects release a draft of the human genome.
2003 The projects

6. History
1980
3 billion $
human genome project

7. Next Generation Sequencing
History
2005
Next Generation Sequencing
454 Life Sciences: Parallelized pyrosequencing
454 Life Sciences markets a parallelized version of pyrosequencing.
The first version of their machine reduced sequencing costs 6-fold compared to automated Sanger sequencing.
Reduce costs 6 fold

8. Next Generation Sequencing
History
2005
Next Generation Sequencing
European Nucleotide Archive (ENA) (
454 Life Sciences markets a parallelized version of pyrosequencing.
The first version of their machine reduced sequencing costs 6-fold compared to automated Sanger sequencing.
(Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Genome Sequencing Program (GSP). Accessed 31-oct-14.)

9. Next generation sequencing Third generation sequencing
Roche, 454 Life Sciences (GS FLX Titanium)
Life Technologies (Ion Torrent & Ion Proton)
Illumina (HiSeq, MiSeq, GenomeAnalyzer)
Pacific Biosciences (PacBio RS)
Oxford Nanopore (MinION, PromethION, GridION)
Third generation sequencing
The different NGS platforms on the marked
----- Meeting Notes (10/2/13 09:46) -----
They differ in how they sequence and have different pros and cons.
First I will gow throug library preparation and then short take about the different platforms.

10. Next generation sequencing
Method outline - library
Amplification primer
Barcode
1. Fragment DNA
2. Ligate adapters
3. Amplification
Sequencing primer
4. Sequencing
----- Meeting Notes (10/2/13 09:07) -----
1. A library of fragment targets is created...
2. Adapters containing universal priming sites are ligated to the target ends.
3. Thise adapters consist of a amplification primer, to amplify the fragments, a sequence primer which is used to sequence the fragments and if your are sequecing more than one isolate/sample the adapter will also contain a barcode which is specifik for each sample.
4. Finally the fragments are amplified and sequenced by one of the NGS mashines…

11. Next generation sequencing technologies
Problem with homopolymers
Fast
Ion Torrent
454:
Sequence machine contains many picoliter wells, each containing a single DNA-amplified bead and sequencing enzyms (beads coupled with sulfhurylase and luciferase)
454 than uses luciferase to generate light for detection of the individual nucletides.
Advantages: long read sizes
Disadvantages: Expensive
Ion Torrent:
Sequencing is based on the detection of hydrogen ions that are relased during the polymerisation of DNA.
Advantages: Cheap
Disadvantages: Low throughput
For both: Fast but problems with homopolymers
Expensive
Long insert sizes
Low throughput
Cheapest

12. Next generation sequencing
Illumina
Genome Analyzer
HiSeq
MiSeq
Illumina:
The most widely used next-generation sequencer on the market and have three
To determine the sequence, four types of reversible terminator bases are added and non-incorporated nucleotides are washed away. A camera takes images of the fluorescently labeled nucleotides, then the dye is chemically removed from the DNA, allowing for the next cycle to begin.
Advantages: Good accuracy and high throughput
Disadvantages: Short reads, long run time,
Short reads (~ bp)
Good Accuracy
High Throughput

13. Third generation sequencing technologies
Expensive (machine)
Lower accuracy
Long reads (on avg bp.)
PacBio
PacBio – the first third generation sequencer on the market
With PacBio platform, single DNA polymerase molecules are attached to the bottom surface of individual detectors (zero-mode waveguide detectors) that can obtain sequence information while phospholinked nucleotides are being incorporated into the growing primer strand.
Advantages: Long reads, quick run time, low cost per sample
Disadvantages: Relatively low accuracy, needs more input material than other platforms, expensive

14. Third generation sequencing technologies
Nanopore
Upcoming technology
Released to select labs

15. Third generation sequencing technologies
Nanopore
80,000+ bp. reads
MinION: 150 mill. Bp pr 6 h.
(30x coverage of E. coli)
GridION
MinION
PromethION
38.2 % error rate on the MinION

16. Sequence data is stored in fasta files
What is sequence data?
Output
Sequence data is stored in fasta files
Fasta example:
Header/ID
Sequence

17. Output
Sample
Raw reads
Reads

18. What is the data? What is Fastq? Fastq example: Output Fastq files
Fasta + quality scores
1 read, 4 lines
Fastq example:
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc
----- Meeting Notes (10/2/13 14:19) -----
When the sequencing is done, you get your output…
----- Meeting Notes (10/2/13 14:27) -----
In this next section I will gow throug the output data.
The sequence data is outputtet as fastq files.
And what is fastq files?
It is a fasta file with quality scores!
So this is an example of a fastq file.

19. What is the data? What is Fastq? Fastq example: Output Fastq files
Fasta + quality scores
Fastq example:
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc
Header/ID
----- Meeting Notes (10/2/13 14:19) -----
When the sequencing is done, you get your output…
----- Meeting Notes (10/2/13 14:27) -----
In this next section I will gow throug the output data.
The sequence data is outputtet as fastq files.
And what is fastq files?
It is a fasta file with quality scores!
So this is an example of a fastq file.

20. What is the data? What is Fastq? Fastq example: Output Fastq files
Fasta + quality scores
Fastq example:
DNA sequence
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc

21. What is the data? What is Fastq? Fastq example: Output Fastq files
Fasta + quality scores
Fastq example:
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc
Name field (optional)

22. What is the data? What is Fastq? Fastq example: Output Fastq files
Fasta + quality scores
Fastq example:
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc
Quality scores

23. Long Insert size (eg. 8000 bp)
Output
Paired and Single End
Fragment gap
Paired end reads
Insert size (e.g. 300 bp.)
Single end reads
Adaptors
Long Insert size (eg bp)

24. Splitting & clipping data
Output
using barcodes
De-multiplexing is usually done by the sequencer
Fastq example:
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaX^bbcccaac[_X]]a[aacXT
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc

25. Data quality
Output

26. Data quality
Output

27. Trimming data Fastq example: Output
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaacc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc

28. Data quality Fastq example: Output Trimming data
@FCC0CD5ACXX:1:1101:1103:2048#ACCGT/1
ACNGTGTTTTTAGTTATTGTTTTGTTAAGTTGGGTTTTTTGTACCCAATAGCCAACAAGCCGCCTTTATGGCGGTTTTTTGTGCCTGAAAAGTGGGCGCA +
_BP`ccceggcegihiiighiifhihfddgfhi^efgfhhhhhegiiiiiiiihiihihggeeccdddcccacWTT^acc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1165:2058#ACGTT/1
ACGTTAGCAGAATCGCTTTCTGTTCGTTTTCCACCTGCGACAGACGCACCGGACCACGGTTGGCGAGATCGTCGCGCAGAATATCGGCGGCACGCTGCGAC
bb_eeceefeggehhdagfghhiihfghighhffhifhhcghfdhiihafgdceba`a\aaccc^V]^baccaccXaaacc[ab_`]`[_b`^BBBBBBBB
@FCC0CD5ACXX:1:1101:1135:2082#AGCGT/1
AGCGTGACAAACATTTTATTGCGCCCGGTTTTATCCAGCTTGAATGCCTGACGAAAGAAGATGATGGTGACGACGATGGAGAGAACAATCAGCACCAGATT
bbbeeeeefggfgiihgiigiiiiiiiffgifgeghiiihhfefffhhhfgh_fhggdgegeaceeacbdcbcc\^aa]``_^bb]bcccccbac_a^bc
@FCC0CD5ACXX:1:1101:1239:2083#AGCGT/1
AGCGTCTGACTCACACAAAAACGGTAACACAGTTATCCACAGAATCAGGGGATAAGGCCGGAAAGAACATGTGAGCAAAAAGGCAAAGCCAGGACAAAAGG
bbbeeeeegggggiiiiiiiiiigifhhiiighiiihhiiiiiiihiiiiiiiiiihiigcdbbdcdcccccdccccccccacccccccbcccacccccc

29. 24 h Data storage & Access Output
International Nucleotide Sequence Database Collaboration (INSDC)
Asia
DNA Data Bank of Japan (DDBJ)
24 h
Europe
European Bioinformatics Institute (EBI)
United States
National Center for Biotechnology Information (NCBI)

30. European Bioinformatics Institute (EBI)
Data storage & Access
Output
European Bioinformatics Institute (EBI)

31. Further analysis (eg. Gene finding)
Data Analysis
De novo
Assembly
Further analysis (eg. Gene finding)
Data splitting, clipping, and trimming
Mapping to a reference
Further analysis (SNPs, metagenomics)

32. denovo & reference based
Data Analysis
Assembly
denovo & reference based
De novo assembly (De Bruijn graphs)
Contigs
Reads
Reference genome
Reference based assembly
Consensus sequence

33. Further data analysis Data Analysis Contigs Resistance MLST
Etc.
Gene finding

34. Assembly Data Analysis De novo
Different sequencers requires different assemblers
Depend on output and error profile
Assembler: Newbler
454
Ion Torrent
Assembler: Velvet, SPAdes
Illumina

35. Further analysis (eg. Gene finding)
Data Analysis
Assembly
Further analysis (eg. Gene finding)
Data splitting, clipping, and trimming
Mapping to a reference

36. Mapping to a reference Data Analysis CGE Mappers: BWA Bowtie MAQ
Do not match any reads
raw reads
Do not match reference
Reference sequence

37. Further analysis (eg. Gene finding)
Data Analysis
Assembly
Further analysis (eg. Gene finding)
Data splitting, clipping, and trimming
Mapping to a reference
Further analysis (eg. SNP, metagenomics)