This document discusses bioinformatics and biology at various levels of organization. It begins by explaining that biology is extremely complex due to the hierarchical organization of life, from molecules to ecosystems. It then provides definitions of bioinformatics from Wikipedia and other sources, emphasizing that it is an interdisciplinary field that uses computer science and other approaches to study vast amounts of biological data. Examples of different types of biological data and areas of bioinformatics research are given, such as sequence analysis, databases, and structural bioinformatics. Overall, the document provides a high-level introduction to bioinformatics and its role in understanding biology.

1. Pravech Ajawatanawong, Ph.D.
Department of Microbiology
Faculty of Science
Mahidol University
Wisdom of the Land
Mahidol University
BIOINFORMATICS
in a Nutshell

2. Biology Is Extremely Complex, Indeed!!!
“… We think that physics is complicated
because it is hard for us to understand, and
because physics books are full of difficult
mathematics. But the objects that physicists
study are still basically simple objects.
…
The objects and phenomena that a physic
book describes are simpler than a single cell
in the body of its author. …”

3. Hierarchy of Organization
molecule
(chlorophyll)
organelle
(chloroplast)
cell
(plant cell)
organ
(leave)
tissue
(plant epithelial)
organism
(maple tree)
population
(maple population)
ecosystem
biome

4. Amazing of Organization

5. Emergent Properties
molecule
(chlorophyll)
organelle
(chloroplast)
cell
(plant cell)
organ
(leave)
tissue
(plant epithelial)
organism
(maple tree)
population
(maple population)
ecosystem
biome
“Each level of biological
organization has
emergent properties.”
We know very little
about the whole biology.

6. Bioinformatics is an interdisciplinary field that develops methods and software
tools for understanding biological data. As an interdisciplinary field of science,
bioinformatics combines computer science, statistics, mathematics, and engineering
to study and process biological data.
— Wikipedia —
Bioinformatics derives knowledge from computer analysis of biological data.
These can consist of the information stored in the genetic code, but also
experimental results from various sources, patient statistics, and scientific
literature. Research in bioinformatics includes method development for storage,
retrieval, and analysis of the data. Bioinformatics is a rapidly developing branch of
biology and is highly interdisciplinary, using techniques and concepts from
informatics, statistics, mathematics, chemistry, biochemistry, physics, and
linguistics. It has many practical applications in different areas of biology and
medicine.
— Michael Nilges & Jens P. Linge, Institut Pasteur —
What is Bioinformatics?

7. Bioinformatics in My Opinion!!!
Bioinformatics is an interdisciplinary subject that uses knowledges
and techniques from computer science, mathematics, statistics,
information technologies and linguistics to get some informations from
the massive biological data.
Synonyms of BIOINFORMATICS
computational biology
biocomputing
computational molecular biology

8. Computer Scientist Biologist
Bioinformatics ≠ Computer + Biology
Bioinformatician
Bioinformaticians are
the bridge between these groups

9. Central Dogma
How information flow?
Ref: http://genius.com/Biology-genius-the-central-dogma-annotated

10. DNA = Coca Cola
phosphoric acidphosphate backbone
sugardeoxyribose
waterwater
caffeineA, T, C and G

11. Sequence = Strings
DNA = {A, T, C, G}
protein = {A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y}
RNA = {A, U, C, G}
CATCAGCTCCACGCATCAGCGACTACACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCGACT
ACACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCGACTACACATTCGACTCAGCATCGACTA
CGCATCAGCTCCACGCATCAGCGACTACACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCGA
CTACACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCGACTACACATTCGACTCAGCATGACT
MSFQDIQQSEHFLLRPSEKVQKLETSQWPLLLKNFDKLNVLTNHYVPIPSGCSPLKRSIEDYVKSGFINLDKPA
NPSSHEVVAWAKRILKVDKTGHSGTLDPKVTGCLIVCIERATRLVKSQQGAGKEYVCIFHLHSPVEDEQKVAKN
IERLTGALFQRPPLISAVKRQLRVRTVYESKMLEYDKDKGMGVFWVSCEAGTYIRTMCVHLGLFLGVGGQMQEL
RRVRSGINSEKEGLVTMHDILDAQWLYENHKDESYLRRAIKPLEALLTSHKRVIMKDTAVNALCYGAKIMLPGV

12. Main Types of Biological Data
Sequence Data
Structural Data
Profile Data

13. (Some) Areas of Bioinformatics
Biodatabase
Sequence Analysis
Structural Bioinformatics
Microarray Data Analysis
Systems Biology

14. Biodatabase

15. Why Biologists Needs Database?

16. PubMed

17. The World Largest Biodatabases
http://www.ncbi.nlm.nih.gov

18. Ref: http://www.nlm.nih.gov/about/2015CJ.html
Growth of GenBank

19. PDBJ

20. KEGG Database

21. Pfam Database

22. Sequence Analysis—a rosetta stone of life
“SEQUENCE ANALYSIS is the process of subjecting a DNA, RNA or
peptide sequence to any of a wide range of analytical methods to
understand its features, function, structure, or evolution.”
— Wikipedia —

23. Charles Darwin
1809–1882
Darwin also spent much time thinking about geology. De-
spite bouts of seasickness, he read Lyell’s Principles of Geology
isms that enhance their survival and reproduction in specific
environments. Later, as he reassessed his observations, he be-
PACIFIC
OCEANPinta
Genovesa
The
Galápagos
Islands
EquatorMarchena
Fernandina Pinzón
Santa
Fe San
Cristobal
Florenza
Isabela Santa
Cruz
Daphne
Islands
Santiago
Española
0 4020
Kilometers
ATLANTIC
OCEAN
PACIFIC
OCEAN
NORTH
AMERICA
Darwin in 1840,
after his return
from the
voyage
SOUTH
AMERICA
Great
Britain
AndesMtns.
Cape Horn
Cape of
Good Hope
Brazil
Argentina
Chile
Equator
Malay Archipelago
AFRICA
EUROPE
HMS Beagle in port
Tasmania
AUSTRALIA
New
Zealand
PACIFIC
OCEAN
᭡ Figure 22.5 The voyage of HMS Beagle. His Voyage with HMS Beagle

24. On the Origin of Species
“… It is a truly wonderful fact—the
wonder of which we are apt to overlook
from familiarity—that all animals and all
plants throughout all time and space
should be related to each other in group
subordinate to group, in the manner
which we everywhere behold namely,
varieties of the same species most
closely related together, species of the
same genus less closely and unequally
related together, forming sections and
sub-genera, species of distinct genera
much less closely related, and genera
related in different degrees, forming sub-
families, families, orders, subclasses,
and classes. …”
— Charles Darwin —

25. Darwin’s Tree

26. Visualization of Phylogeny
shorter than 250 amino acid residues were discarded because they are too
short for reliable control trees. All proteins in the resulting clusters are re-
ferred to here as seed orthologs.
Figure 6. Evolutionary relationships among the 35 eukaryotes used in this thesis
(Hejnol et al., 2009; Parfrey et al., 2010).
Ajawatanawong P. (2014) Mine the gaps, Uppsala University.

27. Carl Richard Woese
1928–2012
professor of microbiology at the
University of Illinois at Urbana–
Champaign
famous for defining the Archaea by
using 16S rRNA phylogeny
originated the idea of RNA world
hypothesis

28. SSU rDNA Structure of Bacteria

29. SSU—Ideal Molecular Marker
Nucleic acid sequencing—16S rRNA gene
(rDNA), oligonucleotide signature (e.g. indels)
Since genome sequencing becomes cheaper,
bacterial systematic using genome-based
method is coming

30. Norman R Pace

31. Deep Evolution of Bacteria
Aquificae
Thermotogae
Chloroflexi
Deinococcus-Thermus
Thermophiles
Archaea
Thermophilic (optimum temperature
around 85ºC)
paraphyletic group
non-peptidoplycan cell wall
similar to Archaea

32. Bacteria with Photosynthesis
Chloroflexi
Firmicutes
Archaea
Cyanobacteria
Chlorobi
α-proteobacteria
PhotosyntheticBacteria
γ-proteobacteria
green non-sulfur bacteria
heliobacteria
cyanobacteria
green sulfur bacteria
purple sulfur bacteria
β-proteobacteria
purple non-sulfur bacteria
purple non-sulfur bacteria

33. Proteobacteria
β-proteobacteria
γ-proteobacteria
Archaea
ε-proteobacteria
δ-proteobacteria
α-proteobacteria
Proteobacteria
Proteus—God of the Ocean

34. Gram Positive Bacteria
Actinobacteria
Firmicutes
Gram Positive Bacteria
Jim Henson

35. Genome Is a Book of Life
ATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAG
CGACTACACATTCGACTCAGCATCGACTACGCATCAGCTCC
ACGCATCAGCGACTACACATTCGACTCAGCATCGACTACGC
ATCAGCTCCACGCATCAGCGACTACACATTCGACTCAGCAT
CGACTACGCATCAGCTCCACGCATCAGCGACTACACATTCG
ACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCGACT
ACACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCA
TCAGCGACTACACATTCGACTCAGCATCGACTACGCATCAG
CTCCACGCATCAGCGACTACACATTCGACTCAGCATCGACT
ACGCATCAGCTCCACGCATCAGCGACTACACATTCGACTCA
GCATCGACTACGCATCAGCTCCACGCATCAGCGACTACACA
TTCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGC
GACTACACATTCGACTCAGCATGACTACACATTCGACTCAG
CATCGACTACGCATCAGCTCCACGCATCAGCGACTACACAT
TCGACTCAGCATCGACTACGCATCAGCTCCACGCATCAGCG
ACTACACATTCGACTCAGCATCGACTACGCATCAGCTCCAC
GCATCAGCGACTACACATTCGACTCAGCATCGACTACGCAT
CAGCTCCACGCATCAGCGACTACACATTCGACTCAGCATCG
ACTACGCATCAGCTCCACGCATCAGCGACTACACATTCGAC
TCAGCATCGACTACGCATCAGCTCCACGCATCAGCGACTAC
ACATTCGACTCAGCATCGACTACGCATCAGCTCCACGCATC
AGCGACTACACATTCGACTCAGCATCGACTACGCATCAGCT
CCACGCATCAGCGACTAAAAACTCGCGCCTACAGCGCATCA
GCATACGACTACAACGACAGCAGCAGCAGCAGCAGCAGCAG
CAGCGCCCCAGAAGAGAGAGAACACATTCGACTCAGCATCG
ACTACGCATCAGCTCCACGCATTCAGCTCCACTACCGACGA
TTAATCTACTACTACTCCCCTATTTCACCTATTTACATCAC
AAAACCGACTCGACATCAGCTCTTCGCATCAGCTACGACGC
ATCAAGCAGACGACTACGACCGCGCGACAGCAGCGACACTC
CCGCGCAACCAACAGATAGATAGATAGAAAAACCGACTCGA
CATCAGCTCTTCGCATCAGCTACGACGCATCAAGCAGACGA
CTACGACCGCGCGACAGCAGCGACACTCCCGCGCAACCAAC
AGATAGATAGATAGAAAAACCGACTCGACATCAGCTCTTCG
CATCAGCTACGACGCATCAAGCAGACGACTACGACCGCGCG
ACAGCAGCGACACTCCCGCGCAACCAACAGATAGATAGATA
GAAAAACCGACTCGACATCAGCTCTTCGCATCAGCTACGAC
GCATCAAGCAGACGACTACGACCGCGCGACAGCAGCGACAC
TCCCGCGCAACCAACAGATAGATAGATAGAAAAACCGACTC
GACATCAGCTCTTCGCATCAGCTACGACGCATCAAGCAGAC
GACTACGACCGCGCGACAGCAGCGACACTCCCGCGCAACCA
ACAGATAGATAGATAGAAAACCGACTCGACATCAGCTCTTC
GCATCAGCTACGACGCATCAAGCAGACGACTACGACCGCGC
GACAGCAGCGACACTCCCGCGCAACCAACAGATAGATAGAT
AGAAAAACCGACTCGACATCAGCTCTTCGCATCAGCTACGA
CGCATCAAGCAGACGACTACGACCGCGCGACAGCAGCGACA
CTCCCGCGCAACCAACAGATAGATAGATAGAAAAACCGACT
CGACATCAGCTCTTCGCATCAGCTACGACGCATCAAGCAGA
CGACTACGACCGCGCGACAGCAGCGACACTCCCGCGCAACC
AACAGATAGATAGATAGAAAAACCGACTCGCTACGACGCAT
CAAGCAGACGACTACGACCGCGCGACAGCAGCGACACTCCC
GCGCAACCAACAGATAGATAGATAGAAAAACCGACTCATCC
GCCCCCCCCCCGCGCGCCGAACTAGACATCAGCTCTTCGCA
TCAGCTACGACGCATCAAGCAGACGACTACGACCGCGCGAC
AGCAGCGACACTCCCGCGCAACCAACAGATAGATAGATAGA

36. Genome Sequencing
think big!!!
The first bacterial genome
(Haemophilus influenzae)
The first eukaryotic genome
(Saccharomyces cerevisiae)
The first archaea genome
(Methanococcus jannaschii)
Homo), plants (Zea), and fungi (Coprinus)
constitute small and peripheral branches of
even eukaryotic cellular diversity. If the
animals, plants, and fungi are taken to com-
prise taxonomic “kingdoms,” then we must
recognize as kingdoms at least a dozen other
eucaryotic groups, all microbial, with as
much or more independent evolutionary
history than that which separates the three
traditional eukaryotic kingdoms (13).
The rRNA and other molecular data
solidly confirm the notion stemming from
the last century that the major organelles of
eukaryotes—mitochondria and chloro-
plasts—are derived from bacterial symbi-
onts that have undergone specialization
through coevolution with the host cell. Se-
quence comparisons establish mitochondria
as representatives of Proteobacteria (the
group in Fig. 1 including Escherichia and
Agrobacterium) and chloroplasts as derived
from cyanobacteria (Synechococcus and
Gloeobacter in Fig. 1) (14). Thus, all respi-
ratory and photosynthetic capacity of eu-
karyotic cells was obtained from bacterial
symbionts; the “endosymbiont hypothesis”
for the origin of organelles is no longer
hypothesis but well-grounded fact. The nu-
clear component of the modern eukaryotic
cell did not derive from one of the pro-
karoytic lineages, however. The rRNA and
other molecular trees show that the eukary-
otic nuclear line of descent extends as deep-
ly into the history of life as do the bacterial
and archaeal lineages. The mitochondrion
and chloroplast came in relatively late. This
late evolution is evidenced by the fact that
mitochondria and chloroplasts diverged
processing mechanisms occurred. Thus,
modern representatives of Eucarya and Ar-
chaea share many properties that differ from
bacterial cells in fundamental ways. One ex-
cleolar structural genes (12). W
tutes a “nucleus?” Certainly the
of the nuclear membrane was
late event in the establishmen
Fig. 1. Un
genetic tre
SSU rRNA
Sixty-four
quences r
of all kno
netic do
aligned, an
produced
NAML (43,
was modi
in the co
shown, by
eages an
branch po
porate res
analyses. T
correspond
changes p

37. The First Plant Genome
Arabidopsis thaliana

38. $1000 per Genome in 2015
$1,000
$10,000
$100,000
$10 million
US$100 million
$1 million
2006
2008
2010
2012
2002
2004
As next-generation
sequencers entered
the market, the
price dropped
precipitously.
The price of sequencing
a whole human genome
hovers around $5,000
and is expected to drop
even lower.
Cost of genome
sequencing.
Moore's law for
computing costs.
I
n Silicon Valley,
Moore’s law seems to stand
on equal footing with the natural
laws codified by Isaac Newton. Intel co-founder
Gordon Moore’s iconic observation that computing
power tends to double — and that its price there-
fore halves — every 2 years has held true for nearly
50 years with only minor revision. But as an exemplar
of rapid change, it is the target of playful abuse from
genome researchers.
In dozens of presentations over the past few years,
scientists have compared the slope of Moore’s law with
theswiftlydroppingcostsofDNAsequencing.Forawhile
they kept pace, but since about 2007, it has not even been
close. The price of sequencing an average human genome
hasplummetedfromaboutUS$10milliontoafewthousand
dollars in just six years. That does not just outpace Moore’s
law— it makes theonce-powerfulpredictor of unbridled pro-
gress look downright sedate. And just as the easy availability of
personal computers changed the world, the breakneck pace of
genome-technology development has revolutionized bioscience
research. It is also set to cause seismic shifts in medicine.
In the eyes of many, a fair share of the credit for this success goes
toagrantschemerunbytheUSNationalHumanGenomeResearch
Institute(NHGRI).OfficiallycalledtheAdvancedSequencingTech-
nologyawards,itisknownmorewidelyasthe$1,000and$100,000
genome programmes. Started in 2004, the scheme has awarded
grantsto97groupsofacademicandindustrialscientists,including
some at every major sequencing company.
Ithasencouragedmobilityandcooperationamongtechnologists,
and helped to launch dozens of competing companies, staving off
the stagnation that many feared would take hold after the Human
GenomeProjectwrappedupin2003.“Themajorcompaniesinthe
space have really changed the way people do sequencing, and it all
startedwiththeNHGRIfunding,”saysGinaCosta,whohasworked
forfiveinfluentialcompaniesandisnowavice-presidentatCypher
Genomics, a genome-interpretation firm in San Diego, California.
A GIANT’S LEGACY
The $1,000 genome programme, now close to achieving its goal,
will award its final grants this year. As technology enthusiasts look
to future challenges, the coming milestone raises questions about
how the roughly $230-million government programme managed
to achieve such success, and whether its winning formula can be
appliedelsewhere.Itbenefitedfromfortuitoustimingandthelackof
anentrenchedindustry.ButJefferySchloss,directorofthedivision
ofgenomesciencesattheNHGRIinBethesda,Maryland,whohas
run the programme from its inception, says that its achievements
alsosuggestthattherearewaystonavigatepublic–privatepartner-
ships successfully. “One of our challenges is to figure out what is
therightroleforthegovernment;tonotgetintheway,butfeedthe
pipeline of private-sector technology development,” he says.
The quest to sequence the first human genome was a massive
BY ERIKA CHECK HAYDEN
With a unique programme, the
US government has managed
to drive the cost of genome
sequencing down towards a
much-anticipated target.
The$1,000
genome
2 9 4 | N A T U R E | V O L 5 0 7 | 2 0 M A R C H 2 0 1 4
© 2014 Macmillan Publishers Limited. All rights reserved
modified from: Hayden EC. (2014) Nature 507:294–295.

39. Human Genome
The human genome is 380,000 longer
than the sequence shown here.

40. From Gene to Genome

41. Human Genome Project

42. Achievements beyond HGP

43. s et al. 2006). At that time,
d bacterial genomes and only
projects; this represented a
from the mere two genomes
er of sequenced genomes has
ly in the last 10 years (Fig. 1),
published. Today, there are more than 20,000 metagenomic
projects publically available, and many terabytes of se-
quencing data have been produced. The myriad of ecosys-
tems includes numerous animal and human microbiomes,
soils of all types, fresh and salt water samples, and even
plant–microbe interaction systems.
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
Numberofgenomessequenced
Year
20 Years of Bacterial Genome Sequencing
Land M, et al. (2015) Funct Integr Genomics 15,141–161.

44. Deinococcus radiodurans
Deinococcus radiodurans
deinos—unusual
extraordinarily resistant to oxidative
stress, including desiccation and
radiation
survive under radiation around 3–5
million rad (100 rad can kill human)

45. genome 1 genome 2
genome 3
core
genes
decorative
genes
Comparative Genomics
based on 16S sequences, DDH and biochemical tests some-
times results in combinations or divisions that are not support-
ed by their genome content. As a result, species, genera, and
complete families are being shifted and reordered, in an ongo-
ing process.
dista
2013
Neg
Acid
quire
of P
Firm
taxo
the a
deve
for f
Ozen
New
Micr
ery eFig. 6 Core and pan-genome of 2085 E. coli genomes. Core gene
decorative genes
core genes

46. MicrobiomeWe are entering to the new era of omics,
a wide variety of large-scale, multi-dimensional biology.

47. Features of Omics approach:
high-throughput, data-driven, holistic, top-down methods
understanding cell metabolism in one ‘integrated system’
high-output, requires bioinformatics to analyze & manipulate
From Standalone Biology to ‘Omics’ Study

48. GENOMICS
TRANSCRIPTOMICS
PROTEOMICS
METABOLOMICS
DNA
Metabolite
Protein
mRNA
transcription
metabolism
translation
Omics Study Relies on Central Dogma

49. Understand Genome is Not Enough
genomics is static
don’t know the set of genes that express in a particular
condition
some phenotypes are consequent of interaction of gene
interaction (emerging property)
lot of changes happen in the downstream processes of genetic
information (not in DNA)

50. DNA Chip
Microarray Chip

51. Microarray Technology

52. Microarray Data

53. Clustering of Microarray Data
microarray data clustering
tree on the top and left are
just dendrogram
always plots between genes
versus conditions
intensity of each color
represents level of expression

54. 2D-gel Electrophoresis
http://elte.prompt.hu/sites/default/files/tananyagok/practical_biochemistry/ch07s03.html

55. Comparative 2D-gel Electrophoresis
http://elte.prompt.hu/sites/default/files/tananyagok/practical_biochemistry/ch07s03.html

56. modified from Venter et al. (2001) Science 291:1304–1351.
pter 15 Genomics
Transfer/carrier protein (203, 0.7%)
Transcription factor (1850, 6.0%)
Nucleic acid enzyme (2308, 7.5%)
Signaling molecule (376, 1.2%)
Receptor (1543, 5.0%)
Kinase (868, 2.8%)
Select regulatory molecule (988, 3.2%)
Transferase (610, 2.0%)
Synthase and synthetase (313, 1.0%)
Oxidoreductase (656, 2.1%)
Lyase (117, 0.4%)
Ligase (56, 0.2%)
Isomerase (163, 0.5%)
Hydrolase (1227, 4.0%)
Viral protein (100, 0.3%)
Miscellaneous (1318, 4.3%)
Cell adhesion (577, 1.9%) Chaperone (159, 0.5%)
Cytoskeletal structural protein (876, 2.8%)
Extracellular matrix (437, 1.4%)
Immunoglobulin (264, 0.9%)
Ion channel (406, 1.3%)
Motor (376, 1.2%)
Structural protein of muscle (296, 1.0%)
Proto-oncogene (902, 2.9%)
Select calcium-binding protein (34, 0.1%)
Intracellular transporter (350, 1.1%)
Transporter (533, 1.7%)
Molecular function unknown (12,809, 41.7%)
Signaltransduction
Enzym
e
Nucleic
acid
binding
None
᭿ FIGURE 15.10 Functional classification of the 26,383 genes predicted by Celera Genomics’ first draft of the
sequence of the human genome. Each sector gives the number and percentage of gene products in each
functional class in parentheses. Note that some classes overlap: a proto-oncogene, for example, may encode
Not All Proteins Are Enzymes

57. Microbiome
microbiome = all microbial population localize in a particular habitat
(e.g.: human gut, skin, vagina, etc.)

58. Human Microbiome Studies

59. Hand–Skin Microbiome
51 college students (after exam)
targeting V2 region of bacterial 16S rRNA gene
>150 species/palm, intra- and interpersonal variation
hand from the same individual share 17% of species-level phylotype
women have higher diversity than men