Bioinformatics and Advanced Programming2014/12/11 · Bioinformatics Jan T. Kim Introduction Mol....

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Bioinformatics and Advanced Programming

Jan T. Kim

BCS Advanced Programming SG, 11 Dec 2014

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Abstract

Bioinformatics can be defined strictly as the science of information in biological systems, or more broadly asdeveloping and applying computational tools for analysing biological data. The biological processes thatgenerate this information, particularly evolution, are highly complex, and therefore analysis of biologicalinformation is often computationally challenging. I will present the following selected topics and highlight theadvanced computing challenges they involve, and also outline advances in the biosciences that have beenenabled by tackling these challenges.Many bioinformatics analyses are based on DNA sequences which today can be determined at very highvolume through ”Next Generation Sequencing” (NGS) techniques. As a result, the volume of publiclyavailable sequence data has reached the range of petabytes. Searching this body of data requires highlyoptimised computational approaches, such as BLAST (”Basic Local Alignment Search Tool”).More recently, NGS methods that generate very large numbers of ”short reads”, i.e. strings of sequence .Central computational challenges resulting from these new technologies are ”de novo assembly” of theoriginal long sequence(s) from short reads, and mapping very large numbers of short reads to a knownreference sequence.Phylogeny analysis, i.e. reconstruction of ancestry relationships among species, is a classical field ofbioinformatics which typically involves two steps, first a multiple alignment of the sequences is computedwhich subsequently is used to compute a tree. Computing multiple alignments is an optimisation problemthat can only be approximately solved.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The Pirbright Institute

Preventing and controlling viral diseases

Core funding:

Project funding by BBSRCand many others.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline

1 IntroductionMolecular Biology BasicsResolving the Phylogeneny of Land PlantsReconstructing Foot and Mouth Disease Transmission Trees

2 Sequence AnalysisPairwise AlignmentBLAST

3 “Next Generation” Sequencing Challenges

4 Multiple Sequence Alignment (MSA)

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Bioinformatics: Definition(s)

• Scientific inquiry into information in biological systems.

• Computational analysis of biological data.

• Computer assisted mining of biological literature.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

DNA: Structure

POH

OH

O

O

CH2

✔✔❚❚

✧✧

❜❜

O✧✧❜❜

❜❜

✧✧❜❜

✧✧ N

N

NH2

N

N

O

POHO

O

CH2

✔✔❚❚

✧✧

❜❜

O

❜❜

✧✧❜❜

✧✧ N

N

NH2

O

HO

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Base Complementarity

http://commons.wikimedia.org/wiki/File:DNA chemical structure.svg

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The “Central Dogma”

http://en.wikipedia.org/wiki/Central_dogma_of_molecular_biologyhttp://en.wikipedia.org/wiki/File:Cdmb.svg

http://en.wikipedia.org/wiki/Central_dogma_of_molecular_biologyhttp://en.wikipedia.org/wiki/File:Cdmb.svg

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The Success of Bioinformatics

• The Object: Information in biological systems:

In living systems, a dynamics of information hasgained control over the dynamics of energy,which determines the behavior of most non-livingsystems.

[Langton, 1992]

• Genetic information is digital.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The Success of Bioinformatics

• The Object: Information in biological systems:

In living systems, a dynamics of information hasgained control over the dynamics of energy,which determines the behavior of most non-livingsystems.

[Langton, 1992]

• Genetic information is digital.

TACCGTCAC

CTACACCAT

ACCTACATG

TTCACATTA

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Sequence Data Is Big Data

• NCBI-GenBank Flat File Release 204.0 (15 Oct 2014):ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt

• 178,322,253 loci,• 181,563,676,918 bases.

[Crosswell and Thornton, 2012]

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Sequence Data Is Big Data

• NCBI-GenBank Flat File Release 204.0 (15 Oct 2014):ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt

• 178,322,253 loci,• 181,563,676,918 bases.

http://www.ebi.ac.uk/ena/about/statistics

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Land Plant Phylogeny

Angiosperms(flowering plants)

Gnetales

Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Phylogeny of MADS Proteins

98

78

ZMM1 (Zea)

FBP11 (Petunia)

BAG1 (Brassica)

AG (Arabidopsis)

PLE (Antirrhinum)

100

GGM3 (Gnetum)

DAL2 (Picea)

GGM10 (Gnetum)

AGL12 (Arabidopsis)

88

98

TOBMADS1 (Nicotiana)

TM3 (Lycopersicon)

SAMADSA (Sinapis)

DEFH24 (Antirrhinum)

AGL14 (Arabidopsis)

98

DAL3 (Picea)

GGM1 (Gnetum)

78

624658

AGL13 (Arabidopsis)

AGL6 (Arabidopsis)

Zag5 (Zea)

ZAG3 (Zea)

62

PRMADS2 (Pinus)

GBM1 (Ginkgo)

PRMADS3 (Pinus)

DAL1 (Picea)

GGM11 (Gnetum)

GGM9 (Gnetum)

100

ZMM6 (Zea)

OM1 (Aranda)

EGM1 (Eucalyptus)

FBP2 (Petunia)

AGL2 (Arabidopsis)

100

ZAP1 (Zea)

AP1 (Arabidopsis)

SQUA (Antirrhinum)

EAP2 (Eucalyptus)

TM4 (Lycopersicon)

100

GGM4 (Gnetum)

GGM8 (Gnetum)

GGM5 (Gnetum)

CRM7 (Ceratopteris)

OPM1 (Ophioglossum)

OPM5 (Ophioglossum)

CRM6 (Ceratopteris)

OPM4 (Ophioglossum)

GGM12 (Gnetum)

GGM6 (Gnetum)

GGM7 (Gnetum)

98

NMHC5 (Medicago)

AGL17 (Arabidopsis)

DEFH125 (Antirrhinum)

ANR1 (Arabidopsis)

OPM3 (Ophioglossum)

100

CRM9 (Ceratopteris)

CRM3 (Ceratopteris)

4852

100

NTGLO (Nicotiana)

GLO (Antirrhinum)

PI (Arabidopsis)

DAPI (Delphinium)

OSMADS2 (Oryza)

100

PMADS1 (Petunia)

NTDEF (Nicotiana)

DEF (Antirrhinum)

TM6 (Lycopersicon)

BOBAP3 (Brassica)

AP3 (Arabidopsis)

58

DAL13 (Picea)

GGM2 (Gnetum)

GGM13 (Gnetum)

98

CRM1 (Ceratopteris)

CRM5 (Ceratopteris)

CRM4 (Ceratopteris)

CRM2 (Ceratopteris)

CERMADS5 (Ceratopteris)

CRM10 (Ceratopteris)

100

AGL15-1 (Brassica)

AGL15-2 (Brassica)

AGL15 (Arabidopsis)

AG

TM

3

AG

L6

AG

L2

SQ

UA

GG

M4

AG

L17

CR

M3

GL

O

DE

F

DE

F/G

LO

CR

M1

AG

L15

Angiosperms Gnetales Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The AG and AGL6 Subfamilies

9878

ZMM1 (Zea)FBP11 (Petunia)BAG1 (Brassica)AG (Arabidopsis)

PLE (Antirrhinum)100 GGM3 (Gnetum)

DAL2 (Picea)GGM10 (Gnetum)

AG subfamily

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


9878




AG subfamily

Angiosperms

Gnetales

Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


9878




AG subfamily

62

46

58AGL13 (Arabidopsis)

AGL6 (Arabidopsis)Zag5 (Zea)ZAG3 (Zea)

62

PRMADS2 (Pinus)GBM1 (Ginkgo)

PRMADS3 (Pinus)DAL1 (Picea)GGM11 (Gnetum)GGM9 (Gnetum)

ZMM6 (Zea)

AGL6 subfamily

Angiosperms

Gnetales

Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The DEF and GLO Subfamilies

4852

100

NTGLO (Nicotiana)GLO (Antirrhinum)PI (Arabidopsis)

DAPI (Delphinium)OSMADS2 (Oryza)

100

PMADS1 (Petunia)NTDEF (Nicotiana)DEF (Antirrhinum)

TM6 (Lycopersicon)BOBAP3 (Brassica)

AP3 (Arabidopsis)58 DAL13 (Picea)

GGM2 (Gnetum)GGM13 (Gnetum)

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

The DEF and GLO Subfamilies

4852

100

NTGLO (Nicotiana)GLO (Antirrhinum)PI (Arabidopsis)

DAPI (Delphinium)OSMADS2 (Oryza)

100

PMADS1 (Petunia)NTDEF (Nicotiana)DEF (Antirrhinum)

TM6 (Lycopersicon)BOBAP3 (Brassica)

AP3 (Arabidopsis)58 DAL13 (Picea)

GGM2 (Gnetum)GGM13 (Gnetum)

Angiosperms

Gnetales

Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Conclusion: Land Plant Phylogeny

MADS-Box Genes Reveal That Gnetophytes Are More CloselyRelated to Conifers than to Flowering Plants[Winter et al., 1999].

Angiosperms

Gnetales

Gymnosperms

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Transmission Trees

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Consensus Sequence Data

site sequences

p0 (ancestor) 2p1b 6p2b 7p2c 3p3b 8p3c 2p4b 2p5 5p6b 3p7 8p8 1

sum 47combinations 967680

• 2007 FMD outbreak inUK

• 10 premises, 2 ancestorsamples (p0)

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Reference Transmission Tree

p0p1b

p2b

p2c

p5p3b

p4b

p3c

p6b

p7 p8

Based on a TCS geneaology of all 47 samples, and additionalbackground knowledge.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Flowchart

consensus sequences

sample with 1 sequence / premise

Hamming distances

TCS genealogy

rooted genealogy

rpetal rfugal closest MST

Analysis carried out for 1000 random samples containing oneconsensus sequence from each site.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Results: Tree Topology

t2 t0 t5 t16 t6 t7 t3 t14

t11

t31

t12

t41

t13

t18

t19 t9 t20

t43

t44

t24

t26

t47

t25

t17

t34

t37 t4 t42

t21

t22

t33

t38

t39 t8 t40

t15

t23

t30 t1 t10

t46

t53

t54

t28

t29

t36

t51

t56

t32

t35

t45

t48

t49

t50

t57

t60

t61

t62

t63

t27

t52

t55

t58

t59

rpetal

topology

freq

uenc

y

0

50

100

150

200

t2 t0 t5 t16 t6 t7 t3 t14

t11

t31

t12

t41

t13

t18

t19 t9 t20

t43

t44

t24

t26

t47

t25

t17

t34

t37 t4 t42

t21

t22

t33

t38

t39 t8 t40

t15

t23

t30 t1 t10

t46

t53

t54

t28

t29

t36

t51

t56

t32

t35

t45

t48

t49

t50

t57

t60

t61

t62

t63

t27

t52

t55

t58

t59

topology

topo

Dis

t to

ref.

tree

0

2

4

6

8

10

radipetal: branch nodes merged towards root (p0)

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


t0 t4 t2 t9 t3 t11 t7 t13 t5 t14 t6 t1 t8 t12

t23

t22

t16

t15

t19

t17

t24

t26

t18

t25

t10

t29

t27

t35

t31

t36

t20

t21

t28

t30

t32

t33

t34

t37

t38

t39

rfugal

topology

freq

uenc

y

0

50

100

150

200

t0 t4 t2 t9 t3 t11 t7 t13 t5 t14 t6 t1 t8 t12

t23

t22

t16

t15

t19

t17

t24

t26

t18

t25

t10

t29

t27

t35

t31

t36

t20

t21

t28

t30

t32

t33

t34

t37

t38

t39

topology

topo

Dis

t to

ref.

tree

0

2

4

6

8

10

radifugal: branch nodes merged away from root (p0)

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


t0 t6 t4 t12 t8 t3 t2 t9 t14

t19

t13 t5 t11

t10

t16 t7 t23

t22

t15 t1 t21

t28

t24

t17

t25

t37

t18

t26

t34

t35

t38

t20

t27

t29

t30

t31

t32

t33

t36

t39

t40

t41

closest

topology

freq

uenc

y

0

50

100

150

200

t0 t6 t4 t12 t8 t3 t2 t9 t14

t19

t13 t5 t11

t10

t16 t7 t23

t22

t15 t1 t21

t28

t24

t17

t25

t37

t18

t26

t34

t35

t38

t20

t27

t29

t30

t31

t32

t33

t36

t39

t40

t41

topology

topo

Dis

t to

ref.

tree

0

2

4

6

8

10

closest: branch nodes merged towards closest premise

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


t4 t9 t2 t0 t3 t10 t1 t12 t7 t13 t8 t5 t11

t16 t6 t14

t15

t17

mst

topology

freq

uenc

y

0

50

100

150

200

t4 t9 t2 t0 t3 t10 t1 t12 t7 t13 t8 t5 t11

t16 t6 t14

t15

t17

topology

topo

Dis

t to

ref.

tree

0

2

4

6

8

10

MST

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Summary: FMD TransmissionTrees

• Algorithms for constructing transmission trees• from TCS genealogies: radipetal, radifugal, closest,• minimum spanning tree (MST).

• Comparison based on the 2007 outbreak.• closest provides TCS based transmission trees best

precision.• MST provides even marginally better precision.

• Outlook:• Try more sophisticated distance measures.• Include further transmission tree reconstruction methods.• Larger data sets, NGS “beyond the consensus”

[Wright et al., 2011]

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Pairwise Alignment: Idea

S = ACATCTCGT = ACTGTA

alignment

Sa = ACATCTCG|| | | :

T a = AC-TGT-A

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Formal Definition

• Extend sequences S and T by inserting gaps to Sa andT a.

• aligned sequences have equal length: |Sa|= |T a|• gaps cannot be paired with gaps

• Biological background: homology, symbols in a columnshould derive from same common ancestor.

• Match: column with equal symbols in Sa and T a.

• Indel: column with a gap symbol in Sa or T a.

• Mismatch: column with different symbols (non-gap) inSa and T a.

ACATCTCG

AC-TGT-A

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Scoring of Alignments

The score m(k) of column k is

• the space penalty m(k) =−g , if one symbol is the gapsymbol, here: g = 2),

• otherwise the pair score m(k) = µ(sa(k), ta(k)), here

µ(x ,y) =

{

1, if x = y ,−1, otherwise

Sa = A C A T C T C G

T a = A C - T G T - Ascore: +1 +1 −2 +1 −1 +1 −2 −1 =−2

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Optimal Alignments

• Objective: Find the alignment with maximal score.

• Problem: The number of alignments is

(

|S |+ |T |)

|S |

)

·

(

|S |+ |T |

|T |

)

• Trying out all alignments is impossible.• Recursion results in trying out all alignments.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Optimal Alignments

• Observation: A prefix alignment of an optimal alignmentis optimal (as well).

• Otherwise, a contradiction results: The optimalalignment could be improved by changing the prefix.

• Dynamic programming: Tabulate optimal scores ofprefix alignments

ACATCTCG

AC-TGT-A

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Table ofPrefix-Alignment Scores

- A G A C

- 0.0 -2.0 -4.0 -6.0 -8.0

A -2.0 1.0 -1.0 -3.0 -5.0

G -4.0 -1.0 2.0 0.0 -2.0

C -6.0 -3.0 0.0 1.0 1.0

The optimal alignment score is 1.0.☞Notice O(n2) complexity.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Backtracking the Alignment- A G A C

- -0.0 -2.0✛ -4.0✛ -6.0✛ -8.0✛

A -2.0

✻1.0

❅❅■-1.0✛ -3.0

❅❅■✛ -5.0✛

G -4.0

✻-1.0

✻2.0

❅❅■0.0✛ -2.0✛

C -6.0

✻-3.0

✻0.0

✻1.0

❅❅■1.0

❅❅■

AG-C

AGAC

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

BLAST: Basic Local AlignmentSearch Tool

• Objective: Given a query sequence, find similarsequences in a database.

• Size of database prohibits pairwise alignment of query toall entries.

• Algorithm outline [Altschul et al., 1997]:

1 Scan for hits, i.e. gapless short word alignments exceedinga threshold score.

2 Extend hits maximally to obtain HSPs (high scoring pairs.3 Combine HSPs to (gapped) alignments.

• E-values indicate expected number of HSPs with givenscore.

• Interesting HSPs have E ≪ 1.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

BLAST:Search Engine for Sequences

http://blast.ncbi.nlm.nih.gov/

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

“Next Generation” Sequencing

• Long DNA sequences cannot be read like a tape.

• Short fragments from random genomic locations can besequenced.

• NGS generates very large number of (very) shortsequencing reads.

http://www.illumina.com/systems/miseq.ilmn

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Illumina NGS Sequencing

Massive numbers of sequencing reactions take place in oneflow cell.

http://www.illumina.com/documents/products/techspotlights/techspotlight sequencing.pdf

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


DNA is fragmented and adapters are ligated.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Fragments (with adapters) are attached to the slide in a flowcell.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


The slide is studded with primers, facilitating bridgeamplification . . .


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


. . . resulting in double stranded fragments . . .


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


. . . which are then denatured.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Multiple rounds of amplification result in a cluster from eachinitial fragment.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Reversible terminator nucleotides are added.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Incorporated nucleotide fluoresce at different wave lengths.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


After removal of the terminator, the next nucleotide is added. . .


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


. . . and the fluorescent light is imaged.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Each image yields one base for each cluster.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 =

read1 =

read2 =

read3 =



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 = G

read1 = T

read2 = C

read3 = A



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 = GC

read1 = TA

read2 = CT

read3 = AG



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 = GCT

read1 = TAA

read2 = CTT

read3 = AGC



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 = GCTG

read1 = TAAG

read2 = CTTA

read3 = AGCC



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


read0 = GCTGA

read1 = TAAGT

read2 = CTTAG

read3 = AGCCG



Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


Reads are further processed, e.g. in sequence assembly.


Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Applications of “Next Generation”Sequencing

• Novel genome sequencing

• Re-sequencing to discover genomic variation• Single nucleotide polumorphisms (SNPs), and their

association to pheonotypic traits,• Evolution of genomic variation patterns.

• Metagenomics

• *-Seq techniques• gene expression measurement: RNA-Seq• binding sites: ChIP-Seq• microRNA-Seq

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Mapping NGS Reads

• Task: align billions of to a known reference genome.

• Not feasible using dynamic programming.

• Feasible using advanced indexing of the reference.• e.g. Burrows-Wheeler transform• Pigeonhole principle

GACTAGAGTAGACGATGAGACCCATGACA

GGC GAGTAGACGAT GACCCATGATAGGCT GAGTAGCCGATG CCCATGACAGGCT AGTAGCCGATGAG CTCATGACAGGCTAG GTAGACGATGAGA CATGACAGGCTAGA AGACGATGAGA ATGACAGGCTAGAG AGCCGATGAGACC ATGACAGGCTAGAGT GACGATGAGACCC

CCGATGAGACCCAT

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Finding Single NucleotidePolymorphisms (SNPs)

GACTAGAGTAGACGATGAGACCCATGACA

GGC GAGTAGACGAT GACCCATGATA

GGCT GAGTAGCCGATG CCCATGACA

GGCT AGTAGCCGATGAG CTCATGACA

GGCTAG GTAGACGATGAGA CATGACA

GGCTAGA AGACGATGAGA ATGACA

GGCTAGAG AGCCGATGAGACC ATGACA

GGCTAGAGT GACGATGAGACCC

CCGATGAGACCCAT

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Aligning Reads to ReferenceSequences

Example: RNA-Seq

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Assembling NGS Reads

GCTGATGTGCCGCCTCACTCCGGTGG

CACTCCGGTGG

CTCACTCCTGTGG

GCTGATGTGCCACCTCA

GATGTGCCGCCTCACTC

GTGCCACCTCACTCCGG

CTCCGGTGG

• Many copies of a genome are fragmented

• Each base has quality, giving its probability of beingcorrect.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

NGS Assembly: Example

C99A99G99A98G95C96A93

C99A99G99A99G99C99A97G95A94C96A89

A99G99A99C99A99A45C26T57A87A85G84T78

A99A99G99T99G99C98T99A96T91C88A82

C99T99A99T99C99A99A96C94T95

T99A99T99C99A99A94C97T95A91G88

A99A99C99T98A91G93

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


C99A99G99A98G95C96A93

C99A99G99A99G99C99A97G95A94C96A89

A99A99A99A99A99A99A99G99A99C99A99A45C26T57A87A85G84T78

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99G99T99G99C98T99A96T91C88A82

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99C99T99A99T99C99A99A96C94T95

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99T99A99T99C99A99A94C97T95A91G88

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99C99T98A91G93

C99A99G99A99G99C99A99G99A99C99A99A99C99T99A99A99G99T99G99C99T99A99T99C99A99A99C99T99A99G99

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA


C99A99G99A98G95C96A93

C99A99G99A99G99C99A97G95A94C96A89

A99A99A99A99A99A99A99G99A99C99A99A45C26T57A87A85G84T78

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99G99T99G99C98T99A96T91C88A82

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99C99T99A99T99C99A99A96C94T95

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99T99A99T99C99A99A94C97T95A91G88

A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99A99C99T98A91G93

C99A99G99A99G99C99A99G99A99C99A99A99N99T99A99A99G99T99G99C99T99A99T99C99A99A99C99T99A99G99

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

NGS Sequence Assembly

• Assembly depends on overlaps among reads.

• Quality of bases must be taken into account.

• Reads that are too short are not informative.

• Repetitive sequences make assembly difficult.

• Insufficient depth results in multiple contigs.

• Sufficient depth is a key success factor:• Joining of contigs depends on sufficient overlap (N50

value).• Resolving low quality bases depends on depth.• Depth does not help resolve repetitive sequences.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

NGS Assembly: Overlap Approach

ATTCCCGTA

CCCGTAA

6

TAATCTACGACTAAG

2

ATTAAGTCA

1

CTACGAT

GTCACAACC

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

NGS Assembly: Overlap Approach

ATTCCCGTA

CCCGTAA

6

TAATCTACGACTAAG

2

ATTAAGTCA

1

1

3

1

GTCACAACC

1CTACGAT

2

1

21

4

2

1

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

De Bruijn Graph

http://commons.wikimedia.org/wiki/File:DeBruijn-as-line-digraph.svg

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

NGS Assembly: De BruijnApproach

Compeau, Pevzner & Tesler, Nature Computational Biology 29 (2011): 987–991, Fig. 3

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Polymorphisms and de BruijnAssembly

[Leggett et al., 2013, Fig. 1]

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Summary ¡: NGS Data Analysis

• Mapping to a reference sequence, using indexing• resequencing• detection of SNPs and other variants,• identification of genes (RNA-seq).

• De novo assembly of genomes or transcriptomes.• Resource intensive (particularly memory)• Overlap assembly: feasible with smaller sets• De Bruijn graph assembly of k-mers

• NGS metagenomics . . .

Software has limitations and is evolving rapidly.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Outline





Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Multiple Alignment

• Extend pairwise approach?• 2 sequences: table of n2 prefix alignments• 3 sequences: table of n3 prefix alignments• Warning: Very large numbers ahead

• Aligning 100 sequences of 300 symbols: about 10170 prefixalignments.

• How much computing time does the universe have?

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Progressive Multiple Alignment

• Compute all pairwise alignments

• Use alignment dissimilarities to produce a guilde tree.

• Align most similar pair of sequences and merge them intoa profile.

• Progressively align profiles.

• Result: All sequences aligned (and merged into oneprofile).

• Programs clustal, muscle

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

More Uses ofContinuous Sequences

• Profile searches (mostly superseded by HMMs)

• Progressive multiple alignment

ACAC

ACCC

AGT

AGAT

AGCT

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA




ACAC

ACCC

AGT

AGAT

AGCTa 1.0 0.0 0.5 0.0c 0.0 0.0 0.5 0.0g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.0 0.0

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA




ACAC

ACCC

AGT

AGAT

AGCTa 1.0 0.0 0.5 0.0c 0.0 0.0 0.5 0.0g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.0 0.0

a 1.0 0.0 0.5 0.0

c 0.0 1.0 0.5 1.0

g 0.0 0.0 0.0 0.0

t 0.0 0.0 0.0 0.0

- 0.0 0.0 0.0 0.0

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA




ACAC

ACCC

AGT

AGAT

AGCTa 1.0 0.0 0.5 0.0c 0.0 0.0 0.5 0.0g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.0 0.0

a 1.0 0.0 0.5 0.0

c 0.0 1.0 0.5 1.0

g 0.0 0.0 0.0 0.0

t 0.0 0.0 0.0 0.0

- 0.0 0.0 0.0 0.0

a 1.0 0.0 0.3 0.0

c 0.0 0.0 0.3 0.0

g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.3 0.0

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA




ACAC

ACCC

AGT

AGAT

AGCTa 1.0 0.0 0.5 0.0c 0.0 0.0 0.5 0.0g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.0 0.0

a 1.0 0.0 0.5 0.0

c 0.0 1.0 0.5 1.0

g 0.0 0.0 0.0 0.0

t 0.0 0.0 0.0 0.0

- 0.0 0.0 0.0 0.0

a 1.0 0.0 0.3 0.0

c 0.0 0.0 0.3 0.0

g 0.0 1.0 0.0 0.0

t 0.0 0.0 0.0 1.0

- 0.0 0.0 0.3 0.0a 1.0 0.0 0.4 0.0

c 0.0 0.4 0.4 0.4

g 0.0 0.6 0.0 0.0

t 0.0 0.0 0.0 0.6

- 0.0 0.0 0.2 0.0

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Multiple Alignment

(program: clustalx)http://www.clustal.org/

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Overview of Molecular Phylogeny

sequences

alignment

aligned sequences

dist. calc.

dist. matrix

neighbor j.

tree

parsimony

tree

max. likelih.

tree

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Acknowledgements

• Kai-Uwe Winter, Thomas Münster, Luzie U. Wingen,Günter Theißen, Heinz Saedler

• Begoña Valdazo-Gonzalez, Nick Knowles, Don King

• Jan Gewehr, Thomas Martinetz, Daniel Polani, SimonMoxon, Vincent Moulton

• Anyela Camargo, Alessandra Devoto, John Turner

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

References

Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang,

J., Zhang, Z., Miller, W., and Lipman, D. J. (1997).Gapped BLAST and PSI-BLAST: A new generation ofprotein database search programs.Nucleic Acids Research, 25:3389–3402.http://nar.oupjournals.org/cgi/content/full/25/17/3389.

Crosswell, L. C. and Thornton, J. M. (2012).

ELIXIR: A distributed infrastructure for Europeanbiological data.Trends in Biotechnology, 30:241–242.

Langton, C. G. (1992).

Preface.In Langton, C. G., Taylor, C., Farmer, J. D., andRasmussen, S., editors, Artificial Life II, volume X ofSanta Fe Institute Studies in the Sciences ofComplexity, Proceedings, pages xiii–xviii, RedwoodCity, CA. Addison-Wesley.

Leggett, R. M., Ramirez-Gonzalez, R. H., Verweij, W.,

Kawashima, C. G., Iqbal, Z., Jones, J. D., Caccamo,M., and MacLean, D. (2013).Identifying and classifying trait linked polymorphismsin non-reference species by walking coloured de bruijngraphs.PLoS One, 8:e60058.

Winter, K.-U., Becker, A., Münster, T., Kim, J. T.,

Saedler, H., and Theißen, G. (1999).MADS-box genes reveal that gnetophytes are moreclosely related to conifers than to flowering plants.Proceedings of the National Academy of Sciences,USA, 96:7342–7347.

Wright, C. F., Morelli, M. J., Thébaud, G., Knowles,

N. J., Merzyk, P., Paton, D. J., Haydon, D. T., andKing, D. P. (2011).Beyond the consensus: Dissecting within-host viralpopulation diversity of foot-and-mouth disease virusby using next-generation genome sequencing.Journal of Virology, 85:2266–2275.

Bioinformatics

Jan T. Kim

Introduction

Mol. Bio. Basics

Plant Phylogeny

FMDTransmission

SequenceAnalysis

PairwiseAlignment

BLAST

NGS

MSA

Thank Youfor your attention and participation

IntroductionMolecular Biology BasicsResolving the Phylogeneny of Land PlantsReconstructing Foot and Mouth Disease Transmission Trees

Sequence AnalysisPairwise AlignmentBLAST

``Next Generation'' Sequencing ChallengesMultiple Sequence Alignment (MSA)

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