Bioinformatics and Connection to Computational Pathology

Fayyaz Minhas

Department of Computer Science

University of Warwick

Why Bioinformatics?• How do we know that humans and

chimpanzees share more than 95% of their DNA?• Human Genome Project

3

How to

compare?

Why Bioinformatics?

• The knapsack problem• Uses dynamic

programming

4

Why Bioinformatics?

• Tree of life

5

Why Bioinformatics?

• How are humans across the Earth related to each other?• Human Genographic project

6

Why Bioinformatics?

• How can we screen for disease?

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Why Bioinformatics?

• Personalized medicine

8

Why Bioinformatics?

• How can we fight against diseases like Cancer?

9

Handling viruses• In Silico Prediction and Validations of Domains

Involved in Gossypium hirsutum SnRK1 Protein Interaction with Cotton leaf curl Multan betasatellite encoded βC1

• βC1, pathogenicity determinant encoded by Cotton leaf curl Multan betasatellite interacts with calmodulin-like protein 11 (CML11) in Gossypium hirsutum

10In Silico Prediction and Validations of Domains Involved in Gossypium hirsutum SnRK1 Protein Interaction with Cotton leaf curl Multan betasatellite encoded βC1, Kamal, Hira, Fayyaz ul Amir Afsar Minhas, Hanu Pappu, Imran Amin et al., in Frontiers in Plant Science 10 (2019): 656.Bioinformatics and molecular analysis of Gossypium hirsutum calmodulin-like protein (CML11) interaction with begomovirus-transcription activator protein C2. Hira Kamal, Fayyaz Minhas, et al., in PLoSOne (In press).

Why Bioinformatics?

• How can we find out what are the effects of a certain disease?

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Why Bioinformatics?

• How can we design new life?

12

https://www.ted.com/talks/craig_venter_unveils_synthetic_life

Molecular Biology Fundamentals

Genome: The ‘Program’

• Genome is the genetic material of an organism

• Deoxyribonucleic acid (DNA)• Encodes these genetic instructions

14

How is the program stored?

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Watson & Crick with DNA model

Rosalind Franklin with X-ray image of DNA

Program size: DNA base pairs (bp)

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Organism # of base pairs # of Chromosomes

Virus

HIV 9193 1

SARS 29751 1

Porcine circovirus 1759 1

Prokayotic

Haemophilus influenzae 1.8x106 1

Escherichia coli (bacterium) 4.6x106 1

Carsonella ruddii 159, 662 (0.16M) 1Eukaryotic

S. cerevisiae (yeast) 1.35x107 17

Drosophila melanogaster (fly) 1.65x108 4

Homo sapiens (human) 2.9x109 23

Paris japonica 150x109 -

http://www.nature.com/news/2006/061009/full/news061009-10.htmlhttp://en.wikipedia.org/wiki/Genome

Execution of the program: Central dogma of molecular biology

17

splicing

(pre-mRNA)

mRNA→ ProteinRibosome

RNA bases to amino acids(A,U,G,C) to (A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Z)

DNA →mRNA: (A,T,G,C) to (A,U,G,C)RNA polymerase

Proteins

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Non-Sense Mutation

• A point mutation in a sequence of DNA that results in a premature stop codon

• Protein product is incomplete or non-functional

• Beta-Thalassemia• Results from a single point mutation

• HBB gene on chromosome 11

• Reduction in production of hemoglobin

• HBB blockage over time leads to decreased Beta-chain synthesis

• Having a single gene for thalassemia may protect against malaria

• One of the most commonly inherited disorders in Pakistan

• With a prevalence rate of 6 % in the Pakistani population

• 5000-9000 children every year

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What is going on in your body?

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What can the cell do?

What is it doing?

How is it doing it?

Sequencing Technologies &

Algorithms

Human Genome Project

• Started in 1990

• Objective: Sequence the human genome by 2005

• Achieved: 2000 • Government consoritium

• Cost: $3 Billion

• Craig Venter’s Celera / Solexa

• $1000 genome project

• 1000 genomes project

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Sequencing Technologies• Sanger Sequencing

• 454 Sequencing / Roche

• GS Junior System

• GS FLX+ System

• Illumina (Solexa)

• HiSeq System

• Genome analyzer IIx

• MySeq

• Applied Biosystems - Life Technologies

• SOLiD 5500 System

• SOLiD 5500xl System

• Ion Torrent - Life Technologies

• Personal Genome Machine (PGM)

• Proton

• Helicos

• Helicos Genetic Analysis System

• Pacific Biosciences

• PacBio RS

• Oxford Nanopore Technologies

• GridION System

• MinION 24

First Generation

2nd Generation(Next Generation Sequencing, NGS)(Deep Sequencing)(High-throughput sequencing)Amplified Single Molecule SequencingMost widely used right now

3rd Generation(Next Next Generation Sequencing)Single molecule sequencing

HiSeq 2000

Steps in Sequencing

• DNA Extraction

• Preprocessing (Amplification , …)

• Sequencing

• Shotgun sequencing• Reads

• Assembly

• Data analysis

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Shotgun Sequencing: The case of exploding newspapers

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Joining overlapping reads

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Completing the overlap puzzle

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Sequencing and newspaper explosions: 1• Take (millions of) copies of the DNA you want to

sequence

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Sequencing and newspaper explosions: 2• Fragment the DNA into smaller pieces

• Because our sequencing technologies can only read very short fragments reliably

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Sequencing and newspaper explosions: 3• The short fragments resulting from DNA

fragmentation are called reads

• Some reads disappear

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Sequencing and newspaper explosions: 3• We get the reads but we have no idea where they

came from in the DNA• No position information

• Need to reconstruct the DNA sequence

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Sequencing and newspaper explosions: 4• Solve it as an overlap puzzle

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Sequencing and newspaper explosions: 5• Reconcile the pieces

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Sequencing as a computational problem

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Comparison of de novo assemblers• Zhang, Wenyu, Jiajia Chen, Yang Yang, Yifei Tang, Jing Shang, and Bairong Shen. “A Practical

Comparison of De Novo Genome Assembly Software Tools for Next-Generation Sequencing Technologies.” PLoS ONE 6, no. 3 (March 14, 2011). doi:10.1371/journal.pone.0017915.

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Sequencing: Costs & Amount

•

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http://sulab.org/2013/06/sequenced-genomes-per-year/

Applications of Sequencing

How sequencing machines work

• Input DNA/RNA Sample

• Output• FASTQ Files: Reads stored

• Also have quality information

• Phred quality scoring

• Different machines use different formats on quality

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!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~

Genome Assembly

• Based on reads, assemble them into a genome

• Whole Genome Sequencing• Input: FASTQ file

• Output: Genome

• Whole Exome or Targeted Sequencing• Input: FASTQ file of reads, Reference Sequence

• Output: Genome

RNA-Seq: What are you doing?• Input: Reference Genome, RNA reads

• Output: Alignment File (SAM or BAM)• Tell where each read is aligned

RNA-Seq

Differential expression

http://www.fejes.ca/labels/figures.html

Gene Expression meets Pathology

https://www.nejm.org/doi/full/10.1056/NEJMoa021967

Knowledge Transfer between spaces

• Platinum-based combination chemotherapy response in Ovarian Cancer

• 224 Cases (159 Sensitive, 65 Resistant)

• Both Gene Expression and H&E WSIs available

Patient-wise bootstrap ROC

Gene Expression: selected 227 genes

47https://github.com/deroneriksson/python-wsi-preprocessing/blob/master/docs/wsi-preprocessing-in-python/index.md

RBF SVM

(MIL-CNN)

Gene Expression Based

https://arxiv.org/abs/1803.04054

WSI: 80Kx80K(Input Space)

20X

ROI Patches: 512x512Top 50 scoring patches per slide

Color factor (pink/purple)Saturation and value factorTissue quantity factorTissue percentageScore

Ref

Stain Normalized

Source

Positive bag (1 per “sensitive” patient)

Negative bag (1 per “resistant” patients)

Stain NormalizationPatch Extraction Formation of bags for MIL

Pretrained on breast cancer classification

If a tumor is sensitive, then all of it may not be sensitive

If a tumor is resistant, then all (or at least most) of it is not sensitive

MIL Based Loss

max 0,1 − 𝑌𝐵𝑚𝑎𝑥𝑖∈𝑩 𝑓 𝒙𝒊; 𝜽CNN

MIL Training for CNN

Gene Expression

Pathology Imaging

Integration of genetic information changes tile selection

Pathology space only

Heterogeneous feature space

TCG

A-1

3-1

48

8TC

GA

-13

-14

97

TCG

A-2

9-1

70

5TC

GA

-25

-23

93

Pathology space only

Heterogeneous feature space

Thoughts on the future

• Multi-view prediction!• Understanding similarity in gene space and pathology

space

• Linking pathways to pathology

• Understanding causal connections

Biology easily has 500 years of exciting problems to work on.

Knuth.