Uma Chandran, MSIS, PhD
Department of Biomedical Informatics
University of Pittsburgh chandran@pitt.edu
412-648-9326
07/17/2013

• What is Bioinformatics?
– Examples of bioinformatics
– Past to present
• What is translational bioinformatics?
• Personalized Medicine
– Bioinformatics and Personalized Medicine

• http://en.wikipedia.org/w iki/Bioinformatics
• Application of information technology to molecular biology
• Databases
• Algorithms
• Statistical techniques

• Sequence analysis
• Genome annotation
• Evolutionary biology
• Literature analysis
• Analysis of Gene Expression
• Analysis of regulation
• Analysis of protein expression
• Analysis of mutations in cancer
• Comparative genomics
• Systems Biology
• Image analysis
• Protein structure prediction
From Wikipedia

• Robert Ledley and
Margaret Dayhoff
– First bioinformaticians
– Using IBM 7090 and punch card analyzed amino acid structure of proteins
– Created amino acid scoring matrix
– Protein evolution
– Protein sequence alignment http://blog.openhelix.eu/?p=1078

• Databases to store sequence info
– Phage Φ-X174 sequenced in 1977
– GenBank
• 30, 000 organisms
• 143 billion base pairs
– BLAST program for sequence searching
• Algorithms, databases, software tools

• Compare relationships between organism by comparing
– DNA sequences
– Now whole genomes
• Can even find single base changes, duplication, insertions, deletions
• Uses advanced algorithms, programs and computational resources

• Millions of articles in the literature
• How to find meaningful information
– Natural language processing techniques
• Example
– Type in p53 or PTEN in Pubmed – will retrieve 1000s of publications
– How to summarize all the information for a particular gene
– Function, disease, mutations, drugs
– IHOP database creates network between genes and proteins for
30000 genes

• Marking genes and other features in DNA
• Algorithms, software

• Interdisciplinary discipline
– Gene/proteins/function/ - Biologist
– In Cancer – Physician/Scientist/Biologist
– Algorithms, for example, BLAST – Math/CS
– Separate Signal from Noise, Diff gene expression, correlation with disease – Statistician
– Tools, Software, Databases – Software developers, programmers
• Aim to make sense of biological data

• Translational = benchside to bedside
– Bringing discoveries made at the benchside to clinical use
• the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health. Translational bioinformatics includes research on the development of novel techniques for the integration of biological and clinical data and the evolution of clinical informatics methodology to encompass biological observations. The end product of translational bioinformatics is newly found knowledge from these integrative efforts that can be disseminated to a variety of stakeholders,
including biomedical scientists, clinicians, and patients.”
• Translational = benchside to bedside
Atul Butte, JAMIA 2008;15:709-714 doi:10.1197

• DNA is transcribed to
RNA
• RNA is translated to protein
• Many regulatory processes control these steps

• 20, 000 genes
• Many transcripts, many proteins
• More than 20, 000 proteins
• Southern, Northern, Western Blots

• DNA
– Are there any mutations
• sickle cell anemia
• Cystic fibrosis
• Hemophilia
• Other diseases such as diabetes, cancer ??
– Polymorphisms
• Variation in the population
• Mutation

• Are there regions of amplification or deletions that correlate with disease
– If so, what genes are present in these regions
– HER2 amplification in breast cancer
– EGFR mutations in lung cancer

• RNA
– DNA is transcribed to RNA
– Approximately 20K genes
• RNA levels will differ in different conditions
– Liver, kidney, cancer, normal, treatment etc
– Diagnosis or prognostic
– microRNAs level
– lnncRNAs
– Splicing differences
mRNA

• DNA level
– Are there mutations or polymorphism between different cancer patient groups
• Good outcome v bad outcome
• Early stage vs late stage
• Therapy responders v non-responders
• Examples: Renal cell, prostate cancer etc
• RNA
– Are there specific transcripts – mRNA, microRNA - that are up or down and are signature for outcome, disease and response
– 1000s of studies
– Consortia projects
• TCGA – The Cancer Genome Atlas projects
• Profile 500 samples of each cancer for DNA, RNA changes

• 20, 000 genes
• Many transcripts, many proteins
• More than 20, 000 proteins
• Southern, Northern, Western Blots

• Microarray and
Northern/Southern blots
– Exploit the ability of nucleotides to hybridize to each other
– Base pairing
– Complementary bases
• A :T (U)
• G: C

Sensitivity and dynamic range low

• Example: Northern blot (measure RNA)
– http://www.youtube.com/watch?v=KfHZFyADnNg
– Workflow of Northern blot
• Key points
– mRNA run on gel – separated by size
– transferred to a membrane – immobilized
– Have a hypothesis – for example studying RNA level for BRCA in normal and cancer
– Only probe for a mRNA or transcript is labeled or tagged
– probe is prepared and labeled with radioactivity
– Hybridized to X-ray film
– Only that mRNA is detected and quantitated

• Solid surface
– Many different technologies
• Affy, Illumina, Agilent
– Probes are synthesized on the solid surface
• Synthesized using proprietary technology
– Probe are selected using proprietary algorithms
– RNA (or DNA) is in solutions
– RNA is labeled or tagged
– Hybridized to the chip
– Tagged RNA is quantitated
– Compare between conditions

• Data Management
– Each file for a chip experiment is large
• 100MG x 10 = 1G
• Generates Gigabytes of data
• Data preprocessing
– Convert raw image into signal values
• Data analysis
– 1000s of genes (or SNPs) and few samples
– How to find differences between samples
– What statistical methods to use?
– Like finding needle in a haystack

Normal Noise reduction
Background subtraction
Normalization
Tumor name id
Samples
2 2
662.7
2
369.7
589.9
2
217.5
883.8
2
489
395.5
2
228
979.5
tyrosine kinase with immunoglobulin and epidermal growth factor homology domains 62.7
---
---
1008_f_at
1017_at
3205.4
33.1
14.6
122
11.5
837.2
275.6
156.4
887.4
1582.4
173.1
58.4
198.8
153.5
33.8
817.4
515.3
125.1
299.3
5618.8
82.8
272.7
161.5
192.6
195.2
31
936.4
620
264.9
1324.8
3589.1
213.7
393.7
243.5
11.3
194.6
238.8
96
662.3
381.3
168.7
132.4
1401.2
24.1
132.6
156.9
592.4
18.4
53.7
126.6
26.1
939.3
417.4
33.7
235.6
93.9
200.4
267.1
231.5
hypoxia-inducible factor 1, alpha subunit (basic helix-loop-helix transcription factor) 103.5
ephrin-A5 1041_at
--melan-A
1047_s_at
1051_g_at
96.1
E2F transcription factor 5, p130-binding
95.4
14.1
81.9
94.8
1055.9
18.8
139.4
332.3
175.1
368.2
48.9
140.8
53.3
210.3
170.5
23
178.5
10.2
125.3
146.4
62.4
182.4
57.5
143.5
99.2
120.9
CCAAT/enhancer binding protein (C/EBP), delta 2984.6
30.1
113.5
89.3
28.3
116.9
162.4
196.9
74.9
--1069_at 45.8
4336
66.9
112.5
40.1
480
319.2
155.7
53.6
290.6
12.8
82.1
1157.3
99
97.1
117.9
10.2
91.9
85.8
118.6
6.9
714.7
96.7
604.1
3959.9
55.6
403
67.8
31.8
157.4
27.4
38.1
8.7
369.1
153.3
561.6
1280.4
34.9
166.8
136.7
13.1
48.7
24.6
52
9
889.6
114.6
212
193
394.6
114.7
358.1
150.2
151.9
45.2
191.7
264.5
131.2
108.4
174
87.6
242.9
100.3
23.5
343.1
60.7
43.1
183.6
144.4
30.2
375
41.8
187.6
15.5
324.2
306.4
432.6
314.3
amyloid beta (A4) precursor protein-binding, family B, member 1 (Fe65) 155.1
25.6
163 14.2
--1104_s_at 16378.1
4845.4
1160.6
338.7
32
2711.3
399.2
143.3
4218.5
325.6
48.7
151.1
49.6
386
53.1
280.2
119
24.5
347.1
249.5
212.6
53.3
1763.4
498.1
110.2
2951.4
17.2
E
393.6
307.1
227.2
N
341.8
145.3
E
708.1
408.3
112.6
S
2
336.3
420.4
131.8
170.8
173
1910.3
47.6
238.7
33.8
78.9
733.4
158.3
773
245.4
166.3
552.6
24.2
276
248.5
33.5
331.5
274.4
163.9
16.1
13180.9
283.5
107.4
167.2
313.8
278.4
124.1
13
118.7
103.5
19.3
4129.2
86.2
294.4
103.4
170.9
246
57.5
187.1
651.7
4.2
37
115
19.3
890.5
332.4
127.7
301.3
110.7
92.4
371.5
73.9
338.4
153.8
18.8
269.7
339
86.4
57.6
3130.4
411.4
183.8
4279.4
10.2
329.7
126
60.2
147.4
17.9
79.5
11
371.3
78.9
958.4
198.8
76.2
698
394.7
23.5
216.6
372.3
185.4
87.2
148.6
100.8
91
221.5
20.6
2
381.7
389.1
59.6
223.6
112.6
1195.2
19.3
166.7
237.2
204.8
517.6
14.3
224.5
57.3
117.7
373.3
73.6
154.1
107.4
12.6
253.3
354.5
111.3
20.2
3238.5
305.3
151.7
2391.4
82.4
586
104.9
5.5
26.3
96.2
19.5
25.7
418.1
173.8
993.5
145.5
34.3
1006.6
435.6
28.9
187.7
77.7
243.7
146.8
94.9
36.6
129.5
166.3
28.8
2
363.2
457.8
54.4
186.5
117.5
1217.8
100.4
242.9
183.4
184.6
643.9
40.6
88.2
153.9
274.7
311.2
54.7
224.9
101.7
32
324.7
288.1
59.1
15.9
3576.2
468.1
115.8
1673.5
245.3
157.6
105
26.6
47.9
25.9
59
81.1
595.7
91.8
3909.5
166.8
32.5
742.3
366.7
56.1
255.8
427.7
183.5
523.9
115.2
77.5
15.7
190.3
149.8
2
373.2
495.7
116.4
42.7
241.5
2928.7
20.4
257.3
81.2
290.2
478.8
122.7
194.4
136.6
109.5
266.4
75
95.6
84.3
15.2
294.5
326.1
92.6
78.5
2802.9
269.3
140.4
4456
54.8
480
20
38.8
117.6
25.1
54.1
4
563.1
219.6
1426.7
101.5
28
838
308.6
38.9
180.6
110.8
317.2
216.6
74.4
57.3
109.8
70.9
12.8
2
263.8
346.3
32.7
93.4
153.9
1305.9
111.7
283.9
103
154
742
6.9
134.5
2
302.8
482.5
22.2
115.1
212.2
589.6
78
390.8
104.4
172.2
Data
43.6
4965.1
100.4
163.9
85.5
14.6
94.8
65.1
87.2
10.6
779.1
186.6
2014.7
12
1093.6
499.9
94
160.7
195
333.4
242.8
54.5
48.6
169.4
87.4
9.7
125.8
109.4
397
156.3
161.2
118.5
10.8
150.4
363.2
71.5
82
1992
386.2
203.7

• Class discovery
– Are there novel subclasses within data?
• Class comparison
– How are tumor and normal different in expression?
– Which SNPs are different?
• Class prediction
– Predict class of new sample
• Advanced pathway
Analysis

Dupuy and Simon, JNCI; 2007

amplification amplification diploid deletion

Hagenkord et al; Modern Pathology, 21:599

• Personalized medicine is the tailoring of medical treatment to the individual characteristics of each patient.
• Based on scientific breakthroughs in understanding of how a person’s unique molecular and genetic profile makes them susceptible to certain diseases.
• ability to predict which medical treatments will be safe and effective for each patient, and which ones will not be.
From ageofpersonalizedmedicine.org

From ageofpersonalizedmedicine.org

From Fernald et al; Bioinformatics, 13: 1741

• Breast cancer
– 30% of patients over express HER2
– Treated with Herceptin
– Oncotype Dx: gene expression predicting recurrence
• Cardiovascular
– Patients response to Warfarin, the blood thinner
– Response determined by polymorphism in a CYP genes

• Examples of personalized medicine resulted from studies that generate
– Lots of data
– Rely on bioinformatics methods to discover these associations
• Oncotype Dx:
– Gene expression studies of large number of patients
• CYP polymorphisms
– Discover single nucleotide polymorphisms in patient polulations and association with response
» Initial studies done with PCR methods

• Current examples are few in numbers
• Making personalized medicine a reality
– Generate the data
– Discover the associations
– Find targeted therapies
– Genome sequences prices are dropping
– Large scale genome information is coming:
• 1000 genome
• TCGA
• ICGC
• Also possible to commercially sequence a person’s genome
• Processing all this data into translating these discoveries into medical practice has many challenges

• Processing large scale robust genomic data
• Interpreting the functional impact of variants
• Integrating data to relate complex interactions with phenotypes
• Translating into medical practice
Fernald et al; Bioinformatics: 13: 1741

• Shift from microarrays to Next Gen
Sequencing

• DNA is transcribed to
RNA
• RNA is translated to protein
• Many regulatory processes control these steps

• Directly sequence DNA to determine
– SNP
– CN
– Expression, mRNA, microRNA
– Protein binding sites
– Methylation
• Initial steps depend not on hybridization but also on base pairing or complementarity and DNA synthesis
• Bioinformatics is extremely challenging

• Whole genome sequencing
– Sequence genomes and find variants (1000 genome project)
• Find variants associated with disease phenotype
• Sequence exomes only
– Find coding region variants associated with phenotypes
• RNA seq
– RNA sequence signatures associated with phenotype

• Restricted to probes on chips
• Only transcripts with probes
• File sizes in MBs to GB
• Algorithms, methods
• Typically done on PCs
• Storage on hard drives
• No – predetermined probes
• Can detect everything that is sequenced
• More applications than microarray
• Very large file sizes
• Computationally very intensive
• Clusters, supercomputers
• Large scale storage solutions

• Dynamic range is low
• Statistic to determine expression based on signal
• Many methods in the last 10 years
• Dynamic range is high
– Based on reads
• Statistics based on counts
– Affected by read length, total number of transcripts, lack of replicates

• Denovo assembly
• Mapping to reference genome
– Based on complementarity of a given 35 nucleotide to the entire genome
– Computationally intensive
• Million of 35 bp reads has to search for alignment against the reference and align spefically to a given regions
– Large file sizes
• Sequence files in the TB
• Aligned file BAM files
– Several hundred GB
Reference genome

• Processing large scale robust genomic data
– Suppose we want to identify DNA variants associated with disease
• Which technology
• How much data
• How to analyze the data
• How to identify variants
• Each genome can have millions of variants
• 300, 000 new variants – i.e, not in existing databases
– Will have to separate error from true variants
– 1 error per 100 kb can lead to 30,000 errors in a single experiment
• Why do these errors happen?
Fernald et al; Bioinformatics: 13: 1741

• Data
• Which technology to use
– Each technology has different error rates , Ion Torrent (higher error rate), SOLID, Illumina
– Speed of generation of data – Ion Torrent is faster
• Application – Whole genome or exome or targeted exome
• Analysis
• Analysis
– Algorithms, speed, accuracy
– BLAST is not good for WGS
– Other new algorithms
• Speed of analysis
– Alignment can take days
• Alignment relies on matches between sequence and reference genome
– How much mismatches to tolerate
– True mismatch or error – sequencing error, true mismatch – is it a SNP
• Quality of reference genome
• Large amounts of data
– Each whole genome sequencing experiment can generate TB of data
• Where to store – patient privacy
– Servers, locations, networking
• Sample sizes – how many samples to sequence to discover the association with disease

• Technology
– Ion Torrent, SoLiD, Illumina
– Each has its own error rates
– Speed of data generation
– Dependent on application – WGS or exome
• Analysis
• Analysis
– Algorithms, speed, accuracy
• Speed of analysis
– Alignment can take days
• Alignment relies on matches between sequence and reference genome
– How much mismatches to tolerate
– True mismatch or error – sequencing error, true mismatch – is it a SNP
• Quality of reference genome

From Mark Boguski’s presentation at the IOM, July 19, 2011

From Mark Boguski’s presentation at the IOM, July 19, 2011

From Mark Boguski’s presentation at the IOM, July 19, 2011

From Mark Boguski’s presentation at the IOM, July 19, 2011

• Infrastructure
– Storage, backup, archive
– Where – HIPAA compliant?
– Network
• How to move data
• Analysis
– Methods – statistics, annotation
– Computing resources
– How many samples can be handled at a time?
– Time to report

From Mark Boguski’s presentation at the IOM, July 19, 2011