Alexei Fedorov, Ph.D.
Associate Professor
Head of Bioinformatics Lab
Department of Medicine
Vice Director
Program in Bioinformatics and Genomics/Proteomics
Tel: (419)-383-5270
Email: alexei.fedorov@utoledo.edu
http://bpg.utoledo.edu/~afedorov/lab/
1
May 2011
Bioinformatics Lab in 2013-2014
PhD students
Shuhao Qiu
Masters students
Ahmed Al-Khudair
Current grants
NSF Career Development 2007-2012
“Investigation of intron cellular roles”
4
MAJOR GOAL:
Bioinformatics
Investigation
of the
Human
Genome
5
Education in Bioinformatics
(TWO TYPES OF STUDENTS)
• Computer/math background  gain experience in
Biology (Sam, Andy)
• Biological background  gain experience in
programming (Dave, Maryam)
• Example of computational projects:
Binary-absrtacted Markov models and their
application to sequence classification
http://etd.ohiolink.edu/view.cgi?acc_num=mco1271271172
http://bpg.utoledo.edu/~sshepard/defense/ video
Genomic MRI
http://bpg.utoledo.edu/gmri/
http://www.jove.com/Details.php?ID=2663
Job perspectives
(example: Ashwin Prakash)
PhD – November 2011, HSC UT
PhD research fellow -- from January 2011
Johns Hopkins School of Medicine
Declined offers:
• Cold Spring Harbor Laboratory
• Baylor College of Medicine
The PI’s students received the following
awards:
• Jason Bechtel, Outstanding MSBS student in 2008 at HSC UT.
• Theodor Rais, Second/Third Poster award by Ohio Bioinformatics
Consortium, 2009.
• Samuel Shepard, Outstanding PhD student in 2010 at HSC UT.
• Lorraine Walters, Undergraduate Research Recognition Award, UT
May 2012.
• Arnab Saha-Mandal, 1) Outstanding MSBS student in 2013 at HSC
UT; and 2) Canadian Institute of Health Research fellowship support
($20,000).
• Jasmine Serpen, 1) Ohio Governor's Thomas Edison Award for
Excellence in Biotechnology & Biomedical Technologies-1st place;
and 2) OSERA Biomedical Research/Bioengineering Award-1st
place (for high school students).
Program in Bioinformatics and
Genomics/Proteomics (BPG)
• http://hsc.utoledo.edu/depts/bioinfo/
• BPG offers a Certificate in association with
the degrees of Doctor of Philosophy (Ph.D.)
or Doctor of Medicine (M.D.). BPG also
offers a Master of Science in Biomedical
Sciences (MSBS).
10
Two courses in Spring semester:
• Application of Bioinformatics, Proteomics, and
Genomics (BIPG 640) or “Advanced Bioinformatics”
(should be taken after “Fundamental Bioinformatics” of Dr. Trumbly)
• Introduction to Bioinformatic Computation (BIPG 610)
The main goal of this course is to provide basic
programming skills to biological and medical students who
may lack a background in computer sciences. Programming
will be specifically taught using important biological
examples, focusing in particular on the PERL language.
No programming skills are required!
11
In the “Introduction to Bioinformatic Computation” course, rather than doing
“cookbook” lab exercises, students participate in real-world, challenging
problems whose resolution advances the field of genome biology. In addition to
learning programming and other bioinformatic skills the students of this course
acquire knowledge in how to present the final product of bioinformatic research
and how to write a scientific paper on the subject.
•In 2005 the class developed a program to identify novel genes for non-coding
RNAs in humans and other mammals. This work resulted in publication of an
article in Nucleic Acids Research1, coauthored by the group of students who
were actively working on this project.
•In 2006 course students created a novel public database (ASMD) and also a
novel computational resource “Splicing Potential”. Ten students were coauthors in two manuscripts2,3.
•In 2007 the class participated in the “Genomic MRI” project. Seven of these
4
students are co-authors in BMC Genomics, 2008
•2008 class continued “Genomic MRI” project. They performed whole genome
comparisons for human, chimpanzee, and macaque and also analyzed
distribution of 4 million SNPs inside and outside MRI regions. The results are in
preparation for publication in Genome Research with 6 students among the
authors.
12
Publications with IBC students
54. Prakash A., Shepard S., Mileyeva-Biebesheimer O., He J., Hart B., Chen M., Amarachiniha
S., Bechtel J., Fedorov A. “Molecular forces shaping human genomic sequence at midrange scales”, BMC Genomics 2009, 10:513.
53. Bechtel J.M., Wittenschlaeger T., Dwyer T., Song J., Arunachalam S., Ramakrishnan S.K.,
Shepard S., Fedorov A. Genomic mid-range inhomogeneity correlates with an abundance
of RNA secondary structures. BMC Genomics 2008, 9:284.
52. Bechtel J. M., Rajesh P., Ilikchyan I., Deng Y., Mishra P.K., Wang G., Wu X., Afonin K.,
Grose W., Wang Y., Khuder S., and Fedorov A. Calculation of Splicing Potential from the
Alternative Splicing Mutation Database Research Notes 2008, 1:4.
51. Bechtel J. M., Rajesh P., Ilikchyan I., Deng Y., Mishra P.K., Wang G., Wu X., Afonin K.,
Grose W., Wang Y., Khuder S., and Fedorov A. The Alternative Splicing Mutation
Database: a hub for investigations of alternative splicing using mutational evidence.
Research Notes 2008, 1:3.
44. Fedorov A, Stombaugh J., Harr M.W., Yu S., Nasalean L., Shepelev V. Computer
identification of snoRNA genes using a Mammalian Orthologous Intron Database. Nucl.
Acids Res. 2005. 33, 4578-4583.
http://www.utoledo.edu/centers/brim/index.html
COURSE: Bioinformatics of Biomarkers and Individualize Medicine,
Spring 2012
• Course time line: 14 Weeks
• No prerequisites, recommended: Introduction
of bioinformatics and molecular biology
• Reserve materials: None
• Unit 1 Biomarker discovery and
validation
• Unit 2 Individualized Medicine
Investigation of the human genome
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taattttgaa
acaaaacttt
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gagacgaagt
caacctgcgt
tacaggcgcc
ccatgttggc
ccaaagtgct
ctggctaaca
gcacatgcct
gaggcagagg
aagactccat
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17
... after next 200 pages
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18
Human chromosome 1
4,814,628 lines =
=100,000 pages
= 100 books
(1000 pages each)
19
Nature 2012, Sept
th
6 ,
v.489, p 46
Lab 2013
THE 1000 GENOME PROJECT
A GUIDE TO YOUR ANCESTRY
The pattern of the human genetic variations believed to be a key to reveal much about the human population
history and diversity. The 1000 Genome project has sequences 1092 genome from different populations and by
identifying the sequence that correspond to LWK, GBR, JPT and FIN, we are aiming to learn more about the
population genetic patterns and to get a picture of the genetic diversity existed within the mentioned populations.
The 1000 genome project effort to catalogue the human genetic variation is utilized in this project to calculate and
compare these genetic differences between 14 populations. I am presenting the results that our bioinformatics
lab’s team obtained so far and working on having it put in a paper.
Using Perl programming to compute the differences between each two individual’s genomes from the 1000
Genome project for the 14 populations
•ASW
HapMap African ancestry individuals from SW US
•CEU
CEPH individuals
•CHB
(CHB) Han Chinese in Beijing
•CHS
(CHB) Han Chinese South
•CLM
Colombian in Medellin, Colombia
•FIN HapMap Finnish individuals from Finland
•GBR
British individuals from England and Scotland (GBR)
•IBS Iberian populations in Spain
•JPT JPT Japanese individuals
•LWK
(LWK) Luhya individuals
•MXL
HapMap Mexican individuals from LA California
•PUR
Puerto Rican in Puerto Rico
•TSI Toscan individuals
•YRI (YRI) Yoruba individuals
Figure 1: Frequency distribution of differences in 14 populations (GBR, JPT, LWK, FIN, ASW, PUR, CLM, CHS, MXL, CEU, IBS, YRI, TSI and CHB), Each peak represents one population and the differences for each of these populations calculated between each two individuals within the same population (example: GBR has 89 individuals
that will make 3916 pair of individuals). The differences ranged between 2.75 million – 5.28 million, and they are plotted on a scale of bins 0.01 million in size
1000
ASW-60
CEU-84
CHB-96
CHS-99
CLM-59
FIN-93
GBR-89
IBS-13
JPT-88
LWK-96
MXL-65
PUR-54
TSI-96
YRI-87
900
800
700
600
500
Number Of Pairs
400
300
200
100
0
2.7
2.9
3.1
3.3
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
5.1
5.3
5.5
The Graph above illustrates the distribution of the genetic differences among the 14 populations.
The X axis shows the range in the number of differences (2.7 million – 5.5 million). The Y axis represents the number of pairs (two individuals
compared by calculating the number of genetic differences between their genomes).
Figure 2:
The Graph below showing the 14 populations consisting 4 distinct origins and lets call them 4 ancestries. 1_African , 2_Hybrid ,
3_European, 4Asian.
4
3
1000
ASW-60
CEU-84
CHB-96
CHS-99
CLM-59
FIN-93
GBR-89
IBS-13
JPT-88
LWK-96
MXL-65
PUR-54
TSI-96
YRI-87
1
900
800
700
600
500
Number Of Pairs
400
2
300
200
100
0
2.7
2.9
3.1
3.3
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
5.1
5.3
5.5
Figure 3:
The three populations that have African origin, they total differences distributed close to each other. The LWK population(Luhya individuals )
showd some individual who had almost half (2.7 million – 4.8 million) the number of differences, almost all of these have been declared as
siblings and relatives.
Some of them are not declared to be relatives by the 100 Genome project so our results suggest that they might be some undeclared
relatives in the 100 genome project.
We further examined some populations for any declared relationships between any of these individuals; the relatives showed
that they have the minimum difference in their genetic variation. For example, In the LWK population as showing in the table
below, the relatives fall at the top of the list when we sorted the total differences from lowest to highest. The green highlighted
cells showing that these individuals are related to each other as been declared by the 1000 genome appendix, The ones that
are not highlighted we suggest that they are somehow relatives but they haven’t been declared by the 1000 genome project.
1
2
3
4
5
6
7
8
9
1
0
1
1
1
2
1
3
1
4
ID1_L
WK
NA193
74
NA193
52
NA194
70
NA193
97
NA194
44
NA193
34
NA193
82
NA194
53
NA194
70
NA193
31
NA193
82
NA194
53
NA193
34
NA194
69
ID2_LW
K
NA1937
3
NA1934
7
NA1944
3
NA1939
6
NA1943
4
NA1933
1
NA1938
1
NA1944
5
NA1946
9
NA1931
3
NA1938
0
NA1944
4
NA1931
3
NA1944
3
Total_LWK
differences
2756691
Siblings
2777456
Siblings
2848500
Aunt/Uncle
2871776
Siblings
3004459
Siblings
3007478
?
3070661
uncertain parent/child relationship
3077137
?
3111728
Niece/Nephew
3119208
?
3970915
Half Siblings
4106949
?
4178970
Unknown relation
4236592
Niece/Nephew
Figure 4:
CLM, PUR and MXL populations, they show a very wide distribution ranged from 3.1-4.86. what our results indicate that these
population have wide range of mixed blood. The PUR population have a second peak showing on the right side (range between
4.74-4.9 million), we expect that these individuals having different blood. More investigation on these people being conducted to
know where do they have blood from.
Figure 5:
Populations from FIN, GBR, TSI, CEU and IBS. All these population fall under European origin. The IBS population show as a really
low curve because only 13 person have been sequenced from this population.
Figure 6:
The population from Asian origin showed how they are close in their blood by having really close shape of distribution that ranged
between 3.4 million- 3.69 million.
We are more investigating the highest differences pairs (the highest differences between pairs of individuals) that
we suggest that they possibly have a different origin. We investigated the highest 40 pairs in some population
and we found that some individuals showed high difference with other individual and that were significantly
repeated. Example in the figure below
80
70
60
50
40
CLM
30
20
10
0
2.7
2.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
The list below is the CLM individuals that showed the highest genetic differences with each other and when we looked at
them individually we noticed that some of them have been repeated significantly more than others as it shows in the right
side list of repeats. We see that HG01551 and HG01342 has been repeated as highest difference for 20 times while
others were
repeated HG01136
2and 3 times. So we decided to investigate the possibility of these individuals having other origin.
•HG01551
4479513
•HG01365
•HG01342
•HG01551
•HG01551
•HG01551
•HG01488
•HG01366
•HG01551
•HG01342
•HG01342
•HG01377
•HG01462
•HG01551
•HG01461
•HG01342
•HG01551
•HG01551
•HG01375
•HG01551
•HG01551
•HG01389
•HG01342
•HG01551
•HG01342
•HG01551
•HG01342
•HG01440
•HG01342
•HG01342
•HG01551
•HG01551
•HG01551
•HG01551
•HG01551
•HG01390
•HG01462
•HG01551
•HG01551
•HG01551
4480834
4481529
4481637
4483529
4485279
4487693
4488647
4490996
4493212
4493218
4494064
4494414
4496682
4497146
4498051
4499694
4499713
4500523
4501432
4503181
4506393
4508562
4510222
4514486
4519187
4520380
4527415
4533004
4535490
4537772
4541901
4542804
4558088
4561600
4562418
4564478
4577349
4608288
4678948
HG01342
HG01250
HG01250
HG01375
HG01125
HG01342
HG01342
HG01259
HG01271
HG01277
HG01342
HG01390
HG01365
HG01342
HG01125
HG01148
HG01345
HG01342
HG01134
HG01495
HG01342
HG01148
HG01377
HG01134
HG01389
HG01124
HG01342
HG01275
HG01272
HG01272
HG01488
HG01461
HG01462
HG01275
HG01342
HG01342
HG01440
HG01390
HG01342
The idea was to take those repeated high difference individuals with 10 other controls from the same population
that showed average number of genetic difference within the same population , we then randomly took
individuals from other populations and calculated the genetic differences between our 10 control +2 high repeats
and the 1 control from the other populations.
The comparison below was
between 10 controls from
CLM plus the 2 high repeated
high genetic difference
(HG01551 and HG01342 ) ,
against one control individual
from YRI population(Yoruba
individuals ) “African Ancestry
“.
HG01551 and HG01342 had
the lowest difference
indicating that these two
persons might be from African
origin.
We more compared CLM controls with individual from African population(LWK) and another individual from
Asian(CHS).
The two control individuals showed lowest genetic difference against LWK control while showed highest
difference when against CHS individual . This suggest that our two individuals from CLM population are
originally belong to an African origin.
CLM - LWK
CLM - CHS
Conclusions
• Total variants showed substantial geographic differentiation,
• Total number of differences determines diverse populations that
are more geographically and ancestrally remote.
• populations are grouped by the predominant component of
ancestry: Europe (CEU, TSI, GBR, FIN and IBS), Africa (YRI,
LWK and ASW), East Asia (CHB, JPT and CHS) and the
Americas (MXL, CLM and PUR).
• Relatives within the same population have significantly less
number of genotype variations “almost half the number”
comparing to the non relatives.
• The study of human genetic variation has evolutionary
significance. It can help to understand ancient human population
migrations as well as how different human groups are
biologically related to one another.