Panagiotis Alexiou, PhD 
Research Associate 
ㅡㅡㅡㅡㅡㅡ 
Panagiotis Alexiou 
Department of  
Pathology and Laboratory Medicine 
 
Division of Neuropathology 
 
613B Stellar Chance Labs 
422 Curie Boulevard 
 
Philadelphia, PA 19104-6100 
 
(+1) 267- 277-2661 
 
palexiou@mail.med.upenn.edu 
 
http://www.panalexiou.com/ 
 
 
27 APRIL 2017 
CEITEC 
Central European Institute of Technology 
Brno, Czech Republic 
 
To whom it may concern, 
 
I am writing to apply for the position of ‘Head of Bioinformatics Unit’                         
posted recently on the ‘nature-jobs’ website. Allow me to briefly expose                     
my skills and experience relevant to the position, following with a brief                       
summary of future research directions. 
 
Research Experience 
 
Following my interdisciplinary education in Genetics, Molecular Biology               
and Bioinformatics, I acquired my PhD in the field of the bioinformatic                       
analysis of microRNA biogenesis and function. The work performed                 
during my PhD introduced me to several subfields of bioinformatics, such                     
as machine learning for microRNA target prediction (classification), the                 
development of relational databases and web-servers, motif analyses,               
scientific knowledge text mining - as well as the theoretical fields of Small                         
RNA Biology and RNA Binding Protein Biology which would be the                     
unifying paths of my research to date. 
 
I continued my research as a Postdoctoral Researcher at the Perelman                     
School of Medicine at the University of Pennsylvania, joining the                   
Mourelatos Lab, one of the premier laboratories worldwide in small RNA                     
biology and RNA binding protein research. During my time at the                     
University of Pennsylvania, I have collaborated directly with biomedical                 
researchers working extensively with Next Generation Sequencing             
(NGS) data. I have developed techniques and algorithms for the analysis                     
of NGS data in the fields of piRNA biology (both biogenesis and function),                         
microRNA biology, and RNA Binding Proteins function in               
neurodegeneration. Specifically pertaining to NGS analysis, I have helped                 
develop both an NGS analysis suite (CLIP-Seq-Tools) and a modern perl                     
object-oriented framework for NGS analysis (GenOO). 
 
Track record of scientific productivity 
 
During my career I have produced 24 publications, which have                   
collectively been cited over 3000 times in the literature ( h-index 17, i-10                         
index 18). The online resources I helped produce during my PhD serve                       
approximately 500 unique users daily. 
 
My work has lead to a number of discoveries including the function of                         
Piwi​ piRNA complexes in germline development, a pre-​miRNA               
surveillance system of quality control of miRNA synthesis and the effects                     
of TAF15 and FUS on the neuronal transcriptome. Some of these                     
publications are in journals of the highest impact in the field such as                         
Molecular Cell, Nature Structural & Molecular Biology, Genes &                 
Development and Nature, a paper that was highlighted in a commentary                     
in Developmental Cell and has been met by overwhelming acceptance by                     
the piRNA Biology community. 
 
An overview of my publications can be found in the attached Curriculum                       
Vitae. 
 
Communication skills 
 
I pride myself in concise, direct and accurate communication. My career                     
to date has always been in radically interdisciplinary environments,                 
collaborating day to day with Biochemists, Computer Scientists,               
Bioinformaticians, Geneticists, Medical Doctors and so on. I often and                   
happily take the role of inter-discipline translator between colleagues                 
coming from different backgrounds. I believe my communication (and                 
teaching) skills to be some of my strongest points as a scientist. 
 
Organizational and leadership capabilities 
 
During the PhD and Postdoctoral phases of my career I have had some                         
opportunities to use my organizational and leadership capabilities. I had                   
pivotal roles in setting up and maintaining the hardware of the two labs                         
of which I was member. Additionally I organized code development and                     
review with coworkers around the agile/xp development model and the                   
pair programming approach.  
 
As a teaching assistant and substitute lecturer, I developed and taught an                       
elective course on Bioinformatics for last year engineering students and                   
MSc students. Due to financial constraints of the university, I was tasked                       
with single handedly teaching and overviewing a student lab of 60                     
students, including 3 students doing their theses in my field. I managed to                         
lead all 3 of these students to produce code fit for inclusion in                         
publications from my lab.  
 
Proposed Future Work 
 
My career up to now has been focused on finding ways to efficiently                         
implement novel technologies that spearhead innovation in every               
subfield of research I have been involved with. I am a proponent of open                           
software and scientific resource development, having produced             
web-tools that are being used by thousands of users per month,                     
open-source tools for the analysis of sequencing data, and even a                     
programming package that codifies genomic and NGS entities into                 
programming objects, allowing for rapid and robust prototyping of future                   
services. My proposed future work involves the development of tools for                     
the classification and exploration of NGS data. Specifically, I am                   
proposing the development of a machine-learning trained function               
classification system for NGS sequencing data of RNA Binding Proteins.                   
Please find attached a brief overview of my research plan. 
 
I believe I have the knowledge, expertise, understanding, and will, to                     
tackle the issues that will face biomedical research in the following years                       
- and I hope that I will be given this opportunity at your Research                           
Institute. 
Sincerely, 
 
Panagiotis Alexiou, Phd 
 
 
Panagiotis Alexiou 
Research Associate
Perelman School of Medicine
University of Pennsylvania, USA
Contact 
Pathology and Laboratory Medicine 
Division of Neuropathology 
613B Stellar Chance Labs 
422 Curie Boulevard 
Philadelphia, PA 19104-6100 
(+1) 267- 277-2661 
palexiou@mail.med.upenn.edu 
http://www.panalexiou.com/ 
Education & Research 
University of Aberdeen, ​UK — ​BSc Genetics with Industrial 
Placement 
1999 - 2004 
Universiteit van Amsterdam, ​Netherlands — ​MSc Molecular 
Cell Biology and Bioinformatics 
2004 - 2006 
Aristotelian University of Thessaloniki, ​Greece​ ​— ​PhD 
BSRC Alexander Fleming, ​Greece​ ​— ​PhD Research 
2007-2011 
University of Pennsylvania, ​USA — ​Postdoctoral Research 
2011 - 2016 
University of Pennsylvania, ​USA — ​Research Associate 
2016 - Present 
 
References 
Zissimos Mourelatos, MD 
Professor of Pathology and Laboratory Medicine 
University of Pennsylvania Perelman School of Medicine 
Department of Pathology and Laboratory Medicine 
Email: ​mourelaz@uphs.upenn.edu 
Artemis Hatzigeorgiou, PhD 
Professor of Bioinformatics 
University of Thessaly, Department of Electrical and Computer 
Engineering 
Email: ​arhatzig@inf.uth.gr 
Theodore Dalamagas, PhD 
Senior Researcher 
ATHENA Research Center 
Email: ​dalamag@imis.athena-innovation.gr 
 
 
Research Focus & Skills 
RNA-binding protein biology 
NGS sequencing data analysis 
Programming (perl, golang) 
Statistics (R) 
Bioinformatics Algorithms 
 
Grants & Scholarships 
2007-2010 
BSRC Alexander Fleming PhD 
funding scholarship 
2010-2012  
IKYDA personnel exchange 
program grant 
(Greece-Germany) in 
collaboration with Martin 
Luther University 
Halle-Wittenberg 
 
Teaching 
2010-2011  
Graduate Teaching Assistant 
University of Thessaly, 
Department of Electrical and 
Computer Engineering 
Bioinformatics (HY501) 
2010-2011  
Substitute Lectures
National and Kapodistrian 
University of Athens,
Department of Informatics 
and Communications,  
MSc Information 
Technologies in Medicine and 
Biology 
 
Publications 
Citations  3008 
h-index  17 
i10-index  18 
 
List in chronological order (* denotes equal contribution): 
 
1. Zhang L, Volinia S, Bonome T, Calin GA, Greshock J, Yang N, Liu CG, Giannakakis A, ​Alexiou ​P​, Hasegawa K,                                       
Johnstone CN, Megraw MS, Adams S, Lassus H, Huang J, Kaur S, Liang S, Sethupathy P, Leminen A, Simossis VA,                                       
Sandaltzopoulos R, Naomoto Y, Katsaros D, Gimotty PA, DeMichele A, Huang Q, Bützow R, Rustgi AK, Weber BL,                                   
Birrer MJ, Hatzigeorgiou AG, Croce CM, Coukos G (2008) Genomic and Epigenetic Alterations Deregulate                           
microRNA Expression in Human Epithelial Ovarian Cancer,​ Proc Natl Acad Sci U S A​ ,105(19) ,7004-9.​ (cited: 500) 
2. Maragkakis M, Reczko M, Simossis VA, ​Alexiou ​P​, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G,                               
Koukis E, Kourtis K, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG. (2009) DIANA-microT web                                 
server: elucidating microRNA functions through target prediction., ​Nucleic Acids Res.​ ,37​ ​W273-6. ​ (cited: 462) 
3. Papadopoulos GL, ​Alexiou ​P​, Maragkakis M, Reczko M, Hatzigeorgiou AG. (2009) DIANA-mirPath: Integrating                         
human and mouse microRNAs in pathways., ​Bioinformatics​. ,2009 Aug 1;25(15):1991-3.​ ​(cited: 252) 
4. Maragkakis M*, ​Alexiou ​P*​, Papadopoulos GL, Reczko M, Dalamagas T, Giannopoulos G, Goumas G, Koukis E,                               
Kourtis K, Simossis VA, Sethupathy P, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG. (2009)                                 
Accurate microRNA target prediction correlates with protein repression levels. ​BMC Bioinformatics ,2009 Sep                         
18;10:295. ​ ​(cited: 318) 
5. Alexiou P​, Maragkakis M, Papadopoulos GL, Reczko M, Hatzigeorgiou AG (2009) Lost in translation: an                             
assessment and perspective for computational microRNA target identification., ​Bioinformatics , 25(23):3049-55.                     
(cited: 285) 
6. Alexiou P​, T. Vergoulis, M. Gleditzsch, G. Prekas, T. Dalamagas, M. Megraw, I. Grosse, T. Sellis, A.G. Hatzigeorgiou                                   
(2009) miRGen 2.0: a database of microRNA genomic information and regulation, ​Nucleic Acids Research                           
,Nucleic Acids Res. 2010 January; 38(Database issue): D137–D141.​ ​(cited: 138)  
7. Alexiou P​, Maragkakis M, Papadopoulos GL, Simmosis VA, Zhang L, Hatzigeorgiou AG (2010) The                           
DIANA-mirExTra web server: from gene expression data to microRNA function., ​PLoS ONE​ ,5(2):e9171.​ ​(cited: 69) 
8. Marotta D, Karar J, Jenkins WT, Kumanova M, Jenkins KW, Tobias JW, Baldwin D, Hatzigeorgiou A, Alexiou P​,                                   
Evans SM, Alarcon R, Maity A, Koch C, Koumenis C. (2011) In vivo profiling of hypoxic gene expression in gliomas                                       
using the hypoxia marker EF5 and laser-capture microdissection.​ Cancer Res​ ,71(3):779-89.​ ​(cited: 33)  
9. Alexiou P, Maragkakis M, Hatzigeorgiou AG (2011) Online resources for microRNA analysis. ​Journal of Nucleic                             
Acid Investigation​, 2(1).​ ​(cited: 7)  
10. Maragkakis M, Vergoulis T, ​Alexiou P​, Reczko M, Plomaritou K, Gousis M, Kourtis K, Koziris N, Dalamagas T,                                   
Hatzigeorgiou AG. (2011) DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and                             
bibliographic miRNA to disease association. ​Nucleic Acids Res.​ ​W145-8. ​(cited: 80)  
11. Vergoulis T, Vlachos IS, ​Alexiou P​, Georgakilas G, Maragkakis M, Reczko M, Gerangelos S, Koziris N, Dalamagas                                 
T, Hatzigeorgiou AG. (2011) TarBase 6.0: capturing the exponential growth of miRNA targets with experimental                             
support.​ Nucleic Acids Res.​ 2011 Dec 1.  ​(cited: 401) 
12. Reczko M, Maragkakis M, ​Alexiou P​, Grosse I, Hatzigeorgiou AG (2012) Functional microRNA targets in protein                               
coding sequences ​Bioinformatics​ 28 (6), 771-776 ​(cited: 188) 
13. Reczko M, Maragkakis M, ​Alexiou P​, Papadopoulos GL, Hatzigeorgiou AG. (2012) Accurate microRNA target                           
prediction using detailed binding site accessibility and machine learning on proteomics data. ​Frontiers in                           
Bioinformatics and Computational Biology​.  ​(cited: 19)  
14. Vourekas A, Zheng Q, ​Alexiou P​, Maragkakis M, Kirino Y, Gregory BD, Mourelatos Z. (2012) Mili and Miwi target                                     
RNA repertoire reveals piRNA biogenesis and function of Miwi in spermiogenesis. ​Nat Struct Mol Biol.                             
19(8):773-81. ​(cited: 103) 
15. Honda S, Kirino Y, Maragkakis M, ​Alexiou P​, Ohtaki A, Murali R, Mourelatos Z, Kirino Y. (2013) Mitochondrial                                   
protein BmPAPI modulates the length of mature piRNAs. ​RNA​. 2013 Oct;19(10):1405-18​(cited: 26) 
16. Ibrahim F, Maragkakis M*, ​Alexiou P*​, Maronski MA, Dichter MA, Mourelatos Z. (2013) Identification of in vivo,                                 
conserved, TAF15 RNA binding sites reveals the impact of TAF15 on the neuronal transcriptome. ​Cell Rep.                               
3(2):301-8. ​(cited: 13) 
17. Nakaya T, ​Alexiou P*​, Maragkakis M*, Chang A, Mourelatos Z. (2013) FUS regulates genes coding for                               
RNA-binding proteins in neurons by binding to their highly conserved introns. ​RNA​ 19(4):498-509. ​(cited: 49) 
18. Liu X, Zheng Q, Vrettos N, Maragkakis M, ​Alexiou P​, Gregory BD, Mourelatos Z. (2014) A MicroRNA precursor                                   
surveillance system in quality control of MicroRNA synthesis. ​Mol Cell.​ 55(6):868-79. ​(cited: 29) 
19. Vourekas A, Zheng K, Fu Q, Maragkakis M, ​Alexiou P​, Ma J, Pillai RS, Mourelatos Z, Wang PJ. (2015) The RNA                                         
helicase MOV10L1 binds piRNA precursors to initiate piRNA processing. ​Genes Dev.​ 29(6):617-29. ​(cited: 22) 
20. Maragkakis M*, ​Alexiou P*​, Mourelatos M. (2015) GenOO: A Modern Perl Framework for High Throughput                             
Sequencing analysis. ​bioRxiv​ doi:http://dx.doi.org/10.1101/019265​. ​(cited: 1) 
21. Maragkakis M*, ​Alexiou P*​, Nakaya T, Mourelatos M. (2016) CLIPSeqTools—a novel bioinformatics CLIP-seq                         
analysis suite. ​RNA​. 22(1):1-9. ​(cited: 5) 
22. Vourekas A*, ​Alexiou P*​, Vrettos N, Maragkakis M, Mourelatos Z. (2016) Sequence-dependent but not                           
sequence-specific piRNA adhesion traps and anchors mRNAs to the germ plasm. ​Nature 531(7594):390-4. ​(cited:                           
8)  
this publication was highlighted in: Voronina E (2016) mRNAs Hit a Sticky Wicket. Dev Cell;37(1):9-10 
23. Vrettos N, Maragkakis M, ​Alexiou P​, Mourelatos Z. (2017) Kc167, a widely used Drosophila cell line, contains an                                   
active primary piRNA pathway. ​RNA​ 23 (1), 108-118  
24. Alexiou P*​, Maragkakis M, Mourelatos Z, Vourekas A* (2017). cCLIP-Seq: Retrieval of chimaeric reads from                             
HITS-CLIP (CLIP-Seq) libraries. ​Methods in Molecular Biology​ (in press) 
Next Generation Sequencing based classification and exploration of               
RNA Binding Protein function. 
 
 
 
Background 
 
The completion of the Human Genome Project has ushered in a new era of genetic and medical research                                   
by defining the human genome in its entirety. The contribution of perhaps even greater importance is                               
the accompanying technological developments in high throughput sequencing that now allow                     
researchers to sequence human genomes in a few days for as little as 1,000 €. High throughput Next                                   
Generation Sequencing (NGS) technologies are continuously lowering the cost of biological molecules                       
sequencing through parallelization, making NGS a widely used tool for genomic analysis and creating an                             
amazing trove of sequencing data openly available to researchers. (NGS reviewed in ​(van Dijk et al.                               
2014)​) 
 
RNA Binding Proteins (RBPs) take part in many physiological cellular functions (splicing, regulation of                           
translation, mRNA decay, transposon suppression etc) and their misregulation has been implicated with                         
several diseases (neurodegenerative diseases, cancer etc). A number of NGS techniques are being used                           
to identify the binding sites of RBPs, HITS-CLIP (high-throughput sequencing of RNA that is isolated                             
after ultraviolet (UV) irradiation-induced crosslinking and immunoprecipitation) ​(Chi et al. 2009; Moore                       
et al. 2014) individual-nucleotide resolution iCLIP ​(König et al. 2010) photoactivatable                     
ribonucleoside-enhanced crosslinking and immunoprecipitation (PAR-CLIP) ​(Hafner et al. 2010) and so                     
on., have been used to identify RBP binding sites on RNA molecules, as well as the small RNA load and                                       
targets of RBPs that utilize small RNA ‘driver’ sequences. Currently, there are hundreds such                           
experiments performed and publicly deposited per year; this rate bound to accelerated increase in the                             
foreseeable future due to reducing costs and ubiquitousness of sequencing machines. 
 
Machine learning systems are currently used to recognize patterns in biomedical images, predict protein                           
structures, classify RNAs as potential targets of RBPs, and many other applications. Recent                         
developments in Machine Learning, such as Deep Learning (reviewed in ​(LeCun et al. 2015)​), are                             
creating new opportunities to harness big data and identify patterns from disparate sources.                         
Conventional machine learning techniques are limited by the need of the development of domain                           
specific features, a process that can introduce bias, is time consuming, and requires extended knowledge                             
of the training sets. Deep learning type techniques instead use several layers of automatically created                             
representation to learn features from raw data in a time efficient manner. 
 
My proposed research focus is the use of Machine Learning techniques to interrogate a large number of                                 
available NGS RBP datasets in order to classify, and eventually predict, RBP function and interplay.                             
Conceptually this proposal can be broken down in three aims: 
 
1. Standardization of NGS analyses for RBPs 
2. Classification of RBPs 
3. Prediction and Exploration of RBP function 
 
 
   
 
 
 
Aim 1: Standardization of NGS analysis for RBPs. 
 
The first focus of my research will be to build a series of modular standardized tools for NGS analysis of                                       
RBPs. The first steps towards this direction have already been done with the development of                             
ClipSeqTools ​(Maragkakis et al. 2016) - a suite for the analysis of NGS reads. Additional modules                               
pertaining to secondary structure, target sequence motifs, and relative positioning between samples are                         
already under way for the next installment of the tool. I am currently working towards a framework that                                   
deals with RBPs that use small RNAs as drivers (such as piRNAs or miRNAs). User friendly and                                 
standardized analyses by themselves are useful for the growing community, but are also the base on                               
which further aims will be built on. 
 
Previously, I have been heavily involved with the development of coding tools used for NGS data                               
analysis (GenOO ​(Maragkakis et al. 2015)​, ClipSeqTools ​(Maragkakis et al. 2016)​), which were in turn                             
applied on several different types of RBPs, helping elucidate the biological functions of said RBPs.                             
Specifically, I was heavily involved in the identification of piRNA biogenesis and function in mouse testis,                               
based on CLIP-Seq and RNA-Seq analysis of the mouse MIWI and MILI RBPs ​(Vourekas et al. 2012)​. The                                   
identification of the role of a mitochondrial RBP (BmPAPI) in the biogenesis and maturation of piRNAs                               
using NGS ​(Honda et al. 2013)​.The identification of a mechanism that performs ‘quality control’ on                             
microRNA precursors, identified via a novel method for circularization and NGS of small RNA                           
precursors ​(Liu et al. 2014)​. The identification of the roles of the RBP MOV10L1 in the processing of                                   
piRNA precursors in which the use of CLIP-Seq data allowed us to create a model that explained the                                   
determination of initiation of piRNA processing ​(Liu et al. 2014; Vourekas et al. 2015)​. My latest large                                 
published project, identified the mechanism by which an RBP (Aubergine) specifies germ cells in fly                             
embryos using small RNAs (piRNAs) as non-specific drivers ​(Vourekas et al. 2016)​. This project by itself                               
involved various analyses such as small RNA targeting prediction using chimeric reads, coverage of                           
mRNAs and piRNA precursors by CLIP reads, enrichment of targeting in localization categories and a                             
total of over 30 NGS samples. I have also worked on two RBPs of the FET family (TAF15 and FUS)                                       
associated with ALS (Amyotrophic Lateral Sclerosis or Lou Gehrig's disease) using NGS techniques                         
(mainly CLIP-Seq and RNA-Seq). For TAF15 ​(Ibrahim et al. 2013) we identified conserved binding sites                             
that affected neuronal transcription. For FUS ​(Nakaya et al. 2013) we identified conserved intron                           
regions, bound by FUS in patients, that affect splicing of target genes. Both of these projects involved                                 
analyses including motif finding, conservation analysis, functional analysis, localization on mRNAs,                     
intron/exon localization.  
 
 
   
Aim 2: Classification of RBPs. 
 
Using the hundreds of available datasets we will classify proteins based on their binding profiles. The                               
aim of this endeavour will be the development of sets of features that will classify experiments in groups                                   
of close similarity based on their binding profiles and the identity of their target genes. Essentially, RBPs                                 
that bind in similar ways, and bind similar genes will be clustered together based on the sets of features                                     
that are deemed most important by the Machine Learning classifier. 
 
Previously, I have been involved in the development of Machine Learning classifiers for the prediction of                               
miRNA target sites with high precision ​(Manolis Maragkakis et al. 2009; Reczko et al. 2012; Reczko et                                 
al. 2011)​. I am currently developing a Machine Learning classifier based on chimeric reads ​(Vourekas et                               
al. 2016)​ for the classification of piRNA binding sites across several species.  
 
 
 
Aim 3: Prediction and Exploration of RBP function. 
 
Finally, the products of Aim 1 and 2 will allow the development of a tool that allows researchers to                                     
predict functional characteristics of their RBP based on an NGS experiment. Briefly, a standardized                           
pipeline (Aim 1) will be used to extract features (defined by Aim 2). Features will then be used to                                     
compare and contrast the RBP of choice against the background of all RBPs used for training (Aim 2).                                   
The new RBP will be classified, and significant differences against the background will be reported to the                                 
user. 
 
Previously, I have been involved in the development of highly used Web servers (~ 2000 unique                               
users/week currently) that allow users to explore miRNA related data such as experimentally verified                           
targets ​(Vergoulis et al. 2011)​, predicted targets ​(M. Maragkakis et al. 2009; Maragkakis et al. 2011)​,                               
miRNA transcripts and their regulatory regions ​(Alexiou, Vergoulis, et al. 2010) and so on ​(Alexiou,                             
Maragkakis, et al. 2010; Papadopoulos et al. 2009)​. 
 
 
 
Conclusion 
 
The pace of technological innovation is ever accelerating in our field, opening new horizons and                             
opportunities. The harnessing of big data using machine learning, as applied on the field of RNA biology,                                 
is a promising field that will create novel biological knowledge, as well as infrastructure - accelerating                               
the research process for future experiments. 
 
 
   
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