About me

Curriculum Vitae

Milos Pjanic, PhD

First name: Milos
Last name: Pjanic
Languages: Serbian, English, French (basic), Russian (basic).
E-mail: mpjanic@stanford.edu; pjanic.milos@gmail.com


Blog

Topics: Life science. Biology. Biotechnology and Biomedical research. Bioinformatics and Computational Biology. Programming for Biologists. Lab protocols and methods. Paper reviews. Data science. Programming in R, C, C++, Perl, Python, Excel, basic and advanced Unix, shell scripting, awk scripting, vim editing, regular expressions. Custom script development.

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Professional experience

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Postdoctoral Scholar
Stanford University School of Medicine
March 2014 – Present

Bioinformatics Analyst
Institute of Molecular genetics and Genetic Engineering
2013 – 2014
Serbia

Home

Postdoc - Bioinformatician
Center for Genomic Regulation (CRG)
2012 – 2013  
Barcelona Area, Spain

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Visiting researcher
Lund University
2011 – 2012
Sweden
Graduate Teaching Assistant
École Polytechnique Fédérale de Lausanne
2006 – 2010
Switzerland


Education

UNIL logo
June 2010

PhD in Life Sciences
Faculty of Biology and Medicine, University of Lausanne, Switzerland.

Supervisor Prof. Nicolas Mermod, Laboratory of Molecular Biotechnology, Institute of Biotechnology, Faculty of Biology and Medicine, University of Lausanne, Switzerland, http://www.unil.ch/biotechAddress: EPFL CH A1 426, Station 6, CH 1015 Lausanne, Switzerland
Thesis topic: “Design and analysis of ChIP-Seq experiments for Nuclear Factor I DNA-binding proteins”


July 2005

M.Sc. in Molecular Biology and Physiology - Genetic Engineering and Biotechnology
Faculty of Biology, University of Belgrade, Serbia.

Average grade: 9.54 (out of max 10), ranked among top 5% of students

Master thesis: “Role of APOE gene SNPs in development of diabetes mellitus type 2 in human population in Serbia”

July 1997

Fifth Belgrade Gymnasium, section of life sciences and mathematics


Awards

2017
Award: Stanford CVI Travel Award 2017, issuer Stanford University School of Medicine Cardiovascular Institute

2016
Reviewers' Choice Abstract Award for top abstracts. American Society of Human Genetics.
Vancouver, 2016.

2016
Stanford University School of Medicine - Stanford University Cardiovascular Institute,
Outstanding Research Poster Award, Stanford-Karolinska Cardiovascular Research Symposium 2016 - In recognition of your work and research in cardiovascular medicine.

2000
University of Belgrade, University Award
For the work performed in a student research project: “BIOFORM”


Scores

Research Gate
27.72
Percentile:
Your score is higher than 85% of ResearchGate members'.

Certificates

University of Lausanne

English (Council of Europe Level C1 - equivalent to Certificate in Advanced English CAE, obtained from Centre de langues, University of Lausanne, Switzerland)

French (Council of Europe Level A2, obtained from Centre de langues, University of Lausanne, Switzerland)

SIB Swiss Institute of Bioinformatics
 
Statistical analysis applied to genome and proteome analyses. March, 2008. EMBnet, EPFL | École polytechnique fédérale de Lausanne, Switzerland

Introduction to statistics for biologists, March, 2007. University of Basel, Switzerland

DNA Microarrays data analysis. September 2006. EMBnet, EPFL | École polytechnique fédérale de Lausanne, Switzerland

Stanford University

PROGRAMMING IN R: PART 1
Li Ka Shing Center for Learning and Knowledge
Stanford, CA

PROGRAMMING IN R: PART 2
Li Ka Shing Center for Learning and Knowledge
Stanford, CA

Scientific Illustration 2: Creating Figures with Illustrator and Flash:
Li Ka Shing Center for Learning and Knowledge
Stanford, CA

Write a Successful NIH Career Development Award
Alway Building
Stanford, CA

Write a Successful NIH Individual NRSA Fellowship
Li Ka Shing Center (LKSC)
Stanford, CA

Analyzing and Visualizing Data with MATLAB
Li Ka Shing Center (LKSC)
Stanford, CA

Bio-Medical Image Processing with MATLAB
Li Ka Shing Center (LKSC)
Stanford, CA


Scripts/code

SingleCellPCAplotMultiGene
SingleCellPCAplotMultiGene is an R script for the principal component analysis of single cell RNAseq data. The script will start from the processed mastertable with RPKM values and perform PCA clustering and highlight in a gradient scale the ratio of expression of the 2 genes of interest (provided as the first and second arguments). bash/R. https://github.com/milospjanic/SingleCellPCAplotMultiGene

SingleCellPCAplot
SingleCellPCAplot is an R script for the principal component analysis of single cell RNAseq data. The script will take as an input the processed mastertable with RPKM values and perform a PCA clustering and highlight in a gradient scale the expression of the gene of interest (provided as the first argument). bash/R. https://github.com/milospjanic/SingleCellPCAplot

GTExExtractor
GTExExtractor is a script that will download and parse individual-level GTEx data set for all tissues and GTEx sample IDs, and output gene RPKMs for the provided gene list that correspond to the tissue of interest, in a form of violin plots. bash/awk/R. https://github.com/milospjanic/GTExExtractor

readCount2RPKM
readCount2RPKM is a combined bash/awk/R script for the conversion of mastertable of per-gene read counts from an RNA-Seq experiment to the mastertable of per-gene RPKM values. It connects to biomaRt to collect transcript lengths and selects the longest transcript to calculate per-gene RPKM values. bash/awk/R. https://github.com/milospjanic/readCount2RPKM

chrPos2rsIDdbSNP147CommonPlusRareVariants
This is a script to convert a list of genomics positions in a format: chrno_position_allele1_allele2 to SNP rsIDs, using dbSNP147 all SNP (common plus rare) data set from UCSC Table Browser. Script will append rsIDs as a column to an existing file. bash/awk. https://github.com/milospjanic/chrPos2rsIDdbSNP147CommonPlusRareVariants

chrStartEnd2rsID
chrStartEnd2rsID is a script to convert a list of genomics positions in a format: chrno \t start \t end \t etc. to SNP rsIDs. Script will append rsIDs as a column to an existing file. chrStartEnd2rsID is useful for quick conversion of to SNP rsIDs for various downstream analysis and lookups. bash/awk. https://github.com/milospjanic/chrStartEnd2rsID

wig2BigWigLiftOverBedgraphPartition
This is a combined bash/perl script that will convert genomics coordinates of a wig file mapped to hg18 genome to the hg19 genome, via converting wig ti bigwig, bigwig to bedgraph, and performing liftover, followed by partitioning the structure of overlapping intervals in a bedgraph file and conversion back of a bedgraph to bigwig. bash/perl. https://github.com/milospjanic/wig2BigWigLiftOverBedgraphPartition

bigWigLiftOverBedgraphPartition
This is a combined bash/perl script that will convert genomics coordinates of a bigwig file mapped to hg18 genome to the hg19 genome, via converting bigwig to bedgraph, and performing liftover, followed by partitioning the structure of overlapping intervals in a bedgraph file and conversion back of a bedgraph to bigwig. bash/perl. https://github.com/milospjanic/bigWigLiftOverBedgraphPartition

wig2BigWigLiftOver
wig2BigWigLiftOver will convert genomics coordinates of a wig file mapped to hg18 genome to the hg19 genome and output bigwig, via converting wig to bigwig, bigwig to bedgraph, and performing liftover, followed by conversion back of a bedgraph to bigwig. bash. https://github.com/milospjanic/wig2BigWigLiftOver

bigWigLiftOver
bigWigLiftOver will convert genomics coordinates of a bigwig file mapped to hg18 genome to the hg19 genome, via converting bigwig to bedgraph, and performing liftover, followed by conversion back of a bedgraph to bigwig. bash. https://github.com/milospjanic/bigWigLiftOver

bedGraphPartitionOverlappingInt
This is a combined bash/perl script will partition the structure of overlapping intervals in a bed or bedgraph file and correct possible errors in manipulating illegal bedgraph files that contain overlapping intervals, e.g. in bedGraphToBigWig tool or in UCSC representations. bash/perl. https://github.com/milospjanic/bedGraphPartitionOverlappingInt

vcfsplitter
This is a combined bash/awk script that will parse a combined vcf file into sample specific vcf files and name individual vcfs according to the sample name. Useful for quick conversions of combined to sample specific files for downstream analysis. bash/awk. https://github.com/milospjanic/vcfsplitter

combinedVCFParser
This is a bash/awk script that will parse a combined vcf file and for your input SNP of interest output a table with Het/Ref homo/Alt homo denotation, sample names/IDs, position in vcf, genotype. Script is useful when you need to quickly assess which sample is Het/Ref homo/Alt homo for a particular SNP of interest. bash/awk. https://github.com/milospjanic/combinedVCFParser

excelDate2GeneName
If you have gene lists previously modified in Excel chances are that 3 genes classes are modified, e.g. SEPT1, DEC1, MARCH1 into dates Sep-01, Dec-01, Mar-01. excelDate2GeneName will test if gene names are lower or upper cases and convert to upper cases, followed by the conversion of dates to gene names. Useful to parallelize on multiple files. Shell. https://github.com/milospjanic/excelDate2GeneName

rsIDmultipleVCFParser
This is a bash/awk script that will parse multiple individual vcf file and for your input SNP of interest output a table with VCF tags including genotype GT, genotype quality GQ, allele depth AD etc, values for VCF tags, sample names/IDs from the VCF file, input SNP ID. Useful to assess genotype and quality for a particular SNP of interest. bash/awk. https://github.com/milospjanic/rsIDmultipleVCFParser

rnaSeqFPro
rnaSeqFPro (beta) is a script for full processing of RNASeq data starting from fastq files. It performs fastqc quality control, mapping to the human genome hg19 using STAR second pass, counting with featurecounts using GENCODE gtf annotation, creates master table, performs differential analysis using DESeq2, generates graphs in gglot2. bash/R. https://github.com/milospjanic/rnaSeqFPro

scientific2StandardDecimal
This awk script will convert a table with E-notations scientific numbers, or mixed E-notations scientific numbers and numbers in a standard form, completely into standard form of numbers. Conversion will transform scientific notation to decimal numbers with 2 decimal places. Awk. https://github.com/milospjanic/scientific2StandardDecimal

scientific2StandardInteger
This awk script will convert a table with E-notations scientific numbers, or mixed E-notations scientific numbers and numbers in a standard form, completely into standard form of numbers. Conversion will round up scientific notation to integers. Awk. https://github.com/milospjanic/scientific2StandardInteger

rsID2Bed
rsID2Bed is a script to convert SNP rsIDs to a list of genomics positions in a bed format: chr, position, position+1. Script is useful for quick conversion of SNPs to a bed format for downstream analysis (e.g. overlaps with bedtools). bash/mysql. https://github.com/milospjanic/rsID2Bed

chrPos2rsID
chrPos2rsID is a script to convert a list of genomics positions in a format: chrno_position_allele1_allele2 to SNP rsIDs. Script will append rsIDs as a column to an existing file. chrPos2rsID is useful for quick conversion of to SNP rsIDs for various downstream analysis and lookups. bash/mysql. https://github.com/milospjanic/chrPos2rsID

ChIPSeqFPro
ChIPSeqFPro (beta) is a script for full processing of ChIPSeq data starting from fastq files. It performs fastqc quality control, mapping to the human genome hg19 or mouse mm10 using bwa, sam to bam conversion, peak calling with MACS, creates bigwig files from bam files using bam2bigwig. Shell. https://github.com/milospjanic/ChIPSeqFPro

moveExt
This script finds all files with the given extension in subfolders of the current folder and copy them into the current folder, and it also changes the name of the files to their last subfolder and adds .file extension. Shell. https://github.com/milospjanic/moveExt

geneName2EntrezIDMusMusculus
Combined bash/R script that use a file containing mouse gene symbols in a first column and prepends an ENREZ ID as a column 1, while keeping the structure of the file intact. bash/R. https://github.com/milospjanic/geneName2EntrezIDMusMusculus

bed2GwasCatalogBinomial
Script will download GWAS Catalog, create bed file, parse and uniq with N-1 input arguments. Last argument provided should be a bed file that will be intersected with parsed GWAS Catalog. Number of overlaps and initial number of entries in parsed files are reported. Finally, it will calculate binomial p-values for each overlap. bash/R. https://github.com/milospjanic/bed2GwasCatalogBinomial

PWMphase
This is a project to develop fine mapping algorithm for GWAS/QTL genomic variants to define causal variant based on phasing of PWM transcription factor binding sites, using the rotational positioning along the DNA helix. https://github.com/milospjanic/PWMphase

nucleoFineMap
This is a project to develop fine mapping algorithm for GWAS/QTL genomic variants to define causal variant based on three main predictors: 1. inward/outward nucleosome - DNA sterical orientation 2. nucleosomal DNA code 3. modeling position weight matrix PWM for transcription factors. https://github.com/milospjanic/nucleoFineMap

moveExt2MasterTable
Script to preprocess RNA-Seq data and create a mastertable. Uses RNA-Seq processed data from multiple subfolders, where each subfolder contains the output file with the same name, and the name of the subfolder is the experimental condition. Shell. https://github.com/milospjanic/moveExt2MasterTable

gwasCatalogFullParseBinomialMod1Ggplot
Script downloads GWAS Catalog, converts it into bed file with columns: chr position position+1 proxy_gene phenotype, then fully parses GWAS Cat. for each unique category, performs modified binomial statistics on overlaps with the input file. Outputs are tables with binomial -log10pvalues and fold changes and graphs in ggplot2. bash/R. https://github.com/milospjanic/gwasCatalogFullParseBinomialMod1Ggplot

matrixeQTL2LocusZoom
Script will use three parameters as inputs, 1) Gene ID from ENSEMBL annotation, 2) chromosome number where the gene is located 3) eQTL file from matrixeQTL and generate output for visualization with LocusZoom Plot containing rsIDs and p-values as columns 1 and 2. Shell. https://github.com/milospjanic/matrixeQTL2LocusZoom

binomialTestForGenomicOverlaps
Combined bash/R script generates a binomial p-value that shows significance for the overlap of two sets of genomic regions (for example from ChIP-Seq experiments). To calculate binomial p-value you need two bed files as inputs. bash/R. https://github.com/milospjanic/binomialTestForGenomicOverlaps

hypergeometricTestForGenomicOverlapsMilosPjanicMod
Combined bash/R script generates a hypergeometric p-value that shows significance for the overlap of two sets of genomic regions. Statistical test includes genomic background i.e. combined ENCODE set of open chromatin regions to calculate constituents for the hypergeometric test. bash/R. https://github.com/milospjanic/hypergeometricTestForGenomicOverlapsMilosPjanicMod

fisherTestForGenomicOverlapsMilosPjanicMod
Combined bash/R script performs specialized Fisher's exact test and generates p-value that shows significance for the overlap of two sets of genomic regions. Statistical test includes genomic background i.e. combined ENCODE set of open chromatin regions to calculate constituents of the Fisher exact test contingency matrix. bash/R. https://github.com/milospjanic/fisherTestForGenomicOverlapsMilosPjanicMod

ensembl2GeneNameMusMusculus
Convert mouse GENCODE/ENSEMBL IDs to gene names. Combined bash/R script will use a file with mouse ENSEMBL geneIDs in a first column of a file and append a gene name to it, while keeping the structure of the file. bash/R. https://github.com/milospjanic/ensembl2GeneNameMusMusculus

gwasCatalog2BedParse
Parse complete GWAS Catalog into unique categories and create bed files. Script will connect to genome.gov, download GWAS Catalog, convert it to a bed file with columns chr;position;position+1;proxy_gene;phenotype, and then create separate bed files for each unique GWAS Catalog category from 5th column. bash/awk. https://github.com/milospjanic/gwasCatalog2BedParse

IntegrativeFunctionalGenomics
Collection of scripts used in:
Miller and Pjanic et al, 2016, Nature Communications,

ensembl2genename
Convert human ENSEMBL IDs to gene names. Combined bash/R script will use a file with human ENSEMBL geneIDs in a first column of a file and append a gene name to it, while keeping the structure of the file. bash/R. https://github.com/milospjanic/ensembl2genename

fileMulti2TableMod2
Create mastertable from multiple RNA-Seq sample files obtained with featureCounts or HTSeq tools. Version 2 modified script will include sample unique rows missing in other samples and set them to NAs. Awk. https://github.com/milospjanic/fileMulti2TableMod2

fileMulti2TableMod1
Create mastertable from multiple RNA-Seq sample files obtained with featureCounts or HTSeq tools. Version 1 script will exclude sample unique rows that are missing in other samples and use only those rows present in every sample. Awk. https://github.com/milospjanic/fileMulti2TableMod1

sam2BigWig
Convert SAM files to BIGWIG files. This script will take a sam file mapped to the human genome hg19 and convert it to a bigwig file for UCSC Genome Browser. Dependencies samtools, bedtools bamtobed, downloads/executes bedItemOverlapCount, bedGraphToBigWig and fetchChromSizes. Shell. https://github.com/milospjanic/sam2BigWig

gwasanalytics
Gwasanalytics consists of a series of scripts that will analyze Gwas Catalog phenotypes and the input bed file from e.g. ChIP-Seq/ATAC-Seq experiment. Gwasanalytics scripts will calculate standard and modified binomial statistics, p-values and fold change for genomic overlaps between the input and various parsed GWAS categories. https://github.com/milospjanic/gwasanalytics

bed2GwasCatalogBinomialMod1
This is a modified version of bed2GwasCatalogBinomial script and it calculates binomial p-value for genomics overlaps using modified binomial probability. This script will download GWAS Catalog, parse to bed files using input terms, intersects parsed GWAS Catalog with input bed file, calculates modified binomial statistics for each overlap. bash/R. https://github.com/milospjanic/bed2GwasCatalogBinomialMod1

bamsplit2bigwig
Convert bam file mapped to the human genome hg19 to two strand specific bigwig files for UCSC Genome Browser. Useful to visualize nucleosomal or protein binding ChIP-Seq 'footprints' marked by reads mapping on different DNA strands. Bamsplit2bigwig depends on bedtools bamtobed, bedItemOverlapCount, bedGraphToBigWig and fetchChromSizes. Shell. https://github.com/milospjanic/bamsplit2bigwig

gwasCatalog2Bed2Category
Download GWAS Catalog, convert it to a bed file with "chr position position+1 proxy_gene phenotype", and then take input terms and select entries that match the term, and create separate bed files. Shell.
https://github.com/milospjanic/gwasCatalog2Bed2Category

bam2bigwig
Convert bam file mapped to the human genome hg19 to a bigwig file for UCSC Genome Browser. Bam2bigwig depends on bedtools bamtobed (aka bamToBed), and it will attempt to download and execute three UCSC scripts, bedItemOverlapCount, bedGraphToBigWig and fetchChromSizes. Shell. https://github.com/milospjanic/bam2bigwig

tabsep
Formatting of multiple spaces and tabs to a single tab, useful for for quick table formatting. Shell. https://github.com/milospjanic/tabsep

Random sampling algorithm
Rero Doc Digital library


Conference papers

- American Society of Human Genetics 67th Annual Meeting, Orlando, USA. October 2017

RNASeqFPro, a full processing pipeline for RNA-Seq differential gene expression analysis. Milos Pjanic1,2, Clint L. Miller1,2, Thomas Quertermous1,2. 1) Division of Cardiovascular Medicine, Stanford University, Stanford, California, United States of America. 2) Cardiovascular Institute, Stanford University, Stanford, California, United States of America.

- American Society of Human Genetics 67th Annual Meeting, Orlando, USA. October 2017

Functional regulatory mechanism of smooth muscle cell-restricted LMOD1 coronary artery disease locus. C. Miller, V. Nanda, T. Wang, M. Pjanic, B. Liu, T. Nguyen, T. Quertermous, N. Leeper. Division of Cardiovascular Medicine, Stanford University, Stanford, California, United States of America.


- American Society of Human Genetics 66th Annual Meeting, Vancouver, Canada. October 2016

Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci. C.L. Miller1, M. Pjanic1, T. Wang1, T. Nguyen1, A. Cohain2, J.D. Lee1,3, L. Perisic4, U. Hedin4, R.K. Kundu1, D. Majmudar1, J.B. Kim1, O. Wang1, C. Betsholtz5,6, A. Ruusalepp7,8, O. Franzen2,8, T.L. Assimes1, S.B. Montgomery3,9, E.E. Schadt2, J.L.M. Bjorkegren2,6,10, T. Quertermous1. 1) Department of Medicine, Division of Cardiovascular Medicine, Stanford Uni-versity School of Medicine, CA, USA; 2) Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, NY, USA; 3) Department of Genetics, Stanford University School of Medicine, CA, USA; 4) Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; 5) Department of Im-munology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden; 6) Department of Medical Biochemistry and Biophysics, Vascular Biology Unit, Karolinska Institutet, Stockholm, Sweden; 7) Depart-ment of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia; 8) Clinical Gene Networks AB, Stockholm, Sweden; 9) Department of Pathology, Stan-ford University School of Medicine, CA, USA; 10) Department of Physiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Estonia.

- American Society of Human Genetics 66th Annual Meeting, Vancouver, Canada. October 2016

Functional fine-mapping of coronary artery disease risk variants. B. Liu1,2, M. Pjanic3, T. Nguyen3, S. Montgomery2,4, C. Miller3, T. Quertermous3. 1) Biology, Stanford University, Palo Alto, CA; 2) Pathology, Stanford University, Palo Alto, CA; 3) Medicine - Division of Cardiovascular Medicine, Stanford University, Palo Alto, CA; 4) Genetics, Stanford University, Palo Alto, CA.

- American Society of Human Genetics 66th Annual Meeting, Vancouver, Canada. October 2016

Interpretable deep learning approaches to understand the genetic and regulatory basis of coronary artery disease. P. Greenside, J. Israeli, C. Mill-er, M. Pjanic, T. Quertermous, A. Kundaje. Stanford University, Stanford, CA.   

- American Society of Human Genetics 66th Annual Meeting, Vancouver, Canada. October 2016

GWAS candidate gene for coronary artery disease  TCF21  interacts with the aryl-hydrocarbon receptor to modify coronary smooth muscle cell response to pro-atherogenic stimuli.   M. Pjanic 1 , J.B. Kim 1 , O. Sazonova 1 , T. Nguyen 1 , T. Wang 1 , C.L. Miller 1 , L. Maegdefessel 2 , U. Hedin 2 , T. Quertermous 1 .   1) Department of Medicine, Cardiovascular Institute, Stanford University, Stan-ford, CA; 2) Karolinska Institute, Stockholm, Sweden.   

- Arteriosclerosis, Thrombosis and Vascular Biology- American Heart Association 2015. San Francisco, US.
Molecular Basis of Regulatory Variation at Coronary Heart Disease-Associated Loci
Clint L. Miller, Milos Pjanic, Jonathan D. Lee, Boxiang Liu, William J. Greenleaf, Stephen B.
Montgomery, Thomas Quertermous, Stanford Univ, Stanford, CA

- American Thoracic Society 2014. San Diego, US. Identification Of Differentially Expressed Genes By Next Generation Sequencing Of RNA Of Formalin-Fixed, Paraffin-Embedded (FFPE) Lung Tissue From Patients With Idiopathic Pulmonary Fibrosis, [Publication Number: A4965]
M. Vukmirovic, J. Blackmon, V. Skodric-Trifunovic, D. Jovanovic, V. Zeljkovic, M. Pjanic, S. Pavlovic, J. Stojsic, N. Kaminski, B. Stefanovic. Yale New Haven, CT/US

- The 83rd European Atherosclerosis Society Congress. Glasgow, UK, 2015 (platform talk). Miller CL, Pjanic M, Assimes TL, Montgomery SB, Greenleaf WJ, Quertermous T. Molecular basis of regulatory variation associated with coronary heart disease.

- EASD-SGGD meeting 2011, Slovakia. Ola Hansson, Peter Osmark, Milos Pjanic, Yuedan Zhou, Erik Renström, Jonathan Esguerra, Petter Vikman, Leif Groop. ”Investigation of the genetic influence on splice pattern distribution in human pancreatic islets”

- EASD-SGGD meeting 2011, Slovakia. Petter Vikman, Emilia Ottosson-Laakso, Ines Möller, Peter Osmark, Milos Pjanic, Yuedan Zhou, Erik Renström, Jonathan Esguerra, Ola Hansson , Leif Groop “Global analysis of splice patterns in human islets: differences due to hyperglycemia.”

- 15th Joint Scientific Meeting of the faculties of Biology and Medecine of the Lausanne and Geneva Universities, Nyon, Switzerland
M. Pjanic, P. Pjanic, P. Bucher, N. Mermod, “False ChIP-Seq peaks originate from artifactual sequence tags over satellite repeats, GC-rich regions and transcription start sites”. In Proceedings of Changins 2009 meeting, OTH - 3.

- Lausanne Life Sciences Festival 2009- Genetics, Epigenetics and Development, Center for Integrative Genomics, University of Lausanne
M. Pjanic, P. Pjanic, and N. Mermod, “Nuclear factor I revealed as family of promoter binding transcription activators using random sampling approach”. In Proceedings of Lausanne Life Sciences Festival 2009.

- D.Day 2009, Center for Integrative Genomics, University of Lausanne,
M. Pjanic, C. Schmid, A. Gaussin, G. Ambrosini, P. Pjanic, P. Bucher and N. Mermod, “Genome-wide analysis of Nuclear Factor I binding sites using ChIP-Seq”. Abstracts book for DDAY 2009, A22.

- D.Day 2008, Center for Integrative Genomics, University of Lausanne,
M. Pjanic, P. Pjanic, A. Jovanovic, N. Mermod, “Detailed genomic maps of matrix-predicted protein-DNA interactions - tool for designing in vivo DNA binding studies”. Abstracts book for DDAY 2008, A19.

- Biology meets engineering - USGEB 2008, Ecole Polytechnique Federale de Lausanne,
P. Pjanic, M. Pjanic, A. Jovanovic, N. Mermod, “Use of the position weight matrices for the prediction of DNA-protein interactions – identification and display of binding sites of defined affinity”. Abstracts book for the 40th Annual Meeting of the Union of Swiss Societies for Experimental Biology USGEB/USSBE, A259.


Talks

- American Society of Human Genetics 2016, Vancouver, Oct 18-22 2016.

American Society of Human Genetics (ASHG) and the American Physiological Society (APS) Joint Symposium: Attaching Physiology to the Genome: Spotlight on Cardiovascular Genetics. Moderator(s): Guillaume Lettre, Univ Montréal, Canada; and Bina Joe, Univ Toledo

Abstract-Selected Talk: GWAS candidate gene for coronary artery disease TCF21 interacts with the aryl-hydrocarbon receptor to modify coronary smooth muscle cell response to proatherogenic stimuli. M. Pjanic, Cardiovascular Inst, Stanford Univ.

- Center for Genomic Regulation, Barcelona, Spain. "Analysis of Nuclear Factor I binding sites and Matrix Attachment Regions and their potential applications”, 2011

- DPLU seminar series, Lund University, Sweden. “Analysis of Nuclear Factor I binding sites and Matrix Attachment Regions and their potential therapeutic and biotechnology applications”, 2011

- Clinical research centre, Malmo, Sweden. “Analysis of Nuclear Factor I binding sites and Matrix Attachment Regions and their potential therapeutic and biotechnology applications”, 2010

- Institute for Biomedical Research Georg-Speyer-Haus, Johann Wolfgang Goethe-Universität Frankfurt, Germany, “Design and analysis of ChIP-Seq experiments for Nuclear Factor I DNA-binding proteins”, informal presentation, 2010

- National Institutes of Health, National Institute of Child Health and Human Development, Bethesda, MD, USA, “Design and analysis of ChIP-Seq experiments for Nuclear Factor I DNA-binding proteins”, informal presentation, 2010

- Lausanne Genomics Days 2008, Center for Integrative Genomics, University of Lausanne, Switzerland, “Genome-wide mapping of Nuclear Factor I binding sites in mouse embryonic cells using ChIP-Seq method”, 2008

- Ultra-High-Throughput Sequencing Symposium, Center for Integrative Genomics, University of Lausanne, Switzerland, “Functional analysis of the NFI/CTF transcription factors using ultra-high-throughput sequencing”, 2008

- School of Mathematics, University of Belgrade, Serbia, “Use of the position weight matrices for the prediction of DNA-protein interactions- identification and display of binding sites of defined affinity”, informal presentation, 2007

- Institute of Biotechnology, University of Lausanne, Switzerland, “Role of APOE gene SNPs in development of diabetes mellitus type 2 in human population in Serbia”, informal presentation, 2005

- Institute of Physiology, University of Zurich, Switzerland, “Role of APOE gene SNPs in development of diabetes mellitus type 2 in human population in Serbia”, informal presentation, 2005

- Department of Infectious Diseases, Parasitology at Heidelberg University Medical School, Heidelberg, Germany, “The role of APOE and HL gene polymorphisms in development of diabetes mellitus type 2 in Serbian population”, informal presentation, 2005


Publications

The role of polycarbonate monomer bisphenol-A in insulin resistance

Pjanic M.

PeerJ, 13 September 2017, Pjanic M. (2017) The role of polycarbonate monomer bisphenol-A in insulin resistance. PeerJ 5:e3809 https://doi.org/10.7717/peerj.3809

Advances in Transcriptomics: Investigating cardiovascular disease at high resolution

Wirka R*, Pjanic M*, Quertermous T.

* Denotes equal contribution.

Circulation Research, in press

TCF21 and the environmental sensor aryl-hydrocarbon receptor cooperate to activate a pro-inflammatory gene expression program in coronary artery smooth muscle cells.

Kim JB*, Pjanic M*, Nguyen T, Miller CL, Iyer D, Liu B, Wang T, Sazonova O, Carcamo-Orive I, Matic LP, Maegdefessel L, Hedin U, Quertermous T.

* Denotes equal contribution.

PLoS Genet. 2017 May 8;13(5):e1006750. doi: 10.1371/journal.pgen.1006750. eCollection 2017 May.
PMID: 28481916

Genetics and Genomics of Coronary Artery Disease

Pjanic M, Miller CL, Wirka R, Kim JB, DiRenzo DM, Quertermous T.

Curr Cardiol Rep. 2016 Oct;18(10):102. doi: 10.1007/s11886-016-0777-y. Review.
PMID: 27586139

Integrative functional genomics identifies regulatory mechanisms at coronary artery disease loci.

Miller CL*, Pjanic M*, Wang T, Nguyen T, Cohain A, Lee JD, Perisic L, Hedin U, Kundu RK, Majmudar D, Kim JB, Wang O, Betsholtz C, Ruusalepp A, Franzén O, Assimes TL, Montgomery SB, Schadt EE, Björkegren JL, Quertermous T.

* Denotes equal contribution.

Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California 94305, USA.
Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.
Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA.
Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm SE-171 77, Sweden.
Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala University, Uppsala SE-751 05, Sweden.
Department of Medical Biochemistry and Biophysics, Vascular Biology Unit, Karolinska Institutet, Stockholm SE-171 77, Sweden.
Department of Cardiac Surgery, Tartu University Hospital, Tartu 50406, Estonia.
Clinical Gene Networks AB, Stockholm SE-114 44, Sweden.
Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA.
Department of Physiology, Institute of Biomedicine and Translation Medicine, University of Tartu, Tartu 50406, Estonia.

Nature Communication. 2016 Jul 8;7:12092. doi: 10.1038/ncomms12092.
PMID: 27386823

Transcription factor regulation as basis of confounding effects between distinct human traits

Pjanic M*, Miller CL*, Quertermous T

F1000 Research, 2015. DOI: 10.12688/f1000research.7336.1

* Denotes equal contribution.

From Locus Association to Mechanism of Gene Causality: The Devil Is in the Details.

Miller CL*, Pjanic M*, Quertermous T.

Arterioscler Thromb Vasc Biol. 2015 Oct;35(10):2079-80. doi: 10.1161/ATVBAHA.115.306366. No abstract available.

PMID: 26399919

* Denotes equal contribution.

Molecular basis of regulatory variation at coronary heart disease associated loci

C Miller, M Pjanic, TL Assimes, SB Montgomery, WJ Greenleaf, T Quertermous

Atherosclerosis 241 (1), e17DOI: 10.1016/j.atherosclerosis.2015.04.077 ·


Characterization of TCF21 downstream target regions identifies a transcriptional network linking multiple independent coronary artery disease loci

Sazonova O, Zhao Y, Nürnberg S, Miller C, Pjanic M, Castano VG, Kim JB, Salfati EL, Kundaje AB, Bejerano G, Assimes T, Yang X, Quertermous T.

Plos Genetics
PLoS Genet. 2015 May 28;11(5):e1005202. doi: 10.1371/journal.pgen.1005202. eCollection 2015 May.

PMID: 26020271

Coronary Artery Disease Associated Transcription Factor TCF21 Regulates Smooth Muscle Precursor Cells that Contribute to the Fibrous Cap

Nurnberg ST, Cheng K, Raiesdana A, Kundu R, Miller CL, Kim JB, Arora K, Carcamo-Oribe I, Xiong Y, Tellakula N, Nanda V, Murthy N, Boisvert WA, Hedin U, Perisic L, Aldi S, Maegdefessel L, Pjanic M, Owens GK, Tallquist MD, Quertermous T.

Plos Genetics

PLoS Genet. 2015 May 28;11(5):e1005155. doi: 10.1371/journal.pgen.1005155.
PMID: 26020946

Molecular characterization of a human matrix attachment region epigenetic regulator

Salina Arope, Niamh Harraghy, Milos Pjanic and Nicolas Mermod

PLoS One. 2013 Nov 14;8(11):e79262. doi: 10.1371/journal.pone.0079262. eCollection 2013.
PMID: 24244463

Nuclear Factor I genomic binding associates with chromatin boundaries.

Pjanic M, Schmid CD, Gaussin A, Ambrosini G, Adamcik J, Pjanic P, Plasari G, Kerschgens J, Dietler G, Bucher P, Mermod N.

BMC Genomics. 2013 Feb 12;14:99. doi: 10.1186/1471-2164-14-99.

PMID: 23402308

Nuclear factor I revealed as family of promoter binding transcription activators.

Pjanic M, Pjanic P, Schmid C, Ambrosini G, Gaussin A, Plasari G, Mazza C, Bucher P, Mermod N.

BMC Genomics. 2011 Apr 7;12:181. doi: 10.1186/1471-2164-12-181.
PMID: 21473784



Teaching

- Graduate teaching assistant for the course Molecular Biology, Faculty of Biology and Medicine, University of Lausanne, Switzerland, in the academic years: 2006/07, 07/08, 08/09, 09/10.

Lab experience

Wet lab: cell culture, molecular biology techniques, cell biology techniques, Illumina NGS sequencing, PCR, ChIP-Seq, RNA-Seq, DNase-Seq, ATAC-Seq, gene expression microarrays.

Compiling and scripting languages: R (basic and advanced), C, C++, bash shell (basic and advanced), awk, perl, python, ggplot, ggplot2, Excel.

Data manipulation and processing: awk (basic and advanced), perl (basic and advanced), python, vim, bash shell.

Bioinformatics tools: Bioconductor R packages, biomaRt, bedtools, samtools, ELAND, Bowtie, BWA, STAR, TopHat, Cufflinks, Cuffdiff, Cuffcompare, CummerBund, picard, PLINK, STAR, featureCounts, HTSeq, DESeq, edgeR, DEXSeq, voom, wasp, shapeit2, impute2.

Other tools. Cytoscape, Galaxy, UCSC Genome Browser, Ensembl, MEME/MAST, BLAST, BLAT, Primer3, DAVID, GREAT.

Custom scripts and code. R, bash shell, perl, python, ggplot, ggplot2, C.

2 comments:

  1. Very cool blog! Surfing for code... not a bad trade.

    ReplyDelete
  2. Great bioinfo resource Milos! Keep it up. -jonathan at CRC Malmö

    ReplyDelete