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Functional Characterization of the New 8Q21 Asthma Risk Locus
Functional characterization of the new 8q21 Asthma risk locus Cristina M T Vicente B.Sc, M.Sc A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2017 Faculty of Medicine Abstract Genome wide association studies (GWAS) provide a powerful tool to identify genetic variants associated with asthma risk. However, the target genes for many allergy risk variants discovered to date are unknown. In a recent GWAS, Ferreira et al. identified a new association between asthma risk and common variants located on chromosome 8q21. The overarching aim of this thesis was to elucidate the biological mechanisms underlying this association. Specifically, the goals of this study were to identify the gene(s) underlying the observed association and to study their contribution to asthma pathophysiology. Using genetic data from the 1000 Genomes Project, we first identified 118 variants in linkage disequilibrium (LD; r2>0.6) with the sentinel allergy risk SNP (rs7009110) on chromosome 8q21. Of these, 35 were found to overlap one of four Putative Regulatory Elements (PREs) identified in this region in a lymphoblastoid cell line (LCL), based on epigenetic marks measured by the ENCODE project. Results from analysis of gene expression data generated for LCLs (n=373) by the Geuvadis consortium indicated that rs7009110 is associated with the expression of only one nearby gene: PAG1 - located 732 kb away. PAG1 encodes a transmembrane adaptor protein localized to lipid rafts, which is highly expressed in immune cells. Results from chromosome conformation capture (3C) experiments showed that PREs in the region of association physically interacted with the promoter of PAG1. -
Accompanies CD8 T Cell Effector Function Global DNA Methylation
Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer, Benjamin G. Barwick, Benjamin A. Youngblood, Rafi Ahmed and Jeremy M. Boss This information is current as of October 1, 2021. J Immunol 2013; 191:3419-3429; Prepublished online 16 August 2013; doi: 10.4049/jimmunol.1301395 http://www.jimmunol.org/content/191/6/3419 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2013/08/20/jimmunol.130139 Material 5.DC1 References This article cites 81 articles, 25 of which you can access for free at: http://www.jimmunol.org/content/191/6/3419.full#ref-list-1 http://www.jimmunol.org/ Why The JI? Submit online. • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists by guest on October 1, 2021 • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2013 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology Global DNA Methylation Remodeling Accompanies CD8 T Cell Effector Function Christopher D. Scharer,* Benjamin G. Barwick,* Benjamin A. Youngblood,*,† Rafi Ahmed,*,† and Jeremy M. -
1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor
xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A -
Integration of 198 Chip-Seq Datasets Reveals Human Cis-Regulatory Regions
Integration of 198 ChIP-seq datasets reveals human cis-regulatory regions Hamid Bolouri & Larry Ruzzo Thanks to Stephen Tapscott, Steven Henikoff & Zizhen Yao Slides will be available from: http://labs.fhcrc.org/bolouri/ Email [email protected] for manuscript (Bolouri & Ruzzo, submitted) Kleinjan & van Heyningen, Am. J. Hum. Genet., 2005, (76)8–32 Epstein, Briefings in Func. Genom. & Protemoics, 2009, 8(4)310-16 Regulation of SPi1 (Sfpi1, PU.1 protein) expression – part 1 miR155*, miR569# ~750nt promoter ~250nt promoter The antisense RNA • causes translational stalling • has its own promoter • requires distal SPI1 enhancer • is transcribed with/without SPI1. # Hikami et al, Arthritis & Rheumatism, 2011, 63(3):755–763 * Vigorito et al, 2007, Immunity 27, 847–859 Ebralidze et al, Genes & Development, 2008, 22: 2085-2092. Regulation of SPi1 expression – part 2 (mouse coordinates) Bidirectional ncRNA transcription proportional to PU.1 expression PU.1/ELF1/FLI1/GLI1 GATA1 GATA1 Sox4/TCF/LEF PU.1 RUNX1 SP1 RUNX1 RUNX1 SP1 ELF1 NF-kB SATB1 IKAROS PU.1 cJun/CEBP OCT1 cJun/CEBP 500b 500b 500b 500b 500b 750b 500b -18Kb -14Kb -12Kb -10Kb -9Kb Chou et al, Blood, 2009, 114: 983-994 Hoogenkamp et al, Molecular & Cellular Biology, 2007, 27(21):7425-7438 Zarnegar & Rothenberg, 2010, Mol. & cell Biol. 4922-4939 An NF-kB binding-site variant in the SPI1 URE reduces PU.1 expression & is GGGCCTCCCC correlated with AML GGGTCTTCCC Bonadies et al, Oncogene, 2009, 29(7):1062-72. SATB1 binding site A distal single nucleotide polymorphism alters long- range regulation of the PU.1 gene in acute myeloid leukemia Steidl et al, J Clin Invest. -
Screen for Multi-SUMO–Binding Proteins Reveals a Multi-SIM–Binding Mechanism for Recruitment of the Transcriptional Regulator ZMYM2 to Chromatin
Screen for multi-SUMO–binding proteins reveals a multi-SIM–binding mechanism for recruitment of the transcriptional regulator ZMYM2 to chromatin Elisa Aguilar-Martineza, Xi Chena, Aaron Webbera, A. Paul Moulda, Anne Seifertb, Ronald T. Hayb, and Andrew D. Sharrocksa,1 aFaculty of Life Sciences, University of Manchester, Manchester M13 9PT, United Kingdom; and bCentre for Gene Regulation and Expression, University of Dundee, Dundee DD1 5EH, United Kingdom Edited by James L. Manley, Columbia University, New York, NY, and approved July 17, 2015 (received for review May 20, 2015) Protein SUMOylation has emerged as an important regulatory human proteins containing two or more motifs corresponding event, particularly in nuclear processes such as transcriptional to the extended negatively charged amino acid-dependent control and DNA repair. In this context, small ubiquitin-like modifier SUMOylation motif (NDSM) (13) Thus, there is a huge poten- (SUMO) often provides a binding platform for the recruitment of tial for widespread multi-SUMOylation of proteins to occur. proteins via their SUMO-interacting motifs (SIMs). Recent discoveries Indeed, several of these proteins have been shown to be point to an important role for multivalent SUMO binding through SUMOylated on multiple sites, including megakaryoblastic leu- multiple SIMs in the binding partner as exemplified by poly- kemia (translocation) 1 (MKL1) (14), CREB-binding protein SUMOylation acting as a binding platform for ubiquitin E3 ligases (CBP) (15), and PEA3/ETV4 (16). Furthermore, two recent such as ring finger protein 4. Here, we have investigated whether proteomic studies emphasize the potential for more widespread other types of protein are recruited through multivalent SUMO multi-SUMOylation as they found that a large proportion of all interactions. -
Structural Basis of Specific DNA Binding by the Transcription Factor
8388–8398 Nucleic Acids Research, 2019, Vol. 47, No. 16 Published online 21 June 2019 doi: 10.1093/nar/gkz557 Structural basis of specific DNA binding by the transcription factor ZBTB24 Ren Ren1,†, Swanand Hardikar1,†, John R. Horton1, Yue Lu1, Yang Zeng1,2,AnupK.Singh1, Kevin Lin1, Luis Della Coletta1, Jianjun Shen1,2, Celine Shuet Lin Kong3, Hideharu Hashimoto4, Xing Zhang1, Taiping Chen1,2,* and Xiaodong Cheng 1,2,* 1Department of Epigenetics and Molecular Carcinogenesis, The University of Texas MD Anderson Cancer Center, 2 Houston, TX 77030, USA, Program in Genetics and Epigenetics, The University of Texas MD Anderson Cancer Downloaded from https://academic.oup.com/nar/article/47/16/8388/5521786 by guest on 23 September 2021 Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA, 3Program in Cancer Biology, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA and 4Department of Biochemistry, Emory University School of Medicine, Atlanta, GA 30322, USA Received October 13, 2018; Revised June 07, 2019; Editorial Decision June 11, 2019; Accepted June 13, 2019 ABSTRACT INTRODUCTION ZBTB24, encoding a protein of the ZBTB family ZBTB (zinc-finger- and BTB domain-containing) proteins, of transcriptional regulators, is one of four known characterized by the presence of an N-terminal BTB genes––the other three being DNMT3B, CDCA7 and (broad-complex, tram-track, and bric-a-brac)` domain [also HELLS––that are mutated in immunodeficiency, cen- known as POZ (poxvirus and zinc finger) domain] and a tromeric instability and facial anomalies (ICF) syn- C-terminal domain with tandem C2H2 Kr¨uppel-type zinc fingers (ZFs), are a large family of evolutionarily con- drome, a genetic disorder characterized by DNA hy- served transcriptional regulators (1). -
Supplementary Figure Legends
1 Supplementary Figure legends 2 Supplementary Figure 1. 3 Experimental workflow. 4 5 Supplementary Figure 2. 6 IRF9 binding to promoters. 7 a) Verification of mIRF9 antibody by site-directed ChIP. IFNβ-stimulated binding of IRF9 to 8 the ISRE sequences of Mx2 was analyzed using BMDMs of WT and Irf9−/− (IRF9-/-) mice. 9 Cells were treated with 250 IU/ml of IFNβ for 1.5h. Data represent mean and SEM values of 10 three independent experiments. P-values were calculated using the paired ratio t-test (*P ≤ 11 0.05; **P ≤ 0.01, ***P ≤ 0.001). 12 b) Browser tracks showing complexes assigned as STAT-IRF9 in IFNγ treated wild type 13 BMDMs. Input, STAT2, IRF9 (scale 0-200). STAT1 (scale 0-150). 14 15 Supplementary Figure 3. 16 Experimental system for BioID. 17 a) Kinetics of STAT1, STAT2 and IRF9 synthesis in Raw 264.7 macrophages and wild type 18 BMDMs treated with 250 IU/ml as indicated. Whole-cell extracts were tested in western blot 19 for STAT1 phosphorylation at Y701 and of STAT2 at Y689 as well as total STAT1, STAT2, 20 IRF9 and GAPDH levels. The blots are representative of three independent experiments. b) 21 Irf9-/- mouse embryonic fibroblasts (MEFs) were transiently transfected with the indicated 22 expression vectors, including constitutively active IRF7-M15. One day after transfection, 23 RNA was isolated and Mx2 expression determined by qPCR. c) Myc-BirA*-IRF9 transgenic 24 Raw 264.7 were treated with increasing amounts of doxycycline (dox) (0,2µg/ml, 0,4µg/ml, 25 0,6µg/ml, 0,8µg/ml, 1mg/ml) and 50µM biotin. -
Supplementary Table 1 Genes Tested in Qrt-PCR in Nfpas
Supplementary Table 1 Genes tested in qRT-PCR in NFPAs Gene Bank accession Gene Description number ABI assay ID a disintegrin-like and metalloprotease with thrombospondin type 1 motif 7 ADAMTS7 NM_014272.3 Hs00276223_m1 Rho guanine nucleotide exchange factor (GEF) 3 ARHGEF3 NM_019555.1 Hs00219609_m1 BCL2-associated X protein BAX NM_004324 House design Bcl-2 binding component 3 BBC3 NM_014417.2 Hs00248075_m1 B-cell CLL/lymphoma 2 BCL2 NM_000633 House design Bone morphogenetic protein 7 BMP7 NM_001719.1 Hs00233476_m1 CCAAT/enhancer binding protein (C/EBP), alpha CEBPA NM_004364.2 Hs00269972_s1 coxsackie virus and adenovirus receptor CXADR NM_001338.3 Hs00154661_m1 Homo sapiens Dicer1, Dcr-1 homolog (Drosophila) (DICER1) DICER1 NM_177438.1 Hs00229023_m1 Homo sapiens dystonin DST NM_015548.2 Hs00156137_m1 fms-related tyrosine kinase 3 FLT3 NM_004119.1 Hs00174690_m1 glutamate receptor, ionotropic, N-methyl D-aspartate 1 GRIN1 NM_000832.4 Hs00609557_m1 high-mobility group box 1 HMGB1 NM_002128.3 Hs01923466_g1 heterogeneous nuclear ribonucleoprotein U HNRPU NM_004501.3 Hs00244919_m1 insulin-like growth factor binding protein 5 IGFBP5 NM_000599.2 Hs00181213_m1 latent transforming growth factor beta binding protein 4 LTBP4 NM_001042544.1 Hs00186025_m1 microtubule-associated protein 1 light chain 3 beta MAP1LC3B NM_022818.3 Hs00797944_s1 matrix metallopeptidase 17 MMP17 NM_016155.4 Hs01108847_m1 myosin VA MYO5A NM_000259.1 Hs00165309_m1 Homo sapiens nuclear factor (erythroid-derived 2)-like 1 NFE2L1 NM_003204.1 Hs00231457_m1 oxoglutarate (alpha-ketoglutarate) -
Association of Cnvs with Methylation Variation
www.nature.com/npjgenmed ARTICLE OPEN Association of CNVs with methylation variation Xinghua Shi1,8, Saranya Radhakrishnan2, Jia Wen1, Jin Yun Chen2, Junjie Chen1,8, Brianna Ashlyn Lam1, Ryan E. Mills 3, ✉ ✉ Barbara E. Stranger4, Charles Lee5,6,7 and Sunita R. Setlur 2 Germline copy number variants (CNVs) and single-nucleotide polymorphisms (SNPs) form the basis of inter-individual genetic variation. Although the phenotypic effects of SNPs have been extensively investigated, the effects of CNVs is relatively less understood. To better characterize mechanisms by which CNVs affect cellular phenotype, we tested their association with variable CpG methylation in a genome-wide manner. Using paired CNV and methylation data from the 1000 genomes and HapMap projects, we identified genome-wide associations by methylation quantitative trait locus (mQTL) analysis. We found individual CNVs being associated with methylation of multiple CpGs and vice versa. CNV-associated methylation changes were correlated with gene expression. CNV-mQTLs were enriched for regulatory regions, transcription factor-binding sites (TFBSs), and were involved in long- range physical interactions with associated CpGs. Some CNV-mQTLs were associated with methylation of imprinted genes. Several CNV-mQTLs and/or associated genes were among those previously reported by genome-wide association studies (GWASs). We demonstrate that germline CNVs in the genome are associated with CpG methylation. Our findings suggest that structural variation together with methylation may affect cellular phenotype. npj Genomic Medicine (2020) 5:41 ; https://doi.org/10.1038/s41525-020-00145-w 1234567890():,; INTRODUCTION influence transcript regulation is DNA methylation, which involves The extent of genetic variation that exists in the human addition of a methyl group to cytosine residues within a CpG population is continually being characterized in efforts to identify dinucleotide. -
Computational Discovery of Transcription Factors Associated with Drug Response
OPEN The Pharmacogenomics Journal (2016) 16, 573–582 www.nature.com/tpj ORIGINAL ARTICLE Computational discovery of transcription factors associated with drug response C Hanson1, J Cairns2, L Wang2 and S Sinha3 This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF–treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing. The Pharmacogenomics Journal (2016) 16, 573–582; doi:10.1038/tpj.2015.74; published online 27 October 2015 INTRODUCTION regulatory activities are associated with cellular response to The field of pharmacogenetics aims to understand the relationship cytotoxic treatments (Figure 1a), with the expectation that in the between individual variation at the genetic level and variation in future the response may be manipulated by intervening with the cellular or physiological response to a drug. -
Xo PANEL DNA GENE LIST
xO PANEL DNA GENE LIST ~1700 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes (at 400 -500x average coverage on tumor) Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 -
Temporal Epigenomic Profiling Identifies AHR As a Dynamic Super-Enhancer Controlled Regulator of Mesenchymal Multipotency
bioRxiv preprint doi: https://doi.org/10.1101/183988; this version posted November 17, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Gerard et al.: Time-series epigenomic profiles of mesenchymal differentiation 1 Temporal epigenomic profiling identifies AHR as a 2 dynamic super-enhancer controlled regulator of 3 mesenchymal multipotency 4 Deborah Gérard1, Florian Schmidt2,3, Aurélien Ginolhac1, Martine Schmitz1, 5 Rashi Halder4, Peter Ebert3, Marcel H. Schulz2,3, Thomas Sauter1 and Lasse 6 Sinkkonen1* 7 1Life Sciences Research Unit, University of Luxembourg, L-4367 Belvaux, 8 Luxembourg 9 2Excellence Cluster for Multimodal Computing and Interaction, Saarland Informatics 10 Campus, Germany 11 3Computational Biology & Applied Algorithmics, Max Planck Institute for 12 Informatics, Saarland Informatics Campus, Germany 13 4Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur- 14 Alzette, L-4362, Luxembourg 15 16 [email protected]; [email protected]; [email protected]; 17 [email protected]; [email protected]; [email protected]; 18 [email protected]; [email protected]; [email protected] 19 20 *Corresponding author: Dr. Lasse Sinkkonen 21 Life Sciences Research Unit, University of Luxembourg 22 6, Avenue du Swing, L-4367 Belvaux, Luxembourg 23 Tel.: +352-4666446839 24 E-mail: [email protected] 25 1 bioRxiv preprint doi: https://doi.org/10.1101/183988; this version posted November 17, 2017.