Researchers and Expertise


Roy Chaudhuri | Lucy Crooks |  Mark Dunning |Eran Elhaik | Win Hide | Marta Milo | Joost Stassen | Ian Sudbery | Dennis Wang

James Bradford | Afsaneh Maleki-Dizaji


Roy Chaudhuri

Lecturer in Bioinformatics, Department of Molecular Biology and Biotechnology
Departmental website

Research Interests

My research interests are primarily in the area of bacterial genomics.

Current research topics include:

  • Comparative genomics and phylogenetics of bacterial pathogens, particularly E. coli and Salmonella
  • Use of transposon insertion sequencing methods (TraDIS/TnSeq/HITS/InSeq) to identify essential bacterial genes and genes important for survival in particular environments such as during infection of a model system
  • Development of user-friendly software tools and online resources for exploring data from -omics technologies. Examples include coliBASE, Xbase and the recently-funded MicrobesNG.


Bacterial Genomics, High-throughput sequencing, Phylogenetics, Transposon Insertion Sequencing (TraDIS/TnSeq/HITS/InSeq), Genome assembly, Variant detection, RNAseq

Recent Publications

  1. Janganan, TK, Mullin, N, Dafis-Sagarmendi, A, Brunt, J, Tzokov, SB, Stringer, S, Moir, A, Chaudhuri, RR, Fagan, RP, Hobbs, JK et al. (2020). Architecture and Self-Assembly of Clostridium sporogenes and Clostridium botulinum Spore Surfaces Illustrate a General Protective Strategy across Spore Formers. mSphere5,.
  2. Haldenby, S, Bronowski, C, Nelson, C, Kenny, J, Martinez-Rodriguez, C, Chaudhuri, R, Williams, NJ, Forbes, K, Strachan, NJ, Pulman, J et al. (2020). Increasing prevalence of a fluoroquinolone resistance mutation amongst Campylobacter jejuni isolates from four human infectious intestinal disease studies in the United Kingdom. PLoS ONE15,e0227535.
  3. Canals, R, Chaudhuri, RR, Steiner, RE, Owen, SV, Quinones-Olvera, N, Gordon, MA, Baym, M, Ibba, M, Hinton, JCD (2019). The fitness landscape of the African Salmonella Typhimurium ST313 strain D23580 reveals unique properties of the pBT1 plasmid. PLoS Pathog.15,e1007948.
  4. Gray, K, Green, LR, Chaudhuri, RR, Shaw, JG (2019). Draft Whole-Genome Sequences of 10 Aeromonas Strains from Clinical and Environmental Sources. Microbiol Resour Announc8,.
  5. Pattrick, CA, Webb, JP, Green, J, Chaudhuri, RR, Collins, MO, Kelly, DJ (2019). Proteomic Profiling, Transcription Factor Modeling, and Genomics of Evolved Tolerant Strains Elucidate Mechanisms of Vanillin Toxicity in Escherichia coli. mSystems4,.
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Mark Dunning

Bioinformatics Core Director, University of Sheffield

Core Website

Research Interests

  • High-throughput technologies such as next generation sequencing (NGS) can routinely produce massive amounts of data that can be used for tasks such as identifying biological samples with aberrant expression patterns or allow us to describe all variants in a genome. However, such datasets pose new challenges in the way the data have to be analyzed, annotated and interpreted which are not trivial and are daunting to the wet-lab biologist. My interests lie in making the analysis and results of high-throughput data accessible to the non-bioinformatician; via specialised training courses and by developing computational pipelines and workflows.I am currently exploring technologies that facilitate reproducible research and promote an open attitude to scientific research, and endeavour to make my talks, code, and analysis available whenever possible.


  • R / Bioconductor
  • Reproducible Research
  • Organising, developing and delivering Bioinformatics training courses
  • Analysis of microarray and RNA-seq data
  • Variant-calling from DNA-seq data
  • Developing pipelines / workflows for the analysis of NGS data

Recent Publications

  1. Rhodes, CJ, Otero-Núñez, P, Wharton, J, Swietlik, EM, Kariotis, S, Harbaum, L, Dunning, MJ, Elinoff, JM, Errington, N, Thomson, AAR et al. (2020). Whole Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome. Am. J. Respir. Crit. Care Med.,.
  2. Hamilton, N, Rutherford, HA, Petts, JJ, Isles, HM, Weber, T, Henneke, M, Gärtner, J, Dunning, MJ, Renshaw, SA (2020). The failure of microglia to digest developmental apoptotic cells contributes to the pathology of RNASET2-deficient leukoencephalopathy. Glia68,1531-1545.
  3. Yatai, KB, Dunning, MJ, Wang, D (2020). Consensus Genomic Subtypes of Muscle-invasive Bladder Cancer: A Step in the Right Direction but Still a Long Way To Go. Eur. Urol.77,434-435.
  4. Westbrook, JA, Wood, SL, Cairns, DA, McMahon, K, Gahlaut, R, Thygesen, H, Shires, M, Roberts, S, Marshall, H, Oliva, MR et al. (2019). Identification and validation of DOCK4 as a potential biomarker for risk of bone metastasis development in patients with early breast cancer. J. Pathol.247,381-391.
  5. Jurmeister, S, Ramos-Montoya, A, Sandi, C, Pértega-Gomes, N, Wadhwa, K, Lamb, AD, Dunning, MJ, Attig, J, Carroll, JS, Fryer, LG et al. (2018). Identification of potential therapeutic targets in prostate cancer through a cross-species approach. EMBO Mol Med10,.
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Eran Elhaik

Lecturer in Bioinformatics, Department of Animal and Plant Sciences
Research group website
Departmental website

Research Interests

My research centres on identifying and addressing biology related to computational, population, and evolutionary genetics. Current research topics include:

  • Identify and measure interbreeding sites between human and ancient hominins.
  • Develop tools to infer the geographical origins of human population
  • Develop tools to improve personalised medicine
  • Develop theoretical framework, tools, and animal models to improve our understanding of complex diseases.


Molecular Evolution, Population Genetics, Genetic epidemiology, Biogeography, Genomics, Paleogenomics, and Epigenetics.

Recent Publications

  1. Baughn, LB, Li, Z, Pearce, K, Vachon, CM, Polley, MY, Keats, J, Elhaik, E, Baird, M, Therneau, T, Cerhan, JR et al. (2020). The CCND1 c.870G risk allele is enriched in individuals of African ancestry with plasma cell dyscrasias. Blood Cancer J10,39.
  2. Elhaik, E (2019). Neonatal circumcision and prematurity are associated with sudden infant death syndrome (SIDS). J Clin Transl Res4,136-151.
  3. Esposito, U, Das, R, Syed, S, Pirooznia, M, Elhaik, E (2018). Ancient Ancestry Informative Markers for Identifying Fine-Scale Ancient Population Structure in Eurasians. Genes (Basel)9,.
  4. Elhaik, E, Ryan, DM (2019). Pair Matcher (PaM): fast model-based optimization of treatment/case-control matches. Bioinformatics35,2243-2250.
  5. Baughn, LB, Pearce, K, Larson, D, Polley, MY, Elhaik, E, Baird, M, Colby, C, Benson, J, Li, Z, Asmann, Y et al. (2018). Differences in genomic abnormalities among African individuals with monoclonal gammopathies using calculated ancestry. Blood Cancer J8,96.
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Win Hide

Professor of Computational Biology and Bioinformatics, SITraN
Departmental website

Research Interests

I am interested in understanding normal and and abnormal development.

  • Can we find omics profiles that diagnose or predict disease progression?
  • Can we integrate model organism, cell line and human cohort omics surveys to yield new understanding of normal and disease processes?
  • How can we develop and exploit next generation sequencing to inform our understanding of biology?
  • How to integrate models of function and disease across networks of interaction to yield targets for intervention?

Research opportunities include:

  • Simplification of molecular profiles to allow integration across biological processes leading to discovery of key processes and targets involved in normal and diseased systems.
  • Interpreting molecular profiling of gene high throughput genome sequencing/RNAseq analyses
  • Integration and data interpretation challenges across human, model organism and cell line systems
  • Building network models of specific diseases that collate and interpret integration across studies.
  • Development of tools to group experiments based upon shared phenotypic and molecular attributes. Providing a sharing environment for data to share functional biological models


Reducing the gap between primary data and its interpretation. We perform computational biology using a systems approach to deliver interventions through understanding of processes underlying disease

Recent Publications

  1. Prokopenko, D, Hecker, J, Kirchner, R, Chapman, BA, Hoffman, O, Mullin, K, Hide, W, Bertram, L, Laird, N, DeMeo, DL et al. (2020). Identification of Novel Alzheimer's Disease Loci Using Sex-Specific Family-Based Association Analysis of Whole-Genome Sequence Data. Sci Rep10,5029.
  2. Carling, PJ, Mortiboys, H, Green, C, Mihaylov, S, Sandor, C, Schwartzentruber, A, Taylor, R, Wei, W, Hastings, C, Wong, S et al. (2020). Deep phenotyping of peripheral tissue facilitates mechanistic disease stratification in sporadic Parkinson's disease. Prog. Neurobiol.187,101772.
  3. Joachim, RB, Altschuler, GM, Hutchinson, JN, Wong, HR, Hide, WA, Kobzik, L (2018). The relative resistance of children to sepsis mortality: from pathways to drug candidates. Mol. Syst. Biol.14,e7998.
  4. Pita-Juárez, Y, Altschuler, G, Kariotis, S, Wei, W, Koler, K, Green, C, Tanzi, RE, Hide, W (2018). The Pathway Coexpression Network: Revealing pathway relationships. PLoS Comput. Biol.14,e1006042.
  5. Zhang, P, Dimont, E, Ha, T, Swanson, DJ, Itoh, M, Kawaji, H, Lassmann, T, Daub, CO, Arner, E, FANTOM Consortium et al. (2018). Correction to: Relatively frequent switching of transcription start sites during cerebellar development. BMC Genomics19,39.
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Marta Milo

Lecturer in Computational Biology, Department of Biomedical Science
Departmental website

Research Interests

To understand and define the source of uncertainty in quantitative biology is a key aspect for improving sensitivity and accuracy in the analysis of high throughput genomic data. My research interests focus on developing computational tools, pipelines, appropriate experimental designs and protocols to assist in improving accuracy and sensitivity in the analysis of biological data.


  • Propagation of uncertainty, associated to low-level data, in downstream analysis of microarray data
  • Quantification and inference of gene expression levels using probabilistic models
  • Inference of gene networks using regulatory data and gene expression data
  • Integrated approaches for the analysis of Next Generation Sequencing data

Recent Publications

  1. Thompson, O, von Meyenn, F, Hewitt, Z, Alexander, J, Wood, A, Weightman, R, Gregory, S, Krueger, F, Andrews, S, Barbaric, I et al. (2020). Low rates of mutation in clinical grade human pluripotent stem cells under different culture conditions. Nat Commun11,1528.
  2. Iannitti, T, Scarrott, JM, Likhite, S, Coldicott, IRP, Lewis, KE, Heath, PR, Higginbottom, A, Myszczynska, MA, Milo, M, Hautbergue, GM et al. (2018). Translating SOD1 Gene Silencing toward the Clinic: A Highly Efficacious, Off-Target-free, and Biomarker-Supported Strategy for fALS. Mol Ther Nucleic Acids12,75-88.
  3. García-Alcántara, F, Murillo-Cuesta, S, Pulido, S, Bermúdez-Muñoz, JM, Martínez-Vega, R, Milo, M, Varela-Nieto, I, Rivera, T (2018). The expression of oxidative stress response genes is modulated by a combination of resveratrol and N-acetylcysteine to ameliorate ototoxicity in the rat cochlea. Hear. Res.358,10-21.
  4. Waller, R, Goodall, EF, Milo, M, Cooper-Knock, J, Da Costa, M, Hobson, E, Kazoka, M, Wollff, H, Heath, PR, Shaw, PJ et al. (2017). Serum miRNAs miR-206, 143-3p and 374b-5p as potential biomarkers for amyotrophic lateral sclerosis (ALS). Neurobiol. Aging55,123-131.
  5. Rolando, C, Erni, A, Grison, A, Beattie, R, Engler, A, Gokhale, PJ, Milo, M, Wegleiter, T, Jessberger, S, Taylor, V et al. (2016). Multipotency of Adult Hippocampal NSCs In Vivo Is Restricted by Drosha/NFIB. Cell Stem Cell19,653-662.
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stassenJoost Stassen

Postdoctoral Research Associate, Department of Animal and Plant Sciences
Group website

Research interests

My research is predominantly focused on different aspects of plant-microbe Interactions. Coming from a molecular biology background, I process sequencing data sets and metabolomic data to identify genes, genomic features and metabolic signatures that are involved in the molecular-genetic basis underpinning pathogen virulence and plant immunity. Examples include identifying disease-promoting effectors of Bremia lactucae by de novo assembly of RNAseq from mixed Bremia/host tissue, as well as improving the genome sequence assembly and annotation of Botrytis cinerea. My current project investigates the molecular and epigenetic basis of the onset and long-term (transgenerational) maintenance of plant immune priming.


Next Generation Sequencing data analysis, Genome assembly, Epigenetics, Plant-Microbe Interactions, non-model systems.

Recent publications

  1. Stassen, JHM, López, A, Jain, R, Pascual-Pardo, D, Luna, E, Smith, LM, Ton, J (2018). The relationship between transgenerational acquired resistance and global DNA methylation in Arabidopsis. Sci Rep8,14761.
  2. Rodenburg, SYA, Terhem, RB, Veloso, J, Stassen, JHM, van Kan, JAL (2018). Functional Analysis of Mating Type Genes and Transcriptome Analysis during Fruiting Body Development of Botrytis cinerea. mBio9,.
  3. López Sánchez, A, Stassen, JH, Furci, L, Smith, LM, Ton, J (2016). The role of DNA (de)methylation in immune responsiveness of Arabidopsis. Plant J.88,361-374.
  4. Van Kan, JA, Stassen, JH, Mosbach, A, Van Der Lee, TA, Faino, L, Farmer, AD, Papasotiriou, DG, Zhou, S, Seidl, MF, Cottam, E et al. (2017). A gapless genome sequence of the fungus Botrytis cinerea. Mol. Plant Pathol.18,75-89.
  5. Pétriacq, P, Stassen, JH, Ton, J (2016). Spore Density Determines Infection Strategy by the Plant Pathogenic Fungus Plectosphaerella cucumerina. Plant Physiol.170,2325-39.
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Ian Sudbery

Lecturer in Bioinformatics, Department of Molecular Biology and Biotechnology

Research Interests

How do cells integrate information to make decisions about what genes should be expressed at a given time and in a given place? How do these processes malfunction to produce disease states? The correct regulation of gene expression is essential for the proper functioning of the cell, and incorrect regulation of genes is central to the mechanisms of many diseases. My interests rest in understanding how the many levels of eukaryotic gene regulation work together to perform these functions, using computational and functional genomics tools.

Examples of recent projects:

  • The Polycomb Repressive Complex is targeted to CpG islands by KDM2b
  • Reconfiguration of the 3D structure of the genome by a disease causing SNP
  • Activation of the WNT pathway by androgen ablation therapy in prostate cancer.
  • Association of RNA binding actors with coding and non-coding transcripts


Genome-wide measurement of gene expression at all stages of regulation:

  • Transcription (ChIP-seq, ChIP-exo, 4/5/HiC)
  • RNA processing (HITS-CLIP/iCLIP)
  • RNA stability (RNA-seq, small RNA-seq)
  • Translation (Ribosome Profiling)

Recent Publications

  1. Baidžajevas, K, Hadadi, É, Lee, B, Lum, J, Shihui, F, Sudbery, I, Kiss-Tóth, E, Wong, SC, Wilson, HL (2020). Macrophage polarisation associated with atherosclerosis differentially affects their capacity to handle lipids. Atherosclerosis305,10-18.
  2. Viphakone, N, Sudbery, I, Griffith, L, Heath, CG, Sims, D, Wilson, SA (2019). Co-transcriptional Loading of RNA Export Factors Shapes the Human Transcriptome. Mol. Cell75,310-323.e8.
  3. Kaneva, IN, Sudbery, IM, Dickman, MJ, Sudbery, PE (2019). Proteins that physically interact with the phosphatase Cdc14 in Candida albicans have diverse roles in the cell cycle. Sci Rep9,6258.
  4. Srivastava, A, Malik, L, Smith, T, Sudbery, I, Patro, R (2019). Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol.20,65.
  5. Lesbirel, S, Viphakone, N, Parker, M, Parker, J, Heath, C, Sudbery, I, Wilson, SA (2018). The m6A-methylase complex recruits TREX and regulates mRNA export. Sci Rep8,13827.
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Dennis Wang

Lecturer in Genomic Medicine, Department of Neuroscience

Departmental Website

Research Interests

We focus on translating the complex patterns of genomic data generated in the lab to enable the development of personalized medicines that can benefit patients suffering from complex diseases. Of computational interest, this involves integrating genomics information of various data types in order to build algorithms that predict clinical outcome and identifying genetic biomarkers through feature selection. Of biomedical interest, we survey the genomic landscape of disease subtypes, as well preclinical models, to better understand how we can stratify patients for different treatments. Both of these aims enable us to work towards the ultimate goal of developing data driven approaches for personalizing medicine.

Examples of recent projects:

  • Prognostic impact of genomic instability and immunogenicity in cancer and pulmonary diseases
  • Personalizing drug combinations based on genomic features
  • Probabilistic modelling of drug response
  • Assessment of variant calling metrics for clinical diagnostics (eg. 100K Genome Project)
  • Molecular stratification of dementia and Alzheimer’s cases


  • Genomic applications in medicine (I co-lead the MSc in Genomic Medicine)
  • Statistical inference and machine learning prediction for pharmaco-genomic applications
  • Performance assessment of diagnostic assays
  • Integrative genomics for patient stratification

Recent Publications

  1. Tay, E, Paul, B, Sharp, J, Wang, D, Chui, ASF, Hazra, PK, Santoso, T, Albers, B, Diener, HC, Lewalter, T et al. (2020). Left atrial appendage occlusion for ischemic stroke prevention in patients with non-valvular atrial fibrillation: clinical expert opinion and consensus statement for the Asian-Pacific region. J Interv Card Electrophysiol,.
  2. Rhodes, CJ, Otero-Núñez, P, Wharton, J, Swietlik, EM, Kariotis, S, Harbaum, L, Dunning, MJ, Elinoff, JM, Errington, N, Thomson, AAR et al. (2020). Whole Blood RNA Profiles Associated with Pulmonary Arterial Hypertension and Clinical Outcome. Am. J. Respir. Crit. Care Med.,.
  3. Freeman, TM, Genomics England Research Consortium, Wang, D, Harris, J (2020). Genomic loci susceptible to systematic sequencing bias in clinical whole genomes. Genome Res.30,415-426.
  4. Bendok, BR, Abi-Aad, KR, Ward, JD, Kniss, JF, Kwasny, MJ, Rahme, RJ, Aoun, SG, El Ahmadieh, TY, El Tecle, NE, Zammar, SG et al. (2020). The Hydrogel Endovascular Aneurysm Treatment Trial (HEAT): A Randomized Controlled Trial of the Second-Generation Hydrogel Coil. Neurosurgery86,615-624.
  5. Yatai, KB, Dunning, MJ, Wang, D (2020). Consensus Genomic Subtypes of Muscle-invasive Bladder Cancer: A Step in the Right Direction but Still a Long Way To Go. Eur. Urol.77,434-435.
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James Bradford

Research Fellow, Department of Oncology

Research Interests

  • Understanding the molecular drivers of tumour development and progression through application of computational methods to integrate cancer genomics datasets.
  • Novel cancer biomarker and drug target identification and characterization with focus on long non-coding RNA and the tumour microenvironment

I am actively seeking cross-disciplinary collaborations to meet the “big data” challenges presented by the above


  • Execution of Next Generation Sequencing pipelines on large cancer datasets
  • Interpretation of complex cancer datasets
  • Multi-dimensional/platform data integration
  • Novel cancer target/biomarker discovery, validation and disease positioning.
  • Application of machine learning approaches to biological problems

Recent Publications

  1. Walters, K, Sarsenov, R, Too, WS, Hare, RK, Paterson, IC, Lambert, DW, Brown, S, Bradford, JR (2019). Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks. BMC Genomics20,454.
  2. Bradford, JR, Cox, A, Bernard, P, Camp, NJ (2018). Correction: Consensus Analysis of Whole Transcriptome Profiles from Two Breast Cancer Patient Cohorts Reveals Long Non-Coding RNAs Associated with Intrinsic Subtype and the Tumour Microenvironment. PLoS ONE13,e0192589.
  3. Bradford, JR, Cox, A, Bernard, P, Camp, NJ (2016). Consensus Analysis of Whole Transcriptome Profiles from Two Breast Cancer Patient Cohorts Reveals Long Non-Coding RNAs Associated with Intrinsic Subtype and the Tumour Microenvironment. PLoS ONE11,e0163238.
  4. Bradford, JR, Wappett, M, Beran, G, Logie, A, Delpuech, O, Brown, H, Boros, J, Camp, NJ, McEwen, R, Mazzola, AM et al. (2016). Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers. Oncotarget7,20773-87.
  5. Wappett, M, Dulak, A, Yang, ZR, Al-Watban, A, Bradford, JR, Dry, JR (2016). Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. BMC Genomics17,65.
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Lucy Crooks

Senior Lecturer in Genomics and Bioinformatics, Department of Biosciences and Chemistry, Sheffield Hallam University
University website

Research Interests

My interest is using next generation sequencing technologies to understand the genetic basis of disease. Next generation sequencing enables us to detect nearly all the genetic variants in an individual but there are hundreds of thousands of these. The difficulty is in pinpointing which of these contribute to a specific disease. My research explores ways to identify processes that are disrupted in disease from moderate-sized patient groups, assuming that individuals have different variants but that affect a common pathway. I am particularly interested in unravelling the genetic causes of schizophrenia.


  • Large-scale human population genomics
  • Clinical genomics
  • Variant calling from DNA-seq with a reference genome
  • Quality Control
  • Statistical and mathematical modelling including generalised mixed linear models and population dynamics
  • Evolutionary biology

Recent Publications

  1. Crooks, L, Guo, Y (2017). Consequences of Epistasis on Growth in an Erhualian × White Duroc Pig Cross. PLoS ONE12,e0162045.
  2. Balasubramanian, M, Hurst, J, Brown, S, Bishop, NJ, Arundel, P, DeVile, C, Pollitt, RC, Crooks, L, Longman, D, Caceres, JF et al. (2017). Compound heterozygous variants in NBAS as a cause of atypical osteogenesis imperfecta. Bone94,65-74.
  3. Singh, T, Kurki, MI, Curtis, D, Purcell, SM, Crooks, L, McRae, J, Suvisaari, J, Chheda, H, Blackwood, D, Breen, G et al. (2016). Rare loss-of-function variants in SETD1A are associated with schizophrenia and developmental disorders. Nat. Neurosci.19,571-7.
  4. Loberg, A, Dürr, JW, Fikse, WF, Jorjani, H, Crooks, L (2015). Estimates of genetic variance and variance of predicted genetic merits using pedigree or genomic relationship matrices in six Brown Swiss cattle populations for different traits. J. Anim. Breed. Genet.132,376-85.
  5. Nelson, RM, Nettelblad, C, Pettersson, ME, Shen, X, Crooks, L, Besnier, F, Alvarez-Castro, JM, Rönnegård, L, Ek, W, Sheng, Z et al. (2013). MAPfastR: quantitative trait loci mapping in outbred line crosses. G3 (Bethesda)3,2147-9.
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Afsaneh Maleki-Dizaji

Research Fellow in Computational Biology, Department of Computer Science

Research Interests

Having years of expertise in bioinformatics and systems biology, my research interests focus on developing computational tools, pipelines, workflows and systems biology models in omics research. I am currently working for AirPROM (€12m EU FP7 Project), which involves the building of individual-based models of cellular interaction, with the aim of using genomic data as the basis for the individual-based model of the cell. My main responsibilities are the analysis and interpretation of high-throughput biological data, with the aim to produce feasible and robust hypothesis for a deeper understanding of the biological systems under study.


  • Quantification and inference of gene expression levels using probabilistic models;
  • Inference of gene networks using regulatory data and gene expression data;
  • Integrated approaches for the analysis of Next Generation Sequencing data.
  • Teaching Modelling and simulation of Natural Systems

Recent Publications

  1. Hamed, FN, Åstrand, A, Bertolini, M, Rossi, A, Maleki-Dizaji, A, Messenger, AG, McDonagh, AJG, Tazi-Ahnini, R . Correction: Alopecia areata patients show deficiency of FOXP3+CD39+ T regulatory cells and clonotypic restriction of Treg TCRβ-chain, which highlights the immunopathological aspect of the disease. PLoS ONE14,e0222473.
  2. Geary, B, Walker, MJ, Snow, JT, Lee, DCH, Pernemalm, M, Maleki-Dizaji, S, Azadbakht, N, Apostolidou, S, Barnes, J, Krysiak, P et al. (2019). Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients. J. Proteome Res.18,3369-3382.
  3. Hamed, FN, Åstrand, A, Bertolini, M, Rossi, A, Maleki-Dizaji, A, Messenger, AG, McDonagh, AJG, Tazi-Ahnini, R (2019). Alopecia areata patients show deficiency of FOXP3+CD39+ T regulatory cells and clonotypic restriction of Treg TCRβ-chain, which highlights the immunopathological aspect of the disease. PLoS ONE14,e0210308.
  4. Almiñana, C, Caballero, I, Heath, PR, Maleki-Dizaji, S, Parrilla, I, Cuello, C, Gil, MA, Vazquez, JL, Vazquez, JM, Roca, J et al. (2014). The battle of the sexes starts in the oviduct: modulation of oviductal transcriptome by X and Y-bearing spermatozoa. BMC Genomics15,293.
  5. Holcombe, M, Adra, S, Bicak, M, Chin, S, Coakley, S, Graham, AI, Green, J, Greenough, C, Jackson, D, Kiran, M et al. (2012). Modelling complex biological systems using an agent-based approach. Integr Biol (Camb)4,53-64.
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