Roy Chaudhuri | Lucy Crooks | Mark Dunning |Eran Elhaik | Win Hide | Marta Milo | Joost Stassen | Ian Sudbery | Dennis Wang
James Bradford | Afsaneh Maleki-Dizaji
Lecturer in Bioinformatics, Department of Molecular Biology and Biotechnology
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
- Oshota, O, Conway, M, Fookes, M, Schreiber, F, Chaudhuri, RR, Yu, L, Morgan, FJE, Clare, S, Choudhary, J, Thomson, NR et al. (2017). Transcriptome and proteome analysis of Salmonella enterica serovar Typhimurium systemic infection of wild type and immune-deficient mice. PLoS ONE12,e0181365.
- Dunne, KA, Chaudhuri, RR, Rossiter, AE, Beriotto, I, Browning, DF, Squire, D, Cunningham, AF, Cole, JA, Loman, N, Henderson, IR et al. (2017). Sequencing a piece of history: complete genome sequence of the original Escherichia coli strain. Microb Genom3,mgen000106.
- Howell, KJ, Weinert, LA, Peters, SE, Wang, J, Hernandez-Garcia, J, Chaudhuri, RR, Luan, SL, Angen, Ø, Aragon, V, Williamson, SM et al. (2017). "Pathotyping" Multiplex PCR Assay for Haemophilus parasuis: a Tool for Prediction of Virulence. J. Clin. Microbiol.55,2617-2628.
- Zis, P, Grünewald, RA, Chaudhuri, RK, Hadjivassiliou, M (2017). Peripheral neuropathy in idiopathic Parkinson's disease: A systematic review. J. Neurol. Sci.378,204-209.
- Bossé, JT, Li, Y, Rogers, J, Fernandez Crespo, R, Li, Y, Chaudhuri, RR, Holden, MT, Maskell, DJ, Tucker, AW, Wren, BW et al. (2017). Whole Genome Sequencing for Surveillance of Antimicrobial Resistance in Actinobacillus pleuropneumoniae. Front Microbiol8,311.
Bioinformatics Core Director, University of Sheffield
- 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
- 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,.
- Dunning, MJ, Vowler, SL, Lalonde, E, Ross-Adams, H, Boutros, P, Mills, IG, Lynch, AG, Lamb, AD (2017). Mining Human Prostate Cancer Datasets: The "camcAPP" Shiny App. EBioMedicine17,5-6.
- Lalonde, E, Alkallas, R, Chua, MLK, Fraser, M, Haider, S, Meng, A, Zheng, J, Yao, CQ, Picard, V, Orain, M et al. (2017). Translating a Prognostic DNA Genomic Classifier into the Clinic: Retrospective Validation in 563 Localized Prostate Tumors. Eur. Urol.72,22-31.
- Whitington, T, Gao, P, Song, W, Ross-Adams, H, Lamb, AD, Yang, Y, Svezia, I, Klevebring, D, Mills, IG, Karlsson, R et al. (2016). Gene regulatory mechanisms underpinning prostate cancer susceptibility. Nat. Genet.48,387-97.
- Shaw, GL, Whitaker, H, Corcoran, M, Dunning, MJ, Luxton, H, Kay, J, Massie, CE, Miller, JL, Lamb, AD, Ross-Adams, H et al. (2016). The Early Effects of Rapid Androgen Deprivation on Human Prostate Cancer. Eur. Urol.70,214-8.
Lecturer in Bioinformatics, Department of Animal and Plant Sciences
Research group website
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.
- Elhaik, E, Yusuf, L, Anderson, AIJ, Pirooznia, M, Arnellos, D, Vilshansky, G, Ercal, G, Lu, Y, Webster, T, Baird, ML et al. (2017). The Diversity of REcent and Ancient huMan (DREAM): A New Microarray for Genetic Anthropology and Genealogy, Forensics, and Personalized Medicine. Genome Biol Evol9,3225-3237.
- Shamarina, D, Stoyantcheva, I, Mason, CE, Bibby, K, Elhaik, E (2017). Communicating the promise, risks, and ethics of large-scale, open space microbiome and metagenome research. Microbiome5,132.
- Elhaik, E (2017). Editorial: Population Genetics of Worldwide Jewish People. Front Genet8,101.
- Das, R, Wexler, P, Pirooznia, M, Elhaik, E (2017). The Origins of Ashkenaz, Ashkenazic Jews, and Yiddish. Front Genet8,87.
- Marshall, S, Das, R, Pirooznia, M, Elhaik, E (2016). Reconstructing Druze population history. Sci Rep6,35837.
Professor of Computational Biology and Bioinformatics, SITraN
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
- 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.
- 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.
- 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.
- Zhang, P, Dimont, E, Ha, T, Swanson, DJ, FANTOM Consortium, Hide, W, Goldowitz, D (2017). Relatively frequent switching of transcription start sites during cerebellar development. BMC Genomics18,461.
- Daily, K, Ho Sui, SJ, Schriml, LM, Dexheimer, PJ, Salomonis, N, Schroll, R, Bush, S, Keddache, M, Mayhew, C, Lotia, S et al. (2017). Molecular, phenotypic, and sample-associated data to describe pluripotent stem cell lines and derivatives. Sci Data4,170030.
Lecturer in Computational Biology, Department of Biomedical Science
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
- 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.
- García-Alcántara, F, Murillo-Cuesta, S, Pulido, S, Bermúdez-Muñoz, JM, Martínez-Vega, R, Milo, M, Varela-Nieto, I, Rivera, T (2017). 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.
- 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.
- 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.
- Boggis, EM, Milo, M, Walters, K (2016). eQuIPS: eQTL Analysis Using Informed Partitioning of SNPs - A Fully Bayesian Approach. Genet. Epidemiol.40,273-83.
Postdoctoral Research Associate, Department of Animal and Plant Sciences
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.
- 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,.
- 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.
- 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.
- Pétriacq, P, Stassen, JH, Ton, J (2016). Spore Density Determines Infection Strategy by the Plant Pathogenic Fungus Plectosphaerella cucumerina. Plant Physiol.170,2325-39.
- Zhang, L, Lubbers, RJ, Simon, A, Stassen, JH, Vargas Ribera, PR, Viaud, M, van Kan, JA (2016). A novel Zn2 Cys6 transcription factor BcGaaR regulates D-galacturonic acid utilization in Botrytis cinerea. Mol. Microbiol.100,247-62.
Lecturer in Bioinformatics, Department of Molecular Biology and Biotechnology
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)
- 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.
- Liao, C, Beveridge, R, Hudson, JJR, Parker, JD, Chiang, SC, Ray, S, Ashour, ME, Sudbery, I, Dickman, MJ, El-Khamisy, SF et al. (2018). UCHL3 Regulates Topoisomerase-Induced Chromosomal Break Repair by Controlling TDP1 Proteostasis. Cell Rep23,3352-3365.
- Smith, T, Heger, A, Sudbery, I (2017). UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res.27,491-499.
- Greig, JA, Sudbery, IM, Richardson, JP, Naglik, JR, Wang, Y, Sudbery, PE (2015). Cell cycle-independent phospho-regulation of Fkh2 during hyphal growth regulates Candida albicans pathogenesis. PLoS Pathog.11,e1004630.
- Rajan, P, Stockley, J, Sudbery, IM, Fleming, JT, Hedley, A, Kalna, G, Sims, D, Ponting, CP, Heger, A, Robson, CN et al. (2014). Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre- and post-treatment prostatic biopsies from patients with advanced prostate cancer. BMC Cancer14,977.
Lecturer in Genomic Medicine, Department of Neuroscience
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
- Ludeman, MJ, Zhong, C, Mulero, JJ, Lagacé, RE, Hennessy, LK, Short, ML, Wang, DY (2018). Developmental validation of GlobalFiler™ PCR amplification kit: a 6-dye multiplex assay designed for amplification of casework samples. Int. J. Legal Med.,.
- Shao, Y, Qiu, J, Huang, H, Mao, B, Dai, W, He, X, Cui, H, Lin, X, Lv, L, Wang, D et al. (2017). Pre-pregnancy BMI, gestational weight gain and risk of preeclampsia: a birth cohort study in Lanzhou, China. BMC Pregnancy Childbirth17,400.
- Kerelsky, A, Nipane, A, Edelberg, D, Wang, D, Zhou, X, Motmaendadgar, A, Gao, H, Xie, S, Kang, K, Park, J et al. (2017). Absence of a Band Gap at the Interface of a Metal and Highly Doped Monolayer MoS2. Nano Lett.17,5962-5968.
- Wang, D, Smyser, K, Rhodes, D, Balicas, L, Pasupathy, A, Herman, IP (2017). Passivating 1T'-MoTe2 multilayers at elevated temperatures by encapsulation. Nanoscale9,13910-13914.
- Silverbush, D, Grosskurth, S, Wang, D, Powell, F, Gottgens, B, Dry, J, Fisher, J (2017). Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia. Cancer Res.77,827-838.
Research Fellow, Department of Oncology
- 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
- 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.
- 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.
- 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.
- 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.
- Bradford, JR, Farren, M, Powell, SJ, Runswick, S, Weston, SL, Brown, H, Delpuech, O, Wappett, M, Smith, NR, Carr, TH et al. (2013). RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib. PLoS ONE8,e66003.
Senior Lecturer in Genomics and Bioinformatics, Department of Biosciences and Chemistry, Sheffield Hallam University
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
- Crooks, L, Guo, Y (2017). Consequences of Epistasis on Growth in an Erhualian × White Duroc Pig Cross. PLoS ONE12,e0162045.
- 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.
- 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.
- 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.
- 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.
Research Fellow in Computational Biology, Department of Computer Science
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
- 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.
- 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.