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Prof. Dr. Thomas Otto

Head of Department

E-Mail: thomas.otto@bnitm.de

BNITM Presse

Telefon: +49 40 285380-264/269

E-Mail: presse@bnitm.de

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Professor Thomas D. Otto is an expert in computational and infection biology, with a research focus on infectious diseases, particularly pathogen genomics and host–pathogen interactions. His vision is to study host–pathogen interactions at the single-cell level to identify new therapeutic targets. His team combines genomics, single-cell, and spatial data to understand how pathogens evolve, evade the immune system, and interact with host cells. The work focuses on three areas: analysing pathogen evolution and complex gene families, exploring host–pathogen interactions using single-cell and spatial data, and developing computational tools, including machine learning methods, to support these efforts.

Professor Otto began his career in computer science, with a minor in bioinformatics, at the University of Lübeck. His early work included developing machine learning approaches for analysing biomedical data (Master’s thesis at Florida State University). He completed his PhD at the Fundação Oswaldo Cruz Institute in Rio de Janeiro (supervised by Professor Wim Degrave), where he developed tools for pathogen genome assembly and annotation.

During his postdoctoral tenure at the Wellcome Sanger Institute (with Matt Berriman and Chris Newbold), Professor Otto specialised in next-generation sequencing for pathogen genome assembly, contributing tools and pipelines that are widely used in the field. He played a leading role in establishing reference genomes for various parasites, including Plasmodium species responsible for malaria, and developed computational pipelines for analysing gene families and understanding host–parasite interactions.

Since 2017, Professor Otto has been at the University of Glasgow (affiliated since 2025), where his research focuses on single-cell sequencing technologies and their applications in immunology and microbiology. He has developed bioinformatics pipelines and tools to explore cell–cell interactions and has established web servers for hosting single-cell atlas data. His collaborative projects extend globally, particularly in Africa, where he works with institutions in Ghana, Kenya, and Malawi to implement advanced genomic techniques for infectious disease research. He also holds an honorary lecturer position at the University of Ghana.

In 2023–2024, he undertook a sabbatical at the University of Heidelberg and the University of Montpellier, focusing on machine learning approaches to host–parasite interactions.

In September 2025, he took on the leadership of the Data Science Centre at the Bernhard Nocht Institute of Tropical Medicine, alongside a W3 professorship in Computational Pathogenomics at the University of Hamburg (Jülicher Model).

With over 150 publications and substantial collaborative grant income, Professor Otto is recognised for his contributions to bioinformatics and his commitment to advancing computational research, particularly in the Global South.

Prof. Dr. Thomas Otto: ein freundlich blickender erfahrener Forscher mit Bart und rosa Hemd
Prof. Dr. Thomas Otto   ©BNITM | Dino Schachten

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Scientific Background

I thought it might be interesting to provide slightly more detail on my background. Hence, here it is (yes, too long for a webpage). I based it on a job application, in case you wonder! 

 

Infectious diseases have shaped human evolution and are a significant threat to global health. Besides the human misery and sadness pathogens bring to us, they also generate billions of dollars in costs every year. Hence, infectious diseases pose formidable, complex challenges that require interdisciplinary and collaborative research, training, and education.

To overcome these challenges and to transcend from genome sequencing and single-cell OMICS, we will study pathogens and perform essential translational work. These technologies include (but are not limited to) genomic sequencing (i.e., novel pathogens), their evolution, single-cell transcriptomics sequencing and spatial biology in animal models/infection to explore host-parasite interaction and computational tool development using machine learning approaches. 

I will “briefly” summarise my previous and current research:

 

Studies

I began my research career studying computer science with a minor in bioinformatics at the University of Lübeck, driven by an interest in computational methods and biology. During my studies, I implemented methods to process biomedical data and visualise results. For example, I created a Java program for interpreting hybridisation images[1]. I performed my master's thesis at Florida State University, where I implemented machine learning approaches for analysing functional magnetic resonance imaging data[2, 3].

Motivated by applying novel computational approaches to analyse biomedical data, I pursued a PhD in molecular and cell biology at Instituto Oswaldo Cruz, Rio de Janeiro with Wim Degrave, financed through my work as a visiting scientist at Fundação Ataulpho de Paiva. In my PhD projects, I developed a tool for assembling and annotating pathogen genomes[4]. At the same time, I supported other colleagues by setting up a bioinformatics computer cluster with pre-installed tools[5] and helped them analyse data.

 

Postdoc

During my postdoc at Wellcome Sanger Institute (Matt Berriman and Chris Newbold), I first specialised in processing next-generation sequencing data for genome assembly in pathogens. My expertise in abstracting biological processes and synthesising algorithms led to significant contributions, including tools and pipelines for genome assembly [6-10]. I still work in this field, and my latest publication is ILRA[11], 2023, which helps polish long-read assemblies. 
I evaluated and visualised sequencing technologies [12-14]. Currently, we are testing different single-cell transcriptomics sequencing (scRNA-Seq) methods that are cheaper than 10X Chromium runs and can be used in the field. 

  In 2016, we built the Companion[15][16] annotation server (https://companion.ac.uk/) to annotate parasite genomes. 

I employed my computational methods to build reference genomes to describe novel parasite species to:

  • Analyse and publish genomes of the humans infecting malaria parasites Plasmodium malariae and P. ovale and present insights into malaria parasite evolution [17]. 

  • Collaborate on the Laverania phylogeny unveiled insights into the evolution of the novel species and how they adapted to infect humans[18]. This was a collaboration with Montpellier and Gabon.

  • Generated many parasite reference genomes[19-25].

  • Established pipelines for genotype-to-phenotype studies, investigating gametogenesis in malaria[26] and the mode-of-action of compounds for malaria[27] and Leishmania[28].

  • Generate methods to integrate omics data around PiggyBac insertion in malaria, an ongoing collaboration with the Adams Laboratory in Tampa[29-33].

  • Explore host-parasite interactions and analyse gene families in parasites[17, 22, 34, 35], especially the var genes family, which is responsible for virulence of malaria[25, 36-39],[40-45].

Professor at the University of Glasgow

In 2017, I continued my research at the University of Glasgow, first as a Senior Lecturer in Bioinformatics for Immunology.  In 2022, I  became Professor of Computational Biology. In Glasgow, I complemented my research portfolio with single-cell transcriptomics sequencing (scRNA-Seq) and its applications in microbiology and immunology. My team developed tools for discovering cell-cell interactions[11], to improve the scRNA-Seq process[46] and predicting patient outcomes[47]. I established the required computational infrastructure to facilitate this research, including HPC hardware, web servers, and personnel, including setting up a bioinformatics core facility. I introduced the 10X Chromium single-cell technology and built bioinformatics pipelines for data analysis, fostering ongoing collaborations in immunology and host-parasite interactions. I established a webserver (http://cellatlas.mvls.gla.ac.uk/) to host single-cell atlas data, expanding it for greater resilience and maintenance with recent funding, currently in submission[48].

My expertise in bioinformatics and omics allowed the funding of several collaborative research projects, for example: 

  • Exploring the niches of malaria parasites in patients (Matt Marti, Universities of Zurich and Glasgow, Swiss National Science Foundation and MRC-FAPESP) 

  • Understanding the brain blood barrier breakdown in severe malaria (Chris Moxon, University of Glasgow (UoG)/College of Medicine Blantyre, MRC funding)

  • Generating the first Trypanosoma cruzi cell atlas (Manu DeRycker, Wellcome Trust Dundee, MRC Funding)

  • Exploring the adaptive role of genomic instability in Trypanosoma cruzi (Martin Llewellyn, UoG, MRC funding, several partners in South America)

  • Studying the immune response to malaria in CHMI from Kenya (James Brewer, UoG/ Francis Udungu KEMRI, Kenya)

  • Establishing single-cell and spatial biology in the bone marrow niche from fractures (Bismark Dinko, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana)

  • Understand immunity to COVID infections in B-cells (Peter Kojo Quashie, West African Centre for Cell Biology of Infectious Pathogens (WACCBIP), Ghana).

In September 2025, he took on the leadership of the Data Science Centre at the Bernhard Nocht Institute of Tropical Medicine, alongside a W3 professorship in Computational Pathogenomics at the University of Hamburg (Jülicher Model). I have assembled a powerful team, so please stay tuned for our impact. Our current work schemes are:

·  Pathogen evolution and immune evasion

We investigate the evolution of gene families that enable pathogens to evade or manipulate the host immune system. This includes generating and annotating genomes across diverse pathogens, as well as modelling complex gene families such as var genes in malaria.

·  Single-cell and spatial host–pathogen interactions
We integrate single-cell transcriptomics and spatial omics data to study how pathogens interact with host cells, and how the immune system responds. A particular focus is on understanding variability in immune responses across individuals.

·  Computational methods and tools 

We develop and apply computational approaches—including machine learning and modern AI methods—to support these research areas and to empower our collaborators.

 

 

It is important to me that collaborations extend to Africa to ensure that my research matters. I collaborate with Ghana, Kenya, and Malawi, mentor several scientists, and bring bioinformatics and genomic know-how. I have helped many groups to sequence and analyse their clinical malaria genomes[10, 17, 21]. My involvement as an honorary lecturer at the University of Ghana contributed to research on patient stratification for malaria outcomes[47] and the establishment of single-cell RNA-Seq analysis from malaria patients in Ghana[49]. 

I would like to note that my main focus are parasites. However, all my computational methods are easily applicable to other species complexes. Moreover, I collaborated on tuberculosis[4, 39, 50] or viruses[6, 51],[52]. I also participate in the IMID-Bio-UK consortium, focusing on immune-mediated inflammatory diseases (IMID) in the UK, where I lead data curation and analysis with Prof Michael Barnes (Queen Mary University of London). Our work involves 30,000 patients across eleven cohorts, addressing disease prediction, patient stratification, multi-omics integration, and AI development. To process this data type, the curation of clinical datasets is essential. This knowledge helped me work with data from malaria patients and use ML to classify outcomes[47]. At the same time, I provide extensive bioinformatics support to colleagues at the UoG who work on immunological datasets[53, 54] and rheumatoid arthritis[55, 56]. My long-term interest is finding similarities between non-communicable diseases and infectious diseases to enable drug repurposing. Therefore, I maintain several industry collaborations with Mariola Kurowska-Stolarska or Carl Goodyear to build a network that allows me to propose compounds for drug repurposing.

 

 

My overall goal is to improve the treatment of pathogens in the Global South. Therefore, I will use genomic and computational methods to explore pathogen diversity and evolution and decipher host-parasite interactions to repurpose drugs.

 

 

References

 

1.         Traut W, Sahara K, Otto TD, Marec F. Molecular differentiation of sex chromosomes probed by comparative genomic hybridization. Chromosoma. 1999;108(3):173-80. PubMed PMID: 10398846.

2.         Otto T, Meyer-Baese A, Hurdal M, Sunmers D, Wismuller A, Auer D, editors. Model-free functional MRI analysis using transformation-based methods. Independent Component Analyses, Wavelets, and Neural Networks; 2003 2003/4/1.

3.         Otto T, Meyer-Baese A, Hurdal M, Sunmers D, Auer D, Wismuller A, editors. Model-free functional MRI analysis using cluster-based methods. International Society for Optics and Photonics; 2003: International Society for Optics and Photonics.

4.         Gomes LH, Otto TD, Vasconcellos EA, Ferrao PM, Maia RM, Moreira AS, et al. Genome sequence of Mycobacterium bovis BCG Moreau, the Brazilian vaccine strain against tuberculosis. Journal of bacteriology. 2011;193(19):5600-1. doi: 10.1128/JB.05827-11. PubMed PMID: 21914899; PubMed Central PMCID: PMCPMC3187452.

5.         Otto TD, Catanho MP, Degrave WM, de Miranda AB. The PDTIS bioinformatics platform: from sequence to function. Revista Eletrônica de Comunicação, Informação e Inovação em Saúde. 2007;1(2). doi: 10.3395/reciis.v1i2.920.

6.         Hunt M, Gall A, Ong SH, Brener J, Ferns B, Goulder P, et al. IVA: accurate de novo assembly of RNA virus genomes. Bioinformatics. 2015;31(14):2374-6. doi: 10.1093/bioinformatics/btv120. PubMed PMID: 25725497; PubMed Central PMCID: PMCPMC4495290.

7.         Hunt M, Kikuchi T, Sanders M, Newbold C, Berriman M, Otto TD. REAPR: a universal tool for genome assembly evaluation. Genome Biol. 2013;14(5):R47. doi: 10.1186/gb-2013-14-5-r47. PubMed PMID: 23710727; PubMed Central PMCID: PMCPMC3798757.

8.         Swain MT, Tsai IJ, Assefa SA, Newbold C, Berriman M, Otto TD. A post-assembly genome-improvement toolkit (PAGIT) to obtain annotated genomes. Nature protocols. 2012;7(7):1260-84. Epub 2012/06/09. doi: 10.1038/nprot.2012.068. PubMed PMID: 22678431.

9.         Hunt M, Silva ND, Otto TD, Parkhill J, Keane JA, Harris SR. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biol. 2015;16:294. doi: 10.1186/s13059-015-0849-0. PubMed PMID: 26714481; PubMed Central PMCID: PMCPMC4699355.

10.       Ruiz JL, Reimering S, Escobar-Prieto JD, Brancucci NMB, Echeverry DF, Abdi AI, et al. From contigs towards chromosomes: automatic improvement of long read assemblies (ILRA). Briefings in bioinformatics. 2023;24(4). Epub 2023/07/05. doi: 10.1093/bib/bbad248. PubMed PMID: 37406192; PubMed Central PMCID: PMCPMC10359078.

11.       Pancheva A, Wheadon H, Rogers S, Otto TD. Using topic modeling to detect cellular crosstalk in scRNA-seq. PLoS Comput Biol. 2022;18(4):e1009975. Epub 2022/04/09. doi: 10.1371/journal.pcbi.1009975. PubMed PMID: 35395014; PubMed Central PMCID: PMCPMC9064087.

12.       Quail MA, Otto TD, Gu Y, Harris SR, Skelly TF, McQuillan JA, et al. Optimal enzymes for amplifying sequencing libraries.  Nat Methods. 9. United States2012. p. 10-1.

13.       Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent. BMC genomics. 2012;13:341. Epub 2012/07/26. doi: 10.1186/1471-2164-13-341. PubMed PMID: 22827831; PubMed Central PMCID: PMCPmc3431227.

14.       Carver T, Bohme U, Otto TD, Parkhill J, Berriman M. BamView: viewing mapped read alignment data in the context of the reference sequence. Bioinformatics. 2010;26(5):676-7. doi: 10.1093/bioinformatics/btq010. PubMed PMID: 20071372; PubMed Central PMCID: PMCPMC2828118.

15.       Steinbiss S, Silva-Franco F, Brunk B, Foth B, Hertz-Fowler C, Berriman M, et al. Companion: a web server for annotation and analysis of parasite genomes. Nucleic Acids Res. 2016;44(W1):W29-34. doi: 10.1093/nar/gkw292. PubMed PMID: 27105845; PubMed Central PMCID: PMCPMC4987884.

16.       Haese-Hill W, Crouch K, Otto TD. Annotation and visualization of parasite, fungi and arthropod genomes with Companion. Nucleic Acids Res. 2024;52(W1):W39-W44. Epub 2024/05/16. doi: 10.1093/nar/gkae378. PubMed PMID: 38752499; PubMed Central PMCID: PMCPMC11223846.

17.       Rutledge GG, Bohme U, Sanders M, Reid AJ, Cotton JA, Maiga-Ascofare O, et al. Plasmodium malariae and P. ovale genomes provide insights into malaria parasite evolution. Nature. 2017;542(7639):101-4. doi: 10.1038/nature21038. PubMed PMID: 28117441; PubMed Central PMCID: PMCPMC5326575.

18.       Otto TD, Gilabert A, Crellen T, Bohme U, Arnathau C, Sanders M, et al. Genomes of all known members of a Plasmodium subgenus reveal paths to virulent human malaria. Nat Microbiol. 2018;3(6):687-97. doi: 10.1038/s41564-018-0162-2. PubMed PMID: 29784978; PubMed Central PMCID: PMCPMC5985962.

19.       Bohme U, Otto TD, Sanders M, Newbold CI, Berriman M. Progression of the canonical reference malaria parasite genome from 2002-2019. Wellcome Open Res. 2019;4:58. Epub 2019/06/06. doi: 10.12688/wellcomeopenres.15194.2. PubMed PMID: 31080894; PubMed Central PMCID: PMCPMC6484455.2.

20.       Muller LSM, Cosentino RO, Forstner KU, Guizetti J, Wedel C, Kaplan N, et al. Genome organization and DNA accessibility control antigenic variation in trypanosomes. Nature. 2018;563(7729):121-5. doi: 10.1038/s41586-018-0619-8. PubMed PMID: 30333624.

21.       Otto TD, Bohme U, Sanders M, Reid A, Bruske EI, Duffy CW, et al. Long read assemblies of geographically dispersed Plasmodium falciparum isolates reveal highly structured subtelomeres. Wellcome Open Res. 2018;3:52. doi: 10.12688/wellcomeopenres.14571.1. PubMed PMID: 29862326; PubMed Central PMCID: PMCPMC5964635.

22.       Bohme U, Otto TD, Cotton JA, Steinbiss S, Sanders M, Oyola SO, et al. Complete avian malaria parasite genomes reveal features associated with lineage-specific evolution in birds and mammals. Genome Res. 2018;28(4):547-60. doi: 10.1101/gr.218123.116. PubMed PMID: 29500236; PubMed Central PMCID: PMCPMC5880244.

23.       Pasini EM, Bohme U, Rutledge GG, Voorberg-Van der Wel A, Sanders M, Berriman M, et al. An improved Plasmodium cynomolgi genome assembly reveals an unexpected methyltransferase gene expansion. Wellcome Open Res. 2017;2:42. doi: 10.12688/wellcomeopenres.11864.1. PubMed PMID: 28748222; PubMed Central PMCID: PMCPMC5500898.

24.       Auburn S, Bohme U, Steinbiss S, Trimarsanto H, Hostetler J, Sanders M, et al. A new Plasmodium vivax reference sequence with improved assembly of the subtelomeres reveals an abundance of pir genes. Wellcome Open Res. 2016;1:4. doi: 10.12688/wellcomeopenres.9876.1. PubMed PMID: 28008421; PubMed Central PMCID: PMCPMC5172418.

25.       Jackson AP, Otto TD, Aslett M, Armstrong SD, Bringaud F, Schlacht A, et al. Kinetoplastid Phylogenomics Reveals the Evolutionary Innovations Associated with the Origins of Parasitism. Curr Biol. 2016;26(2):161-72. doi: 10.1016/j.cub.2015.11.055. PubMed PMID: 26725202; PubMed Central PMCID: PMCPMC4728078.

26.       Sinha A, Hughes KR, Modrzynska KK, Otto TD, Pfander C, Dickens NJ, et al. A cascade of DNA-binding proteins for sexual commitment and development in Plasmodium. Nature. 2014;507(7491):253-7. doi: 10.1038/nature12970. PubMed PMID: 24572359; PubMed Central PMCID: PMC4105895.

27.       Baragana B, Hallyburton I, Lee MC, Norcross NR, Grimaldi R, Otto TD, et al. A novel multiple-stage antimalarial agent that inhibits protein synthesis. Nature. 2015;522(7556):315-20. doi: 10.1038/nature14451. PubMed PMID: 26085270; PubMed Central PMCID: PMC4700930.

28.       Wyllie S, Thomas M, Patterson S, Crouch S, De Rycker M, Lowe R, et al. Cyclin-dependent kinase 12 is a drug target for visceral leishmaniasis. Nature. 2018;560(7717):192-7. doi: 10.1038/s41586-018-0356-z. PubMed PMID: 30046105.

29.       Simmons C, Gibbons J, Wang C, Pires CV, Zhang M, Siddiqui F, et al. A novel Modulator of Ring Stage Translation (MRST) gene alters artemisinin sensitivity in Plasmodium falciparum. mSphere. 2023;8(4):e0015223. Epub 2023/05/23. doi: 10.1128/msphere.00152-23. PubMed PMID: 37219373; PubMed Central PMCID: PMCPMC10449512.

30.       Chawla J, Goldowitz I, Oberstaller J, Zhang M, Pires CV, Navarro F, et al. Phenotypic Screens Identify Genetic Factors Associated with Gametocyte Development in the Human Malaria Parasite Plasmodium falciparum. Microbiol Spectr. 2023;11(3):e0416422. Epub 2023/05/08. doi: 10.1128/spectrum.04164-22. PubMed PMID: 37154686; PubMed Central PMCID: PMCPMC10269797.

31.       Pires CV, Oberstaller J, Wang C, Casandra D, Zhang M, Chawla J, et al. Chemogenomic Profiling of a Plasmodium falciparum Transposon Mutant Library Reveals Shared Effects of Dihydroartemisinin and Bortezomib on Lipid Metabolism and Exported Proteins. Microbiol Spectr. 2023;11(3):e0501422. Epub 2023/04/18. doi: 10.1128/spectrum.05014-22. PubMed PMID: 37067430; PubMed Central PMCID: PMCPMC10269874.

32.       Zhang M, Wang C, Otto TD, Oberstaller J, Liao X, Adapa SR, et al. Uncovering the essential genes of the human malaria parasite Plasmodium falciparum by saturation mutagenesis. Science. 2018;360(6388). doi: 10.1126/science.aap7847. PubMed PMID: 29724925.

33.       Bronner IF, Otto TD, Zhang M, Udenze K, Wang C, Quail MA, et al. Quantitative insertion-site sequencing (QIseq) for high throughput phenotyping of transposon mutants. Genome Res. 2016;26(7):980-9. doi: 10.1101/gr.200279.115. PubMed PMID: 27197223; PubMed Central PMCID: PMCPMC4937560.

34.       Reid AJ, Blake DP, Ansari HR, Billington K, Browne HP, Bryant J, et al. Genomic analysis of the causative agents of coccidiosis in domestic chickens. Genome Res. 2014;24(10):1676-85. doi: 10.1101/gr.168955.113. PubMed PMID: 25015382; PubMed Central PMCID: PMCPMC4199364.

35.       Otto TD, Bohme U, Jackson AP, Hunt M, Franke-Fayard B, Hoeijmakers WA, et al. A comprehensive evaluation of rodent malaria parasite genomes and gene expression. BMC Biol. 2014;12:86. doi: 10.1186/s12915-014-0086-0. PubMed PMID: 25359557; PubMed Central PMCID: PMCPMC4242472.

36.       Woo YH, Ansari H, Otto TD, Klinger CM, Kolisko M, Michalek J, et al. Chromerid genomes reveal the evolutionary path from photosynthetic algae to obligate intracellular parasites. Elife. 2015;4:e06974. doi: 10.7554/eLife.06974. PubMed PMID: 26175406; PubMed Central PMCID: PMCPMC4501334.

37.       Flegontov P, Michalek J, Janouskovec J, Lai DH, Jirku M, Hajduskova E, et al. Divergent mitochondrial respiratory chains in phototrophic relatives of apicomplexan parasites. Molecular biology and evolution. 2015;32(5):1115-31. doi: 10.1093/molbev/msv021. PubMed PMID: 25660376.

38.       Jackson AP, Otto TD, Darby A, Ramaprasad A, Xia D, Echaide IE, et al. The evolutionary dynamics of variant antigen genes in Babesia reveal a history of genomic innovation underlying host-parasite interaction. Nucleic Acids Res. 2014;42(11):7113-31. doi: 10.1093/nar/gku322. PubMed PMID: 24799432; PubMed Central PMCID: PMC4066756.

39.       Abdallah AM, Hill-Cawthorne GA, Otto TD, Coll F, Guerra-Assuncao JA, Gao G, et al. Genomic expression catalogue of a global collection of BCG vaccine strains show evidence for highly diverged metabolic and cell-wall adaptations. Sci Rep. 2015;5:15443. doi: 10.1038/srep15443. PubMed PMID: 26487098; PubMed Central PMCID: PMCPMC4614345.

40.       Otto TD, Assefa SA, Böhme U, Sanders M, Kwiatkowski D, Pf3k consortium, et al. Evolutionary analysis of the most polymorphic gene family in falciparum malaria. Wellcome Open Res. 2019;4(193).

41.       Andrade CM, Fleckenstein H, Thomson-Luque R, Doumbo S, Lima NF, Anderson C, et al. Increased circulation time of Plasmodium falciparum underlies persistent asymptomatic infection in the dry season. Nat Med. 2020. Epub 2020/10/28. doi: 10.1038/s41591-020-1084-0. PubMed PMID: 33106664.

42.       Wichers JS, Tonkin-Hill G, Thye T, Krumkamp R, Kreuels B, Strauss J, et al. Common virulence gene expression in adult first-time infected malaria patients and severe cases. Elife. 2021;10. Epub 2021/04/29. doi: 10.7554/eLife.69040. PubMed PMID: 33908865; PubMed Central PMCID: PMCPMC8102065.

43.       Mackenzie G, Jensen RW, Lavstsen T, Otto TD. Varia: a tool for prediction, analysis and visualisation of variable genes. BMC Bioinformatics. 2022;23(1):52. Epub 2022/01/26. doi: 10.1186/s12859-022-04573-6. PubMed PMID: 35073845; PubMed Central PMCID: PMCPMC8785495.

44.       Andradi-Brown C, Wichers-Misterek JS, von Thien H, Hoppner YD, Scholz JAM, Hansson H, et al. A novel computational pipeline for var gene expression augments the discovery of changes in the Plasmodium falciparum transcriptome during transition from in vivo to short-term in vitro culture. Elife. 2024;12. Epub 2024/01/25. doi: 10.7554/eLife.87726. PubMed PMID: 38270586; PubMed Central PMCID: PMCPMC10945709.

45.       Wichers-Misterek JS, Krumkamp R, Held J, von Thien H, Wittmann I, Hoppner YD, et al. The exception that proves the rule: Virulence gene expression at the onset of Plasmodium falciparum blood stage infections. PLoS Pathog. 2023;19(6):e1011468. Epub 2023/06/29. doi: 10.1371/journal.ppat.1011468. PubMed PMID: 37384799; PubMed Central PMCID: PMCPMC10337978.

46.       Haese-Hill W, Crouch K, Otto TD. peaks2utr: a robust Python tool for the annotation of 3' UTRs. Bioinformatics. 2023;39(3). Epub 2023/03/04. doi: 10.1093/bioinformatics/btad112. PubMed PMID: 36864613; PubMed Central PMCID: PMCPMC10008064.

47.       Morang'a CM, Amenga-Etego L, Bah SY, Appiah V, Amuzu DSY, Amoako N, et al. Machine learning approaches classify clinical malaria outcomes based on haematological parameters. BMC Med. 2020;18(1):375. Epub 2020/12/01. doi: 10.1186/s12916-020-01823-3. PubMed PMID: 33250058; PubMed Central PMCID: PMCPMC7702702.

48.       Agboraw E, Haese-Hill W, Hentzschel F, Briggs EM, Aghabi D, Heawood A, et al. paraCell: A novel software tool for the interactive analysis and visualization of standard and dual host-parasite single cell RNA-Seq data. bioRxiv. 2024:2024.08.29.610375. doi: 10.1101/2024.08.29.610375.

49.       Morang’a CM, Drake RS, Miao VN, Nyakoe NK, Amuzu DSY, Appiah V, et al. scRNA-Seq reveals elevated interferon responses and TNF-α signaling via NFkB in monocytes in children with clinical malaria. medRxiv. 2023:2023.06.02.23290878. doi: 10.1101/2023.06.02.23290878.

50.       Abdallah AM, Weerdenburg EM, Guan Q, Ummels R, Borggreve S, Adroub SA, et al. Integrated transcriptomic and proteomic analysis of pathogenic mycobacteria and their esx-1 mutants reveal secretion-dependent regulation of ESX-1 substrates and WhiB6 as a transcriptional regulator. PLoS One. 2019;14(1):e0211003. Epub 2019/01/24. doi: 10.1371/journal.pone.0211003. PubMed PMID: 30673778; PubMed Central PMCID: PMCPMC6343904.

51.       Nyirenda J, Hardy O, Filho JDS, Herder V, Attipa C, Ndovi C, et al. Spatially resolved single-cell atlas of the lung in fatal Covid19 in an African population reveals a distinct cellular signature and an interferon gamma dominated response. bioRxiv. 2023:2023.11.16.566964. doi: 10.1101/2023.11.16.566964.

52.       MacDonald L, Alivernini S, Tolusso B, Elmesmari A, Somma D, Perniola S, et al. COVID-19 and RA share an SPP1 myeloid pathway that drives PD-L1+ neutrophils and CD14+ monocytes. JCI Insight. 2021;6(13). Epub 2021/06/19. doi: 10.1172/jci.insight.147413. PubMed PMID: 34143756; PubMed Central PMCID: PMCPMC8328085.

53.       Gray JI, Al-Khabouri S, Morton F, Clambey ET, Gapin L, Matsuda JL, et al. Tolerance induction in memory CD4 T cells is partial and reversible. Immunology. 2021;162(1):68-83. Epub 2020/09/16. doi: 10.1111/imm.13263. PubMed PMID: 32931017; PubMed Central PMCID: PMCPMC7730012.

54.       McIntyre CL, Monin L, Rop JC, Otto TD, Goodyear CS, Hayday AC, et al. beta2 Integrins differentially regulate gammadelta T cell subset thymic development and peripheral maintenance. Proceedings of the National Academy of Sciences of the United States of America. 2020;117(36):22367-77. Epub 2020/08/28. doi: 10.1073/pnas.1921930117. PubMed PMID: 32848068; PubMed Central PMCID: PMCPMC7486781.

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Computational Infection Biology