2022 - Research.com Best Female Scientist Award
2020 - Fellow of the Royal Society, United Kingdom
2016 - Fellow of the International Society for Computational Biology
Fellow of The Academy of Medical Sciences, United Kingdom
Member of the European Molecular Biology Organization (EMBO)
Her primary scientific interests are in Computational biology, Genetics, Cell, Single-cell analysis and Transcriptome. Her Computational biology study combines topics in areas such as RNA, Cell type, Bioinformatics and Gene expression profiling. Her Cell research incorporates elements of Cancer, Cell signaling and Parenchyma.
Sarah A. Teichmann has researched Single-cell analysis in several fields, including Renal cell carcinoma, Kidney metabolism and Cell biology. Her Cell biology research is multidisciplinary, incorporating perspectives in Acquired immune system, Immune system, Antigen, Stromal cell and T-cell receptor. Her Transcriptome study incorporates themes from Cancer research, T lymphocyte, T cell, Wilms' tumor and Transcription.
Sarah A. Teichmann mainly focuses on Cell biology, Computational biology, Cell, Immune system and Genetics. Her Cell biology research incorporates themes from T cell, Transcriptome, Transcription factor and Single-cell analysis. The concepts of her Transcriptome study are interwoven with issues in Phenotype, Cancer research, Mesenchymal stem cell and Cellular differentiation.
Her Computational biology research integrates issues from RNA-Seq, Gene expression, Bioinformatics, Genomics and Protein structure. Specifically, her work in Cell is concerned with the study of Cell type. Her studies deal with areas such as Inflammation and Stromal cell as well as Immune system.
Her main research concerns Cell biology, Cell, Immune system, Transcriptome and Computational biology. Her Cell biology study combines topics from a wide range of disciplines, such as T cell and Cell type. Sarah A. Teichmann has included themes like RNA, Cancer research, Homeostasis and T-cell receptor in her Cell study.
She interconnects Inflammation, Neuroscience and Single-cell analysis in the investigation of issues within Immune system. While the research belongs to areas of Transcriptome, Sarah A. Teichmann spends her time largely on the problem of B cell, intersecting her research to questions surrounding Affinity maturation. The various areas that Sarah A. Teichmann examines in her Computational biology study include RNA-Seq, Genome, Gene, DNA sequencing and Profiling.
Sarah A. Teichmann mostly deals with Cell biology, Immune system, Transcriptome, Cell and Computational biology. Her Cell biology research includes themes of T cell, Single cell sequencing, B cell and Transcription factor. Her studies in Immune system integrate themes in fields like Megakaryocyte, Stromal cell, Neuroscience and Single-cell analysis.
Her work carried out in the field of Transcriptome brings together such families of science as Haematopoiesis, Stem cell, Endothelium, breakpoint cluster region and Cell type. Within one scientific family, Sarah A. Teichmann focuses on topics pertaining to RNA under Cell, and may sometimes address concerns connected to Gene expression, Feature and Cluster analysis. The Computational biology study combines topics in areas such as Single cell transcriptomics and Genome, Gene.
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The Transcriptional Landscape of the Mammalian Genome
P. Carninci;T. Kasukawa;S. Katayama;J. Gough.
Evolution of genes and genomes on the Drosophila phylogeny.
Andrew G. Clark;Michael B. Eisen;Michael B. Eisen;Douglas R. Smith;Casey M. Bergman.
A census of human transcription factors: function, expression and evolution
Juan M. Vaquerizas;Sarah K. Kummerfeld;Sarah K. Kummerfeld;Sarah A. Teichmann;Nicholas M. Luscombe;Nicholas M. Luscombe.
Nature Reviews Genetics (2009)
Genomic analysis of regulatory network dynamics reveals large topological changes
Nicholas M. Luscombe;M. Madan Babu;Haiyuan Yu;Michael Snyder.
Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer.
Christine Feig;James O. Jones;Matthew Kraman;Richard J. B. Wells.
Proceedings of the National Academy of Sciences of the United States of America (2013)
Structure and evolution of transcriptional regulatory networks.
M Madan Babu;Nicholas M Luscombe;L Aravind;Mark Gerstein.
Current Opinion in Structural Biology (2004)
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells
Florian Buettner;Kedar N Natarajan;Kedar N Natarajan;F Paolo Casale;Valentina Proserpio;Valentina Proserpio.
Nature Biotechnology (2015)
Computational and analytical challenges in single-cell transcriptomics
Oliver Stegle;Sarah A. Teichmann;John C. Marioni.
Nature Reviews Genetics (2015)
Accounting for technical noise in single-cell RNA-seq experiments
Philip Brennecke;Simon Anders;Jong Kyoung Kim;Aleksandra A Kołodziejczyk;Aleksandra A Kołodziejczyk.
Nature Methods (2013)
An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man
Timothy Ravasi;Harukazu Suzuki;Carlo Vittorio Cannistraci;Shintaro Katayama.
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