Our primary research areas can be broadly classified into two workstreams:
- Use of integration approaches to uncover genotype–phenotype interactions:
We implement bioinformatics pipelines able to integrate different data types to study the interplay between genomics and transcriptomics data with phenotypic data. Our approach can be applied to different types of phenotypes, making it a valuable and versatile strategy with great potential for future research.
- Use of single-cell RNA sequencing to study developmental and stem cell biology:
We have a significant expertise and experience in the analysis of single cell data. We combine cutting-edge techniques in collaboration with dry labs and apply these tools for further understanding of development and diseases.
Our lab is involved in a great network of collaborations to develop multidisciplinary approaches to research efforts, working with faculty members within King’s and other research institutes.
Publications
Awards
- Teaching grant UKRI - Enabling the big data revolution through skills training
- MRC Program Grant - Using iPSC variation to define HIV-1 regulatory networks
Activities
Innovation Scholars Programme: Big Data Skills Training
Funded by UKRI, Kings has launched a new flexible and modular training programme for health care professionals, researchers and industry partners. This training is to upskill all in their ability to use and apply big data in their work and research. Lead of Omics Pillar
Workshops for MRC-dtp and Health Data Science
In the last 3 years, I developed 6 workshops run through Health Data Science DTC, MRC-DTP and Wellcome Trust PhD programme. The workshops are linked in a way to promote skills development pathways, but they can be taken separately and in a sparse order. The current workshops are: - Ensembl: an online resource to access published datasets and basic biological data (1 day course): - Using R for data manipulation – data integration – data visualization (3 days course –twice a year) - Using R for statistical analyses (1 day course–twice a year) - Basics of Unix (1 day course–twice a year) - RNA-seq & Chip-seq data analysis (3 days course) - Single cell RNA-seq data analysis (3 days course)
Other Staff
PhD Students:
- Mahedah Rehman
- Inchul Cho
- Jana Obajdin
Teaching Assistant:
- Daria Belokhvostova
Publications
Awards
- Teaching grant UKRI - Enabling the big data revolution through skills training
- MRC Program Grant - Using iPSC variation to define HIV-1 regulatory networks
Activities
Innovation Scholars Programme: Big Data Skills Training
Funded by UKRI, Kings has launched a new flexible and modular training programme for health care professionals, researchers and industry partners. This training is to upskill all in their ability to use and apply big data in their work and research. Lead of Omics Pillar
Workshops for MRC-dtp and Health Data Science
In the last 3 years, I developed 6 workshops run through Health Data Science DTC, MRC-DTP and Wellcome Trust PhD programme. The workshops are linked in a way to promote skills development pathways, but they can be taken separately and in a sparse order. The current workshops are: - Ensembl: an online resource to access published datasets and basic biological data (1 day course): - Using R for data manipulation – data integration – data visualization (3 days course –twice a year) - Using R for statistical analyses (1 day course–twice a year) - Basics of Unix (1 day course–twice a year) - RNA-seq & Chip-seq data analysis (3 days course) - Single cell RNA-seq data analysis (3 days course)
Other Staff
PhD Students:
- Mahedah Rehman
- Inchul Cho
- Jana Obajdin
Teaching Assistant:
- Daria Belokhvostova
Our Partners
In collaboration with scientists of Genomix4Life, we are studying cell-free DNA and other biomarkers released by tumour cells in circulating blood (liquid biopsy), to investigate the genomic aberrations of primary tumours, to identify key factors linked to disease progression and to screen therapy effectiveness. A bioinformatic pipeline able to analyse and integrate the data will be used to create a better disease classification model that could be applied for other cancer types and other diseases.
![VIGILANTE_Alessandra[4214186]](/newimages/person-profile/2021/vigilante-alessandra4214186.x2b1584e7.jpeg?w=160&h=160&f=webp)