Contact Dr Faisal Rezwan

Areas of expertise

  • Applied Informatics
  • Bioinformatics
  • Computing, Simulation & Modelling
  • Soil Resources


Dr. Rezwan is the Lecturer in Bioinformatics at School of Water, Energy and Environment with over 15 years of experience in bioinformatics and computational methods. He graduated with a Ph.D. in Bioinformatics from the University of Hertfordshire (UK) in 2012 and joined the University of Southampton as a research fellow in bioinformatics. His initial research involved data mining big scale genetic and epigenetic data to identify novel genes and gene regulatory networks. Currently, Dr. Rezwan’s research mainly focuses on basic and applied problems in bioinformatics using machine learning and data integration. He has extensive experience of genome-wide transcriptomic, genetic, and epigenetic analysis and next generation sequencing data analysis. He has track record of developing bioinformatics tools and pipelines, including: GEO import, Single Sample DNA Methylation Data Analysis.

Currently, he is leading a metagenomics project revealing the law of natural succession of soil bacteria community in mining subsidence area. He is the PI in a machine learning project titled "Predictive agricultural models for improving food security in Turkey". In addition, he is involved (as the Co-I) in a NERC funded project titled Restoring Resilient Ecosystems (RestREco)(NE/V006444/1). Dr. Rezwan was awarded the prestigious Turing fellowship and his research at Turing included a pilot project applying deep learning approach on patient data from Electronic Health Record Systems (EHRS).

Dr. Rezwan has over 12 years of experience in teaching and is actively involved in teaching. He is currently leading two modules (Introduction to Bioinformatics using Python and Application of Bioinformatics in Epigenetics, Proteomics and Metagenomics) and teaching in the MSc in Applied Bioinformatics. He is also supervising five doctoral students.

He is also an active member of the Bioinformatics Group in AgriFood. He is the member of FoodBioSystems Doctoral Training Program Training sub-committee. He is a Fellow of Higher Education Academy (FHEA) and has obtained the Postgraduate Certificate in Academic Practice.

Current activities

Dr. Rezwan’s research mainly focuses on basic and applied problems in bioinformatics using data integration and data mining to analyse genetic, epigenetic, and metagenomic data. His interest also involves in pattern recognition and machine learning for analysing, understanding and improving computational prediction for identifying genes, their regulatory regions and other biological features.

16S amplicon analyses
Natural succession of soil bacteria community in mining subsidence area: The aim of the project is to reveal the law of natural succession of soil bacteria community in mining subsidence area. In this project, soil sampling was performed at Daliuta Mine in the Shenfu-Dongsheng coal field (China) in the subsidence area (5years, 10years, 15years and 20years) and in an adjacent unexploited area and further the law and driving factors of natural succession of soil microbial community in mining subsidence area studied using 16S rRNA sequence analysis.

Quantification of biodiversity and community complexity/RestREco: The main objective of the RestREco project is to determine the most efficient approaches to restore complex, multi-functional and resilient ecosystems, thus securing net environmental gain. This part of the project involved multi-trophic assessment of above and below ground diversity aims to quantify net community complexity. It assesses soil microbial community structure using 16S rRNA gene amplicon sequencing where ~400 16S libraries with c. 30,000 raw 150bp paired-end reads per sample will be sequenced using the Illumina NovoSeq platform.

Application of machine learning
Predictive agricultural models for improving food security in Turkey: Climate change is known to have an impact on agricultural production due to temperature increases and changes in precipitation levels and patterns.  In Turkey, where current climate conditions are ideal for wheat production, a temperature increase of 1 degree Celsius is expected to result in a decrease of 10-30% in wheat yield in the area. This project explores and develops preliminary models to predict future wheat yield based on meteorological data (temperature, light, rain and wind) and understand its use in policy intervention and decision making to improve sustainability and climate change adaptation capacity of agriculture to improve food security in Turkey.

Reducing potato losses by controlling black dot disease: The aims of this project are to use sophisticated photonics and associated machine learning and data integration method across the pre and postharvest continuum to create a predictive model for black dot incidence and severity during storage, and evaluate how resilience can be improved in response to different climate change scenarios. Further, metabolomic data is included to further improve the prediction models.

Epigenetic and transcriptomic analyses
Evaluating the epigenetic and transcriptomic adaptations to climate change in oilseed rape (Brassica napus):  The aim of this project is to provide a systems-level molecular and metabolic understanding of the impact of heat stress on yield and seed development of B. napus for UK-specific varieties. It will identify site-specific modification of DNA methylation in the B. napus genome susceptible to heat stress and perform transcriptome profiling using RNA-Seq, to identify key differentially expressed genes as a result of heat stress. The project is a collaboration between Cranfield University and University of Reading.


  • NERC
  • University of Reading
  • Bilecik Seyh Edebali University, Turkey
  • China University of Mining and Technology, China
  • University of Southampton, UK
  • University of Memphis, USA
  • University of Bergen, Norway


Articles In Journals