Happy to announce that my clust algorithm for optimised, rapid, and automatic clustering of gene expression datasets is now published in Genome Biology.
Basel Abu-Jamous and Steven Kelly (2018) Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biology 19: 172; doi: https://doi.org/10.1186/s13059-018-1536-8.
For details, read the paper or visit this page.
I was invited by the Computer Engineering Department at the University of Jordan (Amman, Jordan) to give a seminar on big data and bioinformatics on Thursday the 26th of October 2017. A group of interested students and staff members attended the seminar which lasted for more than 3 hours.
The first part of the seminar, as requested by the hosts, was about my journey from the University of Jordan as an undergraduate student (2006-2010) to the University of Oxford. This was to motivate the students and direct them to one of the paths of success.
The second part of the seminar was about the era of big data research as the fourth paradigm of science and the main domains of big data research.
The rest of the seminar was about bioinformatics as one large area of big data research. A good introduction to molecular biology was delivered followed by listing some types of big biological data that are subject to bioinformatic analysis.
I am very happy to announce that Clust is now freely available as a Package that can be run by single-line command.
Clust is an automated computational framework that performs optimised consensus clustering over one or more heterogeneous (or homogeneous) datasets.
Clust, and its description, are freely available at this GitHub page:
Feedback is much appreciated, and …
I have moved to the Department of Plant Sciences at the University of Oxford on Friday 01/07/2016 to work as a Post-Doctorate Research Assistant with Dr Steven Kelly in his laboratory.
I will be part of the C4 Rice Project which aims to improve photosynthetic efficiency in rice and thus to enhance crop yields. As such, the C4 Rice Project is one of the scientific ‘Grand Challenges’ of the 21st Century, involving the coordinated efforts of researchers from 12 institutions in 8 countries.
I will be involved in the analysis of high-throughput sequence data and in the development of novel data analysis tools. These analyses and tools will be used to help guide the engineering of C4 photosynthesis into rice.I will use these datasets and tools to quantitatively assess the changes in gene expression that have occurred in transgenic rice lines that had been generated as part of the C4 rice project. These transgenic rice lines include those that have been engineered to have altered photosynthetic properties, to contain parts of the C4 cycle, or to have altered leaf venation patterns. My role is to determine what effect (if any) the expression of the transgene(s) has had on global patterns of gene expression. These global changes will be further analysed to identify the key regulators that have facilitated these changes in gene expression. This data will be integrated with current models of C4 photosynthesis to help guide subsequent engineering steps to introduce C4 photosynthesis into rice.
I am the successful recipient of the 2015 (Winter) Dean’s Prize for Innovation and Impact in Doctoral Research in this round within the Department of Electronic and Computer Engineering (ECE) at Brunel University London. This prestigious prize is given to a single PhD graduate every round within each department, and it indicates the distinction of the recipient at the level of his/her class of PhD graduates.
My PhD Thesis is now available at the EURASIP (European Association for Signal Processing) Library of Ph.D. Theses
Link to Thesis
and at the Brunel University Research Archive (BURA)
Link to Thesis