Manal Kalkatawi obtained her PhD Degree in Computer Science with specialization in Bioinformatics. She has an extensive experience in many disciplines related to bioinformatics, but mainly in genomic signals recognition and genome assembly and annotation. She is a hard-working and self-motivated team player with strong interpersonal skills and positive work ethics. Her work has been published in several high impact bioinformatics journals.

Research Interests

Manal research interests are in Bioinformatics more specifically in genome analysis, genomic signals recognition and genomic sequences data extraction and processing. During her PhD, she has been involved in designing methods and supporting systems using machine learning and deep learning algorithms to be applied to genomic signals recognition, genome annotation, and genome assembly.


  • Languages: C/C++/C#, Python, Perl, R, Objective-C, Java, JavaScript, HTML and PHP
  • Software: Theano, Keras (both on CPU and GPU), hyperas, hyperopt, scikit-learn, MATLAB, SQL Database Server, Oracle Database, Altova AML, Sybase and Google Charts
  • Platforms: Linux and MacOS
  • Bioinformatics Skills
  • Machine Learning Models: Neural Networks, Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks and Auto-Encoders
  • Bioinformatics tools: GMAP, Velvet-SC, IDBA-UD, SPAdes, CLC, OPERA, CONTIGuator, MUMmer, QUAST, BWA, SMALT, BG7, RAST, IMG, MEGAN, Artemis, ACT, Blast, and Unipro UGENE
    Data Extraction: Extract genomic signals and regions -TIS and PolyA- from different organisms, human, mouse, cow and fruit fly.

Courses and projects

Pathogen genomics, data mining, genomic signals recognition, drug repurposing, de novo assembly, genome annotation, and chemical interaction prediction.