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Our active research labs & groups:
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Learn about working in Bioinformatics

Bioinformatics entails the development and application of statistical and algorithmic methods to biological data sets. New technologies are giving us unprecedented insights into the inner workings of living things, and reshaping our views of biodiversity. Driving this revolution are leaps in data-generating technologies such as DNA sequencing and protein structure analysis. While these technologies can transform our understanding of the living world, making sense of them is no trivial task, and we are continuously developing new methods that can be used to analyze ever-increasing data sets in increasingly precise ways.

Bioinformatics research in Computer Science encompasses the development of new algorithms and software, and application of tools to new types of data. Our labs combine trainees with backgrounds in disciplines including Computer Science, Biology, and Statistics, providing valuable cross-training experience and unique collaborative opportunities. We work closely with government agencies such as the Public Health Agency of Canada and the Department of Fisheries and Oceans to generate new insights in epidemiology and marine biodiversity from cutting-edge data sets. Our research areas include:

  • Algorithms to tackle new DNA-based approaches to biodiversity analysis;
  • Modeling and simulation of proteins with key roles in disease;
  • "Genomic epidemiology" tools to better track and analyze infectious-disease outbreaks;
  • New tools to investigate the structure, diversity, function, and changes in the human microbiome;
  • Algorithms that can scale up to tens of thousands of genomes;
  • Phylogenetic methods, and application of these methods to large "phylogenomic" data.

We work closely with our Algorithms colleagues to tackle data sets in new and innovative ways.

Faculty Members:

Program Outline

The program requirements are:

  • 12 credit hours of graduate-level courses, mutually agreed upon by the student and the supervisor.
  • Master’s thesis (CSCI 9000) 
  • Present two research seminars at two different venues.
  • The student may also need to audit undergraduate courses to develop necessary background in complementary disciplines such as statistics.

CBBI students may take a maximum of one directed study course as part of their program requirements.

The course requirements allow students to pursue some combination of a directed study course, specialized bioinformatics courses and courses that target the student’s area of specialization within bioinformatics such as Artificial Intelligence, Statistics, Biochemistry, or others. The types of venues for research seminars are not prescribed. Seminars can be given internally to the university and/or at external conferences or workshops. 

 

Courses

 

Courses

There is no prescribed core course content, and the focus is on developing core and complementary skills in the student that reflect their research and career goals. For example, a student with a background in computer science may wish to join the research group of a professor in Biology to apply their skills and knowledge to a particular problem.

Any researcher at pilipiliĀž»­ who has supervisory privileges and whose research includes bioinformatics can supervise students in the program. Many supervisors are affiliated with the Dalhousie Institute for Comparative Genomics (ICG), a network of over 30 researchers across five Faculties at pilipiliĀž»­ who carry out innovative research in genomics. CBBI students join the research culture of their supervisors’ labs and the wider ICG, and gain access to joint seminars, skill-development sessions, symposia, and other activities.

Program delivery is in person, CBBI students are integrated into the research labs of their follow the expectations of the lab in matters such as participation in lab meetings. Program delivery is monitored and managed by the Director, who reviews and approves course plans, audits, and research seminars. 

 

 

Admission Requirements

CBBI admission requires the following additional background / information:

  • The applicant must have past research experience, typically an Honours degree but also potentially fulfilled through research internships or other experiences.
  • The applicant must have an identified supervisor who is willing and able to fund them.

A key objective of the CBBI program is to allow students to work with supervisors in a discipline that is different from the prospective student’s degree. Minimum requirements must be met under all circumstances, but it is the responsibility of the prospective supervisor to decide on the student’s suitability. The funding requirement ensures that the supervisor has a significant stake in the student’s pilipiliĀž»­. 

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Who we are:
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Rob Beiko

RobBeiko
  • Bioinformatics
  • Comparative genomics and phylogenetics
  • Machine learning
  • Visualization of biological data
  • Human microbiome

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Travis Gagie

travis_saturnia
  • Compact Data Structures
  • Data Compression
  • Pattern Matching
  • String Algorithms

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Nauzer Kalyaniwalla

Nauzer
  • Networked information spaces
  • Analysis of massive dynamic graphs
  • Graph and tree compression algorithms
  • Compressibilty as a measure of information content in graphs

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Meng He

MengHe
  • Algorithms & data structures
  • Computational geometry

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Chris Whidden

whidden_headshot
  • Approximation and fixed-parameter algorithms
  • Computational biology
  • Evolutionary trees and networks
  • Graph theory
  • Hybridization and lateral genetic transfer
  • NP-hardness
  • Oceans data analytics

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Norbert Zeh

NorbertZeh
  • Algorithms and data structures
  • I/O-efficient and cache-oblivious algorithms
  • Parallel algorithms
  • Graph algorithms
  • Computational geometry

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Finlay Maguire

profile_picture_FinlayM
  • Genomic Epidemiology
  • Health Data Science
  • Bioinformatics
  • Infectious Diseases
  • Medical Microbiology
  • Machine Learning
  • Computational Social Science

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Somayeh Kafaie

SomayehKafaie_profile
  • Bioinformatics
  • Network Science
  • Knowledge Graphs
  • Graph Neural Networks
  • Explainable AI


 

Manuel Mattheisen

Manuel Mattheisen
  • Community Health and Epidemiology
  • Genetic Epidemiology
  • Bioinformatics
  • Molecular Genetics (especially in complex traits)
  • Genome-wide Association Studies (GWAS)
  • Polygenic Risk Scoring (PRS)
  • Genomics and Epigenomics

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