Zovoilis Group

Email: athanasios.zovoilis@uleth.ca 

Webpage: https://www.genome-intelligence.org/ 


Non-Coding RNAs: The dark matter of our genomes

Our genetic information is encoded mainly in the biochemical sequence of DNA and forms our 3-billion-base-long genome. RNA, which is similar to DNA, is produced based on this genomic information. In humans, less than 3% of the genomic information is transferred into a type of RNA called messenger RNA (mRNA). Through mRNA this small but most studied fraction of the genome is coding for proteins, i.e. is used to produce the chemical molecules that have been believed to enable most biological processes until now (protein-coding genome). However, it is the non-coding genome that holds the lion’s share, that ~97% of the human genomic sequence that has been until recently called ''junk'' DNA and considered to have no functional importance.

To store the extraordinary amount of genomic information in non-digital media we would need more than 150 hefty telephone books; until recently more than a day would have been needed to “read” just a single page of these books. Today, a new generation of sequencers can read all these pages in a matter of hours, enabling us for the first time to take a closer look at the complex architecture of our genome and, of all, of its non-coding part. Novel sequencing technologies have revealed that >80% of the noncoding genome is transcribed into non-coding RNAs (ncRNAs). The function of most of them and their role in human disease remains largely unknown.

All the RNAs available in a cell are collectively referred as the cell’s transcriptome, the study of which is referred as RNA Genomics and uses, among others, RNA Bioinformatics algorithms. The Zovoilis Lab uses next generation sequencing and bioinformatics algorithms to shed light on the non-coding genome and transcriptome and advance our knowledge of the human genome’s dark matter.

Four types of non-coding RNAs are the main focus of our Lab:
i) non-coding RNAs near promoters, splicing and transcription termination sites of the genes,
ii) non-coding RNAs originating from repetitive elements,
iii) chimeric RNAs derived from fusion of different non-coding RNA transcripts, and
iv) circular non-coding RNAs.

Among these non-coding RNAs our Lab seeks candidates that regulate the cell’s response to stress. We study variations in these non-coding RNAs that may be responsible for impaired response to stress during aging. Dissecting the role of these RNAs will help us understand their function in human and their importance in aging-associated diseases, such as cancer and dementia, which are connected to impaired response to cellular stress.

Identifying Disease Associated Non-Coding RNAs and Developing Genome Interpretation Algorithms for Cancer and Dementia

Cancer is a leading cause of morbidity and mortality in Alberta and worldwide. Decades of advances in genomic sequencing technology, therapeutics, and the understanding of the genetic causes of cancer have consolidated into making precision cancer care a reality. Traditionally tumors have been classified based on anatomic site and microscopic morphology and treated according to generalized guidelines or empirical decisions. However, during the recent years large-scale cancer genomics research has revealed the uniqueness of each tumor's genetic profile and has transformed modern cancer diagnosis and treatment. Thus, targeted therapies give nowadays oncologists the ability to customize treatments to their patients instead of applying one-size-fits-all solutions. However, availability of personalized genomics services in Canada is currently limited to a small number of patients in connected to major cancer care centers. One limiting factor that hinders the expansion of cancer precision health is that the process of genome interpretation remains a manual and time-consuming process. The goal of our research is to devise strategies that will enable the process of high volume of patient cases, low turnaround time and consistency in results reported to the physicians. To address these problems our Lab works on the automation of the curation process and the creation of standardized cancer precision medicine reports.  

Dementia is another disease that can benefit from personalized medicine approaches. Along the well-established paradigm of the benefits of precision medicine in cancer care, our Lab focuses on the development of early genomics diagnostics for dementia. We aim to characterize the patterns in non-coding RNAs in dementia patients and establish non-coding RNA biomarkers that will be informative for the disease’s onset.

Exploiting Genome Intellience for IT Purposes

The exponential development and connectivity of our smart devices is transforming positively our society but in the same time creates a number of challenges regarding storage and encryption of the unprecedented amount of data produced every day.

At first sight, digital data storage and computing seems to be rather an information technology task, and involvement of genomicists could be regarded rather as a paradox. However, recent studies have pointed out the potential of storing digital information into a universally readable and immutable storage medium, synthetic DNA molecules, at a rate of 455 exabytes in just 1 gram of single stranded DNA. Although efficient this approach requires the synthesis of a large number of synthetic DNA molecules rendering it financially non viable and does not exploit the inherent encoding potential of the genome but rather copies concepts of electronics into synthetic DNA molecules and devices.

Our approach on the other hand aims to leverage the already existing “logic devices” within the cells, in this case the non-protein coding elements of our genome. Instead of encoding and writing the digitized information into synthetic DNA molecules, our lab implements the encoding of this information in our genomes themselves.

Group Members (Fall 2018)

  1. Babita Gollen (Lab Manager)
  2. Yubo Cheng (PhD Student)
  3. Chris Isaac (MSc Student)
  4. Cody Turner (MSc Student)
  5. Matthew Stuart-Edwards (MSc Student)
  6. Travis Haight (Undergraduate)
  7. Liam Mitchell (Undergraduate)
  8. Luke Saville (Undergraduate)