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Deciphering the Language of DNA to Identify Regulatory Elements and Classify Transcripts Into Functional Classes

Project description

Decoding the language of DNA

Genomic sequences resemble the human language in the sense that nucleotides and codons transmit information in a similar way to how phonemes and syllables comprise the units of the spoken language. A genomic sequence can encode a protein, or it could relay a regulatory or structural message. Scientists of the EU-funded LanguageOfDNA project will employ algorithms designed for the processing of human languages to classify RNA transcripts and untranslated genomic regions. Through the establishment of DNA/RNA language models, they will be able to interpret any genomic sequence and contribute to the functional delineation of the human genome.


The genomics era dawned about two decades ago with the completion of a multi-billion project sequencing the complete human genome. Today a similar task is within reach of any modestly equipped lab, due to the advances in sequencing techniques. Thousands of new species are now having their genome sequenced per year. A volume of produced genomic data challenges the interpretation capacity of classical statistical methods, opening the doors for novel machine learning approaches.

A genomic sequence can be conceptually seen as a close parallel to a human language. Both utilize information (nucleotides/codons and phonemes/syllables) to encode and transmit a signal that can be faithfully decoded, with attention to error minimization, at the receiving end. Genomic messages are a product of multiple and often contradictory evolutionary pressures and are aimed to be decoded at the same time by many different actors in variable ways. For example, a genomic sequence could encode for a protein product, thus displaying a three-nucleotide / codon-based language model. However, it has also subtexts of the regulation (a codon sequence can include motifs aimed at RNA binding proteins), structural information (functional RNA folding patterns pressuring sequences to a specific direction) and so on.

The main challenge of applying machine learning models to the identification of genomic function is to find creative ways to untangle these multiple layers of subtexts and focus on each type of message separately. We will adapt algorithms recently developed for the processing of human languages and use them for the classification of RNA transcripts into functional classes and the classification of untranslated functional genomic regions (enhancers, transcription factor binding sites). We will create ready-to-use datasets to benchmark existing and future methods in this field and make all DNA/RNA language models publicly available.


Masarykova univerzita
Net EU contribution
€ 156 980,64
Zerotinovo namesti 9
601 77 Brno

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Česko Jihovýchod Jihomoravský kraj
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00