2 Days Hands-On Masterclass Deep Learning for Understanding Gene Regulation

INTRODUCTION

Deep learning for understanding gene regulation represents a groundbreaking approach in deciphering the intricate mechanisms that govern gene expression, playing a pivotal role in shaping the complexity of biological systems. Gene regulation orchestrates the precise control of gene activity, dictating when and where genes are expressed and modulating cellular processes crucial for development, homeostasis, and disease.

Deep learning algorithms, particularly neural networks, offer a transformative toolset for dissecting the regulatory grammar encoded within the genome. By leveraging vast amounts of multi-omics data, including DNA sequences, epigenetic modifications, and transcription factor binding profiles, deep learning models can decipher the regulatory landscape with unparalleled accuracy and efficiency.

Moreover, deep learning techniques empower the integration of diverse datasets, facilitating the elucidation of complex gene regulatory circuits underlying cellular processes and disease phenotypes.

APPLICATIONS OF DEEP LEARNING FOR UNDERSTANDING GENE REGULATION

• Enhancer and Promoter Prediction: Deep learning models can identify enhancer and promoter regions within the genome by learning sequence motifs associated with regulatory elements. These predictions aid in understanding the spatial and temporal control of gene expression.
• Transcription Factor Binding Site Prediction: Deep learning algorithms can predict transcription factor binding sites (TFBS) by analyzing DNA sequence patterns and epigenetic features. Accurate TFBS prediction facilitates the study of transcriptional regulation and regulatory networks.
• Gene Expression Prediction: Deep learning models trained on multi-omics data can predict gene expression levels based on genomic sequences, epigenetic modifications, and environmental factors. This helps in understanding the regulation of gene expression under different conditions and cell types.
• Identification of Regulatory Interactions: Deep learning techniques enable the inference of regulatory interactions between transcription factors, enhancers, promoters, and target genes from high-throughput genomic data. This aids in deciphering gene regulatory networks and understanding their dynamics.
• Variant Effect Prediction: Deep learning models can predict the functional impact of genetic variants on gene regulation by integrating sequence data with epigenetic and expression data. This is essential for interpreting the effects of genetic variation on disease susceptibility and phenotype.

DIFFERENT TYPES OF ANALYSIS

• Enhancer Identification and Prediction: Deep learning models can be trained to identify and predict enhancer regions in the genome based on patterns in DNA sequences and epigenetic features.
• Transcription Factor Binding Site (TFBS) Prediction: Deep learning algorithms can analyze genomic sequences to predict the binding sites of transcription factors (TFs). disease pathways.
• Gene Expression Prediction and Regulation: Deep learning models can integrate multi-omics data, including genomic sequences, epigenetic modifications, and gene expression profiles, to predict gene expression levels under different conditions or cell types.

TOPICS COVERED

DAY 1

DAY 2

TAKEAWAY FROM THE MASTERCLASS

• Introductory Documentation: An introductory theory document to help you better understand the subject will also be provided.
• Trainers Slide Deck: After the completion of the session complete access to the trainers slide deck will also be provided
• Access to Trainers Repo: We will also be providing a complete access to trainers repository so that you can use it as reference later
• 8+ Hours of Live Training: During the course of 3 days we will be have live8+ hourse of training sessions with the participants.
• Guided Hands-On Exercises: Hands-On exercises are a must to better learn any technology and be able to reproduce it later.
• Participation Certificate: A Participations certificate is a must after successfully completing the training as a sign of accomplishment.

EXPECTED OUTCOMES

• Identification of Novel Regulatory Elements: Deep learning models can uncover previously unknown regulatory elements in the genome by learning patterns from large-scale genomic data.
• Insights into Regulatory Mechanisms and Networks: Deep learning analyses can provide insights into the complex mechanisms governing gene regulation by elucidating regulatory interactions and networks.
• Prediction of Gene Expression Patterns: Deep learning models trained on genomic and epigenomic data can predict gene expression levels under various conditions or cell types.
• Discovery of Therapeutic Targets and Biomarkers: Deep learning analyses in gene regulation can facilitate the identification of potential therapeutic targets and biomarkers for diseases.

TERMS & CONDITIONS

1. All fee paid is not refundable so please read all the terms & conditions before making any payments. If you still have any doubts please contact us and confirm and then only make the payment.
2. Participants need to bring their registration tickets along with a valid Institutional ID, then only they will be allowed to attend the session. Please reach out to our team in case of any exceptions.
3. Please fill all your details in the form correctly as those details will be used in your certificate as well.
4. Participants need to bring their own computer (laptop) system for the program.
5. The software tools and other required software tools will be provided from our side for the purpose of this program.
6. Participants need to reach the venue and report 30 minutes prior to the start of the sessions.
7. Participants need to wear masks all the time inside the premises and abide by the other rules at the premises.
8. Participants need to attend all the sessions in order to be eligible for getting the certificate.
9. Welcome email will be sent to all the participants with all the details related to the program. Please check your Inbox/Spam folder for the email.
10. All the details of the software installations and how to prepare your system for the Program will be shared with all the participants in the Welcome Email itself.