NDLI: TIANA: transcription factors cooperativity inference analysis with neural attention

Content Provider	Springer Nature : BioMed Central
Author	Li, Rick Z. Han, Claudia Z. Glass, Christopher K.
Abstract	Background Growing evidence suggests that distal regulatory elements are essential for cellular function and states. The sequences within these distal elements, especially motifs for transcription factor binding, provide critical information about the underlying regulatory programs. However, cooperativities between transcription factors that recognize these motifs are nonlinear and multiplexed, rendering traditional modeling methods insufficient to capture the underlying mechanisms. Recent development of attention mechanism, which exhibit superior performance in capturing dependencies across input sequences, makes them well-suited to uncover and decipher intricate dependencies between regulatory elements. Result We present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared with existing tools, TIANA showed superior interpretability and robust performance in identifying putative transcription factor cooperativities from co-occurring motifs. Conclusion Our results suggest that TIANA can be an effective tool to decipher transcription factor cooperativities from distal sequence data. TIANA can be accessed through: https://github.com/rzzli/TIANA .
Related Links	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-024-05852-0.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712105
DOI	10.1186/s12859-024-05852-0
Journal	BMC Bioinformatics
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-08-22
Access Restriction	Open
Subject Keyword	Bioinformatics Microarrays Computational Biology Computer Appl. in Life Sciences Algorithms Self-attention Transcription Factors Deep Learning Integrated gradients Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biochemistry Computer Science Applications Applied Mathematics Structural Biology
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	3.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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