Publications by authors named "O Iu Lazareva"
Single-cell DNA sequencing (scDNA-seq) enables decoding somatic cancer variation. Existing methods are hampered by low throughput or cannot be combined with transcriptome sequencing in the same cell. We propose HIPSD&R-seq (HIgh-throughPut Single-cell Dna and Rna-seq), a scalable yet simple and accessible assay to profile low-coverage DNA and RNA in thousands of cells in parallel.
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- DysRegNet is a new method designed to analyze patient-specific gene-regulatory networks, addressing limitations of existing methods that don't consider important factors like age and treatment history, and that struggle with large samples.
- The method shows improved scalability and relevance by highlighting age-specific biases in gene regulation, particularly in breast cancer, while generating interpretable results comparable to the established SSN method.
- DysRegNet is accessible as a Python package and offers an interactive web interface for analyzing results from various cancer types, making it a useful tool for personalized medicine and bioinformatics research.
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities.
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- The drug development process has become costly and inefficient due to poorly understood molecular mechanisms and the complexity of existing computational tools.
- Drugst.One is a new platform designed to simplify drug repurposing by converting systems biology software into user-friendly web applications with minimal coding.
- With successful integration into 21 computational systems medicine tools, Drugst.One aims to enhance the drug discovery process and help researchers concentrate on important aspects of developing pharmaceutical treatments.
Background: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation.
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