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Related Concept Videos

Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...

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A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae
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pyVIPER: a fast and scalable Python package for protein activity estimation and master regulator analysis of

Alexander L E Wang1, Luca Zanella1, Zizhao Lin1

  • 1Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, 10032, USA.

BMC Bioinformatics
|June 23, 2026
PubMed
Summary

pyVIPER is a new Python tool that makes analyzing large single-cell RNA sequencing datasets faster and more efficient. It significantly speeds up gene regulatory network analysis for biomedical research.

Keywords:
Gene regulatory networksMaster regulator analysisNetwork biologyProtein activity inferenceSingle-cell RNA-seqSystems biologyTranscriptional analysis

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular heterogeneity.
  • Low signal-to-noise ratios in scRNA-seq data hinder quantitative analyses.
  • Gene regulatory network (GRN) analysis, like the VIPER algorithm, helps elucidate cellular state determinants by identifying Master Regulator proteins.

Purpose of the Study:

  • To address the need for scalable tools for analyzing large scRNA-seq datasets.
  • To introduce pyVIPER, a Python-based tool for protein activity inference from transcriptional data.
  • To improve the efficiency and accessibility of GRN analysis in single-cell research.

Main Methods:

  • Developed pyVIPER, a Python toolkit for protein activity inference.
  • Implemented flexible data transformation/postprocessing modules and enrichment analysis algorithms.
  • Integrated a novel data structure for GRN manipulation and compatibility with scverse and scanpy.
  • Leveraged PyTorch-based GPU acceleration and optimized core operations for enhanced performance.

Main Results:

  • pyVIPER demonstrates orders-of-magnitude improvements in runtime efficiency compared to R-based VIPER.
  • Analysis time for large scRNA-seq datasets is reduced from hours to minutes.
  • The tool is memory-efficient and highly scalable for datasets up to hundreds of thousands of cells.

Conclusions:

  • pyVIPER is a fast, memory-efficient, and scalable Python toolkit for protein activity inference in large-scale scRNA-seq data.
  • Its hardware acceleration and scalability enable high-throughput VIPER-based analysis for diverse single-cell datasets.
  • pyVIPER facilitates integration with Python-based machine learning workflows, expanding mechanistic GRN analysis in single-cell research.