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A workflow for pre-processing single-cell RNA-seq data, enabling cell x gene matrix generation and analysis.

kallisto | bustools is a modular and efficient workflow designed for pre-processing single-cell RNA-seq data. It addresses key steps such as associating reads with their cells of origin, collapsing reads based on unique molecular identifiers (UMIs), and generating cell x gene count matrices. The workflow supports diverse technologies like 10x, inDrops, and Dropseq, along with feature barcoding data from CITE-seq, REAP-seq, and MULTI-seq. By providing customizable workflows, kallisto | bustools facilitates quantification, RNA velocity analysis, and QC reporting for single-cell RNA-seq data. Its modular design allows users to adapt it to new technologies and protocols, ensuring robust and scalable single-cell data analysis.
kallisto | bustools is a modular and efficient workflow designed for pre-processing single-cell RNA-seq data.
Explore all tools that specialize in rna velocity analysis. This domain focus ensures kallisto | bustools delivers optimized results for this specific requirement.
Collapses reads based on Unique Molecular Identifiers (UMIs) to reduce amplification bias and improve quantification accuracy.
Generates cell x transcript equivalence class count matrices for accurate transcript-level quantification.
Allows users to tailor workflows for specific single-cell technologies and protocols.
Processes feature barcoding data from technologies like CITE-seq, REAP-seq, and MULTI-seq.
Performs RNA velocity analysis to infer the future state of cells and understand developmental trajectories.
Install kallisto and bustools using a package manager like conda or from source.
Index the transcriptome using kallisto index.
Map reads to the transcriptome using kallisto quant.
Use bustools to correct, collate, and count the reads based on cell barcodes and UMIs.
Generate the cell x gene count matrix using bustools count.
Perform quality control analysis using the generated QC reports.
Integrate the output with downstream analysis tools like Seurat or Scanpy.
All Set
Ready to go
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"Highly regarded for its speed and accuracy in single-cell RNA-seq data pre-processing."
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