Resource for long-read RNA isoform sequencing analysis

This website serves as an updated resource for bulk and single-cell RNA isoform sequencing analysis from Kinnex data. Using latest methods and best practices, we have compiled a series of workflows and notebook vignettes to facilitate data processing and analysis. We hope this resource will serve as a useful guide for this new and feature rich data type.

The Kinnex protocol is based on the MAS-ISO-seq method, developed at the Broad. For information on the approach, please reference the original publication in Nature Biotechnology:

High-throughput RNA isoform sequencing using programmed cDNA concatenation

From receiving sequencing data from PacBio’s Revio sequencing platform, the document steps through various pre-processing workflows for obtaining cleaned s-reads suitable for downstream processing and tertiary analysis workflows and vignettes developed by the MDL team at Broad Clinical Labs to explore Kinnex datatypes.

Repository of Public Datasets for Kinnex, Sequel2e and previous MAS-ISO-seq versions

How do I get more information on available sequencing capabilities?

For more information on sequencing products, services and support available for Kinnex Products visit the BCL website.

Kinnex Full Length

Overview and workflow

A. Kinnex Full Length (bulk) - Overview

Kinnex Single Cell

Overview and workflow

B. Kinnex Single Cell - Overview

Index :

• Best practices and Resources for Kinnex
  • Kinnex Full Length Wet-Lab protocol
  • Kinnex Single Cell Wet-Lab protocol
  • PacBio Documents and Resources

Kinnex Full Length :

• A. Kinnex Full Length (bulk) - Overview
  • Overall Workflow in a nutshell
• 1. Preliminary analysis - Bulk
  • 1.1. pbskera
  • 1.2. lima demux + isoseq refine
  • 1.3. merge
• 2. Full Length Bulk Secondary Processing
  • High-level Workflow for Bulk:
    • Test Data
    • 2.1 minimap2
• 2.2. Read-level Quality Controls
  • Main workflow: LongRNAqcPlusFromBAM
  • LongRNAqc+ Plotting
• 2.3.Integrative Transcriptome Viewer
  • Install notes
  • Getting test data for ITV
  • Setting up config object - Demo start checkpoint
• 2.4. CTAT-LR-FUSION
• 3. Full Length Bulk Downstream Processing
  • High-level Workflow for Bulk:
    • Test Data
    • 3.1 Isoquant - Isoform Discovery
    • 3.2 Stringtie
    • 3.3 Differential Expression with DEseq2
    • 3.4 Gffcompare
    • 3.5 Extracting Long IDs
    • 3.6 isoformSwitchAnalysisR
    • Generating functional annotations
• 3.5. Vignette for extracting Long Ids
  • Matching gene and transcript ids from Gffcompare outs with Isoquant outs
    • Looking at the distribution of class codes (similar to SQANTI)
    • Writing gtf - replacing transcript Ids in strtingtie GTF with Long Ids
• 3.6. Vignette for analyzing and plotting isoform Switching for Kinnex Full Length
  • Module to install packages
  • Reading in gtf
  • Creating Design object
  • importRdata function
  • Pre-filtering switchObject on threholds
  • Analyze ORFs
  • Isoform analyze Part1:
  • DGEList function
  • Mutating isoformSwitchObject
  • Ref Ensmbl Ids in dataset
  • Volcano like plot
  • Switch vs Gene changes
  • isoformSwitchAnalysisPart2 :

Kinnex Single Cell :

• B. Kinnex Single Cell - Overview
  • Kinnex Single Cell workflow overview:
• 1. Preliminary analysis - SC
  • 1.1. pbskera
  • 1.2 pblima + isoseq tag + isoseq refine + isoseq correct
• 2. Single Cell Downstream Processing
  • Test Data
  • 2.1. minimap2
• 2.2. Read-level Quality Controls
  • Main workflow: LongRNAqcPlusFromBAM
  • LongRNAqc+ Plotting
• 2.3.Integrative Transcriptome Viewer
  • Install notes
  • Getting test data for ITV
  • Setting up config object - Demo start checkpoint
• 3.1. LRAA - Long Read Alignment Assembler
• 3.2 Vignette for Tertiary processing for SC-Kinnex
  • Creating sparse matrices for use with Seurat
  • Analysing sparse matrices created above
  • Input counts matrix created above from step1
  • Reading data in using Read10x()
  • UMI counts per cell:
  • sorting:
  • plotting :
  • Creating seurat object from counts matrix
  • UMI counts per cell
  • Feature counts per cell:
  • Performing PCA :
  • Generating UMAP :
  • DE, find markers:
• 3.3 Vignette for creating pseudo-bulk counts matrix
  • Part1: Reading and Exploring Seurat object:
  • Part2: Dividing clusters into sub-clusters
  • Part3 - Create single cell experiment object
  • Part4: Preparing the single-cell dataset for pseudobulk analysis
  • Part5: Subset metadata
  • Part6: Generating matching metadata at the sample-level

Citations :

• Citations and References
• To-do Tasks/ Feedback

Indices and tables

• Index

• Search Page