SCAN-seq2 is a high-throughput, high sensitivity full-length single-cell RNA-seq method with Oxford Nanopore Technology (ONT). Our results proved SCAN-seq2 as a flexible single-cell full-length RNA-seq methods, with potiential implements in isoform quantification, transcriptome assemble, and Immune repertoire profiling.
For more detail about SCAN-seq2, please check our publication.
This repository provide source code for SCAN-seq2 data processing and downstream analysis.
The repository are structured as followed:
.
├── 1.Data_Processing # Pipeline for SCAN-seq2 data processing
│ ├── S01.umi.demultiplex.sh ## Demultiplex ONT reads
│ ├── S02.umi.process.one.sh ## QC, deduplication and trancriptome quantification
│ ├── S03.umi.Summarize.sh ## Summarize statistics, merge UMI count matrix
│ ├── S04.umi.assemble.one.sh ## Reference-guided trancriptome assemble
│ ├── S05.umi.VDJ.BCR.one.sh ## Immune repertoire profiling of immunoglobulin
│ ├── S05.umi.VDJ.TCR.one.sh ## Immune repertoire profiling of T cell receptor
│ ├──merge_counts_tsvs.R ## Merge UMI count matrix
│ └──MergeAssembly
│ ├── MergeAssembly.all.sh ## Merge cell-line assemblies
│ ├── MergeAssembly.sh ## Merge single-cell assemblies
│ └── Sbatch.MergeAssembly.sh
│
├── 2.Dowstream_Libraries # Downstream analyis of each separate SCAN-seq library
│ ├── 9CL.R ## 9CL library
│ ├── 4CL.R ## 4CL library
│ ├── 9CL_Mix.R ## 9CL_Mix library
│ ├── UMI_100.R ## UMI_100 library
│ ├── UMI_200.R ## UMI_200 library
│ └── IGG.R ## IGG6, IGG24 and IGG48 library
│
├── 3.Downstream_Merge # Downstream analysis of all libraries
│ ├── Part1_Technical_Performance
│ │ ├── Technical_Performance.R ## Technical performance of SCAN-seq2
│ │ └── Pseudogene.R ## Systematic evaluation of pseudogene expression
│ ├── Part2_Transcriptome_Assembly
│ │ ├── BCR_TCR_gtf ## Annotation for human immunoglobulin and T cell receptor genes
│ │ │ ├── Hg38.IGH.gtf
│ │ │ ├── Hg38.IGK.gtf
│ │ │ ├── Hg38.IGL.gtf
│ │ │ ├── Hg38.TRA.gtf
│ │ │ └── Hg38.TRB.gtf
│ │ ├── ModifGTF.R ## Modify gtf files for IGV visualization
│ │ ├── Transcriptome_Assembly.R ## Analysis of trancriptome assemblies
│ │ ├── merge_human_6CL.SQANTI3_SQANTI3_report.pdf
│ │ ├── merge_human_6CL.SQANTI3_classification.filtered_lite_SQANTI3_report.pdf
│ │ ├── merge_mouse_3CL.SQANTI3_SQANTI3_report.pdf
│ │ └── merge_mouse_3CL.SQANTI3_classification.filtered_lite_SQANTI3_report.pdf
│ ├── Part3_Isoginkgetin_Treatment
│ │ └── IGGTreatment.R ## IGG treatment of Hela and HepG2 cell lines1
│ └── Part4_Revise ## Additional code during manuscript revision
│
├── primers # Barcode and primer sequences
│
└── README.mdWe provided schematics for the data processing pipelines of SCAN-seq2:
This step includes identifing barcode sequences in raw ONT reads, trimming adaptors, quality control and deduplication.
%%{init: {"theme": "neutral", 'themeVariables': { "fontSize": "30px","fontFamily": "Arial"}}}%%
graph LR
raw("Raw reads</br>(84,801,426)"):::merge --nanoplexer--> demult("Demultiplexed reads</br>74,861 per cell"):::sc --NanoFilt<br/>Pychopper<br/>cutadapt--> QC(QC reads</br>66,606 per cell):::sc --minimap2<br/>UMI-tools dedup-->
dedup(deduped reads</br>43,480 per cell):::sc
classDef merge fill:#bebada,stroke:#000000;
classDef sc fill:#8dd3c7,stroke:#000000;
The main processing step for each single cell, where deduped reads were mapped to transcriptome reference for isoform-level quantification, as well as genome reference for transcriptome assembly. Single-cell assemblies were merged in a hierarchical manner, generating a merged transcriptome assembly.
%%{init: {"theme": "neutral", 'themeVariables': { "fontSize": "30px","fontFamily": "Arial"}}}%%
graph TD
dedup("Deduped reads</br>43,480 per cell</br>(fastq)"):::sc
subgraph Transcriptome Mapping
Align_cDNA("Transcriptome alignments</br>(bam)"):::sc --Salmon quant--> Quant("Gene/Isoform Expression Quantification</br>(tsv)"):::sc --merge_counts_tsvs.R--> Matrix("UMI count matrix</br>cell-by-gene/isoform</br>(tsv)"):::merge
end
subgraph Genome Mapping
Align_Genome("Genome alignments</br>(bam)"):::sc -- StringTie --> sc_Asm("Raw single-cell assembly<br/>4559 genes, 5364 isoforms per cell</br>(gff)"):::sc -- SQANTI3</br>sqanti3_qc.py</br>sqanti3_RulesFilter.py --> sc_Asm_2("Filterd single-cell assembly</br>3685 genes, 4406 isoforms per cell</br>(gff)"):::sc -- TAMA merge --> cl_Asm("Cell-line assembly</br>(bed12)"):::cl -- TAMA merge --> merge_Asm("Raw merged assembly</br>human: 10429 genes, 40712 isoforms</br>mouse: 8923 genes, 23906 isoforms</br>(gff)"):::merge -- SQANTI3</br>sqanti3_qc.py</br>sqanti3_RulesFilter.py --> merge_Asm_2("Filtered merged assembly</br>human: 10428 genes, 40676 isoforms</br>mouse: 8922 genes, 23894 isoforms</br>(gff)"):::merge
end
dedup --minimap2 -ax splice</br>Genome mapping--> Align_Genome
dedup --minimap2 -ax map-ont</br>Transcripome mapping--> Align_cDNA
subgraph Downstream Analysis
sc_Asm_2 --> sc_stat("Statistics of single-cell assemblies"):::down
merge_Asm_2 --> merge_stat("Statistics of merged assembly"):::down
Matrix --> NGS("Compare with NGS methods"):::down
Matrix --> DR_cluster("Dimensional Reduction,<br/>Clustering"):::down
Matrix --> DEG("DEG and DTU"):::down
end
classDef merge fill:#bebada,stroke:#000000;
classDef cl fill:#fb8072,stroke:#000000;
classDef sc fill:#8dd3c7,stroke:#00000;
classDef down fill:#b3de69,stroke:#000000;
This step generated polished transcript assembly for IGH/IGL/IGK/TRA/TRB transcripts, and identify V(D)J elements for each single cell.
%%{init: {"theme": "neutral", 'themeVariables': { "fontSize": "30px","fontFamily": "Arial"}}}%%
graph TD
Align_Genome("Genome alignments</br>(bam)"):::sc -- samtools --> reads("IGH/IGL/IGK/TRA/TRB reads<br/>(fastq)"):::sc --usearch--> cluster("Reads clusters</br>(cluster fastq)"):::sc--> large_clustrer("Largest cluster</br>(fastq)"):::sc--centroid--> centroid("centroid reads"):::sc
large_clustrer --> Other("Other reads in the same cluster"):::sc
subgraph Main
centroid --racon---> pol1(Polished sequence I):::sc
pol1 --another 3 rounds of racon--->pol4(Polished sequence IV):::sc -- Medaka ----> con("Consensus sequence</br>(fasta)"):::sc
end
subgraph Other reads
Other --racon---> pol1
Other -- another 3 rounds of racon-->pol4
Other -- Medaka --> con("Consensus sequence</br>(fasta)")
end
con --IgBlast--> VDJ("V(D)J calls</br>(tsv)"):::sc
classDef merge fill:#bebada,stroke:#000000;
classDef cl fill:#fb8072,stroke:#000000;
classDef sc fill:#8dd3c7,stroke:#00000;
classDef down fill:#b3de69,stroke:#000000;
Please cite the following article if you use SCAN-seq2 in your research:
Liao, Y., Liu, Z., Zhang, Y. et al. High-throughput and high-sensitivity full-length single-cell RNA-seq analysis on third-generation sequencing platform. Cell Discov 9, 5 (2023). https://doi.org/10.1038/s41421-022-00500-4
If you have any question please write issue for this repository or contact me directly at [email protected].