Troubleshooting SMRT Link MAS-Seq Single-cell Iso-Seq output

Last Updated: 02/07/2023

This page serves as a supplement guide to troubleshooting SMRT Link Read Segmentation & Single-cell Iso-Seq analysis for using the MAS-Seq for 10x Single Cell 3’ kit. Please always refer to the official SMRT Link documentation for the latest documentation.

MAS-Seq dataset and reference URL: https://downloads.pacbcloud.com/public/dataset/MAS-Seq/


Read Segmentation

Read segmentation de-concatenates HiFi reads into segmented reads (S-reads) based on segmentation adapters using skera.

Metric Explanation Typical value
Reads Number of HiFi reads Depends on sequencing yield
S-reads Number of segmented reads Depends on HiFi read yield and concatenation success
Mean length of S-reads Mean RL of S-reads 600-800bp for 10x cDNA
Percent of reads with full arrays % HiFi reads with full MAS arrays 85-90%
Mean array size Concatenation factor ~15.xx

A typical HiFi RL will have a clean peak between 10,000-14,000 bp indicating good array formation and successful enrichment of full arrays.

While the S-read read length should large reflect the original 10x cDNA library size.

Single-cell Iso-Seq, Read Statistics

Metric Explanation Typical value
Reads Number of (S-)reads Depends on sequencing yield
Read Type CCS or SEGMENT CCS or SEGMENT
Reads with 5’ and 3’ Primers with extracted UMIs and Barcodes FL tagged reads >95% of reads should be FL tagged
Non-Concatemer Reads with 5’ and 3’ Primers and PolyA Tail FLNC tagged reads >90% of reads should be FLNC tagged
FLNC Reads with Valid Barcodes FLNC reads matching barcode whitelist >90% reads should match barcode
FLNC Reads with Valid Barcodes, corrected FLNC reads matching barcode whitelist w correction >90% reads should match barcode post correction
Reads after Barcode Correction and UMI Deduplication Dedup reads De-duplicated read yield depends on 10x library complexity and PCR duplication rate

Single-cell Iso-Seq, Cell Statistics

Metric Explanation Typical value
Estimated Number of Cells Number of real cells Depends on 10x library
Reads in Cells % of reads in real cells >85%
Mean Reads per Cell mean reads per real cell Depends on 10x library and read yield
Median UMIs per Cell median UMI per real cell Depends on 10x library, read yield, and PCR duplication rate

The number of estimated cells, mean reads/cell and median UMIs/cell are highly dependent on the 10x single cell library and sample complexity. In the case where it is suspected that the cell estimation is incorrect using the default knee method for isoseq correct, the cells can be re-estimated using alternative approach of percentile method. Refer to cell calling for more details.

Single-cell Iso-Seq, Transcript Statistics

Metric Explanation Typical value
FLNC Reads Mapped Confidently to Genome FLNC reads (before dedup) mapped to genome [note1] ~80%
FLNC Reads Mapped Confidently to Transcriptome FLNC reads (before dedup) mapped to transcriptome [note2] 30-50%
Total Unique Genes Total unique genes before pigeon filter [note3] Sample-dependent
Total Unique Genes, filtered Total unique genes after pigeon filter [note3] Sample-dependent
Total Unique Genes, known genes only Total unique known genes before pigeon filter [note3] Sample-dependent
Total Unique Genes, filtered, known genes only Total unique known genes after pigeon filter [note3] Sample-dependent
Total Unique Transcripts Total unique transcripts before pigeon filter Sample-dependent
Total Unique Transcripts, filtered Total unique transcripts after pigeon filter Sample-dependent
Total Unique Transcripts, known transcripts only Total unique known transcripts before pigeon filter Sample-dependent
Total Unique Transcripts, filtered, known transcripts only Total unique known transcripts after pigeon filter Sample-dependent

[note1] FLNC reads mapped to the genome after running isoseq collapse (we actually map dedup reads, but expand it back to reflect the pre-deduplicated FLNC count). Note that isoseq collapse filters for reads that map chimerically or map with low identity, so if there are cancer fusion genes or genes not well represented in the genome, they’d be excluded at this step. In general, we should expect most (~80%) FLNC reads to map to the genome, even if they end up mapping to, say, intergenic regions.

[note2] FLNC reads mapped to known genes (known or novel isoforms) after pigeon classify and pigeon filter. Think of this as the “number of usable reads” that actually go into a standard single-cell analyses. This number includes ribosomal/mitochondrial genes. We typically see 30-50% FLNC reads map to the transcriptome, which is consistent with equivalent 10x short read sequencing data. Most of the non-transcriptomic but genomically mapped reads are attributed to intergenic regions and are filtered out by pigeon filter.

[note3] It is typical to see a very high number of “total number of genes/transcripts” before pigeon filter. This is due to the high number of loci that are intergenic and still being assigned a “novel gene” status before pigeon filter.

Issue Likely cause Solution
Good concatenation factor
Low S-read yield
Low sequencing yield Additional sequencing
Good S-read yield
Poor FLNC yield and beyond
Not using 10x 3’ kit (v3.1) Reanalyze with proper cDNA primer, UMI/BC design, and barcode whitelist [note1]
Good S-read yield
Good cell statistics
Poor read mapping and low gene counts
Wrong reference selected Choose correct reference genome & annotation [note2]
Good S-read yield
Poor cell recovery
Algorithm underestimated number of cells Re-analyze with percentile method in SL or command line
Analysis experienced an error, but was able to recover and complete successfully; High Barcode Errors Incorrect barcode whitelist Re-analyze with correct barcode whitelist [note3]

[note1] Additional 10x cDNA primers and barcode whitelist may be found here

[note2] SMRT Link only supports human and mouse reference genome + Gencode annotation. If using different genomes, refer to pigeon documentation for command line analysis.

[note3] If you see the error message Analysis experienced an error, but was able to recover and complete successfully; High Barcode Errors it means the barcode whitelist provided is incorrect. Note that SMRT Link expects a barcode whitelist that is reverse-complemented, which is not how the 10x whitelist is typically provided. A list of common barcode whitelist in reverse-complement can be found here.


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