Scalable De Novo Isoform Discovery

IsoSeq v3 contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq GUI-based analysis application. A composable workflow of existing tools and algorithms, combined with a new clustering technique, allows to process the ever-increasing yield of PacBio machines with similar performance to IsoSeq versions 1 and 2. Starting with version 3.4, support for UMI and cell barcode based deduplication has been added.

Latest version can be installed via bioconda package isoseq3.

Please refer to our official pbbioconda page for information on Installation, Support, License, Copyright, and Disclaimer.

• Iso-Seq Clustering
• Iso-Seq Deduplication (UMIs and cell barcodes)

• 3.4.0
  • SMRT Link release 10.0.0
  • Add support for UMI and cell barcode handling, by adding tag and dedup
  • Add refine --min-rq to support RQ filtering for unfiltered <movie>.reads.bam input
• 3.3.0
  • SMRT Link release 9.0.0
• 3.2.2
  • Fix polish not generating fasta/q output. This bug was introduced in v3.2.0
• 3.2.1
  • Fix a gff index 1-off bug in collapse
  • We have removed implicit dependencies from the bioconda recipe. Please install pbccs, lima, and pbcoretools as needed.
• 3.2.0
  • polish dropped support for RS II datasets!
  • Add collapse step for aligned transcript BAM input
  • Enable CCS-only workflow cluster --use-qvs
  • Add refine --min-polya-length
  • Add cluster --singletons to output unclustered FLNCs; potential sample prep artifacts!
  • Fix minimap2 bugs. Outputs might change slightly.
• 3.1.2
  • Reduce polish memory footprint
• 3.1.1
  • Edge case fix where polish would not finish and stale
  • Improve polish run time for large scale datasets (> 1M CCS)
  • Improve polish result quality
• 3.1.0
  • We outsourced the poly(A) tail removal and concatemer detection into a new tool called refine. Your custom primers.fasta is used in this step to detect concatemers.

To simplify, unify, and future proof Iso-Seq, we decided to remove documentation starting from unpolished CCS reads. With the ever-increasing polymerase read lengths and improvements of CCS, going forward, it is recommended to generate polished CCS reads first and thus make final transcript polishing optional.

The ever-increasing throughput of the Sequel system gave rise to the need for a scalable software solution that can handle millions of CCS reads, while maintaining sensitivity and accuracy. Internal benchmarks have shown that IsoSeq v3 is orders of magnitude faster than currently employed solutions and SQANTI attributes IsoSeq v3 a higher number of perfectly annotated isoforms:

Additional benefit, single linux binary that requires no dependencies.

Even though we also observe fewer polished transcripts with IsoSeq v3, the overall quality is much higher. Most of the low-quality transcripts are lost in the demultiplexing step. Isoseq v1/2 classify is too relaxed and is not filtering junk molecules to a satisfactory level. In fact, lima calls are spot on and effectively removes most molecules that are wrongly tagged, as in two 5' or two 3' primers. Only a proper 5' and 3' primer pair allows to identify a full-length transcript and its orientation.

Starting with version 3.1, classify functionality has been split into two tools. Removal of (barcoded) primers is performed with PacBio's standard demultiplexing tool lima. Lima does not remove poly(A) tails, nor detects concatemers. For this, isoseq refine generates FLNC reads.

For version 3.0, poly(A) tail removal and concatemer detection is performed in isoseq cluster

Use --require-polya for isoseq refine. This filters for FL reads that have a poly(A) tail with at least 20 base pairs and removes identified tail.

There is no ETA feature. Depending on the sample type, whole transcriptome or targeted amplification, run time varies. The same number of reads from a whole transcriptome sample can finish clustering in minutes, whereas a single gene amplification of 10kb transcripts can take a couple of hours.

In contrast to its predecessors, IsoSeq v3 does not rely on NP-hard clique finding, but uses a hierarchical alignment strategy with O(N*log(N)). Recent advances in rapid alignment of long reads make this this approach feasible.

Cluster uses up to 10 CCS reads to generate the unpolished cluster consensus.

Polish uses up to 60 subreads to polish the cluster consensus.

IsoSeq v3 deems two reads to stem from the same transcript, if they meet following criteria:

There is no upper limit on the number of gaps.

Following BAM tags are being used:

• ib Barcode summary: triplets delimited by semicolons, each triplet contains two barcode indices and the ZMW counts, delimited by comma. Example: 0,1,20;0,3,5
• ic Sum of number of passes from all ZMWs used to create consensus
• im ZMW names associated with this isoform
• is Number of ZMWs associated with this isoform
• it List of barcodes/UMIs clipped during tag
• iz Maximum number of subreads used for polishing
• rq Predicted accuracy for polished isoform
• XA Order of tag names
• XC barcode sequence tag
• XG PacBio's GGG UMI suffix tag
• XM UMI sequence tag
• XO overhang sequence tag

Quality values are capped at 93.

PacBio supports three different SMRTbell designs for IsoSeq libraries. In all designs, transcripts are labelled with asymmetric primers, whereas a poly(A) tail is optional. Barcodes may be optionally added.

Binaries require SSE4.1 CPU support; CPUs after 2008 (Penryn) include it.

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