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
Please refer to our official pbbioconda page for information on Installation, Support, License, Copyright, and Disclaimer.
- SMRT Link release 10.0.0
- Add support for UMI and cell barcode handling, by adding
refine --min-rqto support RQ filtering for unfiltered
- SMRT Link release 9.0.0
polishnot generating fasta/q output. This bug was introduced in v3.2.0
- Fix a gff index 1-off bug in
- We have removed implicit dependencies from the bioconda recipe. Please install
- Fix a gff index 1-off bug in
polishdropped support for RS II datasets!
collapsestep for aligned transcript BAM input
- Enable CCS-only workflow
cluster --singletonsto output unclustered FLNCs; potential sample prep artifacts!
- Fix minimap2 bugs. Outputs might change slightly.
- Edge case fix where
polishwould not finish and stale
polishrun time for large scale datasets (> 1M CCS)
- Edge case fix where
- We outsourced the poly(A) tail removal and concatemer detection into a new tool
refine. Your custom
primers.fastais used in this step to detect concatemers.
- We outsourced the poly(A) tail removal and concatemer detection into a new tool called
Where is the workflow starting from unpolished CCS reads?
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.
Why IsoSeq v3 and not the established versions 1 or 2?
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.
Why is the number of transcripts much lower with IsoSeq v3?
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.
I can't find the classify step
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.
isoseq refine generates FLNC reads.
For version 3.0, poly(A) tail removal and concatemer detection is performed in
My sample has poly(A) tails, how can I remove them?
This filters for FL reads that have a poly(A) tail
with at least 20 base pairs and removes identified tail.
How long will it take until my data has been processed?
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.
Which clustering algorithm is used?
In contrast to its predecessors, IsoSeq v3 does not rely on NP-hard clique
finding, but uses a hierarchical alignment strategy with
Recent advances in rapid alignment of long reads make this this approach
How many CCS reads are used for the unpolished cluster sequence representation?
Cluster uses up to 10 CCS reads to generate the unpolished cluster consensus.
How many subreads are used for polishing?
Polish uses up to 60 subreads to polish the cluster consensus.
When are two reads clustered?
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.
BAM tags explained
Following BAM tags are being used:
ibBarcode summary: triplets delimited by semicolons, each triplet contains two barcode indices and the ZMW counts, delimited by comma. Example:
icSum of number of passes from all ZMWs used to create consensus
imZMW names associated with this isoform
isNumber of ZMWs associated with this isoform
itList of barcodes/UMIs clipped during
izMaximum number of subreads used for polishing
rqPredicted accuracy for polished isoform
Quality values are capped at
What SMRTbell designs are possible?
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.
The binary does not work on my linux system!
Binaries require SSE4.1 CPU support; CPUs after 2008 (Penryn) include it.
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