Scalable De Novo Isoform Discovery
IsoSeq3 contains the newest tools to identify transcripts in PacBio single-molecule sequencing data. Starting in SMRT Link v6.0.0, those tools power the IsoSeq3 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 IsoSeq1 and IsoSeq2.
Latest version can be installed via bioconda package
Please refer to our official pbbioconda page for information on Installation, Support, License, Copyright, and Disclaimer.
The high-level workflow depicts files and processes:
The mid-level workflow schematically explains what happens at each stage:
The low-level workflow explained via CLI calls. All necessary dependencies are installed via bioconda.
Step 0 - Input
For each SMRT cell, the
movieX.subreadset.xml are needed for processing.
Step 1 - Circular Consensus Sequence calling
Each sequencing run is processed by ccs to generate one representative circular consensus sequence (CCS) for each ZMW. Only ZMWs with at least one full pass (at least one subread with SMRT adapter on both ends) are used for the subsequent analysis. Polishing is not necessary in this step and is by default deactivated through.
$ ccs movieX.subreads.bam movieX.ccs.bam --noPolish --minPasses 1
For long movies and short inserts, it is advised to limit the number of subreads used per ZMW; this can decrease run-time (only available in ccs version ≥ 3.1.0):
$ ccs movieX.subreads.bam movieX.ccs.bam --noPolish --minPasses 1 --maxPoaCoverage 10
Step 2 - Primer removal and demultiplexing
Removal of primers and identification of barcodes is performed using lima,
which offers a specialized
Even in the case that your sample is not barcoded, primer removal is performed
If there are more than two sequences in your
primer.fasta file or better said
more than one pair of 5' and 3' primers, please use lima with
to remove spurious false positive signal.
More information about how to name input primer(+barcode)
sequences in this FAQ.
$ lima movieX.ccs.bam barcoded_primers.fasta movieX.fl.bam --isoseq --no-pbi --peek-guess
Following is the
primer.fasta for the Clontech SMARTer and NEB cDNA library
prep, which are the officially recommended protocols:
>NEB_5p GCAATGAAGTCGCAGGGTTGGG >Clontech_5p AAGCAGTGGTATCAACGCAGAGTACATGGGG >NEB_Clontech_3p GTACTCTGCGTTGATACCACTGCTT
Example 2: Following are examples for barcoded primers using a 16bp barcode followed by Clontech primer:
>primer_5p AAGCAGTGGTATCAACGCAGAGTACATGGGG >brain_3p CGCACTCTGATATGTGGTACTCTGCGTTGATACCACTGCTT >liver_3p CTCACAGTCTGTGTGTGTACTCTGCGTTGATACCACTGCTT
Lima will remove unwanted combinations and orient sequences to 5' → 3' orientation.
Output files will be called according to their primer pair. Example for single sample libraries:
If your library contains multiple samples, execute the following workflow for each primer pair:
Step 3 - Refine
Your data now contains full-length reads, but still needs to be refined by:
Input The input file for refine is one demultiplexed CCS file with full-length reads and the primer fasta file:
Output The following output files of refine contain full-length non-concatemer reads:
Actual command to refine:
$ isoseq3 refine movieX.NEB_5p--NEB_Clontech_3p.fl.bam primers.fasta movieX.flnc.bam
If your sample has poly(A) tails, use
This filters for FL reads that have a poly(A) tail
with at least 20 base pairs and removes identified tail:
$ isoseq3 refine movieX.NEB_5p--NEB_Clontech_3p.fl.bam movieX.flnc.bam --require-polya
Step 3b - Merge SMRT Cells
If you used more than one SMRT cells, use
dataset for merging.
Merge all of your
$ dataset create --type TranscriptSet merged.flnc.xml movie1.flnc.bam movie2.flnc.bam movieN.flnc.bam
Similarly, merge all of your source
$ dataset create --type SubreadSet merged.subreadset.xml movie1.subreadset.xml movie2.subreadset.xml movieN.subreadset.xml
Step 4 - Clustering
Compared to previous IsoSeq approaches, IsoSeq3 performs a single clustering technique. Due to the nature of the algorithm, it can't be efficiently parallelized. It is advised to give this step as many coresas possible. The individual steps of cluster are as following:
- Clustering using hierarchical n*log(n) alignment and iterative cluster merging
- Unpolished POA sequence generation
Input The input file for cluster is one FLNC file:
Output The following output files of cluster contain unpolished isoforms:
$ isoseq3 cluster merged.flnc.xml unpolished.bam --verbose
Step 5 - Serial Polishing
The algorithm behind polish is the arrow model that also used for CCS generation and polishing of de-novo assemblies.
Input The input files for polish are:
Output The following output files of polish contain polished isoforms:
<prefix>.hq.fasta.gzwith predicted accuracy ≥ 0.99
<prefix>.lq.fasta.gzwith predicted accuracy < 0.99
<prefix>.hq.fastq.gzwith predicted accuracy ≥ 0.99
<prefix>.lq.fastq.gzwith predicted accuracy < 0.99
$ isoseq3 polish unpolished.bam merged.subreadset.xml polished.bam
Alternative Step 4/5 - Parallel Polishing
Polishing can be massively parallelized on multiple servers by splitting
Split BAM files can be generated by cluster.
$ isoseq3 cluster merged.flnc.xml unpolished.bam --verbose --split-bam 24
This will create up to 24 output BAM files:
unpolished.0.bam unpolished.1.bam ...
Each of those
unpolished.<X>.bam files can be polished in parallel:
$ isoseq3 polish unpolished.0.bam sample.subreadset.xml polished.0.bam $ isoseq3 polish unpolished.1.bam sample.subreadset.xml polished.1.bam $ ...
This is an example of an end-to-end cmd-line-only workflow to get from subreads to polished isoforms:
$ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam $ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreads.bam.pbi $ wget https://downloads.pacbcloud.com/public/dataset/RC0_1cell_2017/m54086_170204_081430.subreadset.xml $ ccs --version ccs 3.1.0 (commit v3.1.0) $ ccs m54086_170204_081430.subreads.bam m54086_170204_081430.ccs.bam \ --noPolish --minPasses 1 --maxPoaCoverage 10 $ cat primers.fasta >primer_5p AAGCAGTGGTATCAACGCAGAGTACATGGGG >primer_3p AAGCAGTGGTATCAACGCAGAGTAC $ lima --version lima 1.9.0 (commit v1.9.0) $ lima m54086_170204_081430.ccs.bam primers.fasta m54086_170204_081430.fl.bam \ --isoseq --no-pbi --peek-guess $ ls m54086_170204_081430.fl* m54086_170204_081430.fl.json m54086_170204_081430.fl.lima.summary m54086_170204_081430.fl.lima.clips m54086_170204_081430.fl.primer_5p--primer_3p.bam m54086_170204_081430.fl.lima.counts m54086_170204_081430.fl.primer_5p--primer_3p.subreadset.xml m54086_170204_081430.fl.lima.report $ isoseq3 refine m54086_170204_081430.fl.primer_5p--primer_3p.bam primers.fasta m54086_170204_081430.flnc.bam $ ls m54086_170204_081430.flnc.* m54086_170204_081430.flnc.bam m54086_170204_081430.flnc.filter_summary.json m54086_170204_081430.flnc.bam.pbi m54086_170204_081430.flnc.report.csv m54086_170204_081430.flnc.consensusreadset.xml $ isoseq3 cluster m54086_170204_081430.flnc.bam unpolished.bam --verbose Read BAM : (197791) 4s 20ms Convert to reads : 1s 431ms Sort Reads : 56ms 947us Aligning Linear : 2m 5s Read to clusters : 9s 432ms Aligning Linear : 54s 288ms Merge by mapping : 36s 138ms Consensus : 30s 126ms Merge by mapping : 5s 418ms Consensus : 3s 597ms Write output : 1s 134ms Complete run time : 4m 32s $ ls unpolished* unpolished.bam unpolished.bam.pbi unpolished.cluster unpolished.fasta unpolished.transcriptset.xml $ isoseq3 polish unpolished.bam m54086_170204_081430.subreadset.xml polished.bam --verbose 14561 $ ls polished* polished.bam polished.hq.fastq.gz polished.bam.pbi polished.lq.fasta.gz polished.cluster_report.csv polished.lq.fastq.gz polished.hq.fasta.gz polished.transcriptset.xml
Or run isoseq3 cluster it in split mode and
isoseq3 polish in parallel:
$ isoseq3 cluster m54086_170204_081430.flnc.bam unpolished.bam --split-bam 24 $ isoseq3 polish unpolished.0.bam m54086_170204_081430.subreadset.xml polished.0.bam $ isoseq3 polish unpolished.1.bam m54086_170204_081430.subreadset.xml polished.1.bam $ ...
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