+
+Additional benefit, single linux binary that requires no dependencies.
### Why is the number of transcripts much lower with IsoSeq3?
Even though we also observe fewer polished transcripts with *IsoSeq3*, the
overall quality is much higher. Most of the low-quality transcripts are lost in the
demultiplexing step. *Isoseq1/2 classify* is too relaxed and is not filtering
-junk molecules to a satifactory level. In fact, *lima* calls are spot on and
+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
-*Classify* has been replaced with PacBio's standard demultiplexing tool *lima*.
-*Lima* does not remove polyA tails, nor detects concatmers. See the next Q.
+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, `isoseq3 refine` generates FLNC reads.
-### Can I perform "classify only" to get FLNC reads?
-One of the early outputs of the `cluster` step is a `*.flnc.bam` file. Feel
-free to abort after this file has been written. This will be addressed in the
-upcoming version.
+For version 3.0, poly(A) tail removal and concatemer detection is performed in
+`isoseq3 cluster`
+
+### My sample has poly(A) tails, how can I remove them?
+Use `--require-polya` for `isoseq3 refine`.
+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
@@ -81,10 +91,6 @@ following criteria:
There is no upper limit on the number of gaps.
-### My sample has poly(A) tails, how can I remove them?
-Use `--require-polya`. This filters for FL reads that have a poly(A) tail
-with at least 20 base pairs and removes identified tail.
-
### BAM tags explained
Following BAM tags are being used:
@@ -96,6 +102,14 @@ Following BAM tags are being used:
Quality values are capped at `93`.
+### 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.
diff --git a/README_v3.0.md b/README_v3.0.md
index 7d95787..88211ee 100644
--- a/README_v3.0.md
+++ b/README_v3.0.md
@@ -25,21 +25,9 @@ for information on Installation, Support, License, Copyright, and Disclaimer.
- [Real-World Example](README_v3.0.md#real-world-example)
- [FAQ](README.md#faq)
-## Changelog
-
- * 3.0.0: Initial release, included in SMRT Link 6.0.0
-
-## SMRTbell designs
-
-PacBio supports three different SMRTbell designs for IsoSeq libraries.
-In all designs, transcripts are labelled with asymmetric primers,
-whereas a polyA tail is optional. Barcodes may be optionally added.
-
-
+
### Input
For each cell, the `
IsoSeq 3.1
+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*. + +## Availability +Latest version can be installed via bioconda package `isoseq3`. + +Please refer to our [official pbbioconda page](https://github.com/PacificBiosciences/pbbioconda) +for information on Installation, Support, License, Copyright, and Disclaimer. + +## Overview + - [Changelog](README_v3.1.md#changelog) + - Workflow Overview: [high](README_v3.1.md#high-level-workflow) / [mid](README_v3.1.md#mid-level-workflow) / [low](README_v3.1.md#low-level-workflow) level + - [Real-World Example](README_v3.1.md#real-world-example) + - [FAQ](README_v3.1.md#faq) + - [SMRTbell Designs](README_v3.1.md#what-smrtbell-designs-are-possible) + +## High-level workflow + +The high-level workflow depicts files and processes: + +
+
+## Mid-level workflow
+
+The mid-level workflow schematically explains what happens at each stage:
+
+
+
+## Low-level workflow
+
+The low-level workflow explained via CLI calls. All necessary dependencies are
+installed via bioconda.
+
+### Step 0 - Input
+For each SMRT cell, the `movieX.subreads.bam`, `movieX.subreads.bam.pbi`,
+and `movieX.subreadset.xml` are needed for processing.
+
+### Step 1 - Circular Consensus Sequence calling
+Each sequencing run is processed by [*ccs*](https://github.com/PacificBiosciences/unanimity)
+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*](https://github.com/pacificbiosciences/barcoding),
+which offers a specialized `--isoseq` mode.
+Even in the case that your sample is not barcoded, primer removal is performed
+by *lima*.
+More information about how to name input primer(+barcode)
+sequences in this [FAQ](https://github.com/pacificbiosciences/barcoding#how-can-i-demultiplex-isoseq-data).
+
+ $ lima movieX.ccs.bam barcoded_primers.fasta movieX.fl.bam --isoseq --no-pbi
+
+**Example 1:**
+Following is the `primer.fasta` for the Clontech SMARTer cDNA library prep,
+which is the officially recommended protocol:
+
+ >primer_5p
+ AAGCAGTGGTATCAACGCAGAGTACATGGG
+ >primer_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:
+
+ movieX.fl.primer_5p--primer_3p.bam
+
+If your library contains multiple samples, execute the following workflow
+for each primer pair:
+
+ movieX.fl.primer_5p--brain_3p.bam
+ movieX.fl.primer_5p--liver_3p.bam
+
+### Step 3 - Refine
+Your data now contains full-length reads, but still needs to be refined by:
+ - [Trimming](https://github.com/PacificBiosciences/trim_isoseq_polyA) of poly(A) tails
+ - Rapid concatmer [identification](https://github.com/jeffdaily/parasail) and removal
+
+**Input**
+The input file for *refine* is one demultiplexed CCS file with full-length reads
+and the primer fasta file:
+ - `