From 8f3aefa5bb355e866cfd6cca008267eefb962bb5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Armin=20To=CC=88pfer?= Date: Tue, 30 Mar 2021 11:35:21 +0200 Subject: [PATCH] Add new docs --- doc/img/isoseq-dedup-faq.png | Bin 0 -> 183650 bytes docs/CNAME | 1 + docs/_config.yml | 38 ++++ docs/_includes/head_custom.html | 8 + docs/_sass/color_schemes/custom.scss | 5 + docs/assets/images/android-chrome-192x192.png | Bin 0 -> 9407 bytes docs/assets/images/android-chrome-512x512.png | Bin 0 -> 28248 bytes docs/assets/images/apple-touch-icon.png | Bin 0 -> 8343 bytes docs/assets/images/favicon-16x16.png | Bin 0 -> 457 bytes docs/assets/images/favicon-32x32.png | Bin 0 -> 995 bytes docs/assets/images/favicon.ico | Bin 0 -> 15406 bytes docs/assets/images/site.webmanifest | 1 + docs/changelog.md | 47 +++++ docs/clustering/cli-workflow.md | 145 +++++++++++++++ docs/clustering/examples.md | 91 ++++++++++ docs/clustering/faq.md | 72 ++++++++ docs/clustering/high-level-workflow.md | 12 ++ 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a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..99cbc89 --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1,47 @@ +--- +layout: default +title: Changelog +nav_order: 99 +--- + +# Version changelog + + * **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 + `.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. diff --git a/docs/clustering/cli-workflow.md b/docs/clustering/cli-workflow.md new file mode 100644 index 0000000..949408c --- /dev/null +++ b/docs/clustering/cli-workflow.md @@ -0,0 +1,145 @@ +--- +layout: default +parent: Clustering +title: CLI Workflow +nav_order: 3 +--- + +# CLI Workflow + +The low-level workflow explained via CLI calls. All necessary dependencies are +installed via bioconda. + +## Step 1 - Input +### CLR data from Sequel / Sequel II / Sequel IIe +For each SMRT cell a `movieX.subreads.bam` is needed for processing. + +Each sequencing run is processed by [*ccs*](https://github.com/PacificBiosciences/ccs) +to generate one representative circular consensus sequence (CCS) for each ZMW. +It is advised to use the latest CCS version 4.2.0 or newer. +_ccs_ can be installed with `conda install pbccs`. + + $ ccs movieX.subreads.bam movieX.ccs.bam --min-rq 0.9 + +You can easily parallelize _ccs_ generation by chunking, please follow [this how-to](https://ccs.how/faq/parallelize). + +### CCS data from Sequel IIe +If on-instrument CCS was performed, you can use the `reads.bam` or +`hifi_reads.bam` as input. + +The `hifi_reads.bam` contains only HiFi reads, with predicted accuracy ≥Q20. No +additional filtering is required. + +The `reads.bam` contains one representative sequence per productive ZMW, +irrespective of quality and passes. Do not forget to use `isoseq3 refine +--min-rq 0.9` in step 3! + +## Step 2 - Primer removal and demultiplexing +Removal of primers and identification of barcodes is performed using [*lima*](https://lima.how/), +which can be installed with \ +`conda install lima` and offers a specialized `--isoseq` mode. +Even in the case that your sample is not barcoded, primer removal is performed +by *lima*. +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 `--peek-guess` +to remove spurious false positive signal. +More information about how to name input primer(+barcode) +sequences in this [lima Iso-Seq FAQ](https://lima.how/faq/isoseq). + + $ lima movieX.ccs.bam barcoded_primers.fasta movieX.fl.bam --isoseq --peek-guess + +**Example 1:** +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: + + movieX.fl.NEB_5p--NEB_Clontech_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 concatemer [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: + - `.fl.bam` or `.fl.consensusreadset.xml` + - `primers.fasta` + +**Output**\ +The following output files of *refine* contain full-length non-concatemer reads: + - `.flnc.bam` + - `.flnc.transcriptset.xml` + +Actual command to refine: + + $ isoseq refine movieX.NEB_5p--NEB_Clontech_3p.fl.bam primers.fasta movieX.flnc.bam + +If your sample has poly(A) tails, use `--require-polya`. +This filters for FL reads that have a poly(A) tail +with at least 20 base pairs (`--min-polya-length`) and removes identified tail: + + $ isoseq refine movieX.NEB_5p--NEB_Clontech_3p.fl.bam movieX.flnc.bam --require-polya + +**Attention!**\ +If your workflow input is `reads.bam`, use `--min-rq 0.9` + +## Step 3b - Merge SMRT Cells +If you used more than one SMRT cells, list all of your `.flnc.bam` in one +`flnc.fofn`, a file of filenames: + + $ ls movie*.flnc.bam movie*.flnc.bam movie*.flnc.bam > flnc.fofn + +## Step 4 - Clustering +Compared to previous IsoSeq approaches, *IsoSeq v3* 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](https://github.com/lh3/minimap2) and iterative cluster merging + - Polished [POA](https://github.com/rvaser/spoa) sequence generation, using a QV guided consensus approach + +**Input** +The input file for *cluster* is one FLNC file: + - `.flnc.bam` or `flnc.fofn` + +**Output** +The following output files of *cluster* contain polished isoforms: + - `.bam` + - `.hq.fasta.gz` with predicted accuracy ≥ 0.99 + - `.lq.fasta.gz` with predicted accuracy < 0.99 + - `.bam.pbi` + - `.transcriptset.xml` + +Example invocation: + + $ isoseq cluster flnc.fofn clustered.bam --verbose --use-qvs diff --git a/docs/clustering/examples.md b/docs/clustering/examples.md new file mode 100644 index 0000000..e81ded4 --- /dev/null +++ b/docs/clustering/examples.md @@ -0,0 +1,91 @@ +--- +layout: default +parent: Clustering +title: Examples +nav_order: 4 +--- + +## Real-world example + +### Single sample +This is an example of an end-to-end cmd-line-only workflow to get from +subreads to transcripts. It's a 1% subsampled Alzheimer dataset. +You can either download the subreads and call HiFi on your own or skip this step +and download the HiFi reads generated by CCS v4.2: + + $ wget https://downloads.pacbcloud.com/public/dataset/IsoSeq_sandbox/2020_Alzheimer8M_subset/alz.1perc.subreads.bam + + $ ccs --version + ccs 4.0.0 + + $ ccs alz.1perc.subreads.bam alz.1perc.ccs.bam --min-rq 0.9 + + # Or download the pre-computed HiFi reads + $ wget https://downloads.pacbcloud.com/public/dataset/IsoSeq_sandbox/2020_Alzheimer8M_subset/alz.1perc.ccs.bam + + $ cat primers.fasta + >NEB_5p + GCAATGAAGTCGCAGGGTTGGGG + >Clontech_5p + AAGCAGTGGTATCAACGCAGAGTACATGGGG + >NEB_Clontech_3p + GTACTCTGCGTTGATACCACTGCTT + + $ lima --version + lima 1.11.0 (commit v1.11.0) + + $ lima alz.1perc.ccs.bam primers.fasta alz.fl.bam --isoseq --peek-guess + + $ ls alz.fl* + alz.fl.json alz.fl.lima.summary + alz.fl.lima.clips alz.fl.NEB_5p--NEB_Clontech_3p.bam + alz.fl.lima.counts alz.fl.NEB_5p--NEB_Clontech_3p.bam.pbi + alz.fl.lima.guess alz.fl.NEB_5p--NEB_Clontech_3p.subreadset.xml + alz.fl.lima.report + + $ isoseq refine alz.fl.NEB_5p--NEB_Clontech_3p.bam primers.fasta alz.flnc.bam + + $ ls alz.flnc.* + alz.flnc.bam alz.flnc.filter_summary.json + alz.flnc.bam.pbi alz.flnc.report.csv + alz.flnc.consensusreadset.xml + + $ isoseq cluster alz.flnc.bam clustered.bam --verbose --use-qvs + Read BAM : (37648) 1s 235ms + Convert to reads : 589ms 797us + Sort Reads : 8ms 409us + Aligning Linear : 23s 63ms + Read to clusters : 861ms 287us + Aligning Linear : 20s 279ms + Merge by mapping : 7s 242ms + Consensus : 4s 663ms + Merge by mapping : 980ms 742us + Consensus : 103ms 913us + Write output : 1s 799ms + + $ ls clustered* + clustered.bam clustered.hq.fasta.gz + clustered.bam.pbi clustered.lq.bam + clustered.cluster clustered.lq.bam.pbi + clustered.cluster_report.csv clustered.lq.fasta.gz + clustered.hq.bam clustered.transcriptset.xml + clustered.hq.bam.pbi + +### Multiplexed samples + + # Download HiFi reads + $ wget https://downloads.pacbcloud.com/public/dataset/IsoSeq_sandbox/2020_MultiplexIsoSeq_toy/m54363_190223_194117.ccs.bam + + # Download barcoded primers + $ wget https://downloads.pacbcloud.com/public/dataset/IsoSeq_sandbox/2020_MultiplexIsoSeq_toy/NEB_barcode16.fasta + + # Demux and primer removal + $ lima m54363_190223_194117.ccs.bam NEB_barcode16.fasta fl.bam --isoseq --peek-guess + + # Combine inputs + $ ls fl.bc1001_5p--bc1001_3p.bam fl.bc1002_5p--bc1002_3p.bam > all.fofn + + # Remove poly(A) tails and concatemer + $ isoseq3 refine all.fofn NEB_barcode16.fasta flnc.bam --require-polya --log-level DEBUG + + $ isoseq3 cluster flnc.bam clustered.bam --use-qvs --verbose diff --git a/docs/clustering/faq.md b/docs/clustering/faq.md new file mode 100644 index 0000000..85c565a --- /dev/null +++ b/docs/clustering/faq.md @@ -0,0 +1,72 @@ +--- +layout: default +parent: Clustering +title: FAQ +nav_order: 99 +--- + +## FAQ + +### 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. + +### How does Iso-Seq v3 compare to 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 +*Iso-Seq v3* is orders of magnitude faster than currently employed solutions and +[SQANTI](https://bitbucket.org/ConesaLab/sqanti) attributes *Iso-Seq 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 Iso-Seq v3? +Even though we also observe fewer polished transcripts with *Iso-Seq v3*, the +overall quality is much higher. Most of the low-quality transcripts are lost in the +demultiplexing step. *Iso-Seq 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. +For this, `isoseq refine` generates FLNC reads. + +For version 3.0, poly(A) tail removal and concatemer detection is performed in +`isoseq cluster` + +### My sample has poly(A) tails, how can I remove them? +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. + +### 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, *Iso-Seq 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. + +### 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. + +### When are two reads clustered? +*Iso-Seq 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. diff --git a/docs/clustering/high-level-workflow.md b/docs/clustering/high-level-workflow.md new file mode 100644 index 0000000..ea2b3c4 --- /dev/null +++ b/docs/clustering/high-level-workflow.md @@ -0,0 +1,12 @@ +--- +layout: default +parent: Clustering +title: High-level Workflow +nav_order: 1 +--- + +## High-level Workflow + +The high-level workflow depicts files and processes: + + diff --git a/docs/clustering/index.md b/docs/clustering/index.md new file mode 100644 index 0000000..34de0e3 --- /dev/null +++ b/docs/clustering/index.md @@ -0,0 +1,8 @@ +--- +layout: default +title: Clustering +has_children: true +nav_order: 4 +--- + +# Clustering diff --git a/docs/clustering/schematic-workflow.md b/docs/clustering/schematic-workflow.md new file mode 100644 index 0000000..8feb730 --- /dev/null +++ b/docs/clustering/schematic-workflow.md @@ -0,0 +1,12 @@ +--- +layout: default +parent: Clustering +title: Schematic Workflow +nav_order: 2 +--- + +## Schematic Workflow + +The mid-level workflow schematically explains what happens at each stage: + + diff --git a/docs/clustering/smrtbell-barcode-design.md b/docs/clustering/smrtbell-barcode-design.md new file mode 100644 index 0000000..6bb491a --- /dev/null +++ b/docs/clustering/smrtbell-barcode-design.md @@ -0,0 +1,14 @@ +--- +layout: default +parent: Clustering +title: SMRTbell designs +nav_order: 0 +--- + +## SMRTbell/Barcode designs + +PacBio supports three different SMRTbell designs for Iso-Seq libraries. +In all designs, transcripts are labelled with asymmetric primers, +whereas a poly(A) tail is optional. Barcodes may be optionally added. + + diff --git a/docs/general-faq.md b/docs/general-faq.md new file mode 100644 index 0000000..630e60c --- /dev/null +++ b/docs/general-faq.md @@ -0,0 +1,27 @@ +--- +layout: default +title: General FAQ +nav_order: 5 +--- + +# General FAQ +## BAM tags explained +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`. + +## The binary does not work on my linux system! +Binaries require **SSE4.1 CPU support**; CPUs after 2008 (Penryn) include it. diff --git a/docs/img/isoseq-barcoding.png b/docs/img/isoseq-barcoding.png new file mode 100644 index 0000000000000000000000000000000000000000..19c091ba5cb7e89c410bb40fd092bac843d4816e GIT binary patch literal 182914 zcmYg&1yodB_x=bdN(j}& z-S9uW@ArMb|6Pm4x-)asn!!XJip;t^ObbS&skVNvwHFhdt@D-&^WIFMs^?#&2M&`}Nj 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