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A program for the analysis of single cell nanopore long read data

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Scywalker
========= 
A program for the analysis of single cell Oxford nanopore long read data
Copyright VIB and University of Antwerp

Scywalker
-------
scywalker is a package designed to analyse single cell (10x) Oxford
nanopore long read data (without the need for matching short read data),
but it can also analyse PacBio data with the appropriate options.
It provides end-to-end analysis in one command: Starting from fastqs, it
will find and assign cellbarcodes, align reads, and reconstruct (based on
[IsoQuant](https://github.com/ablab/IsoQuant)) and quantify isoforms and
genes, producing both bulk and per cell counts.

If cell specific markersets are provided, it will also assign cell-types
and generate pseudobulk (per cell-type) counts for each sample, and make
count files allowing comparison between samples for these pseudobulk
counts.

scywalker is implemented within
[genomecomb](https://github.com/derijkp/genomecomb/). The scywalker
distribution contains all needed dependencies, including the full
genomecomb. This provides, besides the core scywalker algorithms, also
several commands/tools useful for scywalker set-up, analysis and
downstream analysis.

Installation
------------
Binary packages for Linux can be downloaded from github
([https://github.com/derijkp/scywalker](https://github.com/derijkp/scywalker))

Scywalker is distributed as a portable application directory: A
self-contained directory with the scywalker executable (scywalker) and all
needed depencies compiled in a way that they should work on all (except very
ancient) Linux systems.

Installation of the package is as simple as downloading the
[distribution](https://github.com/derijkp/scywalker/releases/download/0.112.0/scywalker-0.112.0-linux-x86_64.tar.gz)
from github
([https://github.com/derijkp/scywalker](https://github.com/derijkp/scywalker))
and unpacking it, e.g.: 
```
cd ~/bin
wget https://github.com/derijkp/scywalker/releases/download/0.112.0/scywalker-0.112.0-linux-x86_64.tar.gz
tar xvzf scywalker-0.112.0-linux-x86_64.tar.gz
rm scywalker-0.112.0-linux-x86_64.tar.gz
```

You can call the executables (scywalker, cg) directly from the directory
using the path (e.g. `~/bin/scywalker-0.112.0-linux-x86_64/scywalker ..`) 
or by placing the directory in the PATH environment variable (e.g. using 
`export PATH=~/bin/:$PATH`)
You can also place soft-links to the executables in a directory already in
the PATH. (remark: The executable itself needs to stay in the application
directory to find it's dependencies), e.g.
```
cd ~/bin
ln -s scywalker-0.112.0-linux-x86_64/scywalker .
ln -s scywalker-0.112.0-linux-x86_64/scywalker_makerefdir .
ln -s scywalker-0.112.0-linux-x86_64/sw .
ln -s scywalker-0.112.0-linux-x86_64/cg .
```

Scywalker is largely implemented within [genomecomb](https://github.com/derijkp/genomecomb), 
and its distribution comes with an appropriate full version of genomecomb,
which can be run using the cg or sw executable, which also provides
multiple usefull extra tools for querying tsv files, etc.

Example/test run
----------------
As an example/test, the following code shows you how to download an example data set and run scywalker on it:
```
# download and unpack test data
wget https://github.com/derijkp/scywalker/releases/download/v0.108.0/scywalker_test.tar.gz
tar xvzf scywalker_test.tar.gz

cd scywalker_test
# make refdir; This test data is limited to chromosome 17, so there are no organelles included
# The command will accept compressed source fasta and gtf files (.gz, .zst, ..)
scywalker_makerefdir -organelles '' g17 genome.fa genes.gtf

# run scywalker using 8 cores and 64G of memory on local machine; adapt these numbers to what you have available 
# on your machine (or use e.g. sge to run on a grid engine cluster)
# Of course we cannot properly determine celltypes on this limited data set.
# The "marker" genes in the included markers_chr17.tsv are not good
# markers, they are just made to allow celltyping to at least run on this specific limited dataset
scywalker -v 1 -d 8 \
	-dmaxmem 64G \
	-refdir g17 \
	-sc_expectedcells 183 \
	-cellmarkerfile markers_chr17.tsv \
	-threads 6 \
	test10x
```

Reference data
--------------
A scywalker analysis needs a reference genome and a set of known isoforms
in a specific format, with different types of indexes and supporting
files. These must be provided in a reference directory.

You can use the included command scywalker_makerefdir to make a reference
directory starting from a fasta and a gtf transcript file.
If there are organelles in the genome sequence, it is important to specify them so the organelle
specific algorithm can be used: The isoquant based code often hangs or crashes on the
very different organelle data.
```
scywalker_makerefdir -organelles organelleslist refdir genomesequence.fasta transcripts.gtf
```
where 
* `organelleslist` is a space separated list of chromosomes that represent organelles, e.g. 'chrM chrPt',
* `genomesequence.fasta` is a multifasta file with the genomesequence and transcripts.gtf
* `transcripts.gtf` is a gtf file with transcripts for the given genome sequence. It is also possible
to give a (genomecomb) gene tsv file here.

You can also use genomecomb reference directories for this; These can be downloaded from
the genomecomb website for a number of species (or created new) as
described in the [genomecomb installation
documentation](https://derijkp.github.io/genomecomb/install.html).

When downloadinga reference, be sure to also download and install the matching minimap2 indexes, e.g.
```
wget https://genomecomb.bioinf.be/download/refdb_hg38-0.110.0.tar.gz
tar xvzf refdb_hg38-0.110.0.tar.gz
wget https://genomecomb.bioinf.be/download/refdb_hg38-minimap2-0.110.0.tar.gz
tar xvzf refdb_hg38-minimap2-0.110.0.tar.gz
```
### PacBio indexes
By default scywalker_makerefdir will only create an ont minimap2 index for the genome.
If you want to analyze PacBio data, the appropriate index can be added by giving the 
option '-pacbioindex 1' to scywalker_makerefdir

### Chromosome grouping
When distributing over chromosomes (or regions), by default alt and
unplaced chromosomes (usually small) are grouped together. This default
relies on the fact that these (often) contain a "_", and so, e.g.
chr1_KI270706v1_random, chr1_KI270707v1_random are grouped under chr1_.
For genomes where this pattern is not good, the scywalker_makerefdir
command supports the option `-groupchromosomes` to specify a different way
of grouping: Each chromosome is matched to the given list of regular
expressions, and if it matches one the chromosome is assigned to a group
named after the match.

Sample data
-----------
Scywalker works on data coming in the form of a **project** or **sample directory**:
A **sample directory** is a directory that has (at least) a subdirectory named fastq. This
fastq directory must contain the fastq files (or softlinks to them). The
sample name is determined by the name of the sample directory. Results of the
analysis specific for the sample will be added in this directory

A **project directory** can be used to analyse multiple samples in one run. 
This is a directory (name of the dir determines the name of the
run/project) that at least contains a subdirectory named samples. This
samples subdir contains a sample directory from each sample in the run. On
analysis of a projectdir, all samples are analysed individually, and files
providing comparisons of multiple samples will be made in a subdirectory
compar.

The starting project directory should look thus like:

* project_directory/
  * sample1/
    * fastq/
      * file1.fq.gz
      * file2.fq.gz
  * sample2/
    * fastq/
      * file1.fq.gz
      * file2.fq.gz

Sample data using a samplesheet
-------------------------------
You can also provide a samplesheet using the -samplesheet option to create
a new project directory based on the data in the samplesheet. 
The **samplesheet** is a tab-separated value file with at least the fields
"sample" and "seqfiles" (The command will also recognize fieldnames
"fastq" or "fastqs" instead of "seqfiles")

For each in the samplesheet line the sample (name given by the field
"sample") will be created in the directory ```<projectdir>/samples```.
seqfiles gives the
location of sequencing data files in fastq or ubam format that will be
added to the sample. The value can be the path to a specific file, a
directory (containing the sequencing files), or a (glob) pattern matching
one or more sequencing files (e.g. data/sample1_*.fastq.gz) The data files
are (by default) soflinked in the directory ```<projectdir>/samples/<samplename>/fastq```
 for fastq files (extension .fastq, .fq, .fastq.gz, or .fq.gz), and in
```<projectdir>/samples/<samplename>/ubam``` for unaligned bam files
(extension .bam)

You can have more than one line for the same sample, possibly merging
sequencing data from different sources into one sample. (You can not mix
fastq and ubam sources this way; if both a fastq and ubam directory are
present, only the ubam will be used for analysis)

The extra fields in the samplesheet (if not empty) are added to the projects
options.tsv file, which allows you to set specific analysis options for each
sample, e.g. the samplesheet
```
sample	seqfiles	sc_expectedcells
sample1	sample1/*.fastq.gz	2000
sample2	sample2/*.fastq.gz	10000
```
will setup a projectdir where sample1 will be analysed using 2000 expected
cells, whereas for sample2 10000 are expected.

Running scywalker
-----------------
You can run scywalker using the following command
```
scywalker ?options? sampledir/projectdir
```
This will analyse the sample in **sampledir** or all samples in
**projectdir** using the reference data in **refdir**. Results of the
analysis (or intermediate files) are added in place in the sampledir when
finsihed. If analysis is interupted or has an error, you can continue the
analysis by issuing the same command (after fixing what caused the error)

Options typically included (some required) for basic analysis of a 10x v3 (default)
data set would be
`-refdir`
    reference directory with genomesequence, etc. as described previously. This option is required.

`-sc_expectedcells`
    gives the the number of cells expected. This information is required for filtering cells using emptydrops
    However when processing a projectdir, this number is not always the same for all samples. You
    can give different values for this option by writing a tsv (tab separated value) file named
    options.tsv in the projectdir with the following fields: sample option value
    For each sample (that differs from the general option if given) you add a line with the samplename, 
    the option (sc_expectedcells without the -) and the value (the number of expected cells in this case)

`-cellmarkerfile`
    A tsv file providing genes that are indicative specific cell cell types. If not given, scsorter will not
    be used to determine celltypes and making pseudobulk files
    It can contain the following fields:
    **marker**: gene indicative of celltype (obligatory field)
    **celltype**: celltype for which gene is indicative (obligatory field)
    **tissue**: can be used in combination with the -tissue option; only markers of the given tissue will be used
    **markertype**: is the marker expressed "up" or not expressed "down" in celltype (only used by sctype currently)
    **weight**: weight of the marker (only used by sctype currently)

`-samplesheet`
    A tsv file providing the samples to be analysed with where there raw data is found. If given,
    the project directory will be automatically created (or added to) based on
    this data (see "Sample data using a samplesheet")

`-tissue`
    The tissue type of the sample. If cellmarkerfile is given, only markers of the given tissue are used.
    If cellmarkerfile is not given, scsorter is not run; however sctype will use its internal database 
    with the given tissue. If neither is given, no celltyping or pseudobulk generation is done.

`-preset`
    You can set this to `pacbio` for analyzing Pacific Biosciences long reads data, or to `ont` (default) for the analysis of Oxford nanopore data.

`-d`
    By default the command is run using a single core (=slow). Use the `-d` option to specify the
    manner of job distribution/parallelisation. Use a number to specify distribution
    over (max) the given number of cores on the local machine, while specifying "sge" or
    "slurm" here is one way (see next for other) to distribute jobs over a Grid Engine or SLURM cluster.
    On a cluster the command will finish after submitting all jobs (with dependencies).
    For distributed runs a tab separated log file is created (in the projectdir) named
    process_project_<projectname>.<starttime>.running
    This contains information on all started jobs, and when all jobs are finished, 
    this log file will be renamed to process_project_<projectname>.<starttime>.finished on success
    or to process_project_<projectname>.<starttime>.error when there was an error
    encountered. In this case, specific jobs that had errors can be found in the 
    logfile.
    More information on options for distribution options can be found in the
    [genomecomb joboptions help](https://derijkp.github.io/genomecomb/joboptions.html)

`-dsubmit`
    Some clusters limit the number of concurrent submitted jobs. This option provides an alternative
    method for distribution that works on those: You specify the (maximum) number of jobs
    that can be submitted using the -d <number> option, and add sge or slurm here to indicate
    which job management system will be submitted to. Using this method, the command will have keep
    running until all jobs are finished.

`-dmaxmem`
    The maximum memory to be used (reserved) when running distributed on a local machine.
    This will stop only jobs from starting as long as they request more memory than currently 
    available (based on requested memory by running jobs). Jobs that use more memory than requested
    will not be stopped by this. This option will have no effect when running on a cluster

Joint Analysis
--------------

The level of proof for adding a novel isoform is a lot higher than for counting a known one. This sometimes causes
real isoforms to be only detected in some samples, while they are present (typically in low abundance) in the other samples.
joint analysis solves this by reanalysing each sample with selected novel isoforms detected in the first pass treated
as "known" samples in the second pass

`-iso_joint isoquant`
    perform joint analysis on the isoquant analysis: the resulting isoforms of all per sample runs are combined, 
    and each sample is recalled with the combination of isoforms (including novel ones found in at least 2 samples default) 
    treated as reference/known transcripts
`-iso_joint_min`
    integer: minimum number of samples an isoform has to be found in to be included in the joint analysis.
    The default value is 2. You can set this to 1, but this will include a lot more artifact isoforms 
    detected in only one of the samples (increasing runtime considerably). If you have a lot of samples,
    you can increase this value to reduce the number random/artefacts isoforms.

Other options
-------------
Scywalker defaults to analysis of the 10x v3 protocol. 
The following settings influence how **barcodes and UMIs** are found, and some can be used to analyse 10x v2

`-sc_whitelist`
    Used to provide a file with all possible correct barcodes. By default the whitelist with the 10x version 3
    barcodes is used. You can specify to use version 2 of the whitelist by using the shortcut
    `-sc_whitelist v2`
    Other versions you can use are v4 for the 3' version 4 and p5v3 for the 5' version 3 (for version 2 the 5' whitelist is the same as the 3').
    You can also specify a different whitelist by giving a file containing the barcodes, e.g. for 10x v2 using
    `-sc_whitelist ~/bin/scywalker-0.112.0-linux-x86_64/whitelists/737K-august-2016.txt.gz`
    You can also choose to not use a whitelist by specifying an empty for the option using
    `-sc_whitelist ''`

`-sc_umisize`
    The default UMI size is 12 (v3). 10 v2 has a smaller UMI of 10; you can specify this using 
    `-sc_umisize 10`

`-sc_barcodesize`
    The default UMI size is 16  for both v2 and v3. This should normally not be changed

`-sc_adaptorseq`
    The default adapter sequence used to find the barcode and UMI is CTACACGACGCTCTTCCGATCT.
    This should normally not be changed

Some options influence **distribution** (besides `-d` and other [joboptions](https://derijkp.github.io/genomecomb/joboptions.html))

`-threads`
    The number of threads commands in jobs that support threading will use. On a cluster, this many cores
    will be reserved for the job. As threading in this commands often does not scale very well, keep the number
    typically low (4, max 8)

`-distrreg`
    determines how jobs are distributed over regions. Default is g5000000,
    which will distribute over regions of approximately 5M where splits can
    only occur in larger regions without known genes. Other possibilities are
    chr for per chromosome or 0 for no distribution over regions. It is advised
    to keep the default as larger regions also require more (peak) memory
    and

`-maxfastqdistr`
    alignment is run per fastq; if there are very many small fastqs the overhead to 
    processes (alignment etc.) them separately (default) can become too large.
    The number given here limits the number of separately processed fastqs:
    if there are more separate input fastqs than the number given, they will be merged 
    before processing.

Various **other options** are

`-v`
    default 0, increase (up to 2) to increase the verbosity level, i.e. how much information 
    starting up jobs, dependencies, etc. is displayed

`-stack`
    set to 1 (default 0) to show an extended stack trace on error (mainly for debugging)

`-aligners`
    The default aligner is minimap2_splice (minimap2 with the splice preset). You could (experimentally)
    try to change this to minimap2_splicesmall to run with settings optimized for finding 
    small exons (but probably making more mistakes elsewhere)

Results
-------
After (successful) analysis, a sampledir contains various results files
following the genomecomb naming conventions (what-methods-samplename.extension). 
Result files are often tab separated files that are zstandard
(http://facebook.github.io/zstd/) compressed (extension .zst)

Compressed tsv files can also be easily read in e.g. R using
```
data=read.table(pipe("zstdcat file.tsv.zst"), sep="\t",header=T)
```
or if zstdcat is not installed, the included genomecomb command for this can be used:
```
data=read.table(pipe("sw zcat file.tsv.zst"), sep="\t",header=T)
```

Result files also usualy have an accompanying file with the .analysisinfo
extension that lists the tools (and their versions) used to generate the
file.

### Sample Results
The most important result files found in the sample directories are:

#### sc_gene_counts_filtered-isoquant_sc-sminimap2_splice-sample1.tsv.zst
zstandard compressed, tab separated file with gene information and UMI counts per
gene/per cell. There are several counts for different ways of correcting
multimapping reads:
- `count`: UMI count per gene, multimapping reads are weighed (if maps to N genes -> each gets 1/N count)
- `nicount`: same, but intronic reads are not counted towards the gene they are in (as in count)
- `maxcount`: UMI count per gene, multimappers count 1 to each gene
- `uniquecount`: multimappers are not counted

The `cell` field indicates which cell the counts apply to. In this file (_filtered) 
only information on the emptydrops approved cells is given, the file
with _raw provides counts for all detected "cells".

The basic data (only the counts field, less info on the genes) is also supplied 
in the 10x (MEX) format in the directory
sc_gene_counts_filtered-isoquant_sc-sminimap2_splice-sample1.10x

#### sc_isoform_counts_filtered-isoquant_sc-sminimap2_splice-sample1.tsv.zst
zstandard compressed, tab separated file with isoform/transcript information and UMI counts per
isoform/per cell.
Transcripts are described in fields using the genePred convention as described 
in the [genomecomb gene/transcript format](https://derijkp.github.io/genomecomb/format_gene.html).  
The **cell** field again indicates which cell the counts apply to. In this file (_filtered) 
only information on the emptydrops approved cells is given, the file
with _raw provides counts for all detected "cells".

This file also provides different ways counting/correcting for
reads supporting multiple isoforms (many reads are incomplete and could be derived from multiple isoforms):
- `counts_weighed`: reads supporting multiple (N) transcripts are weighed as 1/N
- `counts_unique`: count only reads uniquely supporting this one transcript
- `counts_strict`: only unique reads that cover >= 90% of the transcript
- `counts_aweighed`, `counts_aunique`, `counts_astrict`: same as above, but only reads with polyA (detected) counted

The basic data (using the counts_weighed field) is also supplied in the
10x (MEX) format in the directory as
`sc_isoform_counts_filtered-isoquant_sc-sminimap2_splice-sample1.weighed_count.10x`

#### sc_cellinfo_raw-isoquant_sc-sminimap2_splice-sample1.tsv.zst
A zstd compressed tsv file containing information on the detected cells
(one line for each cell), wth the following main fields:
- `cell` : cell barcode
- `readcount` : nr of reads assigned to to this cell
- `umicount` : nr of UMIs assigned to to this cell
- `is_cell` : 1 if emptydrops categorized the cell as real, 0 if not (the sc_cellinfo_filtered contains only info on cells where is_cell is 1)
- `nCount_RNA` : Seurat UMI count (can be lower than umicount due to filtering))
- `nFeature_RNA` : number of genes/features detected for the cell
Other information from Seurat, and two doublet finders is also added.

#### sc_group-scsorter-isoquant_sc-sminimap2_splice-sample1.tsv
tab separated file assigning cells to specific groups, in this case 
the celltype as determined by scsorter. More than one groupfile may be
present, e.g. based on sctype analysis (sc_group-sctype-isoquant_sc-sminimap2_splice-sample1.tsv)
Main fields are
- `cell` : cell barcode
- `group` : assigned group/cell type
- `group_filtered` : assigned group/cell type filtering put uncertains (foor tools that support this)
- `score` : asigned by celltyper
- `ncells` : number of cells in the group
- `UMAP_1` : UMAP coordinate 1 (for display)
- `UMAP_2` : UMAP coordinate 2 (for display)

#### pb_gene_counts-scsorter-isoquant_sc-sminimap2_splice-sample1.tsv.zst
zstandard compressed, tab separated pseudobulk gene counts file based on the scsorter
celltyper (there can more such files, e.g. one extra for the sctype celltyper).
This file contains the gene info similar to the sc_gene_counts file, and
provides the same types of counts, but the counts here are in wide format
(counts for all cell types on one line); The fields names indicate which
count and which celltype (and sample) is provided using the following
format: 
`<count_type>-<celltype>-scsorter-isoquant_sc-sminimap2_splice-<sample>`
e.g. `count-A549-scsorter-isoquant_sc-sminimap2_splice-mix1`

#### pb_isoform_counts-scsorter-isoquant_sc-sminimap2_splice-sample1.tsv.zst
zstandard compressed, tab separated pseudobulk isoformq counts file based on the scsorter
celltyper (there can more such files, e.g. one extra for the sctype celltyper).
This file contains the gene info similar to the sc_gene_counts file, and
provides the same types of counts, but the counts here are in wide format
(counts for all cell types on one line); The fields names indicate which
count and which celltype (and sample) is provided using the following
format: 
`<count_type>-<celltype>-scsorter-isoquant_sc-sminimap2_splice-<sample>`
e.g. `count-A549-scsorter-isoquant_sc-sminimap2_splice-mix1`

#### report_scywalker-sample1.html
A html reports summarizing the results, and including various informative graphs.

#### read_assignments-isoquant_sc-sminimap2_splice-sample1.tsv.zst
Gives a line for each assignment of a read to an isoform: Each line
contains information about the read, the alignment, the supported isoform,
etc. (in case of assignments to novel isoforms, the events/differences
are with regards to the closest known isoform, not the novel one)

#### map-sminimap2_splice-sample1.bam
bam file created by aligning the reads of sample1. The read names have
embedded cellbarcode and UMI information in both the read name and
comments

### Multi-sample results

The results covering multiple samples are found in the `compar` subdirectory of the projectdir.

#### pb_isoform_counts-scsorter-project.tsv.zst
A compressed tsv file containing the combination of all per sample
pseudobulk isoform count files. It has the same format as the individual
pb_isoform_count files (with fields like
`<count_type>-<celltype>-scsorter-isoquant_sc-sminimap2_splice-<sample>`),
but is wider (having more than one sample).

Novel transcripts often differ slightly on the ends between different
samples (depending on reads present). Such transcripts are matched over
samples (if the have the same junctions) keeping the most outer ends for
the combination.

#### pb_gene_counts-scsorter-project.tsv.zst
A compressed tsv file containing the combination of all per sample pseudobulk gene count files.
It has the same format as the individual pb_gene_count files (with fields 
like `<count_type>-<celltype>-scsorter-isoquant_sc-sminimap2_splice-<sample>`), but is wider
(having more than one sample).

Novel genes with slightly different ends are similarly matched as isoforms.

Troubleshooting
---------------
Errors in the submission command (e.g. the given reference dir does not exists)
are returned immediately. You can get more extensive information on such errors
by adding the -stack 1 option (and possible the optiuon -v 2)
When running distributed (option -d with sge, slurm, or a number of cores), scywalker
can also encounter errors in the submitted jobs. Information on submitted jobs is
gathered in a directory log_jobs (files per job) and in a tsv log file name typically named
process_*.<ext>, where <ext> can be
* **submitting** when not all jobs are submitted yet
* **running** when the submission command is finished, but some jobs are still running (e.g. on cluster)
* **finished** on successful completion (when all jobs are ready and there were no errors, the log_jobs directory is deleted)
* **error** when all jobs are ready, but some had errors

When there was an error in one job, all jobs that depend on results of that job will also have errors
(dependencies that are not found), so you typically want to look for the first error. You can do
this by checking/querying the (tsv) log file. The convenience function error_report can be used to get a
more nicely formatted overview of the errors (if you do not specify the
logfile, it will take the most recent one in the current working
directory):
```
sw error_report ?logfile?
```
In this you can check the error messages, time run, etc.
With the **runfile** given in this output, you can try to run specific jobs separately

Tools
-----
The version of genomecomb included in the scywalker distribution
provides many tools useful for analysis of scywalker results.
You can call these using `cg toolname ...` or `sw toolname ...` if you want
to specifically use the scywalker version. You can get an overview
of all tools in genomecomb using
```
sw help
```
and help on specific tools using
```
sw toolname -h
```
Following tools are typically useful for scywalker analysis:

```
sw viz_transcripts ?options? isoform_counts_file gene output_file
```
[viz_transcripts](https://derijkp.github.io/genomecomb/cg_viz_transcripts.html)
can be used to create a visual presentation of isoform usage of a given gene. get more help

```
sw sc_pseudobulk scgenefile scisoformfile groupfile
```
[sc_pseudobulk](https://derijkp.github.io/genomecomb/cg_sc_pseudobulk.html)
make pseudobulk files of sc_gene and sc_transcript files based on an sc_group file

```
sw sc_demultiplex sampledir dmfile refseq
```
[sc_demultiplex](https://derijkp.github.io/genomecomb/cg_sc_demultiplex.html) can be used to 
demultiplex ont single cell results into separate samples based on a demultiplexing file

```
sw multitranscript ?options? multitranscriptfile transcriptfile transcriptfile ?transcriptfile? ...
```
[multitranscript](https://derijkp.github.io/genomecomb/cg_multitranscript.html)
can be used to combine separate per sample transcript files in one multisample transcript file

```
sw viz file.tsv
```
The [viz](https://derijkp.github.io/genomecomb/cg_viz.html) tool
can be used to browse and query (compressed) tsv result files using a graphical interface

```
sw select ?options? ?datafile? ?outfile?
```
Using the [select](https://derijkp.github.io/genomecomb/cg_select.html) tool,
you can query the tsv result files on the command-line

```
sw zcat file ...
```
[zcat](https://derijkp.github.io/genomecomb/cg_zcat.html)
will concatenate (potentially compressed) files to standard output. It differs from
normal zcat in that it supports multiple compression types (based on the
file extension) including zstandard (.gz .zst .lz4 .rz .bz2)

License
-------
The use of this application is governed by the GPL (license.txt).

How to contact me
-----------------

Peter De Rijk
VIB - UAntwerp Center for Molecular Neurology, Neuromics Support Facility - Bioinformatics
University of Antwerp
Universiteitsplein 1
B-2610 Antwerpen, Belgium

tel.: +32-03-265.10.40
E-mail: Peter.DeRijk@uantwerpen.vib.be

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