Appendix
--------

Although *FuSViz* is designed for interpretation and visualization of
SVs in a sample cohort, it can be utilized for single sample analysis
together with read alignments as well. Read alignments from DNA-seq and
RNA-seq data are able to be imported in **Linear** and **Two-way**
modules.

Quality control of SVs via read alignment in Linear module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

1. Load annotation resource data of human genome **hg19** version, then
   upload RNA-seq alignment and index files at the path
   ``~/inst/example/RNA-seq-BRAF.bam`` and
   ``~/inst/example/RNA-seq-BRAF.bam.bai`` of *FuSViz* package.

|image1|

2. Import an example file of RNA SV calls at the path
   ``~/inst/example/BRAF_demo.txt`` of *FuSViz* package. Click on the
   index of the 6th row (i.e., *HNRNPDL-BRAF*) that links to the linear
   view session, then genomic coordinates of *HNRNPDL* and *BRAF*
   breakpoints are shown in two split windows.

|image2|

3. Inspect read alignment and search for supporting split reads at the
   breakpoints.

|image3|

A **split-window** mode is used to investigate alignment quality of
split reads mapped to *HNRNPDL* and *BRAF* genes (NOTE: please click on
the search box in the toolbar if a **split-window** mode does not show
properly). Soft-clipped read sequences (black box) match exactly the
fusion sequences at breakpoints of the partner genes *HNRNPDL* and
*BRAF* (black dash lines), respectively. Sometimes, the genomic
coordinates of breakpoints need an adjustment if the provisonal ones are
inaccurate.

Load alignment track from URL address in Linear module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If alignment data is hosted in a remote server or a cloud, users can
load it via URL web address. For example,

|image4|

|image5|

A **split-window** mode is also able to evaluate alignment quality of
discordant read pairs mapped to different genomic loci. For another
example, a read pair highlighted in dash boxes shows a discordant
mapping feature to partner genes *ABHD12B* and *RCC1*, respectively.

Visualize SV event together with read coverage using Two-way module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This functionality is performed under CLI environment (**NOT** available
via web interface). Firstly, load *FuSViz* package in R:

::

   library(FuSViz)
   options(uscsChromosomeName=FALSE)

Set gene/transcript annotation version (e.g. **hg19**) and RNA-seq
alignment path

::

   version = "hg19"; # or “hg38”
   rna_bam_path=file.path(extdata = system.file("extdata", package = "FuSViz"), "RNA-seq-example.bam")

Use ‘plot_separate_individual_bam’ to visualize fusion together with RNA-seq read coverage
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For example, plot a fusion event of “PIK3R1-HSD17B4”

::

   plot_separate_individual_bam(first_name = "PIK3R1", second_name = "HSD17B4", breakpoint_A = 67576834, breakpoint_B = 118792010, coverage_plot_trans = F, version=version, rna_bam_path = rna_bam_path, split = 9, span = 1, fusion_strandA="+", fusion_strandB="-")

|image6|

From the top it shows the position of partner genes in a chromosome
ideogram, the fusion event (a curved line marked by read support [9 -
split read, 1 – spanning read pair]; arrow indicates transcription
direction of the fusion), exon annotations of different transcript
isoforms for upstream (colored by **green**) and downstream (colored by
**orange**) partners, RNA expression level measured by read counts and
genomic coordinates of partner gene loci in Mb from chromosome.
``coverage_plot_trans = F`` suggests RNA read coverage is plotted using
reads mapped to exons and introns of all transcript isforms of geneA and
geneB.

Visualize fusion and read coverage calculated using specific transcript isoforms
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

   plot_separate_individual_bam(first_name = "PIK3R1", second_name = "HSD17B4", breakpoint_A = 67576834, breakpoint_B = 118792010, coverage_plot_trans = T, version=version, rna_bam_path = rna_bam_path, transcriptA="ENST00000521381 ENST00000274335", transcriptB="ENST00000414835", split = 9, span = 1, fusion_strandA="+", fusion_strandB="-")

``coverage_plot_trans = T`` suggests RNA-seq read coverage is plotted
using the exons of selected transcripts **ENST00000521381** and
**ENST00000414835**. If breakpoint falls within a intron, read coverage
of the related intron is plotted as well.

|image7|

Visualize fusion and read coverage calculated by duplicated aligned reads
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

By default, the coverage is plotted using un-duplicated aligned reads
(i.e. ``duplicate=F``). If users would like to plot coverage using
duplicated aligned reads, please set ``duplicate=T`` (NOTE:
``duplicate=T`` only works when alignment is processed by Picard or
Samtools with the setting **MarkDuplicate=T**).

|image8|

|image9|

Visualize fusion if DNA-seq read coverage is available
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

   dna_bam_path=file.path(extdata = system.file("extdata", package = "FuSViz"), "DNA-seq-example.bam");
   plot_separate_individual_bam(first_name = "PIK3R1", second_name = "HSD17B4", breakpoint_A = 67576834, breakpoint_B = 118792010, coverage_plot_trans = F, version=version, chrom_notation_rna = T, chrom_notation_dna = F, split = 9, span = 1, rna_bam_path = rna_bam_path, dna_bam_path = dna_bam_path, fusion_strandA="+", fusion_strandB="-")

Read alignment from DNA-seq data can be whole genome sequencing,
Exome-seq or gene-panel target sequencing. ``chrom_notation_rna = T``
suggests the chromosome notation in RNA-seq alignment file is named like
**‘chrX’** (i.e. UCSC syntax); ``chrom_notation_dna = F`` denotes the
chromosome notation in DNA-seq alignment file is named like **‘X’**
(i.e. ensembl syntax).

|image10|

An example of fusion and read coverage plot using docker engine
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

::

   version = 'hg19';
   docker run --rm -v `pwd`:/data senzhao/fusviz_shiny_app:1.0 R -e "library(FuSViz); options(uscsChromosomeName=F); pdf(file='/data/fusion_plot.pdf', height=7, width=14); plot_separate_individual_bam(first_name='PIK3R1', second_name='HSD17B4', breakpoint_A=67576834, breakpoint_B=118792010, coverage_plot_trans = T, version='$version', rna_bam_path=file.path(extdata=system.file('extdata', package='FuSViz'), 'RNA-seq-example.bam'), transcriptA='ENST00000521381 ENST00000274335', transcriptB='ENST00000414835', split=9, span=1, fusion_strandA='+', fusion_strandB='-'); dev.off();"

**NOTE:** the ouptput file ``fusion_plot.pdf`` is generated at the
current path ``pwd`` as the path ``pwd`` in host machine is binded to
the volume path ``/data`` in the container.

A full usage of ‘plot_separate_individual_bam’
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

See reference, ``?plot_separate_individual_bam``

.. |image1| image:: 6.1.Load_local_alignment_track_in_Linear_module_1.png
.. |image2| image:: 6.1.Load_local_alignment_track_in_Linear_module_2.png
.. |image3| image:: 6.1.Load_local_alignment_track_in_Linear_module_3.png
.. |image4| image:: 6.2.Load_alignment_track_from_URL_address.png
.. |image5| image:: 6.1.Load_local_alignment_track_in_Linear_module_4.png
.. |image6| image:: 6.3.fusion_RNA-seq_1.png
.. |image7| image:: 6.3.fusion_RNA-seq_2.png
.. |image8| image:: 6.3.fusion_RNA-seq_3.png
.. |image9| image:: 6.3.fusion_RNA-seq_4.png
.. |image10| image:: 6.3.fusion_RNA-seq_DNA-seq_5.png