### Create RaggedExperiment from GRangesList

Source: https://github.com/sa-lee/superintronic/wiki/Design-Doc

Converts a list of CompressedGRanges to a RaggedExperiment object. This is useful for representing coverage data in a matrix form for downstream analysis.

```r
library(RaggedExperiment)
x <- as(lapply(design$bam, compute_coverage), "CompressedGRangesList")
wide <- RaggedExperiment(x, colData = design)
```

--------------------------------

### Summarise Coverage in RaggedExperiment

Source: https://github.com/sa-lee/superintronic/wiki/Design-Doc

Summarises coverage over specified features within a RaggedExperiment object. This function is equivalent to the rangle-join-rangle strategy.

```r
qreduceSummarizedExperiment(wide, features, fun_to_summarise_cov)
```

--------------------------------

### Load Libraries and Collect Gene Parts

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Loads necessary libraries and collects intronic/exonic regions for a specific gene from a GTF file using plyranges.

```r
library(superintronic)
#> Warning: replacing previous import 'IRanges::slice' by 'plyranges::slice' when
#> loading 'superintronic'
suppressPackageStartupMessages(library(plyranges))

features <- system.file("extdata", 
                        "Homo_sapiens.GRCh37.75_subset.gtf", 
                        package = "airway") %>%
  collect_parts() %>%
  filter(gene_name == "SRM")

features
#> GRanges object with 1 range and 9 metadata columns:
#>       seqnames            ranges strand |         gene_id   gene_name
#>          <Rle>         <IRanges>  <Rle> |           <Rle> <character>
#>   [1]        1 11114641-11120081      - | ENSG00000116649         SRM
#>          gene_source   gene_biotype     type         source   n_olaps
#>          <character>    <character> <factor>       <factor> <integer>
#>   [1] ensembl_havana protein_coding     gene protein_coding         1
#>                                                                exonic_parts
#>                                                               <GRangesList>
#>   [1] 1:11114641-11115261:-,1:11115838-11115983:-,1:11116068-11116151:-,...
#>                                                              intronic_parts
#>                                                               <GRangesList>
#>   [1] 1:11115262-11115837:-,1:11115984-11116067:-,1:11116152-11116660:-,...
#>   -------
```

--------------------------------

### Compute Coverage from BAM Files

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Computes coverage scores from BAM files using a design data frame. Ensure the design data frame includes a column with paths to BAM files. The function can propagate metadata from the design to the resulting GRanges object.

```r
design <- read.csv(system.file("extdata", 
                               "sample_table.csv", 
                               package = "airway")) %>% 
  dplyr::select(sample_id = X, cell, dex, albut) %>% 
  dplyr::mutate(bam = dir(system.file("extdata", package = "airway"), 
                          pattern = "*.bam",
                          full.names = TRUE)
  )

cvg <- compute_coverage_long(design, source = "bam")

cvg
```

--------------------------------

### Combine Coverage and Gene Tracks

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Combines the coverage plot and the gene track plot into a single figure using the `patchwork` library. This provides a comprehensive view of coverage in relation to gene structure.

```r
p / gene_track
```

--------------------------------

### Facet Coverage by Cell Type

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Visualizes coverage scores faceted by a specific variable, such as 'cell' type. This allows for comparison of coverage across different experimental conditions. Requires `patchwork` for layout adjustments.

```r
p <- cvg_over_features %>%
  mutate(strand = feature_strand) %>%
  view_coverage(score = score, 
                colour = feature_type, 
                facets = vars(cell)) + 
  scale_color_brewer(palette = "Dark2") + 
  guides(colour = FALSE) + 
  labs(title = "Coverage over SRM")
p / gene_track + patchwork::plot_layout(heights = c(3, 1))
```

--------------------------------

### Create Gene Body Track

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Generates a track plot representing gene segments. This function is useful for overlaying gene structures onto coverage plots. Requires the `features` data.

```r
gene_track <- view_segments(unnest_parts(features), 
                            colour = feature_type)
gene_track
```

--------------------------------

### Visualize Coverage Over a Gene

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Generates a ggplot object for coverage over a genomic region. Requires the `ggplot2` library and `cvg_over_features` data. The coverage is aligned from 5' to 3' by default.

```r
library(ggplot2)
p <- cvg_over_features %>%
  mutate(strand = feature_strand) %>%
  view_coverage(score = score, colour = feature_type) + 
  scale_color_brewer(palette = "Dark2") + 
  guides(colour = FALSE) + 
  labs(title = "Coverage over SRM")
p
```

--------------------------------

### Filter BAM Reads by Overlap and Extract Sequence

Source: https://github.com/sa-lee/superintronic/wiki/Design-Doc

Filters reads from a BAM file that overlap with a given feature and extracts the sequence. It then mutates the sequence to count occurrences of polyA strings and their positions.

```r
read_bam("bam.bam") %>%
   filter_by_overlaps(feature) %>%
   select(seq) %>%
   mutate(a_runs = Biostrings::vcountPattern("AAAAA", seq),
          a_runs_inx = Biostrings::vmatchPattern("AAAAA", seq)
   )
```

--------------------------------

### Intersect Coverage with Genomic Features

Source: https://github.com/sa-lee/superintronic/blob/master/README.md

Intersects computed coverage GRanges with genomic features. This is useful for analyzing coverage specifically within gene parts or other defined regions. Ensure the 'features' object is properly defined.

```r
cvg_over_features <- cvg %>% 
  select(-bam) %>% 
  join_parts(features)
cvg_over_features
```

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