---
title: "ProActive"
author: "Jessie Maier"
output: 
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 3
description: >
  The `ProActive` R package automatically detects regions of gapped and elevated 
  read coverage using a pattern-matching algorithm. `ProActive` can detect, characterize 
  and visualize read coverage patterns in both genomes and metagenomes. Optionally, 
  users may provide gene annotations associated with their genome or metagenome 
  in the form of a .gff file. In this case, `ProActive` will generate an additional 
  output table containing the annotations found within the detected regions of gapped 
  and elevated read coverage.
vignette: >
  %\VignetteIndexEntry{ProActive}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  out.width = "100%",
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, echo=FALSE, warning=FALSE, include=FALSE}
library(ProActive)
library(kableExtra)
library(ggplot2)
library(stringr)
library(dplyr)
```

# Introduction

**`ProActive` automatically detects regions of gapped and elevated read coverage 
using a 2D pattern-matching algorithm. `ProActive` detects, characterizes and 
visualizes read coverage patterns in both genomes and metagenomes. Optionally, 
users may provide gene annotations associated with their genome or metagenome 
in the form of a .gff file. In this case, `ProActive` will generate an additional 
output table containing the gene annotations found within the detected regions of 
gapped and elevated read coverage. Additionally, users can search for gene 
annotations of interest in the output read coverage plots.** 

Visualizing read coverage data is important because gaps and elevations in coverage can 
be indicators of a variety of biological and non-biological scenarios, for example-

* Elevations and gaps in read coverage may be caused by some types of structural 
variants. Deletions can cause gaps while duplications can cause elevations in read coverage [1]. 
* Highly active and/or abundant mobile genetic elements, like transposable 
elements [2] and prophage [3] for example, can create elevations in read coverage 
at their respective integration sites. 
* Genetic regions with high mutation rates and/or high variability within the population 
can generate gaps in read coverage [4]. 
* Poor quality sequencing reads and chimeric reference sequences may cause gaps
and elevations in read coverage.

**Since the cause for gaps and elevations in read coverage can be ambiguous, 
ProActive is best used as a screening method to identify genetic regions for further 
investigation with other tools!**

**References:**

1.  Tattini L., D'Aurizio R., & Magi A. (2015). Detection of Genomic Structural 
Variants from Next-Generation Sequencing Data. Frontiers in bioengineering and biotechnology, 
3, 92. https://doi.org/10.3389/fbioe.2015.00092 
2.  Kleiner M., Bushnell B., Sanderson K.E. et al. (2020) Transductomics: sequencing-based 
detection and analysis of transduced DNA in pure cultures and microbial communities. 
Microbiome 8, 158. https://doi.org/10.1186/s40168-020-00935-5
3.  Kieft K., Anantharaman K. (2022). Deciphering Active Prophages from Metagenomes. mSystems 7:e00084-22.
https\://doi.org/10.1128/msystems.00084-22
4. Fogarty E., Moore R. (2019). Visualizing contig coverages to better understand 
microbial population structure. https://merenlab.org/2019/11/25/visualizing-coverages/ 

# Installation
## CRAN install
```{r, eval=FALSE}
install.packages("ProActive")
library(ProActive)
```

## GitHub install
```{r, eval=FALSE}
if (!require("devtools", quietly = TRUE)) {
  install.packages("devtools")
}

devtools::install_github("jlmaier12/ProActive")
library(ProActive)
```

# Input data

## Pileups

ProActive detects read coverage patterns using a pattern-matching algorithm that 
operates on pileup files. A pileup file is a file format where each row 
summarizes the 'pileup' of reads at specific genomic locations. Pileup files 
can be used to generate a rolling mean of read coverages and associated base 
pair positions which reduces data size while 
preserving read coverage patterns. **ProActive requires that input pileups files** 
**be generated using a 100 bp window/bin size.**  

Pileup files are generated using the .bam files produced after mapping sequencing reads to a 
metagenome or genome fasta file. **Read mapping should be performed using a high** 
**minimum identity (0.97 or higher) and random mapping of ambiguous reads.** 

Some read mappers, like 
[BBMap](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/bb-tools-user-guide/bbmap-guide/), 
allow for the generation of pileup files in the 
[`bbmap.sh`](https://github.com/BioInfoTools/BBMap/blob/master/sh/bbmap.sh) 
command with use of the `bincov` output with the `covbinsize=100` 
parameter/argument. **Otherwise, BBMap's** 
**[`pileup.sh`](https://github.com/BioInfoTools/BBMap/blob/master/sh/pileup.sh)** 
**can convert .bam files produced by any read mapper to pileup files compatible** 
**with ProActive using the `bincov` output with `binsize=100`.**

**The input pileup file for metagenomes must have the following format:**

Dataframe with four columns:

* V1: Contig accession
* V2: Mapped read coverage values averaged over 100 bp windows
* V3: Starting position (bp) of each 100 bp window. Restarts from 100 at the 
start of each new contig.
* V4: Starting position (bp) of each 100 bp window. Does NOT restart at the 
start of each new contig.

```{r echo=FALSE}
kable(head(sampleMetagenomePileup), row.names = FALSE) %>%
  kable_styling(latex_options = "HOLD_position")
```

**Note that the format for a genome pileup will be slightly different! The third** 
**column (V3) does not restart and the fourth column (V4) starts from 0. ProActive**
**accounts for the differences in pileup formats between genomes and metagenomes.** 

Users may use the 'sampleMetagenomePileup' and 'sampleGenomePileup' files that 
come pre-loaded with ProActive as references for proper input file format.

## gff TSV
Optionally, ProActive will accept a .gff file as additional input. The .gff file must be 
associated with the same metagenome or genome used to create your pileup file. 
The .gff file should be in TSV format and should follow the same layout described [here](https://en.wikipedia.org/wiki/General_feature_format#:~:text=In%20bioinformatics%2C%20the%20general%20feature,DNA%2C%20RNA%20and%20protein%20sequences.).

**The input .gff file must have the following format exactly:**
```{r echo=FALSE}
kable(head(sampleMetagenomegffTSV), row.names = FALSE) %>%
  kable_styling(latex_options = "HOLD_position")
```

(**Hint**- if you are using a gff file output by [PROKKA](https://github.com/tseemann/prokka),
you may need to remove some unnecessary (for ProActive) lines of text at the top 
of the file. There are various ways one can remove these additional lines, however, a
nice command-line solution is:)
```{bash eval=FALSE}
grep ^COMMONID metagenomeAnnots.gff > metagenomeAnnotsForProActive.gff
```

The 'COMMONID' should be a value that all of your contig or genome accessions start with. 
For example, the 'COMMONID' for the contig accessions in the sampleMetagenomegffTSV 
displayed above could be "NODE" since all the accessions start with "NODE".

# ProActiveDetect()
`ProActiveDetect()` is the main function in the ProActive R package. This function 
filters contigs/chunks based on length and read coverage, performs pattern-matching 
to detect gaps and elevations in read coverage, identifies start and stop positions 
and sizes of pattern-matches, and, optionally, extracts gene annotations that fall
within detect gaps and elevations in read coverage.    

## Function components

### Chunking
Currently, `ProActiveDetect()` can only detect one gap or elevation pattern per contig. 
Until `ProActiveDetect()` is able to detect multiple read coverage patterns per contig, we 
implemented a 'chunking' functionality which (if `chunkContigs` = TRUE) chunks large contigs into smaller subsets 
(defined by `chunkSize`) so that pattern-matching can be performed on each chunk as if 
it were an individual contig. The chunking mechanism is what allows `ProActiveDetect()` to perform
pattern-matching on entire genome sequences. When contigs/genomes are chunked, they are assigned 
a sequential value to link chunks back together (i.e. "NODE_1_chunk_1, NODE_1_chunk_2, NODE_1_chunk_3, ...).
Note that the remaining 'chunk' of a contig/genome may not be long enough to 
perform pattern-matching on. Chunks too small for pattern-matching will be put 
in the output FilteredOut table. If a chunk splits a gap or elevation pattern in half,
`ProActiveDetect()` will attempt to detect this and report it to the user as a 'possible 
pattern-match continuity' between contig/genome chunks. Pattern-match continuity is
detected when two sequential chunks have a partial elevation/gap pattern going off the 
right and left side of the chunks, respectively.

### Filtering
Contigs/chunks that are too short or have little to no read coverage are filtered out 
prior to pattern-matching. `ProActiveDetect()` filters out contigs/chunks that do 
not have at least 10x coverage on a total of 5,000 bp across the whole contig/chunk. 
The read coverage filtering was done in this way to avoid filtering out long 
contigs/chunks with small elevations in read coverage that might get removed if filtering 
was done with read coverage averages or medians. Additionally, contigs/chunks less 
than 30,000 bp are filtered out by default, however this can be changed with 
the `minContigLength` parameter which can be set to a minimum of 25,000 bp. 
**If you would like to reduce the size of your input metagenome pileup file for** 
**`ProActiveDetect()`, consider pre-filtering your assembly for contigs greater than** 
**25,000 bp prior to read mapping!** 

### Changing pileup windowSize
The input pileup files have 100 bp windows in which the mapped read coverage is 
averaged over. `ProActiveDetect()` increases the window size prior to pattern-matching by 
averaging the read coverages over a value specified with `windowSize`. In 
many cases, read coverage patterns don't require the resolution that 100 bp windows 
provide, however, starting with a 100 bp windowSize means the higher resolution is 
available if needed. While users can use the 100 bp `windowSize` for `ProActiveDetect()`, 
the processing time will be increased **significantly** and noisy data may interfere 
with pattern-matching. We find that the default 1,000 bp `windowSize` provides a 
nice balance between processing time and read coverage pattern resolution. If you'd 
like more resolution than the 1,000 bp `windowSize` provides, consider dropping the
`windowSize` to 500. If you'd like fine scale read coverage resolution, consider 
viewing the contigs/genome with a software like Integrative Genomics Viewer 
[IGV](https://github.com/igvteam/igv).

### Pattern-matching
`ProActiveDetect()` detects read coverage patterns using a 2D
pattern-matching algorithm. Several predefined patterns, described below, are 
built using the specific length and read coverage values of the contig/chunk being 
assessed. Patterns are translated across each contig/chunk in 1,000 bp sliding 
windows and at each translation, a pattern-match score is calculated by taking 
the mean absolute difference of the read coverage and the pattern 
values. The smaller the match-score, the better the pattern-match. After a 
pattern is fully translated across a contig/chunk, certain aspects of the pattern are 
changed (i.e. height, base, width) and translation is repeated. This process 
of translation and pattern re-scaling is repeated until a large number of 
pattern variations are tested. After pattern-matching is complete, the pattern 
associated with the best match-score is used for contig/chunk classification. 
Contigs/chunks are classified as ‘Elevation’, ‘Gap’, or 'NoPattern' during 
pattern-matching. 

#### Elevation pattern:

The 'elevation' class is defined by a 'block' pattern. During 
pattern-matching, the height (max.), base (min.) and width are altered and all 
pattern variations are translated across the contig/chunk. The block width never 
gets smaller than 10,000 bp by default, however this can be changed with the `minSize` 
parameter. 

```{r echo=FALSE, out.width = "50%"}
dataframe <- cbind.data.frame(c(1:100), c(rep(0, 20), rep(100, 60), rep(0, 20)))
colnames(dataframe) <- c("mockpos", "mockcov")
plot1 <- ggplot(dataframe, aes(x = mockpos, y = mockcov)) +
  geom_line(linewidth = 1) +
  labs(x = NULL, y = NULL) +
  theme_classic() +
  ggplot2::theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.title = element_text(size = 10),
    panel.border = element_rect(colour = "black", fill = NA, linewidth = 2)
  )

plot1
```


#### Gap pattern:
The 'gap' class is essentially the reverse of the values used to build the block 
pattern in the 'elevation' class. The same pattern-matching steps (alteration of 
pattern max., min. and width values and pattern translation) used for the
elevation pattern are used for the gap pattern.

```{r echo=FALSE, out.width = "50%"}
dataframe <- cbind.data.frame(c(1:100), c(rep(100, 20), rep(5, 60), rep(100, 20)))
colnames(dataframe) <- c("mockpos", "mockcov")
plot1 <- ggplot(dataframe, aes(x = mockpos, y = mockcov)) +
  geom_line(linewidth = 1) +
  labs(x = NULL, y = NULL) +
  theme_classic() +
  ggplot2::theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.title = element_text(size = 10),
    panel.border = element_rect(colour = "black", fill = NA, linewidth = 2)
  )

plot1
```

#### Elevation/Gap pattern:
Elevations and gaps that trail off one side of a contig/chunk are hard to classify
as the read coverage can be interpreted as a gap or elevation depending on how you're 
looking at it. We classify contigs/chunk as 'Gap' if the elevated region is greater 
than 50% of the length of the contig/chunk. Conversely, if the elevated region is less 
than 50% of the contig/chunk length, the classification is 'Elevation'.

```{r echo=FALSE, out.width = "50%"}
dataframe <- cbind.data.frame(c(1:100), c(rep(100, 50), rep(5, 50)))
colnames(dataframe) <- c("mockpos", "mockcov")
plot1 <- ggplot(dataframe, aes(x = mockpos, y = mockcov)) +
  geom_line(linewidth = 1) +
  labs(x = NULL, y = NULL) +
  theme_classic() +
  ggplot2::theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.title = element_text(size = 10),
    panel.border = element_rect(colour = "black", fill = NA, linewidth = 2)
  )

plot1
```

#### noPattern:
Since the best pattern-match for each contig/chunk is determined by comparing 
match-scores amongst all pattern-variations from all pattern classes, we needed 
a ‘negative control’ pattern to compare against. The 'NoPattern' 'pattern' 
serves as a negative control by matching to contigs/chunks with no read coverage 
patterns. We made two NoPattern patterns which consist of a horizontal line the 
same length as the contig/chunk being assessed at the contig/chunk's 
average and median read coverage value. This pattern is not re-scaled or translated in 
any way. 

```{r echo=FALSE, out.width = "50%"}
dataframe <- cbind.data.frame(c(1:100), rep(10, 100))
colnames(dataframe) <- c("mockpos", "mockcov")
plot1 <- ggplot(dataframe, aes(x = mockpos, y = mockcov)) +
  geom_line(linewidth = 1) +
  ylim(0, 100) +
  labs(x = NULL, y = NULL) +
  theme_classic() +
  ggplot2::theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    plot.title = element_text(size = 10),
    panel.border = element_rect(colour = "black", fill = NA, linewidth = 2)
  )
plot1
```

### Calculating elevation ratios
Every gap and elevation classification receives an 'elevation ratio' value which is simply 
the pattern-match's maximum value divided by the minimum value. For Elevation classifications,
you can think of the elevation ratio as how many times greater the read coverage of the elevated 
region is compare to the non-elevated region'. Conversely, for Gap classifications, the elevation ratio
is how many times less the read coverage of the gap region is compared to the non-gapped region.

### Extracting gene annotations in elevated/gapped regions
If a .gff file is provided, then `ProActiveDetect()` will extract the gene annotations
found within the gapped and elevated pattern-match regions and provide them to the user in 
an output table (GeneAnnotTable). An additional column will be added with the classification 
information (Gap or Elevation) associated with the gene annotations. If the input .gff file 
contains a gene 'product' field in the attributes column (9th column in the dataframe), then 
`ProActiveDetect()` will extract the product information into a separate column for easy 
visualization and filtering of annotations of interest. 

## Usage

**Default arguments in metagenome mode:**
```{r}
ProActiveOutputMetagenome <- ProActiveDetect(
  pileup = sampleMetagenomePileup,
  mode = "metagenome",
  gffTSV = sampleMetagenomegffTSV
)
```

**Default arguments in genome mode:**
```{r}
ProActiveOutputGenome <- ProActiveDetect(
  pileup = sampleGenomePileup,
  mode = "genome",
  gffTSV = sampleGenomegffTSV
)
```

**Note that `ProActiveDetect()` *can* be run without the gffTSV file!** 

## Arguments/parameters
```{r, eval=FALSE}
ProActiveDetect(
  pileup,
  mode,
  gffTSV,
  windowSize = 1000,
  minSize = 10000,
  maxSize = Inf,
  minContigLength = 30000,
  chunkSize = 50000,
  chunkContigs = FALSE,
  IncludeNoPatterns = FALSE,
  verbose = TRUE,
  saveFilesTo
)
```

- **`pileup`**: A .txt file containing mapped sequencing read coverages averaged over 
100 bp windows/bins.
- **`mode`**: Either "genome" or "metagenome". 
- **`gffTSV`**: Optional, a .gff file (TSV) containing gene annotations associated with 
the .fasta file used to generate the pileup. 
- **`windowSize`**: The number of basepairs to average read coverage values over. 
Options are 100, 200, 500, 1000 ONLY.  Default is 1000.
- **`minSize`**: The minimum size (in bp) of elevation or gap patterns. Default is 10000.
- **`maxSize`**: The maximum size (in bp) of elevation or gap patterns. Default is NA
(i.e. no maximum).
- **`minContigLength`**: The minimum contig/chunk size (in bp) to perform pattern-matching 
on. Default is 25000.
- **`chunkSize`**: If `mode = "genome"` OR if `mode = "metagenome"` and `chunkContigs = TRUE`, 
chunk the genome or contigs, respectively, into smaller subsets for pattern-matching. 
`chunkSize` determines the size (in bp) of each 'chunk'. Default is 100000.
- **`chunkContigs`**: TRUE or FALSE, If TRUE and `mode = "metagenome"`, contigs longer 
than the `chunkSize` will be 'chunked' into smaller subsets and pattern-matching 
will be performed on each subset. Default is FALSE.
- **`IncludeNoPatterns`**: TRUE or FALSE, If TRUE the noPattern pattern-matches will 
be included in the PatternMatches output list. If you would like to visualize 
the read coverage of noPattern classifications in `plotProActiveResults()`, this should 
be set to TRUE. 
- **`verbose`**: TRUE or FALSE. Print progress messages to console. Default is TRUE.
- **`saveFilesTo`**: Optional, Provide a path to the directory you wish to save 
output to. A folder will be made within the provided directory to store 
results.

## Output

The output of `ProActiveDetect()` is a list containing six objects:

1. SummaryTable: A table containing all pattern-matching classifications
2. CleanSummaryTable: A table containing only Gap and Elevation pattern-match
classifications (i.e. noPattern classifications removed)
3. PatternMatches: A list object containing information needed to visualize the 
pattern-matches in `plotProActiveResults()`
4. FilteredOut: A table containing contigs/chunks that were filtered out for being 
too small or having too low read coverage
5. Arguments: A list object containing arguments used for pattern-matching (windowSize, 
mode, chunkSize, chunkContigs)
6. GeneAnnotTable: A table containing gene annotations associated with elevated 
or gapped regions in pattern-matches

Save the desired list item to a new variable using its associated name.

**Metagenome results summary table:**
```{r}
MetagenomeCleanSummaryTable <- ProActiveOutputMetagenome$CleanSummaryTable
```

```{r, echo=FALSE}
kable(MetagenomeCleanSummaryTable) %>%
  kable_styling(latex_options = "HOLD_position")
```

**Subset of genome results summary table:**
```{r}
GenomeCleanSummaryTable <- head(ProActiveOutputGenome$CleanSummaryTable)
```

```{r, echo=FALSE}
kable(GenomeCleanSummaryTable) %>%
  kable_styling(latex_options = "HOLD_position")
```

**Subset of GeneAnnotTable for metagenome results:**
```{r}
MetagenomeResultsGenePredictTable <- head(ProActiveOutputMetagenome$GeneAnnotTable)
```

```{r, echo=FALSE}
kable(MetagenomeResultsGenePredictTable) %>%
  kable_styling(latex_options = "HOLD_position")
```

**Subset of GeneAnnotTable for genome results:**
```{r}
GenomeResultsGenePredictTable <- head(ProActiveOutputGenome$GeneAnnotTable)
```

```{r, echo=FALSE}
kable(head(GenomeResultsGenePredictTable)) %>%
  kable_styling(latex_options = "HOLD_position")
```


# plotProActiveResults()

`plotProActiveResults()` allows users to visualize both the read coverage and 
the pattern-match associated with each Gap or Elevation classification.

## Function components

### Re-building pattern-matches
The `ProActiveDetect()` output contains information needed to re-build each 
pattern-match used for classification. To re-build a complete 
pattern-match for visualization, `plotProActiveResults()` uses the 
pattern-match's minimum and maximum values and the start and stop positions.

### Plotting read coverage and associated pattern-matches
By default, the read coverage is plotted for each contig/chunk
classified as having a Gap or Elevation in read coverage. If you wish to see 
the read coverage for noPattern classifications, be sure to set
`IncludeNoPatterns = TRUE` when running `ProActiveDetect()`. The pattern-match
associated with each classification is overlaid on the coverage plot.

## Usage

**Default arguments:**
```{r}
MetagenomeResultsPlots <- plotProActiveResults(
  pileup = sampleMetagenomePileup,
  ProActiveResults = ProActiveOutputMetagenome
)

GenomeResultsPlots <- plotProActiveResults(
  pileup = sampleGenomePileup,
  ProActiveResults = ProActiveOutputGenome
)
```

**Note**- There is no need to set 'genome' or 'metagenome' mode. `plotProActiveResults()`
will get this information from the `ProActiveDetect()` output.

## Arguments/parameters
```{r, eval=FALSE}
plotProActiveResults(pileup,
  ProActiveResults,
  elevFilter,
  saveFilesTo
)
```

- **`pileup`**: A .txt file containing mapped sequencing read coverages averaged over 
100 bp windows/bins.
- **`ProActiveResults`**: The output from `ProActiveDetect()`.
- **`elevFilter`**: Optional, only plot results with pattern-matches that achieved an 
elevation ratio (max/min) greater than the specified value. Default is no filter.
- **`saveFilesTo`**: Optional, Provide a path to the directory you wish to save 
output to. A folder will be made within the provided directory to store 
results.

## Output

The output of `plotProActiveResults()` is a list of ggplot objects.

### View select metagenome plots

```{r fig.width=6}
MetagenomeResultsPlots$NODE_1884
MetagenomeResultsPlots$NODE_368
MetagenomeResultsPlots$NODE_617
```

### View select genome plots

**Notice the 'chunk' information in the plot titles**
```{r fig.width=6}
GenomeResultsPlots$NC_003197.2_chunk_36
GenomeResultsPlots$NC_003197.2_chunk_8
```

# geneAnnotationSearch()

`geneAnnotationSearch()` helps users explore gene annotations of interest in and
around detected gaps and elevations in read coverage.

## Function components

### Search for gene annotations
`geneAnnotationSearch()` utilizes a .gff file and the pattern-matching results from 
`ProActiveDetect()` to locate gene annotations that match provided `keyWords`. The 
.gff file should be in the same format described previously in the Input Files section 
of this vignette. First, the information associated with the gene or gene product 
(depending on what the user selects for the `geneOrProduct`  parameter) is extracted 
from the attributes column of the .gff file. Then, the .gff file is subset to include 
only the annotations associated with the contig/chunk being assessed. From here, the 
search can vary quite a bit depending on the parameters the user selects for the 
`inGapOrElev` and `bpRange` parameters. If `inGapOrElev = FALSE` (the default), 
then gene annotations located anywhere on the contig/chunk that match 
one or more of the provided `keyWords` will be visualized. If `inGapOrElev = TRUE`, 
then only gene annotations within the gap/elevation region of the pattern-match will
be searched for matches to the provided `keyWords`. The `bpRange` parameter can be used if 
`inGapOrElev = TRUE` and allows the search range to be extended a specified number
of base pairs to the left and right of the gap/elevation pattern-match borders. Gene annotation
are included in the search if the end of the open reading frame (defined by the 'end' 
values in the .gff file) falls within the search region. 

### Plot gene annotation locations
The read coverage and locations of gene annotations that match the provided `keyWords` 
are visualized for each contig/chunk with matches. The read coverage is plotted using 
a 100 bp windowSize to allow for greater resolution of read coverage patterns and gene
annotation locations. The borders of the elevated/gapped regions of read coverage
detected by `ProActiveDetect()` are marked on the plot with orange vertical 
lines. If `inElevOrGap = TRUE` and `bpRange` is set to a non-zero value, then the extended search
range outside the gap/elevation borders are marked on the plot with orange dashed vertical
lines. The matching gene annotation locations are marked on the plot with
black vertical lines at the start position of the associated open reading frames. 

**Note**- The pattern-matching used to identify the borders of elevated and gapped 
regions of read coverage was *likely* performed using a `windowSize` larger than 100 bp in
`ProActiveDetect()`. This means that the locations of the borders may not perfectly translate 
to the borders of gaps and elevations at 100 bp resolution. 

## Usage

**Default arguments:**

With defaults, all contigs/chunks classified as having a gap or elevation 
in read coverage are searched for gene annotations that match any of the provided keywords.
The entire contig/chunk is searched, not just the gapped or elevated region.   
```{r}
MetagenomeGeneMatches <- geneAnnotationSearch(
  ProActiveResults = ProActiveOutputMetagenome, 
  pileup = sampleMetagenomePileup, 
  gffTSV = sampleMetagenomegffTSV, 
  geneOrProduct = "product", 
  keyWords = c("transport", "chemotaxis")
  )
```

**Non-default arguments**

With the following parameters/arguments, all classified contigs/chunks are searched 
for gene annotations that match the provided keywords (same as default), BUT with the 
use of `inGapOrElev = TRUE`, only the gapped or elevated region is searched for matching 
annotations. Additionally, the use of `bpRange = 5000` means that the search region 
is extended 5,000 bp from the left and right of the gapped or elevated region.
```{r}
GenomeGeneMatches <- geneAnnotationSearch(
  ProActiveResults = ProActiveOutputGenome,
  pileup =  sampleGenomePileup, 
  gffTSV = sampleGenomegffTSV, 
  geneOrProduct = "product", 
  keyWords = c("ribosomal"), 
  inGapOrElev = TRUE, 
  bpRange = 5000
  )
```


## Arguments/parameters
```{r eval=FALSE}
geneAnnotationSearch(
  ProActiveResults, 
  pileup, 
  gffTSV,
  geneOrProduct, 
  keyWords, 
  inGapOrElev = FALSE,
  bpRange = 0, 
  elevFilter, 
  saveFilesTo, 
  verbose = TRUE
  ) 
```

- **`ProActiveResults`**: The output from `ProActiveDetect()`. 
- **`pileup`**: A .txt file containing mapped sequencing read coverages averaged over 
100 bp windows/bins.
- **`gffTSV`**: A .gff file (TSV) containing gene annotations associated with 
the .fasta file used to generate the pileup. 
- **`geneOrProduct`**: "gene" or "product". Search for keyWords associated with genes or gene products.
- **`keyWords`**: The keyWord(s) to search for. Case independent. Searches will return the string
#' that contains the matching keyWord. KeyWord(s) must be in quotes, comma-separated, and surrounded by
#' c() i.e( c("antibiotic", "resistance", "drug") )
- **`inGapOrElev`**: TRUE or FALSE. If TRUE, only search for gene-annotations in
#' the gap/elevation region of the pattern-match. Default is FALSE (i.e search the
#' entire contig/chunk for the gene annotation key-words)
- **`bpRange`**: If `inGapOrElev = TRUE`, the user may specify the region (in base pairs) that should
#' be searched to the left and right of the gap/elevation region. Default is 0.
- **`elevFilter`**: Optional, only plot results with pattern-matches that achieved an 
elevation ratio (max/min) greater than the specified value. Default is no filter.
- **`saveFilesTo`**: Optional, Provide a path to the directory you wish to save 
output to. A folder will be made within the provided directory to store 
results.
- **`verbose`**: TRUE or FALSE. Print progress messages to console. Default is TRUE.

## Output

The output of `geneAnnotationSearch()` is a list of ggplot objects. 

Default search parameters:
```{r fig.width=6, fig.height=5}
MetagenomeGeneMatches$NODE_617
```

Non-default search parameters (use of `inGapOrElev` = TRUE and `bpRange` = 5000)
```{r fig.width=6, fig.height=5}
GenomeGeneMatches$NC_003197.2_chunk_3
GenomeGeneMatches$NC_003197.2_chunk_36
```

# Session Information
```{r}
sessionInfo()
```
