ProActive"

In ProActive: Detect Elevations and Gaps in Mapped Sequencing Read Coverage

knitr::opts_chunk$set(
  out.width = "100%",
  collapse = TRUE,
  comment = "#>"
)

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

install.packages("ProActive")
library(ProActive)

GitHub install

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, allow for the generation of pileup files in the bbmap.sh command with use of the bincov output with the covbinsize=100 parameter/argument. Otherwise, BBMap's 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.

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.

The input .gff file must have the following format exactly:

kable(head(sampleMetagenomegffTSV), row.names = FALSE) %>%
  kable_styling(latex_options = "HOLD_position")

(Hint- if you are using a gff file output by 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
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.

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'.

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.

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:

ProActiveOutputMetagenome <- ProActiveDetect(
  pileup = sampleMetagenomePileup,
  mode = "metagenome",
  gffTSV = sampleMetagenomegffTSV
)

Default arguments in genome mode:

ProActiveOutputGenome <- ProActiveDetect(
  pileup = sampleGenomePileup,
  mode = "genome",
  gffTSV = sampleGenomegffTSV
)

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

Arguments/parameters

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:

MetagenomeCleanSummaryTable <- ProActiveOutputMetagenome$CleanSummaryTable

kable(MetagenomeCleanSummaryTable) %>%
  kable_styling(latex_options = "HOLD_position")

Subset of genome results summary table:

GenomeCleanSummaryTable <- head(ProActiveOutputGenome$CleanSummaryTable)

kable(GenomeCleanSummaryTable) %>%
  kable_styling(latex_options = "HOLD_position")

Subset of GeneAnnotTable for metagenome results:

MetagenomeResultsGenePredictTable <- head(ProActiveOutputMetagenome$GeneAnnotTable)

kable(MetagenomeResultsGenePredictTable) %>%
  kable_styling(latex_options = "HOLD_position")

Subset of GeneAnnotTable for genome results:

GenomeResultsGenePredictTable <- head(ProActiveOutputGenome$GeneAnnotTable)

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:

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

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

MetagenomeResultsPlots$NODE_1884
MetagenomeResultsPlots$NODE_368
MetagenomeResultsPlots$NODE_617

View select genome plots

Notice the 'chunk' information in the plot titles

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.

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.

GenomeGeneMatches <- geneAnnotationSearch(
  ProActiveResults = ProActiveOutputGenome,
  pileup =  sampleGenomePileup, 
  gffTSV = sampleGenomegffTSV, 
  geneOrProduct = "product", 
  keyWords = c("ribosomal"), 
  inGapOrElev = TRUE, 
  bpRange = 5000
  )

Arguments/parameters

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:

MetagenomeGeneMatches$NODE_617

Non-default search parameters (use of inGapOrElev = TRUE and bpRange = 5000)

GenomeGeneMatches$NC_003197.2_chunk_3
GenomeGeneMatches$NC_003197.2_chunk_36

Session Information

sessionInfo()

ProActive documentation built on April 4, 2025, 12:58 a.m.