Package 'AssessORF'

Tabulate the Category Assignments for Assessment Results Objects

Description

The as.matrix method for Assessment and subclass Results objects

Usage

## S3 method for class 'Assessment'
as.matrix(x, ...)

Arguments

x	An object of class Assessment and subclass Results.
...	Additional arguments.

Details

as.matrix.Assessment tabulates and returns the number of times each category appears in the CategoryAssignments vector within the given Results object. If the number of genes for any the 14 main gene / ORF categories is zero, a count (of zero) will still be included for that category.

Value

A one-row matrix with the counts for the number of genes/ORFs that fall into each category. The corresponding category codes serve as the column names, and the name of the row is the strain ID.

See Also

Assessment-class

Examples

as.matrix(readRDS(system.file("extdata",
                              "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                              package = "AssessORF")))

---

Assess Genes

Description

Assess and categorize a set of genes for a genome using proteomics hits, evolutionarily conserved starts, and evolutionarily conserved stops as evidence

Usage

AssessGenes(geneLeftPos,
            geneRightPos = NA_integer_,
            geneStrand = NA_character_,
            inputMapObj,
            geneSource = "",
            minCovNum = 10,
            minCovPct = 5,
            minConCovRatio_Strong = 0.99,
            limConCovRatio_NotCon = 0.8,
            maxN_AltConStart = 200,
            frac_AltConStart = 0.5,
            minConCovRatio_Stop = 0.5,
            noConStopsGeneFrac = 0.5,
            minNumProtHitsNORFs = 2L,
            minLenNORFs = 0,
            allowNestedNORFs = FALSE,
            useNTermProt = FALSE,
            verbose = TRUE)

Arguments

geneLeftPos	An integer vector with the left positions of each gene, in terms of the forward strand. Can also be a GRanges object from the GenomicRanges package that holds all of the positional information (including strand) for the genes. In that case, the next two parameters should be left as NA.
geneRightPos	An integer vector with the right positions of each gene, in terms of the forward strand. Should be left at the default value of NA_integer_ if geneLeftPos is a GRanges object.
geneStrand	A character vector consisting of "+" and "-", specifying which strand each gene is on. Should be left at the default value of NA_character_ if geneLeftPos is a GRanges object.
inputMapObj	EITHER an object of class Assessment and subclass DataMap OR a character string corresponding to the strain identifier for one of such objects from AssessORFData.
geneSource	Optional character string that describes the source of the gene set, i.e. a database or gene prediction program. Used when viewing and identifying the object returned by the function.
minCovNum	Minimum number of related genomes required to have synteny to a position in the central genome. Recommended to use the default value.
minCovPct	Minimum percentage of related genomes required to have synteny to a position in the central genome. Must be an integer ranging from 0 to 100. Recommended to use the default value.
minConCovRatio_Strong	Minimum value of the start codon conservation to coverage ratio needed to call a start strongly conserved. Must range from 0 to 1. Lower values allow more conserved starts through. Recommended to use the default value.
limConCovRatio_NotCon	Maximum, non-inclusive value of the conservation to coverage ratio needed to call a possible conserved start not conserved. Used when making a decision on how to categorize the conserved start evidence. Must range from 0 to 1 Recommended to use the default value.
maxN_AltConStart	Maximum nucleotide distance that non-predicted, conserved starts can be away from the start of an ORF (the previous in-frame stop) in order for such starts to be considered an alternative to the predicted start. Recommend to use the default value.
frac_AltConStart	Value from 0 to 1 describing the fractional range of positions in a ORF, starting from the previous in-frame stop and moving towards the ORF-ending stop to use in search for non-predicted, conserved starts in order for such starts to be considered an alternative to the predicted start. For example, a value of 0.25 means that the first quarter of the ORF is checked, a value of 0.5 correspond to the first half of the ORF, etc. Recommended to use the default value.
minConCovRatio_Stop	Minimum value of the stop codon conservation to coverage ratio needed to say a position in the central genome corresponds to a conserved stop across the related genomes. Must range from 0 to 1. Lower values allow more conserved stops through. Recommended to use the default value.
noConStopsGeneFrac	Value from 0 to 1 describing the fractional range of positions in a gene, starting from the start of the gene and moving towards the stop of the gene, to use in searching for conserved stops. For example, a value of 0.25 means that the first quarter of the gene is checked for conserved stops, a value of 0.5 correspond to the first half of the gene, etc. Recommended to use the default value.
minNumProtHitsNORFs	Number of peptide hits required to be in an ORF with protein hits but no given/predicted gene start in order for such an ORF to be included in the final output.
minLenNORFs	Minimum ORF length required to include an ORF with protein hits but no given/predicted gene start in the final output.
allowNestedNORFs	Logical indicating whether or not to include ORFs with protein hits but no given/predicted gene starts that are completely nested within an ORF in another frame in the final output.
useNTermProt	Logical indicating whether or not to treat proteomics evidence in the given mapping object as originating from N-terminal proteomics experiments. The mapping object must be built with N-terminal proteomics data. Default value is FALSE.
verbose	Logical indicating whether or not to display progress and status messages.

Details

For each of the given genes, AssessGene assigns a category based on where conserved starts, conserved stops, and/or proteomics hits are located in relation to the start of the gene. The category assignments for the genes are stored in the CategoryAssignments vector in the Results object returned by the function. Please see Assessment-class for a list of all possible categories and their descriptions.

If geneLeftPos is a GRanges object, then the left and right positions of each gene along with the strand of each gene are extracted from the object. Any sequence names given for the genes within the GRanges object are ignored, and the CategoryAssignments in the returned Results object follows the same order as to how the genes are listed within the GRanges object.

If gene positional information is instead given as three vectors, then the three vectors, geneLeftPos, geneRightPos, and geneStrand, must all be of the same length. The same index within each vector must provide information on the same gene (think of the vectors as columns of the same table). geneLeftPos and geneRightPos describe the upstream and downstream positions (respectively) for each gene in terms of the forward strand. For genes on the forward strand, geneLeftPos corresponds to the start positions and geneRightPos corresponds to stop positions. For genes on the reverse strand, geneLeftPos corresponds to the stop positions and geneRightPos corresponds to the start positions. Gene positions on the reverse strand must be relative to the 5' to 3' direction of the forward strand (as opposed to being relative to the 5' to 3' direction of the reverse strand). This means that none of the elements of geneLeftPos can be greater than (or equal to) the corresponding element in geneRightPos. The CategoryAssignments in the returned Results object has the same length as and aligns with the indexing of the three given gene positional information vectors.

Please ensure that the same genome used in the mapping function is also used to derive the set of genes for this assessment function. The function will only error if any gene positions are outside the bounds of the genome and does not make any other checks to make sure the genes are valid for the genome.

The maximum of either minCovNum (option 1) or minCovPct divided 100 then multiplied by the number of related genomes (option 2) is used as the minimum coverage required in determining conserved starts and stops.

Additionally, open reading frames with proteomics evidence but no gene start are categorized based on whether or not there is a conserved start upstream of the proteomic evidence. The positions and lengths of these open reading frames are included in the N_CS-_PE+_ORFs and N_CS+_PE+_ORFs matrices within the final object that is returned.

If the proteomics evidence provided in the given mapping object comes from N-terminal proteomics experiments (i.e., if the value of the NTermProteomics item within the mapping object is TRUE), the useNTermProt can be set to TRUE to impose stricter requirements on the use of proteomics evidence in determining the correctness of the given genes. When useNTermProt is set to TRUE, the start of first peptide mapping to an ORF where there is a given gene must directly align with the start of that gene or be one codon off from the start (in cases where the protein product of the gene has undergone N-terminal methionine excision) in order for the gene to be considered as having supporting protein evidence. If the first peptide hit does not align like that, the gene is considered as having disproving protein evidence. Currently, N-terminal proteomics does not produce enough N-terminal peptides so setting this flag as TRUE does not provide meaningful results. It is recommended to leave this flag as FALSE in all situations.

Value

An object of class Assessment and subclass Results

See Also

Assessment-class

Examples

## Example showing the minimum number of arguments that need to be specified:

## Not run: 
myResObj <- AssessGenes(geneLeftPos = myGenesLeft,
                        geneRightPos = myGenesRight,
                        geneStrand = myGenesStrand,
                        inputMapObj = myMapObj)

## End(Not run)



## Example from vignette is shown below

currMapObj <- readRDS(system.file("extdata",
                                  "MGAS5005_PreSaved_DataMapObj.rds",
                                  package = "AssessORF"))

currProdigal <- readLines(system.file("extdata",
                                      "MGAS5005_Prodigal.sco",
                                      package = "AssessORF"))[-1:-2]

prodigalLeft <- as.numeric(sapply(strsplit(currProdigal, "_", fixed=TRUE), `[`, 2L))
prodigalRight <- as.numeric(sapply(strsplit(currProdigal, "_", fixed=TRUE), `[`, 3L))
prodigalStrand <- sapply(strsplit(currProdigal, "_", fixed=TRUE), `[`, 4L)

currResObj <- AssessGenes(geneLeftPos = prodigalLeft,
                          geneRightPos = prodigalRight,
                          geneStrand = prodigalStrand,
                          inputMapObj = currMapObj,
                          geneSource = "Prodigal")

print(currResObj)

---

Assessment objects

Description

In order to assess the quality of a set of (predicted) genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for that gene or against that gene. Class Assessment furnishes objects that can store the necessary information for assessing a set of genes for a genome and also provides functions for viewing and visualizing assessment information. Specifically, class Assessment objects utilize proteomic hits and evolutionarily conserved start & stop codons as evidence to determine the correctness for each gene in a given set.

DataMap Objects

Objects of class Assessment and subclass DataMap are used to store the mapping of proteomics and evolutionary conservation to the genome of interest (central genome). They are generated through the function MapAssessmentData, and they have a list structure containing the following elements:

StrainID

Equal to strainID if it was specified; otherwise ""

Species

Equal to speciesName if it was specified; otherwise ""

GenomeLength

Length of the central genome

StopsByFrame

Where the stops are in each frame, used to bound open reading frames in downstream functions

N-TermProteomics

Logical describing whether or not the proteomics hits are from N-terminal proteomics

FwdProtHits

Proteomic hit information that maps to the three forward frames of the central genome

RevProtHits

Proteomic hit information that maps to the three reverse frames of the central genome

FwdCoverage

Coverage of the forward strand of the central genome

FwdConStarts

Start codon conservation of the forward strand of the central genome

FwdConStops

Stop codon conservation of the forward strand of the central genome

RevCoverage

Coverage of the reverse strand of the central genome

RevConStarts

Start codon conservation of the reverse strand of the central genome

RevConStops

Stop codon conservation of the reverse strand of the central genome

NumRelatedGenomes

Final number of related genomes that were mapped to the central genome

HasProteomics

Logical describing whether or not proteomics evidence has been mapped to the central genome

HasConservation

Logical describing whether or not evolutionary conservation evidence has been mapped to the central genome

Results Objects

Objects of class Assessment and subclass Results are used to store how correct a set of genes for a given genome. The function AssessGenes generates Results using a DataMap object and information on a set of genes for the genome corresponding to the DataMap object. Results objects have a list structure containing the following elements:

StrainID

Equal to the strainID of the corresponding DataMap object

Species

Equal to speciesName of the corresponding DataMap object

GenomeLength

Length of the genome

GeneLeftPos

Left positions of the given set of genes (in forward strand terms)

GeneRightPos

Right positions of the given set of genes (in forward strand terms)

GeneStrand

Strand information of the given set of genes ("+" or "-")

GeneSource

The source of the given set of genes

NumGenes

Number of genes given

N_CS-_PE+_ORFs

Data for open reading frames with no gene start but with proteomics evidence

N_CS<_PE+_ORFs

Data for open reading frames with no gene start but with proteomics evidence and at least one valid evolutionarily conserved start

CategoryAssignments

A character vector that stores the category assignment for each of the given genes in the same order as the gene information (please see below for a list of all possible categories, their descriptions, and their character string codes)

Gene Categories

The CategoryAssignments vector in Results objects describes how the proteomics evidence and evolutionarily conserved start/stop codon evidence support or disprove the corresponding set of genes. In the vector, each gene is assigned a character string code that has the following format: "Y CS[_] PE[_]". The first part, the "Y", signifies that for this ORF contains a predicted gene. The second part, the "CS[_]", describes how the conserved start(s) lines up with the given gene start. The third part, the "PE[_]", describes how the proteomics hits line up with the given gene start.

Y CS+ PE+

There is a good conserved start aligned with the gene start with protein evidence downstream.

Y CS+ PE-

There is a good conserved start aligned with the gene start without protein evidence downstream.

Y CS- PE+

There is no good conserved start aligned with the predicted start, and there is protein evidence downstream of the gene start.

Y CS- PE-

There is no good conserved start aligned with the predicted start, and there is no protein evidence downstream of the gene start.

Y CS! PE-

There are either multiple good conserved stops in the middle of the gene, or the most downstream, good conserved stop is followed by a good conserved start. There is no protein evidence downstream of the gene start

Y CS! PE+

The most downstream, good conserved stop is followed by a good conserved start, and there is protein evidence downstream of the gene start.

Y CS< PE!

The protein evidence disagrees with/is upstream of the gene start, and there is a good conserved start upstream of the protein evidence.

Y CS- PE!

The protein evidence disagrees with/is upstream of the gene start, and there is no good conserved start upstream of the protein evidence.

Y CS> PE+

The best conserved starts are downstream of the predicted start, and there is protein evidence downstream of the gene start.

Y CS> PE-

The best conserved starts are downstream of the predicted start, and there is no protein evidence downstream of the gene start.

Y CS< PE+

At least one of the best conserved starts is upstream of the predicted start, and there is protein evidence downstream of the gene start.

Y CS< PE-

At least one of the best conserved starts is upstream of the predicted start, and there is no protein evidence downstream of the gene start.

S3 Methods

as.matrix.Assessment (only works with objects of class Results)

print.Assessment

plot.Assessment

mosaicplot.Assessment (only works with objects of class Results)

---

Compare Assessment Results

Description

Compare two objects of class Assessment. subclass Results to determine how their gene sets and the corresponding category assignments vary

Usage

CompareAssessmentResults(obj1,
                         obj2,
                         printSummary = TRUE,
                         returnDetails = FALSE)

Arguments

obj1, obj2	Objects of class Assessment and subclass Results to compare against each other. Alternatively, either obj1 or obj2 (or both) can be a two-element character vector that specifies one of such objects from AssessORFData. The first element in the vector should be the strain identifier, and the second element should be the gene source identifier. Both objects should have been generated from the same mapping object.
printSummary	Logical indicating whether or not to print out a summary of the comparison analysis.
returnDetails	Logical indicating whether or not to return a list of details from the comparison analysis. See the next section for what items are in the list.

Details

Since the same mapping object (an object class Assessment and subclass DataMap) can be used to assess multiple sets of genes for genome, it is meaningful to compare how those gene sets and their category assignments from AssessGenes vary from one another. To make describing this function easier, let us assume that one set of genes consists of the complete set of predictions made by a gene-finding program on a particular strain's genome and that the other set of genes consists of the complete set of predictions from a second gene-finding program.

When gene-finding programs predict genes for a genome, they make a decision on which regions of the genome code for proteins. There is (usually) only one option for the stop codon that ends a particular coding region, but there are typically multiple options available for the start codon that will mark the beginning of a region. It is therefore useful to find out which (general) coding regions the two programs agree on by determining which stops are found in both sets of predicted genes. From there, the starts each program picked for those shared coding regions can be compared to see whether they agree or not. If the same start is chosen by both programs for a particular shared stop / coding region, then that is an example of a gene predicted by both programs. If the starts chosen by the two programs for a particular shared stop / coding region are different, then that it is an example of both programs agreeing that that particular region of the genome codes for protein but disagreeing on where in the genome that region starts. It would be interesting to see what category was assigned to each start, especially if one start has evolutionary conservation and the other does not.

This function compares the set of genes and corresponding category assignments in the object specified by obj1 (object 1) to the set of genes and corresponding category assignments in the object specified by obj2 (object 2). It then reports the results of the comparison analysis in the format specified by the logical parameters printSummary and returnDetails.

If printSummary is true, the function prints out the following information: the number of shared coding regions (i.e., the number of stops in both gene sets), the number of shared genes (i.e., the number of times both a start and its corresponding stop are found in both sets), and the number of instances where a stop is found in both gene sets but the corresponding starts in each set disagree. For the shared stop - different start set, the function also prints the number of instances where the start from one object has conservation evidence while the corresponding start in the other object does not.

If returnDetails is true, the function returns a 11-item list. Each item of the list is described below. The contents of the object 1 and object 2 gene vectors correspond to the ordering of the genes inside object 1 or object 2, respectively. For the category assignment matrix for shared stop - different start set, it is possible for the gene in object 1 to be assigned to the same category as the corresponding gene from object 2, and the table reflects that.

• "StrainID"Same as the strain identifier inside obj1 and obj2

• SpeciesSame as the strain identifier inside objects 1 and 2

• Obj1_GeneSourceSame as the gene source identifier inside obj1

• Obj2_GeneSourceSame as the gene source identifier inside obj2

• Obj1_Genes_SharedCodingRegionsThe genes from object 1 that share a stop with a gene from the object 2

• Obj2_Genes_SharedCodingRegionsThe genes from object 2 that share a stop with a gene from the object 1

• Obj1_Genes_SharedGenesThe genes from object 1 that share a start and stop with a gene from the object 2

• Obj2_Genes_SharedGenessThe genes from object 2 that share a start and stop with a gene from the object 1

• Obj1_Genes_SharedStopDiffStartThe genes from object 1 that share a stop with a gene from the object 2 but have a different start from the corresponding object 2 gene

• Obj2_Genes_SharedStopDiffStartThe genes from object 2 that share a stop with a gene from the object 1 but have a different start from the corresponding object 1 gene

• CategoryTable_SharedStopDiffStartA 12-by-12 matrix describing the number of times the gene from object 1 was assigned one category and the gene from object 2 was assigned some other category for the shared stop - different start set

Please ensure that obj1 and obj2 come from the same strain / mapping object. The function will do its best to make sure the identifying information for obj1 and obj2 match.

printSummary and returnDetails cannot both be FALSE.

Value

If returnDetails is true, the function returns a 11-item list. Otherwise, the function invisibly returns object 1.

See Also

Assessment-class, AssessGenes

Examples

## Example showing how to use the function with the AssessORFData package:

## Not run: 
compare1 <- CompareAssessmentResults(obj1 = c("MGAS5005", "Prodigal"),
                                     obj2 = c("MGAS5005", "GeneMarkS2"),
                                     printSummary = TRUE,
                                     returnDetails = TRUE)

## End(Not run)

resObj1 <- readRDS(system.file("extdata",
                               "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                               package = "AssessORF"))
                               
resObj2 <- readRDS(system.file("extdata",
                               "MGAS5005_PreSaved_ResultsObj_GeneMarkS2.rds",
                               package = "AssessORF"))
                               
compare2 <- CompareAssessmentResults(obj1 = resObj1,
                                     obj2 = resObj2,
                                     printSummary = TRUE,
                                     returnDetails = TRUE)

---

Map Evidence to a Genome

Description

Maps proteomics hits and evolutionarily conserved starts to a central genome

Usage

MapAssessmentData(genomes_DBFile,
                  tblName = "Seqs",
                  central_ID,
                  related_IDs,
                  protHits_Seqs,
                  protHits_Scores = rep.int(1, length(protHits_Seqs)),
                  strainID = "",
                  speciesName = "",
                  protHits_Threshold = 0,
                  protHits_IsNTerm = FALSE,
                  related_KMerLen = 8,
                  related_MinDist = 0.01,
                  related_MaxDistantN = 1000,
                  startCodons = c("ATG", "GTG", "TTG"),
                  ema_AlphaVal = 0.1,
                  ema_MinVal = 0.6,
                  useProt = TRUE,
                  useCons = TRUE,
                  processors = 1,
                  verbose = TRUE)

Arguments

genomes_DBFile	A SQLite connection object or a character string specifying the path to the database file.
tblName	Character string specifying the table where the genome sequences are located.
central_ID	Character string specifying which identifier corresponds to the central genome, the genome to which the proteomics data and evolutionary conservation data will be mapped.
related_IDs	Character vector of strings specifying identifiers that correspond to related genomes, the genomes that will be used to determine which start codons (ATG, GTG, and TTG) are evolutionarily conserved.
protHits_Seqs	Character vector of amino acid strings that correspond to the sequences for the proteomics hits.
protHits_Scores	Numeric vector of (confidence) scores for the proteomics hits. Scores cannot be negative. The default option assigns a score of one to each proteomics hit.
strainID	Optional character string that specifies the strain identifier that the central genome corresponds to.
speciesName	Optional character string that specifies the name of the species that the central genome corresponds to.
protHits_Threshold	Optional number that specifies what percent of the lowest scoring proteomics hits should be dropped. Must be a non-negative integer less than 100.
protHits_IsNTerm	Logical describing whether or not the proteomics hits come from N-terminal proteomics. Default value is false.
related_KMerLen	The k-mer length to be used when measuring distances between the central genome and related genomes. Default value is 8. Recommended to use the default value.
related_MinDist	The minimum fractional distance required for a related genome to be used in finding evolutionary conservation. Used to prevent the inclusion of related genomes that are too similar to the central genome. Default value is 0.01. Recommended to use the default value.
related_MaxDistantN	The maximum number of related genomes to use in finding evolutionary conservation after the related genomes have been sorted from most distantly related to most closely related in relation to the central genome. Default value is 1000.
startCodons	A character vector consisting of three-letter DNA strings to use as the start codons when finding evolutionarily conserved starts.
ema_AlphaVal	The alpha value to use when calculating the exponential moving average over an alignment derived from a synteny map. Default value is 0.1. Recommended to use the default value.
ema_MinVal	The minimum exponential moving average value required for an alignment position to be incorporated into the conservation vectors. Default value is 0.6. Recommended to use the default value.
useProt	Logical indicating whether or not proteomics evidence should be mapped to the genome. Default value is true. Cannot be false if useCons is false.
useCons	Logical indicating whether or not evolutionary conservation evidence should be mapped to the genome. Default value is true. Cannot be false if useProt is false.
processors	Number describing the how many processors to use with DECIPHER functions. Should be either a positive integer that describes the number of processors to use or NULL to detect and use all available processors.
verbose	Logical indicating whether or not to display progress and status messages.

Details

MapAssessmentData maps the given data (either proteomics data, evolutionary conservation data, or both) to the given central genome and stores those mappings in the object outputted by the function. The object that is outputted can then be used to assess the quality of genes predicted for that same central genome.

All genomes used inside this function, including the central genome, must be inside the specified table of the specified database. If the central genome is not found, the function returns an error. Please see the Using AssessORF vignette for details on how to populate a database with genomic sequences.

Information on the proteomics hits is primarily given by protHits_Seqs and protHits_Scores. The sequences (protHits_Seqs) are mapped to the six-frame translations of the central genome, and the scores (protHits_Scores) are used in thresholding and plotting the proteomics hits.

protHits_Scores can be a single number. In that case, that number is used the as the score for all proteomics hits. Otherwise, the protHits_Scores must be of the same length as protHits_Seqs.

Only proteomics hits with a score greater than the value of the percentile that corresponds to the value of protHits_Threshold will be kept and the rest of the hits will be dropped. If all the proteomics hits have the same score or if protHits_Threshold is zero, no thresholding will occur and no hits will be dropped.

Please note that the logical parameter protHits_IsNTerm has no effect on how the proteomics evidence is mapped to the central genome but it can be used to affect how genes are assessed and categorized in AssessGenes. The NTermProteomics item in the outputted object is set to the value of protHits_IsNTerm (TRUE or FALSE). Users then have the option of requiring that AssessGenes specifically perform N-terminal proteomics assessment when categorizing genes via the useNTermProt parameter to the AssessGenes function. To summarize, the protHits_IsNTerm parameter in the MapAssessmentData function and the useNTermProt in the AssessGenes function must both be set to TRUE in order to perform N-terminal proteomics assessment. See AssessGenes for more details.

Evolutionarily conserved starts and conserved stop are found by first measuring how far the related genomes are from the central genome using k-mer frequencies. Next, synteny is mapped between the central genome and each of the most distant related genomes, and alignments are built from those synteny maps. An exponential moving average (EMA) is calculated over the alignment (based on whether the central genome is identical to the related genome at that position) to filter out areas of poor alignment. The synteny maps and filterd alignments provide information on how often each position in the central genome is covered by syntenic matches to related genomes (coverage), how often those positions correspond to the start codons (start codon conservation) in both genomes, and how often those positions correspond to stop codons in related genomes (stop codon conservation). A ratio of conservation to coverage is used in downstream functions to measure the strength of both conserved starts and conserved stops.

Related genomes should be from species that are closely related to the given strain. related_IDs specifies the identifiers for the sequences of the related genomes inside the database. A related genome identifier (each element of related_IDs) is considered invalid and not used when finding evolutionary conservation if it is not found in the databse. Please note that the function will only error when none of the related genomes are found.

If there are less valid related genomes in the sequence database than value of related_MaxDistantN, all valid related genomes will be used in finding evolutionary conservation.

The logical flag useProt is used to indicate whether or not proteomics evidence has been provided and should be mapped to the genome. Error checking will not occur for any arguments that involve proteomics if it is false.

The logical flag useCons is used to indicate whether or not evolutionary conservation evidence has been provided and should be mapped to the genome. Error checking will not occur for any arguments that involve evolutionary conservation if it is false.

Value

An object of class Assessment and subclass DataMap

See Also

Assessment-class

Examples

## Example showing the minimum number of arguments that need to be specified
## to map both proteomics and evolutionary conservation data:

## Not run: 
myMapObj <- MapAssessmentData(myDBFile, central_ID = "1",
                              related_IDs = as.character(2:1001),
                              protHits_Seqs = myProtSeqs)

## End(Not run)


## Runnable example that uses evolutionary conservation data only:
## Human adenovirus 1 is the strain of interest, and the set of Adenoviridae
## genomes will serve as the set of genome. The cenral genome, also known as
## the genome of human adenovirus 1, is at identifier 1. The related genomes
## are at identifiers 2 - 13.

myMapObj <- MapAssessmentData(system.file("extdata",
                                          "Adenoviridae.sqlite",
                                          package = "AssessORF"),
                              central_ID = "1",
                              related_IDs = as.character(2:13),
                              speciesName = "Human adenovirus 1",
                              useProt = FALSE)

---

Plot Genes by Category and Length

Description

The mosaicplot method for Assessment object

Usage

## S3 method for class 'Assessment'
mosaicplot(x, ...)

Arguments

x	An object of class Assessment and subclass Results.
...	Further mosaicplot parameters.

Details

mosaicplot.Assessment plots all the genes in the given Results object by category and length. This set of genes includes both the supplied predicted genes as well as open reading frames with proteomics evidence but no predicted start.

The set of genes are separated into ten quantile bins based on the length of the gene/open reading frame. The genes are then plotted by length bin and category in a mosaic format, with each column representing a length bin and each row/block representing a category.

Value

Invisibly returns the input object x

See Also

Assessment-class

Examples

mosaicplot(readRDS(system.file("extdata",
                               "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                               package = "AssessORF")))

---

Plot Assessment Objects

Description

The plot method for Assessment objects

Usage

## S3 method for class 'Assessment'
plot(
  x,
  y = NULL,
  minConCovRatio_GV = 0.8,
  interactive_GV = TRUE,
  rangeStart_GV = NA_integer_,
  rangeEnd_GV = NA_integer_,
  ...
)

Arguments

x	An object of class Assessment and of either subclass DataMap or subclass Results.
y	An optional object of class Assessment and of either subclass DataMap or subclass Results. Its subclass must be different than the subclass of x
minConCovRatio_GV	Minimum value of the conservation to coverage ratio needed to call a start conserved. Must range from 0 to 1. Lower values allow more conserved starts through. Only used with the genome viewer. Default value is recommended.
interactive_GV	Logical specifying whether or not the genome viewer plot should be interactive. Default is TRUE.
rangeStart_GV, rangeEnd_GV	Optional positive integer values that can be specified when generating a genome viewer plot in order to have the plot zoom into the range of genomic positions between those values. Both values must be within the bounds of the genome. Omitted (NA) by default, which results in a plot spanning the whole genome.
...	Further plotting parameters.

Details

If out of x and y only x is specified and x is of subclass Results, a bar chart describing the number of genes in each category is plotted. For the predicted gene categories, bars are colored by the correctness of that category, where dark green represents "definitely correct", light green represents "likely correct", white represents "no evidence", dark red represents "definitely incorrect", light red represents "likely incorrect", and grey represents "potentially incorrect". For the two categories that come from ORFs without predicted genes, dark blue represents "likely missing" and light blue represents "potentially missing".

If out of x and y only x is specified and x is of subclass DataMap, a genome viewer plot showing how the proteomics data and evolutionary conservation data map to the central genome is generated.

If both x and y are specified, each of a different subclass, a genome viewer plot showing how the proteomics data, evolutionary conservation data, and set of predicted genes map to the central genome is generated.

## The genome viewer

In the genome viewer plot, predicted starts are magenta lines, predicted stops are cyan lines, genome stops are yellow lines, conserved starts are gray lines, and proteomic hits are blue / red / green blocks.

If interactive_GV is set to FALSE, a static genome viewer plot is generated. If interactive_GV is set to TRUE, a genome viewer plot that can be interacted with using the locator is generated. In order to interact with the plot, the user needs to click on the graphics window one or more times and then terminate the locator. One click will scroll the viewer either to the left or the right (based on which side is closer to the click). Two clicks will zoom the viewer into the horizontal range between the two click points. Three clicks will zoom out 10-fold, and four clicks will zoom out completely to the entire genome. To stop interaction with the locator, click zero times then terminate the locator. Depending on the graphical device, terminating the locator can either done by pressing the 'Finish' / 'Stop' button, hitting the 'Esc' key, or right-clicking the graphics device.

If both rangeStart_GV and rangeEnd_GV are validly specified, the genome viewer will only span the range of genomic positions between those two values. If interaction is turned on with interactive_GV and a static plot is not being generated, this only applies to the initial state of the genome viewer plot. By default, rangeStart_GV and rangeEnd_GV are not specified, resulting in the genome viewer covering all positions in the genome (at least to start). WARNING: plotting the whole genome at once may overwhelm some graphical devices and cause R to slow down or crash, so it is recommended to avoid doing so unless absolutely necessary.

Value

Invisibly returns the input object x

See Also

Assessment-class, locator

Examples

currMapObj <- readRDS(system.file("extdata",
                                  "MGAS5005_PreSaved_DataMapObj.rds",
                                  package = "AssessORF"))

currResObj <- readRDS(system.file("extdata",
                                  "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                                  package = "AssessORF"))

plot(currMapObj)

plot(currResObj)

plot(currMapObj, currResObj)

plot(currResObj, currMapObj)

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Print Assessment Objects

Description

The print method for Assessment objects

Usage

## S3 method for class 'Assessment'
print(x, ...)

Arguments

x	An object of class Assessment and of either subclass DataMap or subclass Results.
...	Further printing parameters.

Details

If x is of subclass DataMap, the length of the genome is printed along with any supplied identifying information for the genome.

If x is of subclass Results, the number of genes in each category and the accuracy scores are printed out along with any supplied identifying information.

Value

Invisibly returns the input object x

See Also

Assessment-class

Examples

print(readRDS(system.file("extdata",
                          "MGAS5005_PreSaved_DataMapObj.rds",
                          package = "AssessORF")))

print(readRDS(system.file("extdata",
                          "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                          package = "AssessORF")))

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Score Gene Assessment Results

Description

Scores the results from the assessment of a set of genes using one of three modes

Usage

ScoreAssessmentResults(x, mode = "a")

Arguments

x	An object of class Assessment and subclass Results.
mode	Must either be "a" (use all evidence), "p" (use proteomics evidence only), or "c" (use evolutionary conservation evidence only)

Details

ScoreAssessmentResults calculates an accuracy-like score for the categorization of genes within the given Results object using the given mode of calculation. The score for a mode is equal to the number of genes that were categorized to be correct for that mode divided by the total number of genes that could have been categorized as correct for that mode (i.e. a count of the number of genes that had available and useable evidence for that particular mode).

Open reading frames with proteomics evidence but no predicted start are included in the total gene count when calculating the accuracy-like score for the proteomics mode and for the all evidence mode.

Value

A numeric vector of length one containing the calculated accuracy-like score.

See Also

Assessment-class

Examples

currResObj <- readRDS(system.file("extdata",
                                  "MGAS5005_PreSaved_ResultsObj_Prodigal.rds",
                                  package = "AssessORF"))

ScoreAssessmentResults(currResObj, "a")

ScoreAssessmentResults(currResObj, "c")

ScoreAssessmentResults(currResObj, "p")

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