npcooley commented 4 years ago

Update the following URL to point to the GitHub repository of the package you wish to submit to Bioconductor

Repository: https://github.com/npcooley/SynExtend

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bioc-issue-bot commented 4 years ago

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The DESCRIPTION file for this package is:

Package: SynExtend
Type: Package
Title: Tools for working with Synteny Objects
Version: 0.99.05
Date: 2020-02-12
Authors@R: c(
    person("Nicholas", "Cooley", email = "npc19@pitt.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6029-304X")),
    person("Adelle", "Fernando", email = "Adelle.fernando@gmail.com", role = "ctb"),
    person("Erik", "Wright", email = "ESWRIGHT@pitt.edu", role = "aut"))
BiocVews: Genetics, Clustering, ComparativeGenomics
Description: Tools for working with Synteny Objects
Depends: R (>= 3.6.0), DECIPHER (>= 2.14.0), igraph (>= 1.2.4.1)
Imports: Biostrings, IRanges, S4Vectors
Suggests: knitr
License: GPL-3
Encoding: UTF-8
LazyData: true
NeedsCompilation: no
VignetteBuilder: knitr

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mtmorgan commented 4 years ago

please provide a more extensive Description:, like a paper abstract describing the unique contributions your package is making.

Please mark the first code chunk of your vignette with {r, eval = FALSE} so that building the vignette does not trigger installing the package.

In the vignette, please use rtracklayer::import() to import the gff file into a standard R data representation; make sure that this representation can be used as inputs to your functions.

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lshep commented 4 years ago

Build Report

[ ] We highly suggest having unit tests
[ ] We highly suggest adding a NEWS file. The NEWS file is what we use to generate the release announcements to recognize changes and updates to code. example release announcement

README

[ ] Add Bioconductor installation instructions as well.

DESCRIPTION

[ ] Remove Date field. Should be generated automatically and not needed to put directly in the DESCRIPTION
[ ] Please remove LazyData: true we have found this rarely proves useful and can actually slow down the installation of a package

NAMESPACE

[X] Okay

INST

[ ] Include an inst/scripts directory that has information on how the data in inst/extdata was generated. It should be an R script, sudo-code, or text information the source and any relavalent processing

Data

[X] Okay

Vignette

[ ] As @mtmorgan previously stated: please use rtracklayer::import() to import the gff file into a standard R data representation; make sure that this representation can be used as inputs to your functions. GenomicRanges GRanges object is the common class structure in Bioconductor to represent genomic positions.

man pages

data man pages

[ ] man files for data seem incomplete. TBA

NucleotideOverlap

[ ] I would make it more explicit that the Synteny object is from DECIPHER.
[ ] GeneCalls should be GRange or GRangeList from GenomicRanges.
[ ] List the options for OutputFormat. I don't know what else besides "Normal" can be used
[ ] inluced references and note or remove
[ ] based on the code it seems like GeneCalls also needs more information like that it should be named

PairSummaries

[ ] How would one create the DBPATH or get the DBPATH? This is unclear to me? Where does this come from? Is it common output when creating the DECIPER object?
[ ] remove TBA sections
[ ] What are the options for Correction?
[ ] Elobrate if possible for all descriptions

gffToDataFrame

[ ] Remove this entirely and use already implemented Bioconductor function rtracklayer::import

R code In general the code need clean up and compression.

PairSummaries

[ ] GeneCalls should be GRange or GRangeList
[ ] Do all checking for valid arguments at the start of the function. Include additional checks for model, correction, etc.
[ ] can some of the conditional code be together. Example: All if(PID) in the same location instead of multiple separate conditionals. condense where possible

[ ] Instead of multiple

TotalCoverage <- vector("list",
                      length = Total)
MinCoverage <- vector("list",
                    length = Total)
MaxCoverage <- vector("list",
                    length = Total)

It would be more readable to do

TotalCoverage <- MinCoverage <- MaxCoverage <- vector("list", length = Total)

It looks like you did this for some but not all.

[ ] Minimize function calls. It would be cleaner to assign arguments in conditional and have a single call to the functions. This is an example but should be applied throughout


if (!(GeneCalls[[m1]][SyntenyLinks[[m1, m2]][i, "QueryGene"], "Coding"]) |
          !(GeneCalls[[m2]][SyntenyLinks[[m1, m2]][i, "SubjectGene"], "Coding"]) |
          IgnoreDefaultStringSet) {
        # align as nucleotides
        IDType[[Count]][i] <- "DNA"
        Scores[[Count]][[i]] <- 1 - DistanceMatrix(myXStringSet = AlignSeqs(myXStringSet = Scores[[Count]][[i]],
                                                                            verbose = FALSE),
                                                   penalizeGapLetterMatches = GapPenalty,
                                                   includeTerminalGaps = TerminalPenalty,
                                                   correction = Correction,
                                                   type = "matrix",
                                                   verbose = FALSE)[1, 2]
      } else {
        # align as AAs
        IDType[[Count]][i] <- "AA"
        Scores[[Count]][[i]] <- 1 - DistanceMatrix(myXStringSet = AlignTranslation(myXStringSet = Scores[[Count]][[i]],
                                                                                   sense = "+",
                                                                                   direction = "5' to 3'",
                                                                                   type = "DNAStringSet",
                                                                                   readingFrame = 1L,
                                                                                   verbose = FALSE),
                                                   penalizeGapLetterMatches = GapPenalty,
                                                   includeTerminalGaps = TerminalPenalty,
                                                   correction = Correction,
                                                   type = "matrix",
                                                   verbose = FALSE)[1, 2]

Would become.

if (!(GeneCalls[[m1]][SyntenyLinks[[m1, m2]][i, "QueryGene"], "Coding"]) |
          !(GeneCalls[[m2]][SyntenyLinks[[m1, m2]][i, "SubjectGene"], "Coding"]) |
          IgnoreDefaultStringSet) {
myXStringSet = AlignSeqs(myXStringSet = Scores[[Count]][[i]], verbose = FALSE)
type = "DNA"

}else{ myXStringSet = AlignTranslation(myXStringSet = Scores[[Count]][[i]], sense = "+", direction = "5' to 3'",type = "DNAStringSet", readingFrame = 1L, verbose = FALSE) type = "AA } IDType[[Count]][i] <- type Scores[[Count]][[i]] <- 1 - DistanceMatrix(myXStringSet = myXStringSet, penalizeGapLetterMatches = GapPenalty, includeTerminalGaps = TerminalPenalty, correction = Correction, type = "matrix", verbose = FALSE)[1, 2]

This eliminates redundant code and condense the code to make it easier to read. Please apply throughout this script and NucleotideOverlap.R 
Another example

if (PIDs) { DF <- data.frame("TotalCoverage" = unlist(TotalCoverage), "MaxCoverage" = unlist(MaxCoverage), "MinCoverage" = unlist(MinCoverage), "NormDeltaStart" = unlist(NormDeltaStart), "NormDeltaStop" = unlist(NormDeltaStop), "NormGeneDiff" = unlist(NormGeneDiff), "ExactMatch" = unlist(ExactMatch),

"LeftDeviation" = unlist(AbsStartDelta),

                 # "RightDeviation" = unlist(AbsStopDelta),
                 # "SubjectLeft" = unlist(ExactLeftS),
                 # "SubjectRight" = unlist(ExactRightS),
                 # "QueryLeft" = unlist(ExactLeftQ),
                 # "QueryRight" = unlist(ExactRightQ),
                 "PID" = unlist(Scores),
                 "PIDType" = unlist(IDType),
                 stringsAsFactors = FALSE)
rownames(DF) <- unlist(LabelNames)

} else { DF <- data.frame("TotalCoverage" = unlist(TotalCoverage), "MaxCoverage" = unlist(MaxCoverage), "MinCoverage" = unlist(MinCoverage), "NormDeltaStart" = unlist(NormDeltaStart), "NormDeltaStop" = unlist(NormDeltaStop), "NormGeneDiff" = unlist(NormGeneDiff), "ExactMatch" = unlist(ExactMatch),

"LeftDeviation" = unlist(AbsStartDelta),

                 # "RightDeviation" = unlist(AbsStopDelta),
                 # "SubjectLeft" = unlist(ExactLeftS),
                 # "SubjectRight" = unlist(ExactRightS),
                 # "QueryLeft" = unlist(ExactLeftQ),
                 # "QueryRight" = unlist(ExactRightQ),
                 stringsAsFactors = FALSE)
rownames(DF) <- unlist(LabelNames)

}

You could construct the overlapping DF first and then apply the additional columns in the conditional only. 
And again with choosing the model. The conditional should be the model difference
and then only a single call

shortened for example

if (Model == "Global") { data("GlobalSelect", envir = environment(), package = "SynExtend") modelData = GlobalSelect else if (Model == "Local") { data("LocalSelect", envir = environment(), package = "SynExtend") modelData = LocalSelect }

KeepSet <- predict(object = modelData, DF, type = response") DF <- cbind(DF,"ModelSelect" = KeepSet >= 0.5)


- [ ] Remove commented out code

_NucleotideOverlap_

- [ ] OutputFormat doesn't change between options? Also have a check for valid
  value of OutFormat at the start of the function.

Links <- NucleotideOverlap(SyntenyObject = Syn,

GeneCalls = GeneCalls,

LimitIndex = FALSE,

Verbose = FALSE) Links 1 2 3 1 1683 Pairs 1760 Pairs 2 609223 Nucleotides 1781 Pairs 3 802650 Nucleotides 623239 Nucleotides
Links <- NucleotideOverlap(SyntenyObject = Syn,GeneCalls=GeneCalls, LimitIndex=FALSE, Verbose=FALSE, OutputFormat="Comprehensive") Links 1 2 3 1 1794 Genes 1683 Pairs 1760 Pairs 2 609223 Nucleotides 2264 Genes 1781 Pairs 3 802650 Nucleotides 623239 Nucleotides 2216 Genes Links <- NucleotideOverlap(SyntenyObject = Syn,GeneCalls=GeneCalls, LimitIndex=FALSE, Verbose=FALSE, OutputFormat="Sparse") Links 1 2 3 1 1683 Pairs 1760 Pairs 2 609223 Nucleotides 1781 Pairs 3 802650 Nucleotides 623239 Nucleotides


- [ ] Check valid argument values at the start of function.  
- [ ] GeneCalls should be GRange or GRangeList
- [ ] Apply comments to avoid redundant code and to condense where possible to this script as well.

_gffToDataFrame_
- [ ] Remove this entirely and use already implemented Bioconductor function `rtracklayer::import`

Please apply the above corrections and clean up.  When ready make sure there is a new build with a valid version bump and comment back here with a summary of the updates.  

Thank you for your submission to Bioconductor
~ Lori

npcooley commented 4 years ago

Hi Lori,

Thanks for all the reviewing! I'll get started on your comments as I have time.

I would be happy to make NucleotideOverlap flexible in that it could take in a Genomic Ranges object, but just the wholesale import of the GFF is actually not useful to us. The text parsing that the gffToDateFrame performs is just sort of speculative, but all the parent/id matching and condensing would still have to be performed on the Genomic Ranges object anyway. It made more sense for us to parse it straight from the original file.

I.e. a naive user would be just fine pulling the gene bounds from a gff generated by Prodigal or Prokka using a rtracklayer::import because those programs don't think about genes with complex bounds, but if I pull a generic refseq or genbank gff, it will need additional condensing to drop redundant bound sets (such as were the same bound is marked for "gene", "mrna", "exon", and "cds"), and to correctly think about the coding space for cases where multiple exons exist.

Keeping a condensed format is useful for me internally because it allows me to correctly reconcile CDS and gene bounds in a single place, and keep track of indices in the source FASTA file. It seems useful for a naive user as well because the data format is just simpler.

Once again I'm happy to make it flexible, and if GRanges objects can support ragged ranges for gene positions I can convert my output to that, but those are changes I would prefer to implement in a later release if possible.

Cheers, Nick

lshep commented 4 years ago

In Bioconductor the recommended and almost always required structure for data involving genomic position information is GenomicRanges in either a GRanges or GRangesList object. Users that have been using Bioconductor pipelines and workflows will expect this and at minimum expect the function to be able to handle this type of object. There is also some consideration of how your parser handles the range and range intervals. Bioconductor standards for ranges are 1-based, closed intervals and this would have to be implemented so there is no discrepancy from standard usage throughout Bioconductor; this is the reasoning for using the rtracklayer::import function as it takes all of this into consideration when doing the parsing; it is a fast and developed function that would guarantee correctness. It seems like your other manipulations (parent/id matching, etc) and additions of information can be performed on the imported GenomicRanges object and this would be preferred. The GenomicRanges package has been highly developed over the years, as a very mature package, and has many useful functionalities that could prove useful in development. Please update the package in the current version before acceptance; in our experience promises of updating in future implementations never come to fruition.

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lshep commented 4 years ago

Is the package ready for a re-review? If so please summarize changes that have been made here.

lshep commented 4 years ago

Please remember all packages must be reviewed and accepted by april 22 to be included in the 3.11 release else they will be in devel until the released in fall under bioc 3.12

npcooley commented 4 years ago

Ohp, I missed your earlier notification, sorry! Yes, the package is ready for review.

Attempted to address all comments on documentation and vignette.
Addressed changes to code, including simplifications and removal of commented out lines
Added GRange objects as an option for inputs to functions.
gffToDataFrame remains in the package because it is not simply just importing the gff files, it is perfroming parsing functions that are essential. That parsing either has to occur upfront, or interior to later functions, and my preference is for it to happen up front.

npcooley commented 4 years ago

Hi,

I know this is cuttiing it close to the deadline, but is it possible to get a review back for this package today?

Cheers, Nick

On Thu, Apr 16, 2020 at 9:51 AM lshep notifications@github.com wrote:

Please remember all packages must be reviewed and accepted by april 22 to be included in the 3.11 release else they will be in devel until the released in fall under bioc 3.12

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Bioconductor / Contributions

SynExtend #1369

"LeftDeviation" = unlist(AbsStartDelta),

"LeftDeviation" = unlist(AbsStartDelta),

shortened for example