R/makeTxDbFromGRanges.R

Defines functions test_makeTxDbFromGRanges_on_Ensembl_gtf test_makeTxDbFromGRanges_on_Ensembl_organism_gtf .light_txdb_dump makeTxDbFromGRanges .normarg_metadata .extract_chrominfo_from_GRanges .extract_genes_from_gtf_GRanges .extract_genes_from_gff3_GRanges .get_gene_ids .make_splicings .find_exon_cds .add_missing_transcripts .infer_transcripts_from_exons .extract_transcripts_from_GRanges .merge_transcript_parts .add_missing_exons .extract_exons_from_GRanges .infer_rank_from_position .extract_rank_from_id .decorate_drop_msg .get_rejected_transcripts .reject_transcripts .flush_rejected_tx_envir .ls_rejected_tx_envir .get_cds_with_noextx_parent_IDX .get_noextx_IDX .get_tx_IDX .get_gene_as_tx_IDX .get_exon_with_gene_parent_IDX .collect_parent_types .get_exon_IDX .get_stop_codon_IDX .get_cds_with_gene_parent_IDX .get_cds_IDX .get_gene_IDX .get_geneID .get_Dbxref .extract_mcols0 .get_Name .drop_ID_prefix .get_Parent .get_ID .is_gtf_format .get_transcript_id .no_id .get_gene_id .get_phase .get_type .rank.CompressedList

Documented in makeTxDbFromGRanges

### =========================================================================
### makeTxDbFromGRanges()
### -------------------------------------------------------------------------
###


### TODO: Move this to IRanges and make it the S4 "rank" method for
### CompressedList objects. Note that the same trick can be used to implement
### a fast "rank" method for list and SimpleList objects. So it would be
### great to implement a .rank.List() in S4Vectors that does the same as
### below but without using IRanges idioms like PartitioningByEnd() and
### togroup().
.rank.CompressedList <- function(x, na.last=TRUE,
                                 ties.method=c("average", "first",
                                               "random", "max", "min"))
{
    ties.method <- match.arg(ties.method)
    if (!identical(ties.method, "first"))
        stop(wmsg("\"rank\" method for CompressedList objects ",
                  "only supports 'ties.method=\"first\"' at the moment"))
    x_partitioning <- PartitioningByEnd(x)
    unlisted_x <- unlist(x, use.names=FALSE)
    oo <- orderIntegerPairs(togroup(x_partitioning), unlisted_x)
    unlisted_ans <- integer(length(oo))
    unlisted_ans[oo] <- seq_along(oo)
    ans <- relist(unlisted_ans, x_partitioning)
    x_len <- length(x)
    if (x_len != 0L) {
        offsets <- c(0L, end(x_partitioning)[-x_len])
        ans <- ans - offsets
    }
    ans
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the metadata columns of interest
###

### Expected metadata columns for GRanges in GFF3 format:
###   - required: type, ID
###   - optional: 1) phase, Parent, Name, Dbxref
###               2) gene_id, transcript_id, exon_id, protein_id (used
###                  by Ensembl for their gene, transcript, exon, and CDS
###                  features, note that exon_id and protein_id are ignored
###                  at the moment)
###               3) geneID (used by Flybase)
### Used in R/makeTxDbFromGFF.R
GFF3_COLNAMES <- c("type", "phase", "ID", "Parent", "Name", "Dbxref",
                   "gene_id", "transcript_id", "exon_id", "protein_id",
                   "geneID")

### Expected metadata columns for GRanges in GTF format:
###   - required: type, gene_id, transcript_id
###   - optional: phase, exon_id
### Used in R/makeTxDbFromGFF.R
GTF_COLNAMES <- c("type", "phase", "gene_id", "transcript_id", "exon_id")

### Only valid SO terms (Sequence Ontology terms) that are "gene"
### offsprings via the is_a relationship should be added to .GENE_TYPES.
.GENE_TYPES <- c(
    "gene",

    ## protein_coding_gene and its offsprings:
    "protein_coding_gene",
        "gene_with_edited_transcript",
        "gene_with_mRNA_with_frameshift",
        "gene_with_polyadenylated_mRNA",
        "gene_with_recoded_mRNA",
            "gene_with_mRNA_recoded_by_translational_bypass",
            "gene_with_transcript_with_translational_frameshift",
            "gene_with_stop_codon_read_through",
                "gene_with_stop_codon_redefined_as_pyrrolysine",
                "gene_with_stop_codon_redefined_as_selenocysteine",

    ## ncRNA_gene and its offsprings:
    "ncRNA_gene",
        "snoRNA_gene",
        "gRNA_gene",
        "RNase_P_RNA_gene",
        "tmRNA_gene",
        "SRP_RNA_gene",
        "tRNA_gene",
        "rRNA_gene",
            "rRNA_25S_gene",
            "rRNA_16S_gene",
            "rRNA_21S_gene",
            "rRNA_5S_gene",
            "rRNA_28S_gene",
            "rRNA_23S_gene",
            "rRNA_5_8S_gene",
            "rRNA_18S_gene",
        "lncRNA_gene",
            "bidirectional_promoter_lncRNA",
            "sense_overlap_ncRNA_gene",
            "lincRNA_gene",
            "antisense_lncRNA_gene",
            "sense_intronic_ncRNA_gene",
        "enzymatic_RNA_gene",
            "ribozyme_gene",
        "RNase_MRP_RNA_gene",
        "snRNA_gene",
        "telomerase_RNA_gene",
        "miRNA_gene",
        "piRNA_gene",
        "scRNA_gene",

    ## pseudogene and its offsprings:
    "pseudogene",
        "non_processed_pseudogene",
            "duplicated_pseudogene",
            "cassette_pseudogene",
            "nuclear_mt_pseudogene",
            "translated_unprocessed_pseudogene",
            "pseudogene_by_unequal_crossing_over",
            "transcribed_unprocessed_pseudogene",
            "unitary_pseudogene",
                "transcribed_unitary_pseudogene",
                "allelic_pseudogene",
        "processed_pseudogene",
            "transcribed_processed_pseudogene",
            "translated_processed_pseudogene",
        "polymorphic_pseudogene",
            "polymorphic_pseudogene_with_retained_intron",
        "vertebrate_immune_system_pseudogene",
            "T_cell_receptor_pseudogene",
                "TR_J_pseudogene",
                "TR_V_pseudogene",
            "immunoglobulin_pseudogene",
                "IG_J_pseudogene",
                "IG_V_pseudogene",
                "IG_C_pseudogene",
        "transposable_element_pseudogene",

    ## transposable_element_gene and its only child (no grandchildren):
    "transposable_element_gene",
        "engineered_foreign_transposable_element_gene"
)

### Only valid SO terms (Sequence Ontology terms) that are "transcript"
### offsprings via the is_a relationship should be added to .TX_TYPES.
### HOWEVER we've made exceptions for "pseudogenic_transcript" and
### the 4 "vertebrate_immunoglobulin_T_cell_receptor_segment"
### children ("V_gene_segment", "D_gene_segment", "C_gene_segment",
### "J_gene_segment") which, according to SO, are NOT offsprings
### of "transcript" via the is_a relationship. The reason for these
### exceptions is that Ensembl assigns transcript-like IDs to these
### features (e.g. ENST00000488147 for pseudogenic_transcript WASH7P-201,
### or ENST00000542354 for V_gene_segment TRAV1-1-201), and this is
### reinforced by their use of "transcript:ENST00000488147" and
### "transcript:ENST00000542354" for the corresponding "ID" attribute in
### the GFF3 files that they produce. In addition, some GFF3 files produced
### by NCBI (e.g. ref_GRCh38.p12_top_level.gff3.gz found at
### ftp://ftp.ncbi.nlm.nih.gov/genomes/H_sapiens/GFF/) link some
### of their exons to parents of type "C_gene_segment".
.TX_TYPES <- c(
    "transcript",

    "primary_transcript",
    "mRNA",
    "ncRNA",
    "rRNA",
    "snoRNA",
    "snRNA",
    "tRNA",
    "tmRNA",
    "miRNA",
    "miRNA_primary_transcript",
    "RNase_P_RNA",
    "RNase_MRP_RNA",
    "SRP_RNA",
    "misc_RNA",
    "antisense_RNA",
    "antisense",
    "lnc_RNA",
    "antisense_lncRNA",
    "transcript_region",
    "scRNA",
    "guide_RNA",
    "telomerase_RNA",
    "vault_RNA",
    "Y_RNA",

    ## predicted_transcript and its only child (no grandchildren):
    "predicted_transcript",
        "unconfirmed_transcript",

    ## pseudogenic_transcript and its offsprings:
    "pseudogenic_transcript",
        "pseudogenic_rRNA",
            "unitary_pseudogenic_rRNA",
            "allelic_pseudogenic_rRNA",
            "unprocessed_pseudogenic_rRNA",
            "processed_pseudogenic_rRNA",
        "pseudogenic_tRNA",
            "unitary_pseudogenic_tRNA",
            "allelic_pseudogenic_tRNA",
            "processed_pseudogenic_tRNA",
            "unprocessed_pseudogenic_tRNA",

    ## vertebrate_immunoglobulin_T_cell_receptor_segment and its 4
    ## children (no grandchildren via the is_a relationship):
    "vertebrate_immunoglobulin_T_cell_receptor_segment",
        "V_gene_segment",
        "D_gene_segment",
        "C_gene_segment",
        "J_gene_segment"
)

### Only valid SO terms (Sequence Ontology terms) that are "exon"
### offsprings via the is_a relationship should be added to .EXON_TYPES.
.EXON_TYPES <- c(
    "exon",

    "pseudogenic_exon",
    "coding_exon",
    "five_prime_coding_exon",
    "interior_coding_exon",
    "three_prime_coding_exon",
    "exon_of_single_exon_gene",
    "interior_exon",
    "noncoding_exon",
    "five_prime_noncoding_exon",
    "three_prime_noncoding_exon"
)

### Only valid SO terms (Sequence Ontology terms) that are "CDS"
### offsprings via the is_a relationship should be added to .CDS_TYPES.
.CDS_TYPES <- c(
    "CDS",

    "transposable_element_CDS",
    "CDS_predicted",
    "edited_CDS"
)

.STOP_CODON_TYPES <- "stop_codon"

### Used in R/makeTxDbFromGFF.R
GFF_FEATURE_TYPES <- c(.GENE_TYPES, .TX_TYPES, .EXON_TYPES,
                       .CDS_TYPES, .STOP_CODON_TYPES)

.get_type <- function(gr_mcols)
{
    type <- gr_mcols$type
    if (is.null(type))
        stop(wmsg("'gr' must have a \"type\" metadata column"))
    ## Return a factor where all levels are in use.
    if (is.factor(type)) {
        levels_in_use <- levels(type)[tabulate(type, nbins=nlevels(type)) != 0L]
    } else {
        if (!is.character(type))
            stop(wmsg("the \"type\" metadata column must be ",
                      "a character vector or factor"))
        levels_in_use <- unique(type)
    }
    factor(type, levels=levels_in_use)
}

### Return an integer vector or NULL.
.get_phase <- function(gr_mcols)
{
    phase <- gr_mcols$phase
    if (!(is.null(phase) || is.integer(phase)))
        stop(wmsg("the \"phase\" metadata column must be an integer vector"))
    phase
}

### Return a character vector or NULL.
.get_gene_id <- function(gr_mcols)
{
    gene_id <- gr_mcols$gene_id
    if (is.null(gene_id))
        return(NULL)
    if (!is.character(gene_id))
        gene_id <- as.character(gene_id)
    gene_id
}

.no_id <- function(id) {is.null(id) || all(is.na(id))}

### Return a character vector, or integer vector (if inferred ids), or NULL.
.get_transcript_id <- function(gr_mcols, gene_id, type)
{
    transcript_id <- gr_mcols$transcript_id
    ## We've seen silly GTF files that contain only lines of type "transcript"
    ## but no "transcript_id" attribute.
    if (!.no_id(gene_id) && .no_id(transcript_id) && all(type %in% .TX_TYPES))
        return(seq_along(type))  # inferred ids
    if (is.null(transcript_id))
        return(NULL)
    if (!is.character(transcript_id))
        transcript_id <- as.character(transcript_id)
    transcript_id
}

### If we have no "ID" metadata column but "gene_id" and "transcript_id"
### metadata columns (and if they don't contain only NAs) then we assume the
### GRanges object is in GTF format. Otherwise we assume it's in GFF3 format.
.is_gtf_format <- function(ID, gene_id, transcript_id)
    (.no_id(ID) && !.no_id(gene_id) && !.no_id(transcript_id))

.get_ID <- function(gr_mcols, type, gene_id, transcript_id, gtf.format=FALSE)
{
    if (gtf.format) {
        ## GTF format
        ID <- rep.int(NA_character_, nrow(gr_mcols))
        idx1 <- which(type %in% .GENE_TYPES)
        ID[idx1] <- gene_id[idx1]
        idx2 <- which(type %in% .TX_TYPES)
        ID[idx2] <- transcript_id[idx2]
        exon_id <- gr_mcols$exon_id
        if (!is.null(exon_id)) {
            idx3 <- which(type %in% .EXON_TYPES)
            ID[idx3] <- exon_id[idx3]
        }
    } else {
        ## GFF3 format
        ID <- gr_mcols$ID
        if (is.null(ID))
            ID <- rep.int(NA_character_, nrow(gr_mcols))
        if (!is.character(ID))
            ID <- as.character(ID)
        ID[ID %in% ""] <- NA_character_
    }
    ID
}

.get_Parent <- function(gr_mcols, type, gene_id, transcript_id,
                        gtf.format=FALSE)
{
    if (gtf.format) {
        ## GTF format
        Parent <- character(nrow(gr_mcols))
        idx1 <- which(type %in% .TX_TYPES)
        Parent[idx1] <- gene_id[idx1]
        idx2 <- which(type %in% c(.EXON_TYPES, .CDS_TYPES, .STOP_CODON_TYPES))
        Parent[idx2] <- transcript_id[idx2]
    } else {
        ## GFF3 format
        Parent <- gr_mcols$Parent
        if (is.null(Parent))
            Parent <- rep.int(NA_character_, nrow(gr_mcols))
    }
    if (!is(Parent, "CompressedCharacterList")) {
        Parent0 <- Parent
        Parent <- as(Parent, "CompressedCharacterList")
        if (is.atomic(Parent0)) {
            na_idx <- which(is.na(Parent0))
            Parent[na_idx] <- CharacterList(character(0))
        }
    }
    Parent
}

### Remove "<type_level>:" prefix (like in "gene:Si016158m.g" or
### "CDS:Si016158m" or "CDS:ENSP00000493376") but only if it's present
### in *all* non-NA elements of 'ID'.
.drop_ID_prefix <- function(ID, type_level)
{
    prefix <- paste0(type_level, ":")
    if (all(substr(ID, start=1L, stop=nchar(prefix)) == prefix, na.rm=TRUE))
        return(substr(ID, start=nchar(prefix)+1L, stop=nchar(ID)))
    prefix <- paste0(type_level, "-")
    if (all(substr(ID, start=1L, stop=nchar(prefix)) == prefix, na.rm=TRUE))
        return(substr(ID, start=nchar(prefix)+1L, stop=nchar(ID)))
    ID
}

.get_Name <- function(gr_mcols, type, ID, transcript_id)
{
    Name <- gr_mcols$Name
    if (is.null(Name))
        Name <- rep.int(NA_character_, nrow(gr_mcols))
    if (!is.character(Name))
        Name <- as.character(Name)
    Name[Name %in% ""] <- NA_character_
    ## If, for a given type (e.g. "exon"), none of the features of that type
    ## has a Name (i.e. Name is NA for all the features of that type), then we
    ## infer their Name from their ID.
    for (type_level in levels(type)) {
        ## We make an exception for transcripts with inferred ids.
        if ((type_level %in% .TX_TYPES) && is.integer(transcript_id))
            next
        idx <- which(type == type_level)
        if (all(is.na(Name[idx])))
            Name[idx] <- .drop_ID_prefix(ID[idx], type_level)
    }
    Name
}

.extract_mcols0 <- function(gr_mcols)
{
    type <- .get_type(gr_mcols)
    phase <- .get_phase(gr_mcols)
    gene_id <- .get_gene_id(gr_mcols)
    transcript_id <- .get_transcript_id(gr_mcols, gene_id, type)
    gtf.format <- .is_gtf_format(gr_mcols$ID, gene_id, transcript_id)
    ID <- .get_ID(gr_mcols, type, gene_id, transcript_id,
                  gtf.format=gtf.format)
    Parent <- .get_Parent(gr_mcols, type, gene_id, transcript_id,
                          gtf.format=gtf.format)
    Name <- .get_Name(gr_mcols, type, ID, transcript_id)

    mcols0 <- DataFrame(
        type=type,
        ID=ID,
        Name=Name,
        Parent=Parent)
    if (!is.null(gene_id))
        mcols0$gene_id <- gene_id
    if (!is.null(transcript_id))
        mcols0$transcript_id <- transcript_id
    if (!is.null(phase))
        mcols0$phase <- phase
    metadata(mcols0)$gtf.format <- gtf.format

    mcols0
}

.get_Dbxref <- function(gr_mcols)
{
    Dbxref <- gr_mcols$Dbxref
    if (is.null(Dbxref))
        return(NULL)
    if (is(Dbxref, "List") || is.list(Dbxref)) {
        if (!is(Dbxref, "CompressedCharacterList"))
            Dbxref <- as(Dbxref, "CompressedCharacterList")
    } else {
        if (!is.character(Dbxref))
            Dbxref <- as.character(Dbxref)
    }
    Dbxref
}

### The FlyBase people use the "geneID" attribute to assign an external gene
### id to each transcript in their GFF3 files. There is no corresponding
### "gene" line in the file.
.get_geneID <- function(gr_mcols)
{
    geneID <- gr_mcols$geneID
    if (is.null(geneID))
        return(NULL)
    if (!is.character(geneID))
        geneID <- as.character(geneID)
    geneID
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Get the gene, cds, stop_codon, exon, and transcript indices.
###

.get_gene_IDX <- function(type)
{
    which(type %in% .GENE_TYPES)
}

.get_cds_IDX <- function(type, phase)
{
    is_cds <- type %in% .CDS_TYPES
    if (!is.null(phase)) {
        if (S4Vectors:::anyMissingOrOutside(phase[is_cds], 0L, 2L))
            warning(wmsg("some CDS phases are missing or not between 0 and 2"))
        types_with_phase <- type[!is.na(phase) & type %in% GFF_FEATURE_TYPES]
        types_with_phase <- setdiff(as.character(unique(types_with_phase)),
                                    .CDS_TYPES)
        if (length(types_with_phase) != 0L) {
            in1string <- paste0(as.character(types_with_phase), collapse=", ")
            warning(wmsg("The \"phase\" metadata column contains non-NA ",
                         "values for features of type ", in1string,
                         ". This information was ignored."))
        }
    }
    which(is_cds)
}

### Returns the index of CDS whose Parent is a gene (this happens with some
### GFF files e.g. inst/extdata/GFF3_files/NC_011025.gff). These CDS will
### also be considered exons (i.e. added to the set of exons).
.get_cds_with_gene_parent_IDX <- function(cds_IDX, Parent, gene_IDX, ID,
                                          gtf.format=FALSE)
{
    if (gtf.format)
        return(integer(0))
    cds_IDX[as.logical(unique(Parent[cds_IDX] %in% ID[gene_IDX]))]
}

.get_stop_codon_IDX <- function(type)
{
    which(type %in% .STOP_CODON_TYPES)
}

.get_exon_IDX <- function(type, cds_with_gene_parent_IDX)
{
    sort(c(which(type %in% .EXON_TYPES), cds_with_gene_parent_IDX))
}

### 'IDX' will typically contain the index returned by .get_exon_IDX().
### Not used at the moment. Maybe we should make something like this
### available to the user, and it should work directly on a GFF file.
### That way they could get a quick glance at the parent types of all
### the exons in the file. A toggle could let them collect only the
### types that don't belong to .TX_TYPES. This would provide a way to
### know in advance which exons (and how many) are going to be dropped
### by makeTxDbFromGRanges().
### This could be combined with an 'extra_tx_types' argument to
### makeTxDbFromGRanges() that would let the user supply some of the
### exon parent types to be treated as transcripts.
.collect_parent_types <- function(IDX, Parent, ID, type)
{
    types <- type[ID %in% unlist(Parent[IDX], use.names=FALSE)]
    table(as.character(types))
}

### Returns the index of exons whose Parent is a gene.
.get_exon_with_gene_parent_IDX <- function(exon_IDX, Parent, gene_IDX, ID,
                                           gtf.format=FALSE)
{
    if (gtf.format)
        return(integer(0))
    has_gene_parent <- as.logical(unique(Parent[exon_IDX] %in% ID[gene_IDX]))
    ## We've seen silly GFF files where exons have no parents. This introduces
    ## NAs in 'has_gene_parent' so we use which() here to get rid of them.
    exon_IDX[which(has_gene_parent)]
}

### Returns the index of genes that have exons as direct children. These genes
### will also be considered transcripts (i.e. added to the set of transcripts).
.get_gene_as_tx_IDX <- function(gene_IDX, ID, exon_with_gene_parent_IDX, Parent)
{
    gene_IDX[ID[gene_IDX] %in% unlist(Parent[exon_with_gene_parent_IDX],
                                      use.names=FALSE)]
}

.get_tx_IDX <- function(type, gene_as_tx_IDX)
{
    sort(c(which(type %in% .TX_TYPES), gene_as_tx_IDX))
}

### noextx :== transcript with no exons
.get_noextx_IDX <- function(tx_IDX, ID, exon_IDX, Parent)
{
    tx_IDX[!(ID[tx_IDX] %in% unlist(Parent[exon_IDX], use.names=FALSE))]
}

### CDS in transcripts with no exons will also be considered exons (i.e. added
### to the set of exons).
.get_cds_with_noextx_parent_IDX <- function(cds_IDX, Parent, noextx_IDX, ID)
{
    ## as.logical() will fail if a CDS has at least 2 parents and 1 is a
    ## transcript with no exons and the other is not.
    cds_IDX[as.logical(unique(Parent[cds_IDX] %in% ID[noextx_IDX]))]
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Management of rejected transcripts
###

.rejected_tx_envir <- new.env(hash=TRUE, parent=emptyenv())

.ls_rejected_tx_envir <- function()
    ls(envir=.rejected_tx_envir, all.names=TRUE, sorted=FALSE)

.flush_rejected_tx_envir <- function()
{
    objnames <- .ls_rejected_tx_envir()
    rm(list=objnames, envir=.rejected_tx_envir)
}

.reject_transcripts <- function(tx_ids, because)
{
    tx_ids <- sort(as.character(tx_ids))
    in1string <- paste0(tx_ids, collapse=", ")
    warning(wmsg("The following transcripts were dropped because ",
                 because, ": ", in1string))

    objnames <- .ls_rejected_tx_envir()
    nobj <- length(objnames)
    if (nobj == 0L) {
        new_objname <- 1L
    } else {
        new_objname <- as.integer(objnames[nobj]) + 1L
    }
    new_objname <- sprintf("%08d", new_objname)
    assign(new_objname, tx_ids, envir=.rejected_tx_envir)
}

.get_rejected_transcripts <- function()
{
    objnames <- .ls_rejected_tx_envir()
    unique(unlist(mget(objnames, envir=.rejected_tx_envir), use.names=FALSE))
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'exons', 'cds', and 'stop_codons' data frames
###

.decorate_drop_msg <- function(msg, features, mcols0)
{
    Parent_type <- as.character(mcols0$type[match(features$Parent, mcols0$ID)])
    features$Parent_type <- Parent_type
    if (nrow(features) > 6L)
        msg <- c(msg, " (showing only the first 6)")
    c(wmsg(msg), ":\n", paste(capture.output(print(head(features, n=6L))),
                              collapse="\n"))
}

.extract_rank_from_id <- function(id, parent_id)
{
    if (length(id) == 0L)
        return(integer(0))
    id_parts <- strsplit(id, "\\.|:")
    ## Fix non-sensical output of strsplit() on empty strings.
    id_parts[elementNROWS(id_parts) == 0L] <- ""
    unlisted_id_parts <- unlist(id_parts, use.names=FALSE)
    idx <- cumsum(elementNROWS(id_parts))
    rank <- unlisted_id_parts[idx]
    rank <- suppressWarnings(as.integer(rank))
    if (any(is.na(rank)))
        return(NULL)
    ## Sanity check.
    tmp <- unname(splitAsList(rank, parent_id))
    if (any(any(duplicated(tmp))))
        return(NULL)
    if (!(all(min(tmp) == 1L) && all(max(tmp) == elementNROWS(tmp))))
        return(NULL)
    rank
}

.infer_rank_from_position <- function(tx_id, exon_chrom, exon_strand,
                                             exon_start, exon_end)
{
    chrom_by_tx <- split(Rle(exon_chrom), tx_id)
    tx_chrom <- runValue(chrom_by_tx)
    bad_tx1 <- names(which(elementNROWS(tx_chrom) > 1L))

    strand_by_tx <- split(Rle(exon_strand), tx_id)
    tx_strand <- runValue(strand_by_tx)
    is_bad <- elementNROWS(tx_strand) > 1L
    bad_tx2 <- names(which(is_bad))
    tx_strand[is_bad] <- "*"
    minus_idx <- which(as.character(tx_strand) == "-")

    ex_by_tx <- split(IRanges(exon_start, exon_end), tx_id)
    reduced_ex_by_tx <- reduce(ex_by_tx, min.gapwidth=0L)
    bad_tx3 <- names(which(elementNROWS(reduced_ex_by_tx) !=
                           elementNROWS(ex_by_tx)))

    start_by_tx <- start(ex_by_tx)
    start_by_tx[minus_idx] <- start_by_tx[minus_idx] * (-1L)
    rank <- .rank.CompressedList(start_by_tx, ties.method="first")
    ans <- unsplit(rank, tx_id)

    bad_tx <- unique(c(bad_tx1, bad_tx2, bad_tx3))
    ans[tx_id %in% bad_tx] <- NA_integer_

    ans
}

### Can be used to extract exons, cds, or stop codons.
.extract_exons_from_GRanges <- function(exon_IDX, gr, mcols0, tx_IDX,
                                        feature=c("exon", "cds", "stop_codon"),
                                        gtf.format=FALSE, phase=NULL)
{
    feature <- match.arg(feature)
    what <- switch(feature, exon="exon", cds="CDS", stop_codon="stop codon")
    exon_Parent <- mcols0$Parent[exon_IDX]
    if (any(any(duplicated(exon_Parent))))
        stop(wmsg("some ", what, "s are mapped twice to the same transcript"))

    tx_id <- factor(unlist(exon_Parent, use.names=FALSE))
    nparent_per_ex <- elementNROWS(exon_Parent)
    exon_id <- rep.int(mcols0$ID[exon_IDX], nparent_per_ex)
    exon_name <- rep.int(mcols0$Name[exon_IDX], nparent_per_ex)
    exon_chrom <- rep.int(seqnames(gr)[exon_IDX], nparent_per_ex)
    exon_strand <- rep.int(strand(gr)[exon_IDX], nparent_per_ex)
    exon_start <- rep.int(start(gr)[exon_IDX], nparent_per_ex)
    exon_end <- rep.int(end(gr)[exon_IDX], nparent_per_ex)
    if (feature == "cds" && !is.null(phase))
        cds_phase <- rep.int(phase[exon_IDX], nparent_per_ex)

    if (!gtf.format) {
        ## Drop orphan exons (or orphan cds or orphan stop codons).
        ## This happens typically when 'gr' is only a subset of the original
        ## GFF file. Because of this subsetting, an exon can loose its parent.
        m <- match(tx_id, mcols0$ID)
        drop_me <- is.na(m)
        ## We also drop exons (or cds) that have a parent that is not
        ## considered a transcript.
        if (!is.null(tx_IDX))
            drop_me <- drop_me | !(m %in% tx_IDX)
        if (sum(drop_me) != 0L) {
            dropped <- data.frame(seqid=exon_chrom[drop_me],
                                  start=exon_start[drop_me],
                                  end=exon_end[drop_me],
                                  strand=exon_strand[drop_me],
                                  ID=exon_id[drop_me],
                                  Name=exon_name[drop_me],
                                  Parent=tx_id[drop_me],
                                  stringsAsFactors=FALSE)
            msg <- paste0(nrow(dropped), " ", what,
                          ifelse(what == "CDS", "", "s"), " ",
                          "couldn't be linked to a transcript so were dropped")
            warning(.decorate_drop_msg(msg, dropped, mcols0))
            keep_idx <- which(!drop_me)
            tx_id <- tx_id[keep_idx]
            exon_id <- exon_id[keep_idx]
            exon_name <- exon_name[keep_idx]
            exon_chrom <- exon_chrom[keep_idx]
            exon_strand <- exon_strand[keep_idx]
            exon_start <- exon_start[keep_idx]
            exon_end <- exon_end[keep_idx]
            if (feature == "cds" && !is.null(phase))
                cds_phase <- cds_phase[keep_idx]
        }
    }

    exons <- data.frame(
        tx_id=tx_id,
        exon_id=exon_id,
        exon_name=exon_name,
        exon_chrom=exon_chrom,
        exon_strand=exon_strand,
        exon_start=exon_start,
        exon_end=exon_end,
        stringsAsFactors=FALSE
    )

    if (feature == "exon") {
        exon_rank <- .extract_rank_from_id(exon_id, tx_id)
        if (is.null(exon_rank))
            exon_rank <- .infer_rank_from_position(tx_id,
                                                   exon_chrom, exon_strand,
                                                   exon_start, exon_end)
        exons$exon_rank <- exon_rank
    } else {
        colnames(exons) <- sub("^exon_", "", colnames(exons))
        if (feature == "cds" && !is.null(phase))
            exons$phase <- cds_phase
    }
    exons
}

### For each transcript with no exons, infer a single exon that is the same as
### the transcript itself. Other heuristics might have been used earlier to
### give exons to these poor transcripts with no exons (like inferring the
### exons from the CDS, to support the GeneDB case) and .add_missing_exons()
### should only be used as the last and desperate solution to give them an
### exon after the other heuristics have failed to give them one.
.add_missing_exons <- function(exons, transcripts)
{
    is_noextx <- !(transcripts$tx_id %in% exons$tx_id)
    missing_exons <- S4Vectors:::extract_data_frame_rows(transcripts, is_noextx)
    if (nrow(missing_exons) == 0L)
        return(exons)
    missing_exons$tx_type <- NULL
    TX2EXON_COLNAMES <- c(
        tx_id="tx_id",
        tx_name="exon_name",
        tx_chrom="exon_chrom",
        tx_strand="exon_strand",
        tx_start="exon_start",
        tx_end="exon_end"
    )
    colnames(missing_exons) <- TX2EXON_COLNAMES[colnames(missing_exons)]
    missing_exons$exon_rank <- 1L
    rbind(exons, missing_exons)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'transcripts' data frame
###

.merge_transcript_parts <- function(transcripts)
{
    tx_id <- transcripts$tx_id
    if (!is.character(tx_id))
        tx_id <- as.character(tx_id)
    ## 'transcripts_by_id' is a SplitDataFrameList object.
    transcripts_by_id <- splitAsList(transcripts, tx_id, drop=TRUE)

    tx_name <- unique(transcripts_by_id[ , "tx_name"])  # CharacterList
    drop_idx <- which(elementNROWS(tx_name) != 1L)
    if (length(drop_idx) != 0L) {
        in1string <- paste0(sort(names(drop_idx)), collapse=", ")
        warning(wmsg("The following transcripts were dropped because they ",
                     "have multiple parts that could not be merged due to ",
                     "incompatible \"Name\" attributes: ", in1string))
        transcripts_by_id <- transcripts_by_id[-drop_idx]
        tx_name <- tx_name[-drop_idx]
    }
    tx_name <- as.character(tx_name)

    tx_type <- unique(transcripts_by_id[ , "tx_type"])  # CharacterList
    drop_idx <- which(elementNROWS(tx_type) != 1L)
    if (length(drop_idx) != 0L) {
        in1string <- paste0(sort(names(drop_idx)), collapse=", ")
        warning(wmsg("The following transcripts were dropped because they ",
                     "have multiple parts that could not be merged due to ",
                     "incompatible types: ", in1string))
        transcripts_by_id <- transcripts_by_id[-drop_idx]
        tx_name <- tx_name[-drop_idx]
        tx_type <- tx_type[-drop_idx]
    }
    tx_type <- as.character(tx_type)

    tx_chrom <- unique(transcripts_by_id[ , "tx_chrom"])  # CharacterList
    drop_idx <- which(elementNROWS(tx_chrom) != 1L)
    if (length(drop_idx) != 0L) {
        in1string <- paste0(sort(names(drop_idx)), collapse=", ")
        warning(wmsg("The following transcripts were dropped because they ",
                     "have multiple parts that could not be merged due to ",
                     "incompatible seqnames: ", in1string))
        transcripts_by_id <- transcripts_by_id[-drop_idx]
        tx_name <- tx_name[-drop_idx]
        tx_type <- tx_type[-drop_idx]
        tx_chrom <- tx_chrom[-drop_idx]
    }
    tx_chrom <- as.character(tx_chrom)

    tx_strand <- unique(transcripts_by_id[ , "tx_strand"])  # FactorList
    drop_idx <- which(elementNROWS(tx_strand) != 1L)
    if (length(drop_idx) != 0L) {
        in1string <- paste0(sort(names(drop_idx)), collapse=", ")
        warning(wmsg("The following transcripts were dropped because they ",
                     "have multiple parts that could not be merged due to ",
                     "incompatible strands: ", in1string))
        transcripts_by_id <- transcripts_by_id[-drop_idx]
        tx_name <- tx_name[-drop_idx]
        tx_type <- tx_type[-drop_idx]
        tx_chrom <- tx_chrom[-drop_idx]
        tx_strand <- tx_strand[-drop_idx]
    }
    tx_strand <- as.character(tx_strand)

    tx_start <- unname(min(transcripts_by_id[ , "tx_start"]))
    tx_end <- unname(max(transcripts_by_id[ , "tx_end"]))

    data.frame(
        tx_id=names(transcripts_by_id),
        tx_name=tx_name,
        tx_type=tx_type,
        tx_chrom=tx_chrom,
        tx_strand=tx_strand,
        tx_start=tx_start,
        tx_end=tx_end,
        stringsAsFactors=FALSE
    )
}

.extract_transcripts_from_GRanges <- function(tx_IDX, gr, type, ID,
                                              transcript_id, Name)
{
    tx_id <- ID[tx_IDX]
    if (is.character(transcript_id) && !all(is.na(transcript_id))) {
        what <- "transcript_id"
        tx_name <- transcript_id[tx_IDX]
    } else {
        what <- "Name"
        tx_name <- Name[tx_IDX]
    }
    if (anyNA(tx_name))
        warning(wmsg("some transcripts have no \"", what, "\" attribute ==> ",
                     "their name (\"tx_name\" column in the TxDb object) ",
                     "was set to NA"))
    if (anyDuplicated(tx_name))
        warning(wmsg("the transcript names (\"tx_name\" column in the TxDb ",
                     "object) imported from the \"", what, "\" attribute ",
                     "are not unique"))
    transcripts <- data.frame(
        tx_id=tx_id,
        tx_name=tx_name,
        tx_type=type[tx_IDX],
        tx_chrom=seqnames(gr)[tx_IDX],
        tx_strand=strand(gr)[tx_IDX],
        tx_start=start(gr)[tx_IDX],
        tx_end=end(gr)[tx_IDX],
        stringsAsFactors=FALSE
    )
    merged_tx <- unique(tx_id[duplicated(tx_id)])
    if (length(merged_tx) != 0L) {
        transcripts <- .merge_transcript_parts(transcripts)
        #in1string <- paste0(sort(merged_tx), collapse=", ")
        #warning(wmsg("The following transcripts have multiple parts that ",
        #             "were merged: ", in1string))
    }
    ## We've seen silly GFF/GTF files where each transcript in the file is
    ## reported to be on its own contig and spans it (start=1) but no strand
    ## is reported for the transcript. We set the strand to "+" for all these
    ## transcripts.
    if (all(transcripts$tx_strand == "*")
     && anyDuplicated(transcripts$tx_chrom) == 0L
     && all(transcripts$tx_start == 1L)) {
        transcripts$tx_strand <- rep.int(strand("+"), nrow(transcripts))
    }
    transcripts
}

.infer_transcripts_from_exons <- function(exons)
{
    exons_tx_id <- exons$tx_id
    if (!is.character(exons_tx_id))
        exons_tx_id <- as.character(exons_tx_id)
    if (!is.character(exons$exon_chrom))
        exons$exon_chrom <- as.character(exons$exon_chrom)
    if (!is.character(exons$exon_strand))
        exons$exon_strand <- as.character(exons$exon_strand)
    exons_by_id <- splitAsList(exons, exons_tx_id, drop=TRUE)

    tx_id <- names(exons_by_id)

    tx_type <- rep.int("inferred_from_exons", length(tx_id))

    tx_chrom <- unique(exons_by_id[ , "exon_chrom"])
    tx_chrom[elementNROWS(tx_chrom) != 1L] <- NA_character_
    tx_chrom <- as.character(tx_chrom)

    tx_strand <- unique(exons_by_id[ , "exon_strand"])
    tx_strand[elementNROWS(tx_strand) != 1L] <- NA_character_
    tx_strand <- as.character(tx_strand)

    tx_start <- unname(min(exons_by_id[ , "exon_start"]))
    tx_end <- unname(max(exons_by_id[ , "exon_end"]))

    data.frame(
        tx_id=tx_id,
        tx_name=tx_id,
        tx_type=tx_type,
        tx_chrom=tx_chrom,
        tx_strand=tx_strand,
        tx_start=tx_start,
        tx_end=tx_end,
        stringsAsFactors=FALSE
    )
}

.add_missing_transcripts <- function(transcripts, exons)
{
    is_orphan <- !(exons$tx_id %in% transcripts$tx_id)
    orphan_exons <- S4Vectors:::extract_data_frame_rows(exons, is_orphan)
    missing_transcripts <- .infer_transcripts_from_exons(orphan_exons)
    rbind(transcripts, missing_transcripts)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Make the 'splicings' data frame
###

.find_exon_cds <- function(exons, cds, what="CDS")
{
    query <- GRanges(cds$chrom,
                     IRanges(cds$start, cds$end),
                     cds$strand)
    subject <- GRanges(exons$exon_chrom,
                       IRanges(exons$exon_start, exons$exon_end),
                       exons$exon_strand)
    hits <- findOverlaps(query, subject, type="within")
    hits <- hits[cds$tx_id[queryHits(hits)] == exons$tx_id[subjectHits(hits)]]
    q_hits <- queryHits(hits)
    s_hits <- subjectHits(hits)

    bad_tx <- unique(cds$tx_id[q_hits[duplicated(q_hits)]])
    if (length(bad_tx) != 0L) {
        because <- c("they have ", what, "s that are mapped to ",
                     "more than one exon")
        .reject_transcripts(bad_tx, because)
    }

    cds2exon <- selectHits(hits, select="arbitrary")
    bad_tx <- unique(cds$tx_id[is.na(cds2exon)])
    if (length(bad_tx) != 0L) {
        because <- c("they have ", what, "s that cannot be mapped to an exon")
        .reject_transcripts(bad_tx, because)
    }

    bad_tx <- unique(exons$tx_id[s_hits[duplicated(s_hits)]])
    if (length(bad_tx) != 0L) {
        in1string <- paste0(sort(bad_tx), collapse=", ")
        warning(wmsg("The following transcripts have exons that contain ",
                     "more than one ", what, " (only the first ", what,
                     " was kept for each exon): ", in1string))
    }
    selectHits(t(hits), select="first")
}

.make_splicings <- function(exons, cds, stop_codons=NULL)
{
    cds_tx_id <- factor(cds$tx_id, levels=levels(exons$tx_id))
    if (any(is.na(cds_tx_id)))
        stop(wmsg("some CDS cannot be mapped to an exon"))
    cds$tx_id <- cds_tx_id

    exon2cds <- .find_exon_cds(exons, cds)
    cds_name <- cds$name[exon2cds]
    cds_strand <- cds$strand[exon2cds]
    cds_start <- cds$start[exon2cds]
    cds_end <- cds$end[exon2cds]
    if (!is.null(cds$phase))
        cds_phase <- cds$phase[exon2cds]

    if (!is.null(stop_codons)) {
        stop_codons_tx_id <- factor(stop_codons$tx_id,
                                    levels=levels(exons$tx_id))
        if (any(is.na(stop_codons_tx_id)))
            stop(wmsg("some stop codons cannot be mapped to an exon"))
        stop_codons$tx_id <- stop_codons_tx_id

        exon2stop_codon <- .find_exon_cds(exons, stop_codons,
                                          what="stop codon")
        stop_codon_name <- stop_codons$name[exon2stop_codon]
        stop_codon_strand <- stop_codons$strand[exon2stop_codon]
        stop_codon_start <- stop_codons$start[exon2stop_codon]
        stop_codon_end <- stop_codons$end[exon2stop_codon]

        ## Exons with no CDS get the stop codon as CDS (with phase set to 0).
        replace_idx <- which(is.na(exon2cds) & !is.na(exon2stop_codon))
        cds_name[replace_idx] <- stop_codon_name[replace_idx]
        cds_strand[replace_idx] <- stop_codon_strand[replace_idx]
        cds_start[replace_idx] <- stop_codon_start[replace_idx]
        cds_end[replace_idx] <- stop_codon_end[replace_idx]
        if (!is.null(cds$phase))
            cds_phase[replace_idx] <- 0L

        ## Exons with a CDS and a stop codon have the latter merged into the
        ## former (with phase adjusted).
        merge_idx <- which(!is.na(exon2cds) & !is.na(exon2stop_codon))
        start1 <- cds_start[merge_idx]
        end1 <- cds_end[merge_idx]
        start2 <- stop_codon_start[merge_idx]
        end2 <- stop_codon_end[merge_idx]
        gap <- pmax.int(start1, start2) - pmin.int(end1, end2) - 1L
        bad_tx <- unique(exons$tx_id[merge_idx[gap > 0L]])
        if (length(bad_tx) != 0L) {
            because <- "they have incompatible CDS and stop codons"
            .reject_transcripts(bad_tx, because)
        }
        cds_start[merge_idx] <- pmin.int(start1, start2)
        cds_end[merge_idx] <- pmax.int(end1, end2)
        ## Set phase to 0 if stop codon is upstream of CDS (i.e.
        ## if start2 < start1 on + strand and end2 > end1 if on - strand).
        if (!is.null(cds$phase)) {
            strand1 <- cds_strand[merge_idx]
            cds_phase[merge_idx] <- ifelse((strand1 == "+" & start2 < start1) |
                                           (strand1 == "-" & end2 > end1),
                                           0L, cds_phase[merge_idx])
        }
    }
    splicings <- cbind(exons, cds_name, cds_start, cds_end,
                       stringsAsFactors=FALSE)
    if (!is.null(cds$phase))
        splicings$cds_phase <- cds_phase
    splicings
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'genes' data frame
###

.get_gene_ids <- function(ID, Name, gene_id, gene_IDX)
{
    ## First we try to get the gene ids from the "gene_id" attribute.
    ## For example, gene features in GFF3 files from Ensembl have
    ## a "gene_id" attribute set to their Ensembl id (e.g. ENSG00000223972)
    ## so we will import that in the TxDb object instead of the "Name"
    ## attribute which might contain duplicates.
    if (!is.null(gene_id)) {
        gene_ids <- gene_id[gene_IDX]
        if (!anyNA(gene_ids))
            return(gene_ids)
    }
    ## If we can't use the "gene_id" attribute, then we get the gene ids
    ## from the "Name" attribute.
    gene_ids <- Name[gene_IDX]
    if (!anyNA(gene_ids))
        return(gene_ids)
    ## If both attempts failed, we return NULL.
    NULL
}

.extract_genes_from_gff3_GRanges <- function(gene_IDX, tx_IDX,
                                             ID, Parent,
                                             gene_id, Name,
                                             Dbxref=NULL, geneID=NULL)
{
    if (anyNA(ID[gene_IDX]))
        stop(wmsg("some genes have no \"ID\" attribute"))

    tx_id <- ID[tx_IDX]
    tx2genes <- Parent[tx_IDX]

    ## Genes that we consider transcripts are therefore their own parents.
    idx0 <- which(tx_IDX %in% gene_IDX)
    tx2genes[idx0] <- tx_id[idx0]

    ## Transcripts with no parents are sometimes linked to a gene via the
    ## "Dbxref" or "geneID" attribute.

    ## First we try to find their parent via the "Dbxref" attribute, if present.
    if (!is.null(Dbxref)) {
        idx0 <- which(elementNROWS(tx2genes) == 0L)
        tx_Dbxref <- Dbxref[tx_IDX[idx0]]
        gene_Dbxref <- Dbxref[gene_IDX]
        tx_Dbxref_unlisted <- unlist(tx_Dbxref, use.names=FALSE)
        gene_Dbxref_unlisted <- unlist(gene_Dbxref, use.names=FALSE)
        hits <- findMatches(tx_Dbxref_unlisted, gene_Dbxref_unlisted)
        hits <- remapHits(hits,
            Lnodes.remapping=S4Vectors:::quick_togroup(tx_Dbxref),
            new.nLnode=length(tx_Dbxref),
            Rnodes.remapping=S4Vectors:::quick_togroup(gene_Dbxref),
            new.nRnode=length(gene_Dbxref))
        tx2genes[idx0] <- relist(ID[gene_IDX][subjectHits(hits)],
                                 as(hits, "PartitioningByEnd"))
    }

    ## Try to get the gene ids from the "gene_id" or "Name" attributes.
    gene_ids <- .get_gene_ids(ID, Name, gene_id, gene_IDX)
    if (is.null(gene_ids)) {
        gene_ids <- .drop_ID_prefix(ID[gene_IDX], "gene")
    } else {
        ## The mapping between 'ID[gene_IDX]' and 'gene_ids' must be
        ## one-to-one. If it's not, then we can't use 'gene_ids' to reliably
        ## identify genes so we get the gene ids from the "ID" attribute.
        sm1 <- match(ID[gene_IDX], ID[gene_IDX])
        sm2 <- match(gene_ids, gene_ids)
        if (!identical(sm1, sm2))
            gene_ids <- .drop_ID_prefix(ID[gene_IDX], "gene")
    }

    ## Replace the gene IDs in the 'tx2genes' mapping with the gene ids
    ## in 'gene_ids'.
    names(gene_ids) <- ID[gene_IDX]
    gene_ids <- gene_ids[unlist(tx2genes, use.names=FALSE)]
    tx2genes <- relist(unname(gene_ids), tx2genes)

    ## Remove NAs.
    tx2genes <- tx2genes[!is.na(tx2genes)]

    ## Then, if we still have transcripts with no parent, we use the "geneID"
    ## attribute (if present) to assign them an external gene id.
    if (!is.null(geneID)) {
        idx0 <- which(elementNROWS(tx2genes) == 0L)
        tx2genes[idx0] <- geneID[tx_IDX[idx0]]
        tx2genes <- tx2genes[!is.na(tx2genes)]
    }

    tx_id <- rep.int(tx_id, elementNROWS(tx2genes))
    gene_id <- unlist(tx2genes, use.names=FALSE)
    data.frame(tx_id=tx_id, gene_id=gene_id, stringsAsFactors=FALSE)
}

.extract_genes_from_gtf_GRanges <- function(transcript_id, gene_id,
                                            transcripts)
{
    ## Keep only unique (tx_id, gene_id) pairs.
    tx_id <- factor(transcript_id, levels=unique(transcript_id))
    gene_id <- factor(gene_id, levels=unique(gene_id))
    keep_idx <- which(!duplicatedIntegerPairs(tx_id, gene_id))
    tx_id <- tx_id[keep_idx]
    gene_id <- gene_id[keep_idx]

    ## Keep only (tx_id, gene_id) pairs that point to a row in 'transcripts'.
    keep_idx <- which(tx_id %in% transcripts$tx_id)
    tx_id <- tx_id[keep_idx]
    gene_id <- gene_id[keep_idx]

    data.frame(tx_id=tx_id, gene_id=gene_id, stringsAsFactors=FALSE)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'chrominfo' data frame
###

.extract_chrominfo_from_GRanges <- function(gr)
{
    gr_seqinfo <- seqinfo(gr)
    data.frame(
        chrom=seqnames(gr_seqinfo),
        length=seqlengths(gr_seqinfo),
        is_circular=isCircular(gr_seqinfo),
        stringsAsFactors=FALSE
    )
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### makeTxDbFromGRanges()
###

.normarg_metadata <- function(metadata, Genome, taxonomyId)
{
    if (!is.null(metadata)) {
        if (!is.data.frame(metadata))
            stop("'metadata' must be NULL or a data.frame")
        if (!setequal(colnames(metadata), c("name", "value")))
            stop("'metadata' columns must be \"name\" and \"value\"")
    }
    if (!("Genome" %in% metadata$name)) {
        df1 <- data.frame(name="Genome", value=Genome, stringsAsFactors=FALSE)
        metadata <- rbind(metadata, df1)
    }
    if (!is.na(taxonomyId)) {
        org <- GenomeInfoDb:::lookup_organism_by_tax_id(taxonomyId)
        organism <- paste(org[1,2], org[1,3])
        idx <- match("Organism", metadata$name)
        if (!is.na(idx))
            metadata <- S4Vectors:::extract_data_frame_rows(metadata, -idx)
        idx <- match("Taxonomy ID", metadata$name)
        if (!is.na(idx))
            metadata <- S4Vectors:::extract_data_frame_rows(metadata, -idx)
        df2 <- data.frame(name=c("Organism", "Taxonomy ID"),
                          value=c(organism, taxonomyId),
                          stringsAsFactors=FALSE)
        metadata <- rbind(metadata, df2)
    }
    metadata
}

### If 'drop.stop.codons' is TRUE then the "stop_codon" lines are ignored.
### Otherwise (the default) the stop codons are considered to be part of the
### CDS and merged to them.
makeTxDbFromGRanges <- function(gr, drop.stop.codons=FALSE, metadata=NULL,
                                taxonomyId=NA)
{
    if (!is(gr, "GenomicRanges"))
        stop("'gr' must be a GRanges object")

    Genome <- unique(genome(gr))
    if (length(Genome) > 1L)
        stop("all the sequences in 'seqinfo(gr)' must belong ",
             "to the same genome")
    if (length(Genome) == 0L)
        Genome <- NA_character_

    if (!isTRUEorFALSE(drop.stop.codons))
        stop("'drop.stop.codons' must be TRUE or FALSE")

    metadata <- .normarg_metadata(metadata, Genome, taxonomyId)

    gr_mcols <- mcols(gr)

    ## Get the metadata columns of interest.
    mcols0 <- .extract_mcols0(gr_mcols)
    gtf.format <- metadata(mcols0)$gtf.format

    ## Get the gene, cds, stop_codon, exon, and transcript indices.
    gene_IDX <- .get_gene_IDX(mcols0$type)
    cds_IDX <- .get_cds_IDX(mcols0$type, mcols0$phase)
    cds_with_gene_parent_IDX <- .get_cds_with_gene_parent_IDX(cds_IDX,
                                          mcols0$Parent, gene_IDX, mcols0$ID,
                                          gtf.format=gtf.format)
    if (!drop.stop.codons)
        stop_codon_IDX <- .get_stop_codon_IDX(mcols0$type)
    exon_IDX <- .get_exon_IDX(mcols0$type, cds_with_gene_parent_IDX)
    exon_with_gene_parent_IDX <- .get_exon_with_gene_parent_IDX(exon_IDX,
                                          mcols0$Parent, gene_IDX, mcols0$ID,
                                          gtf.format=gtf.format)
    gene_as_tx_IDX <- .get_gene_as_tx_IDX(gene_IDX, mcols0$ID,
                                          exon_with_gene_parent_IDX,
                                          mcols0$Parent)
    tx_IDX <- .get_tx_IDX(mcols0$type, gene_as_tx_IDX)

    noextx_IDX <- .get_noextx_IDX(tx_IDX, mcols0$ID, exon_IDX, mcols0$Parent)
    if (length(noextx_IDX) != 0L) {
        ## Infer exons for transcripts with no exons (noextx).

        ## For a noextx with CDS (the GeneDB case): infer 1 exon per CDS that
        ## is the same as the CDS itself.
        cds_with_noextx_parent_IDX <- .get_cds_with_noextx_parent_IDX(
                                              cds_IDX, mcols0$Parent,
                                              noextx_IDX, mcols0$ID)
        exon_IDX <- sort(c(exon_IDX, cds_with_noextx_parent_IDX))

        ## For a noextx with no CDS (the miRBase case): we'll take care of
        ## them later with .add_missing_exons().
    }

    ## Extract the 'exons' and 'cds' data frames.
    exons <- .extract_exons_from_GRanges(exon_IDX, gr, mcols0, tx_IDX,
                                         feature="exon", gtf.format=gtf.format)
    cds <- .extract_exons_from_GRanges(cds_IDX, gr, mcols0, tx_IDX,
                                       feature="cds", gtf.format=gtf.format,
                                       phase=mcols0$phase)
    ## 'cds$tx_id' should contain the ID of the CDS Parent and this ID is
    ## expected to be a transcript ID. However, for some rare GFF3 files (e.g.
    ## Pabies01b-gene.gff3.gz at
    ## ftp://plantgenie.org/Data/ConGenIE/Picea_abies/v1.0/GFF3/Gene_Prediction_Transcript_assemblies/)
    ## this ID is an **exon** ID instead of a transcript ID so we need to
    ## fix 'cds$tx_id'.
    cds_tx_id <- cds$tx_id
    cds2exon <- match(cds_tx_id, exons$exon_id)
    exons$exon_id <- NULL
    fixme_idx <- which(!is.na(cds2exon))
    if (length(fixme_idx) != 0L) {
        cds_tx_id <- as.character(cds_tx_id)
        cds_tx_id[fixme_idx] <- as.character(exons$tx_id[cds2exon[fixme_idx]])
        cds$tx_id <- factor(cds_tx_id)
    }

    ## Extract the 'stop_codons', 'transcripts', and 'genes' data frames.
    if (!drop.stop.codons) {
        stop_codons <- .extract_exons_from_GRanges(
                                   stop_codon_IDX, gr, mcols0, NULL,
                                   feature="stop_codon", gtf.format=gtf.format)
    } else {
        stop_codons <- NULL
    }
    transcripts <- .extract_transcripts_from_GRanges(tx_IDX, gr,
                                                     mcols0$type,
                                                     mcols0$ID,
                                                     mcols0$transcript_id,
                                                     mcols0$Name)
    if (gtf.format) {
        transcripts <- .add_missing_transcripts(transcripts, exons)
        genes <- .extract_genes_from_gtf_GRanges(mcols0$transcript_id,
                                                 mcols0$gene_id,
                                                 transcripts)
    } else {
        Dbxref <- .get_Dbxref(gr_mcols)
        geneID <- .get_geneID(gr_mcols)
        genes <- .extract_genes_from_gff3_GRanges(gene_IDX, tx_IDX,
                                                  mcols0$ID, mcols0$Parent,
                                                  mcols0$gene_id, mcols0$Name,
                                                  Dbxref, geneID)
    }

    ## Drop hopeless transcripts.
    drop1_tx <- as.character(exons$tx_id[is.na(exons$exon_rank)])
    drop2_tx <- as.character(transcripts$tx_id[is.na(transcripts$tx_chrom)])
    drop3_tx <- as.character(transcripts$tx_id[is.na(transcripts$tx_strand)])
    drop1_tx <- unique(drop1_tx)
    drop2_tx <- unique(drop2_tx)
    drop3_tx <- unique(drop3_tx)
    drop_tx <- unique(c(drop1_tx, drop2_tx, drop3_tx))
    if (length(drop_tx) != 0L) {
        exons <- S4Vectors:::extract_data_frame_rows(exons,
                                     !(exons$tx_id %in% drop_tx))
        cds <- S4Vectors:::extract_data_frame_rows(cds,
                                     !(cds$tx_id %in% drop_tx))
        stop_codons <- S4Vectors:::extract_data_frame_rows(stop_codons,
                                     !(stop_codons$tx_id %in% drop_tx))
        transcripts <- S4Vectors:::extract_data_frame_rows(transcripts,
                                     !(transcripts$tx_id %in% drop_tx))
        genes <- S4Vectors:::extract_data_frame_rows(genes,
                                     !(genes$tx_id %in% drop_tx))
        if (length(drop1_tx) != 0L) {
            in1string <- paste0(sort(drop1_tx), collapse=", ")
            warning(wmsg(
                "The following transcripts were dropped because their exon ",
                "ranks could not be inferred (either because the exons are ",
                "not on the same chromosome/strand or because they are not ",
                "separated by introns): ", in1string))
        }
        if (length(drop2_tx) != 0L) {
            in1string <- paste0(sort(drop2_tx), collapse=", ")
            warning(wmsg(
                "The following transcripts were dropped because no genomic ",
                "ranges could be found for them and their ranges could not ",
                "be inferred from their exons either (because they have them ",
                "on more than one chromosome): ", in1string))
        }
        if (length(drop3_tx) != 0L) {
            in1string <- paste0(sort(drop3_tx), collapse=", ")
            warning(wmsg(
                "The following transcripts were dropped because no genomic ",
                "ranges could be found for them and their ranges could not ",
                "be inferred from their exons either (because they have them ",
                "on both strands): ", in1string))
        }
    }

    ## We might still have transcripts with no exons (noextx). Take care of
    ## them now.
    exons <- .add_missing_exons(exons, transcripts)

    ## .make_splicings() can also reject some transcripts.
    .flush_rejected_tx_envir()
    splicings <- .make_splicings(exons, cds, stop_codons)
    drop_tx <- .get_rejected_transcripts()
    if (length(drop_tx) != 0L) {
        transcripts <- S4Vectors:::extract_data_frame_rows(transcripts,
                                           !(transcripts$tx_id %in% drop_tx))
        splicings <- S4Vectors:::extract_data_frame_rows(splicings,
                                         !(splicings$tx_id %in% drop_tx))
        genes <- S4Vectors:::extract_data_frame_rows(genes,
                                     !(genes$tx_id %in% drop_tx))
    }

    ## Turn the "tx_id" column in 'splicings', 'genes', and 'transcripts' into
    ## an integer vector.
    ## TODO: Maybe makeTxDb() could take care of that.
    splicings_tx_id <- match(splicings$tx_id, transcripts$tx_id)
    orphan_idx <- which(is.na(splicings_tx_id))
    if (length(orphan_idx) != 0L) {
        ## Not really expected to happen.
        stop(wmsg("The input GRanges object contains ",
                  length(orphan_idx), " exons linked to ",
                  "transcripts not found in the object. ",
                  "Did you subset the object before ",
                  "calling makeTxDbFromGRanges() on it?"))
    }
    splicings$tx_id <- splicings_tx_id

    genes_tx_id <- match(genes$tx_id, transcripts$tx_id)
    orphan_idx <- which(is.na(genes_tx_id))
    if (length(orphan_idx) != 0L) {
        ## Not really expected to happen.
        stop(wmsg("The input GRanges object contains ",
                  length(orphan_idx), " genes linked to ",
                  "transcripts not found in the object. ",
                  "Did you subset the object before ",
                  "calling makeTxDbFromGRanges() on it?"))
    }
    genes$tx_id <- genes_tx_id
    transcripts$tx_id <- seq_along(transcripts$tx_id)

    chrominfo <- .extract_chrominfo_from_GRanges(gr)

    makeTxDb(transcripts, splicings,
             genes=genes, chrominfo=chrominfo,
             metadata=metadata, reassign.ids=TRUE)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Some non-exported tools to test makeTxDbFromGRanges() on real data.
###
### NOTE: These tests take too long to be part of the formal unit tests!
###

.light_txdb_dump <- function(txdb)
{
    txdb_dump <- as.list(txdb)

    keep_cols <- c("tx_name", "tx_chrom", "tx_strand", "tx_start", "tx_end")
    transcripts <- txdb_dump$transcripts[keep_cols]
    oo <- order(transcripts$tx_chrom,
                transcripts$tx_start, transcripts$tx_end,
                transcripts$tx_strand,
                transcripts$tx_name)
    transcripts <- S4Vectors:::extract_data_frame_rows(transcripts, oo)

    keep_cols <- c("exon_rank", "exon_name", "exon_chrom", "exon_strand",
                   "exon_start", "exon_end", "cds_start", "cds_end")
    splicings <- txdb_dump$splicings[keep_cols]
    oo <- order(splicings$exon_chrom,
                splicings$exon_start, splicings$exon_end,
                splicings$exon_strand,
                splicings$exon_name)
    splicings <- S4Vectors:::extract_data_frame_rows(splicings, oo)

    list(transcripts=transcripts, splicings=splicings)
}

test_makeTxDbFromGRanges_on_Ensembl_organism_gtf <- function(organism)
{
    cat("Download GTF file ... ")
    gtf_url <- ftp_url_to_Ensembl_gtf()
    url <- paste0(gtf_url, organism, "/")
    organism_files <- ls_ftp_url(url)
    gtf_file <- grep("gtf", organism_files, ignore.case=TRUE, value=TRUE)
    if (length(gtf_file) != 1L) {
        cat("ABORT (0 or more than 1 GTF file found)\n")
        return(NA)
    }
    url <- paste0(url, gtf_file)
    local_gtf_file <- file.path(tempdir(), gtf_file)
    download.file(url, local_gtf_file)

    cat("Import ", local_gtf_file, " as GRanges object 'gr' ... ", sep="")
    gr <- import(local_gtf_file, colnames=GTF_COLNAMES,
                                 feature.type=GFF_FEATURE_TYPES)
    cat("\n")

    cat("txdb1 <- makeTxDbFromGRanges(gr) ... ", sep="")
    txdb1 <- makeTxDbFromGRanges(gr)
    cat("\n")

    dataset <- strsplit(organism, "_", fixed=TRUE)[[1L]]
    dataset <- paste0(substr(dataset[[1L]], 1L, 1L),
                      dataset[[2L]],
                      "_gene_ensembl")
    cat("txdb2 <- makeTxDbFromBiomart(\"", dataset, "\") ... ", sep="")
    txdb2 <- try(makeTxDbFromBiomart(dataset))
    if (is(txdb2, "try-error")) {
        cat("ABORT\n")
        return(NA)
    }
    cat("\n")

    cat("'txdb1' and 'txdb2' have the same transcripts, exons, and CDS: ")
    dump1 <- .light_txdb_dump(txdb1)
    dump2 <- .light_txdb_dump(txdb2)
    OK <- identical(dump1, dump2)
    if (OK) {
        cat("YES\n")
    } else {
        cat("NO!!!!!!\n")
    }
    return(OK)
}

test_makeTxDbFromGRanges_on_Ensembl_gtf <- function(all=FALSE)
{
    gtf_url <- ftp_url_to_Ensembl_gtf()
    if (all) {
        organisms <- ls_ftp_url(gtf_url, subdirs.only=TRUE)
    } else {
        organisms <- c("caenorhabditis_elegans",
                       "ciona_intestinalis",
                       "danio_rerio",
                       "drosophila_melanogaster",
                       "gallus_gallus",
                       "homo_sapiens",
                       "mus_musculus",
                       "rattus_norvegicus",
                       "saccharomyces_cerevisiae")
    }
    norganism <- length(organisms)
    for (i in seq_len(norganism)) {
        organism <- organisms[[i]]
        cat("\n")
        cat("*************************************************************\n")
        cat("[", i , "/", norganism, "] ",
            "Testing makeTxDbFromGRanges() on ", organism, "\n", sep="")
        cat("*************************************************************\n")
        cat("\n")
        test_makeTxDbFromGRanges_on_Ensembl_organism_gtf(organism)
    }
    cat("\n")
    cat("DONE.\n")
}


if (FALSE) {
library(GenomicFeatures)
source("GenomicFeatures/R/makeTxDbFromGRanges.R")
library(rtracklayer)

## Test with GRanges obtained from GFF3 files
## ==========================================

GFF3_files <- system.file("extdata", "GFF3_files", package="GenomicFeatures")

file1 <- file.path(GFF3_files, "TheCanonicalGene_v1.gff3")
gr1 <- import(file1, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb1 <- makeTxDbFromGRanges(gr1)
txdb1

file2 <- file.path(GFF3_files, "TheCanonicalGene_v2.gff3")
gr2 <- import(file2, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb2 <- makeTxDbFromGRanges(gr2)
txdb2

file3 <- file.path(GFF3_files, "a.gff3")
gr3 <- import(file3, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb3 <- makeTxDbFromGRanges(gr3)
txdb3

file4 <- file.path(GFF3_files, "dmel-1000-r5.11.filtered.gff")
gr4 <- import(file4, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb4 <- makeTxDbFromGRanges(gr4)
txdb4  # exactly the same as with makeTxDbFromGFF()

file5 <- file.path(GFF3_files, "NC_011025.gff")
gr5 <- import(file5, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb5 <- makeTxDbFromGRanges(gr5)
txdb5

genome <- "GCA_000364345.1_Macaca_fascicularis_5.0"
filename <- paste0(genome, "_genomic.gff.gz")
url <- paste0("ftp://ftp.ncbi.nlm.nih.gov/genomes/all/", genome, "/", filename)
file6 <- file.path(tempdir(), file6)
download.file(url, file6)
gr6 <- import(file6, format="gff3", colnames=GFF3_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)

## Compared with makeTxDbFromGFF():
##
## (a) makeTxDbFromGFF() fails on TheCanonicalGene_v1.gff3
##
## (b) gene_id, tx_name, exon_name, and cds_name are now imported from the
##     "Name" attribute instead of the "ID" attribute (GFF3 Spec: "IDs do
##     not have meaning outside the file in which they reside")


## Test with GRanges obtained from GTF files
## =========================================

GTF_files <- system.file("extdata", "GTF_files", package="GenomicFeatures")

## test1.gtf grabbed from http://mblab.wustl.edu/GTF22.html (5 exon gene with
## 3 translated exons).
file1 <- file.path(GTF_files, "test1.gtf")
gr1 <- import(file1, format="gtf", colnames=GTF_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb1 <- makeTxDbFromGRanges(gr1)
txdb1

file2 <- file.path(GTF_files, "Aedes_aegypti.partial.gtf")
gr2 <- import(file2, format="gtf", colnames=GTF_COLNAMES,
                     feature.type=GFF_FEATURE_TYPES)
txdb2 <- makeTxDbFromGRanges(gr2)
txdb2
}
Bioconductor/GenomicFeatures documentation built on Aug. 29, 2021, 1:03 p.m.