R/makeTxDbFromUCSC.R

Defines functions makeTxDbFromUCSC .make_TxDb_from_UCSC_txtable .prepare_UCSC_metadata .make_UCSC_chrominfo .warn_about_circularity_guess .make_UCSC_genes .extract_splicings_from_UCSC_txtable .extract_cds_locs_from_UCSC_txtable .extract_UCSC_cds_start_end .extract_transcripts_from_UCSC_txtable .extract_txname2geneid_from_UCSC_txtable .fetch_txname2geneid_from_UCSC .fetch_UCSC_table .fetch_UCSC_txtable .get_txname2geneid_mapdef .tablename2track browseUCSCtrack supportedUCSCtables .get_supported_tx_tables .get_GENCODE_tx_tables

Documented in browseUCSCtrack makeTxDbFromUCSC supportedUCSCtables

### =========================================================================
### makeTxDbFromUCSC()
### -------------------------------------------------------------------------


### makeTxDbFromUCSC() expects a UCSC transcript table to have at least
### the following columns:
.UCSC_TXCOL2CLASS <- c(
    name="character",
    chrom="character",
    strand="character",
    txStart="integer",
    txEnd="integer",
    cdsStart="integer",
    cdsEnd="integer",
    exonCount="integer",
    exonStarts="list",    # list of raw vectors
    exonEnds="list"       # list of raw vectors
)

### .get_GENCODE_tx_tables("All GENCODE %s", "V34") or
### .get_GENCODE_tx_tables("GENCODE Genes %s", "V19")
.get_GENCODE_tx_tables <- function(track, version, only.first.3=FALSE)
{
    tx_tables <- c(
        "wgEncodeGencodeBasic%s",          track, "Basic",
        "wgEncodeGencodeComp%s",           track, "Comprehensive",
        "wgEncodeGencodePseudoGene%s",     track, "Pseudogenes"
    )
    if (!only.first.3) {
        tx_tables <- c(tx_tables,
            "wgEncodeGencode2wayConsPseudo%s", track, "2-way Pseudogenes",
            "wgEncodeGencodePolya%s",          track, "PolyA"
        )
    }
    sprintf(tx_tables, version)
}

### NAs in the tablename and track columns are resolved via
### 'rtracklayer::trackNames(session)'.
.hg38_TX_TABLES <- c(
  ## tablename (unique key)           track                subtrack
  "knownGene",                        NA,                  NA,
  "ncbiRefSeq",                       "NCBI RefSeq",       "RefSeq All",
  "ncbiRefSeqCurated",                "NCBI RefSeq",       "RefSeq Curated",
  "ncbiRefSeqPredicted",              "NCBI RefSeq",       "RefSeq Predicted",
  "ncbiRefSeqOther",                  "NCBI RefSeq",       "RefSeq Other",
  #"ncbiRefSeqPsl",                    "NCBI RefSeq",       "RefSeq Alignments",
  #"ncbiRefSeqGenomicDiff",            "NCBI RefSeq",       "RefSeq Diffs",
  "refGene",                          "NCBI RefSeq",       "UCSC RefSeq",
  "ncbiRefSeqSelect",                 "NCBI RefSeq",       "RefSeq Select+MANE",
  "ncbiRefSeqHgmd",                   "NCBI RefSeq",       "RefSeq HGMD",
  "xenoRefGene",                      "Other RefSeq",      NA,
  .get_GENCODE_tx_tables("All GENCODE %s", "V34"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V33"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V31"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V29"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V28"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V27"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V26"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V25"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V24"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V23"),
  .get_GENCODE_tx_tables("All GENCODE %s", "V22"),
  .get_GENCODE_tx_tables("GENCODE %s (Ensembl 76)", "V20"),
  "augustusGene",                     "AUGUSTUS",          NA,
  "ccdsGene",                         "CCDS",              NA,
  "geneid",                           "Geneid Genes",      NA,
  "genscan",                          "Genscan Genes",     NA,
  NA,                                 "Old UCSC Genes",    NA,
  "sgpGene",                          "SGP Genes",         NA,
  "sibGene",                          "SIB Genes",         NA
)

### NAs in the tablename and track columns are resolved via
### 'rtracklayer::trackNames(session)'.
.hg19_TX_TABLES <- c(
  ## tablename (unique key)           track                subtrack
  "knownGene",                        NA,                  NA,
  "ncbiRefSeq",                       "NCBI RefSeq",       "RefSeq All",
  "ncbiRefSeqCurated",                "NCBI RefSeq",       "RefSeq Curated",
  "ncbiRefSeqPredicted",              "NCBI RefSeq",       "RefSeq Predicted",
  "ncbiRefSeqOther",                  "NCBI RefSeq",       "RefSeq Other",
  #"ncbiRefSeqPsl",                    "NCBI RefSeq",       "RefSeq Alignments",
  #"ncbiRefSeqGenomicDiff",            "NCBI RefSeq",       "RefSeq Diffs",
  "refGene",                          "NCBI RefSeq",       "UCSC RefSeq",
  "ncbiRefSeqSelect",                 "NCBI RefSeq",       "RefSeq Select",
  "ncbiRefSeqHgmd",                   "NCBI RefSeq",       "RefSeq HGMD",
  "xenoRefGene",                      "Other RefSeq",      NA,
  "acembly",                          "AceView Genes",     NA,
  "augustusGene",                     "AUGUSTUS",          NA,
  "ccdsGene",                         "CCDS",              NA,
  "ensGene",                          "Ensembl Genes",     NA,
  "exoniphy",                         "Exoniphy",          NA,
  .get_GENCODE_tx_tables("GENCODE %s", "V34lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE %s", "V33lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE %s", "V31lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE %s", "V28lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE Gene %s", "V27lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE Gene %s", "V24lift37", only.first.3=TRUE),
  .get_GENCODE_tx_tables("GENCODE Genes %s", "V19"),
  .get_GENCODE_tx_tables("GENCODE Genes %s", "V17"),
  .get_GENCODE_tx_tables("GENCODE Genes %s", "V14"),
  .get_GENCODE_tx_tables("GENCODE Genes %s", "V7"),
  "geneid",                           "Geneid Genes",      NA,
  "genscan",                          "Genscan Genes",     NA,
  "nscanGene",                        "N-SCAN",            NA,
  NA,                                 "Old UCSC Genes",    NA,
  "sgpGene",                          "SGP Genes",         NA,
  "sibGene",                          "SIB Genes",         NA,
  "vegaGene",                         "Vega Genes",        "Vega Protein Genes",
  "vegaPseudoGene",                   "Vega Genes",        "Vega Pseudogenes",
  "pseudoYale60",                     "Yale Pseudo60",     NA
)

### NAs in the tablename and track columns are resolved via
### 'rtracklayer::trackNames(session)'.
.ALL_SUPPORTED_TX_TABLES <- c(
  ## tablename (unique key)           track                subtrack

  ## Tables/tracks for hg18.
  ## All the tables/tracks listed in this section belong to the "Genes and
  ## Gene Prediction" group of tracks for hg18, mm10, and sacCer2.
  ## On Aug 13 2010, makeTxDbFromUCSC() was successfully tested by hand on
  ## all of them for hg18 (i.e. with 'genome="hg18"').
  ## Note: the "acembly" table contains more than 250000 transcripts!
  "knownGene",                        NA,                  NA,
  "knownGeneOld11",                   "Old UCSC Genes",    NA,
  "knownGeneOld8",                    "Old UCSC Genes",    NA,
  "knownGeneOld7",                    "Old UCSC Genes",    NA,
  "knownGeneOld6",                    "Old UCSC Genes",    NA,
  "knownGeneOld4",                    "Old UCSC Genes",    NA,
  "knownGeneOld3",                    "Old UCSC Genes",    NA,
  "knownGenePrevious",                "Old Known Genes",   NA,
  "ccdsGene",                         "CCDS",              NA,
  "refGene",                          "NCBI RefSeq",       NA,
  "refGene",                          "RefSeq Genes",      NA,
  "xenoRefGene",                      "Other RefSeq",      NA,
  "vegaGene",                         "Vega Genes",        "Vega Protein Genes",
  "vegaPseudoGene",                   "Vega Genes",        "Vega Pseudogenes",
  "ensGene",                          "Ensembl Genes",     NA,
  "acembly",                          "AceView Genes",     NA,
  "sibGene",                          "SIB Genes",         NA,
  "nscanPasaGene",                    "N-SCAN",            "N-SCAN PASA-EST",
  "nscanGene",                        "N-SCAN",            "N-SCAN",
  "sgpGene",                          "SGP Genes",         NA,
  "geneid",                           "Geneid Genes",      NA,
  "genscan",                          "Genscan Genes",     NA,
  "exoniphy",                         "Exoniphy",          NA,
  "augustusGene",                     "AUGUSTUS",          NA,
  "augustusHints",                    "Augustus",          "Augustus Hints",
  "augustusXRA",                      "Augustus",          "Augustus De Novo",
  "augustusAbinitio",                 "Augustus",          "Augustus Ab Initio",
  "acescan",                          "ACEScan",           NA,
  "lincRNAsTranscripts",              "lincRNAsTranscripts", NA,

  ## Tables/tracks specific to hg18.
  "wgEncodeGencodeManualV3",          "Gencode Genes",     "Gencode Manual",
  "wgEncodeGencodeAutoV3",            "Gencode Genes",     "Gencode Auto",
  "wgEncodeGencodePolyaV3",           "Gencode Genes",     "Gencode PolyA",

  ## Tables/tracks specific to mm10/rn6/danRer10/danRer11/ce11/dm6/sacCer3.
  "ncbiRefSeq",                       "NCBI RefSeq",       "RefSeq All",
  "ncbiRefSeqCurated",                "NCBI RefSeq",       "RefSeq Curated",
  "ncbiRefSeqPredicted",              "NCBI RefSeq",       "RefSeq Predicted",
  "ncbiRefSeqOther",                  "NCBI RefSeq",       "RefSeq Other",
  #"ncbiRefSeqPsl",                    "NCBI RefSeq",       "RefSeq Alignments",
  #"ncbiRefSeqGenomicDiff",            "NCBI RefSeq",       "RefSeq Diffs",
  "ncbiRefSeqSelect",                 "NCBI RefSeq",       "RefSeq Select+MANE",
  "ncbiRefSeqHgmd",                   "NCBI RefSeq",       "RefSeq HGMD",

  ## Tables/tracks specific to mm10/rn6/danRer10/danRer11.
  "ncbiRefSeqPredicted",              "NCBI RefSeq",       "RefSeq Predicted",

  ## Tables/tracks specific to D. melanogaster.
  "flyBaseGene",                      "FlyBase Genes",     NA,

  ## Tables/tracks specific to sacCer2.
  ## makeTxDbFromUCSC(genome="sacCer2", tablename="sgdGene")
  ## successfully tested on On Aug 13 2010.
  "sgdGene",                          "SGD Genes",         NA
)

.get_supported_tx_tables <- function(genome)
{
    tx_tables <- switch(genome,
        hg38=.hg38_TX_TABLES,
        hg19=.hg19_TX_TABLES,
        .ALL_SUPPORTED_TX_TABLES
    )
    m <- matrix(tx_tables, ncol=3L, byrow=TRUE)
    colnames(m) <- c("tablename", "track", "subtrack")
    as.data.frame(m)
}

### Return a data.frame with 3 columns (tablename, track, and subtrack) and
### 1 row per UCSC table known to work with makeTxDbFromUCSC().
### A note about the current implementation:
### Current implementation uses hard-coded matrices .hg38_TX_TABLES,
### .hg19_TX_TABLES, and .ALL_SUPPORTED_TX_TABLES defined above which
### is not satisfying in the long run (the matrices need to be manually
### updated from times to times, a long and boring and error-prone
### process, and is probably out-of-sync at the moment). Ideally we'd like
### to be able to generate the 3-column data.frame programmatically in
### reasonable time. For this we need to be able to retrieve all the "central
### tables" for all the transcript-centric tracks available for a given
### organism. Using a combination of calls to rtracklayer::trackNames(session)
### and rtracklayer::ucscTables(genome(session), track) would partly achieve
### this but is unfortunately very slow.
supportedUCSCtables <- function(genome="hg19",
                                url="http://genome.ucsc.edu/cgi-bin/")
{
    if (is(genome, "UCSCSession")) {
        if (!missing(url))
            warning("'url' is ignored when 'genome' is a UCSCSession object")
        session <- genome
        genome <- genome(session)
    } else {
        if (!isSingleString(genome))
            stop("'genome' must be a single string")
        if (!isSingleString(url))
            stop("'url' must be a single string")
        session <- browserSession(url=url)
        genome(session) <- genome
    }

    ans <- .get_supported_tx_tables(genome)

    ## trackNames() returns a mapping from track names to "central table" names
    ## in the form of a named character vector where the names are the track
    ## names and the values the "central table" names (more than 1 table can
    ## be connected to a given track via joins thru the "central table").
    ## Unfortunately such mapping cannot handle the situation where a track is
    ## mapped to more than 1 "central table". This happens for example when a
    ## track has subtracks (e.g. the Augustus track for hg18 has 3 subtracks),
    ## in which case there is 1 "central table" per subtrack. So trackNames()
    ## alone cannot be used to get the one-to-many mapping from tracks to
    ## "central tables". Calling ucscTables(genome(session), track) in a loop
    ## on all the tracks returned by trackNames() would work but is very slow.
    genome_tracknames <- trackNames(session)

    ## Resolve NAs in the tablename column.
    na_idx <- which(is.na(ans[ , "tablename"]))
    m <- match(ans[na_idx, "track"], names(genome_tracknames))
    ans[na_idx, "tablename"] <- genome_tracknames[m]

    ## Resolve NAs in the track column.
    na_idx <- which(is.na(ans[ , "track"]))
    m <- match(ans[na_idx, "tablename"], genome_tracknames)
    ans[na_idx, "track"] <- names(genome_tracknames)[m]

    if (!(genome %in% c("hg38", "hg19"))) {
        ## Keep only existing tracks.
        ans <- ans[ans$track %in% names(genome_tracknames), , drop=FALSE]
        rownames(ans) <- NULL

        ## Associate subtrack "UCSC RefSeq" to table "refGene" for a few
        ## genome builds.
        if (genome %in% c("mm10", "rn6",
                          "bosTau9", "danRer10", "danRer11",
                          "ce11", "dm6", "galGal6", "panTro6",
                          "rheMac10", "sacCer3"))
        {
            ans_subtrack <- ans[ , "subtrack"]
            ans_subtrack[ans[ , "tablename"] == "refGene"] <- "UCSC RefSeq"
            ans[ , "subtrack"] <- ans_subtrack
        }
    }

    ans$track <- factor(ans$track, levels=unique(ans$track))
    ans
}

### Can be used to quickly check that a combination of genome/tablename
### actually exists.
browseUCSCtrack <- function(genome="hg19",
                            tablename="knownGene",
                            url="http://genome.ucsc.edu/cgi-bin/")
{
    if (!isSingleString(genome))
        stop("'genome' must be a single string")
    if (!isSingleString(tablename))
        stop("'tablename' must be a single string")
    if (!isSingleString(url))
        stop("'url' must be a single string")
    url <- sprintf("%s/hgTrackUi?db=%s&g=%s", url, genome, tablename)
    ## Avoid "file association for 'http://...' not available or invalid"
    ## error during 'R CMD check' on some Windows systems (e.g. riesling1).
    if (interactive())
        browseURL(url)
}

.tablename2track <- function(tablename, session)
{
    if (!isSingleString(tablename))
        stop("'tablename' must be a single string")
    supported_tables <- supportedUCSCtables(session)
    idx <- which(supported_tables$tablename == tablename)
    if (length(idx) == 0L)
        stop("UCSC table \"", tablename, "\" is not supported")
    ## Sanity check.
    stopifnot(length(idx) == 1L)  # should never happen
    track <- as.character(supported_tables$track[idx])
    track_tables <- ucscTables(genome(session), track)
    if (!(tablename %in% track_tables))
        stop("UCSC table \"", tablename, "\" does not exist ",
             "for genome \"", genome(session), "\", sorry")
    track
}

### The table names above (unique key) must be used to name the top-level
### elements of the list below. If no suitable tx_name-to-gene_id mapping is
### available in the UCSC database for a supported table, then there is no
### entry in the list below for this table and makeTxDbFromUCSC() will leave
### the gene table empty.
.UCSC_TXNAME2GENEID_MAPDEFS <- list(
    knownGene=list(
        L2Rchain=list(
            c(tablename="knownToLocusLink",
              Lcolname="name",
              Rcolname="value")
        ),
        gene_id_type="Entrez Gene ID"
    ),
    refGene=list(
        L2Rchain=list(
            c(tablename="hgFixed.refLink",
              Lcolname="mrnaAcc",
              Rcolname="locusLinkId")
        ),
        gene_id_type="Entrez Gene ID"
    ),
    vegaGene=list(
        L2Rchain=list(
            c(tablename="vegaGtp",
              Lcolname="transcript",
              Rcolname="gene")
        ),
        gene_id_type="HAVANA Pseudogene ID"
    ),
    vegaPseudoGene=list(
        L2Rchain=list(
            c(tablename="vegaGtp",
              Lcolname="transcript",
              Rcolname="gene")
        ),
        gene_id_type="HAVANA Pseudogene ID"
    ),
    ## UCSC changed its db schema in September 2011 and the ensGtp table became
    ## unavailable for some genomes (sacCer2, hg18, etc..., apparently for
    ## those assemblies that are not the latest). But the (new?) name2 column
    ## in the ensGene table seems to contain the Ensembl gene ids so the join
    ## with the ensGtp table is not needed anymore.
    #ensGene=list(
    #    L2Rchain=list(
    #        c(tablename="ensGtp",
    #          Lcolname="transcript",
    #          Rcolname="gene")
    #    ),
    #)
    ensGene=c(
        colname="name2",
        gene_id_type="Ensembl gene ID"
    ),
    lincRNAsTranscripts=c(
        colname="name",
        gene_id_type="Name of gene"
    ),
    wgEncodeGencodeManualV3=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeClassesV3",
              Lcolname="name",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeAutoV3=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeClassesV3",
              Lcolname="name",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePolyaV3=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeClassesV3",
              Lcolname="name",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeBasicV17=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV17",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeCompV17=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV17",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePseudoGeneV17=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV17",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencode2wayConsPseudoV17=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV17",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePolyaV17=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV17",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeBasicV14=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV14",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeCompV14=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV14",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePseudoGeneV14=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV14",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencode2wayConsPseudoV14=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV14",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePolyaV14=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV14",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeBasicV7=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV7",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodeCompV7=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV7",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePseudoGeneV7=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV7",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencode2wayConsPseudoV7=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV7",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    wgEncodeGencodePolyaV7=list(
        L2Rchain=list(
            c(tablename="wgEncodeGencodeAttrsV7",
              Lcolname="transcriptId",
              Rcolname="geneId")
        ),
        gene_id_type="Ensembl gene ID"
    ),
    flyBaseGene=list(
        L2Rchain=list(
            c(tablename="flyBaseIsoforms",
              Lcolname="transcript",
              Rcolname="clusterId"),
            c(tablename="flyBaseCanonical",
              Lcolname="clusterId",
              Rcolname="transcript")
        ),
        gene_id_type="Name of canonical transcript in cluster"
    ),
    sgdGene=list(
        L2Rchain=list(
            c(tablename="sgdIsoforms",
              Lcolname="transcript",
              Rcolname="clusterId"),
            c(tablename="sgdCanonical",
              Lcolname="clusterId",
              Rcolname="transcript")
        ),
        gene_id_type="Name of canonical transcript in cluster"
    )
)

.get_txname2geneid_mapdef <- function(tablename)
    .UCSC_TXNAME2GENEID_MAPDEFS[[tablename]]

.fetch_UCSC_txtable <- function(genome, tablename, transcript_ids=NULL)
{
    if (is.null(transcript_ids)) {
        where <- NULL
    } else {
        where <- sprintf("name IN (%s)",
                         paste(paste0("'", transcript_ids, "'"), collapse=","))
    }
    columns <- names(.UCSC_TXCOL2CLASS)
    mapdef <- .get_txname2geneid_mapdef(tablename)
    if (is.character(mapdef))
        columns <- c(columns, mapdef[["colname"]])
    message("Download the ", tablename, " table ... ", appendLF=FALSE)
    on.exit(message("OK"))
    ucsc_txtable <- UCSC_dbselect(genome, tablename,
                                  columns=columns, where=where)
    current_classes <- head(sapply(ucsc_txtable, class),
                            n=length(.UCSC_TXCOL2CLASS))
    stopifnot(identical(current_classes, .UCSC_TXCOL2CLASS))
    ucsc_txtable$exonStarts <- toListOfIntegerVectors(ucsc_txtable$exonStarts)
    ucsc_txtable$exonEnds <- toListOfIntegerVectors(ucsc_txtable$exonEnds)
    if (!identical(lengths(ucsc_txtable$exonStarts),
                   ucsc_txtable$exonCount))
        stop(wmsg("UCSC data anomaly in table ", genome, ".", tablename, ": ",
                  "columns exonStarts and exonCount are inconsistent"))
    if (!identical(lengths(ucsc_txtable$exonEnds),
                   ucsc_txtable$exonCount))
        stop(wmsg("UCSC data anomaly in table ", genome, ".", tablename, ": ",
                  "columns exonEnds and exonCount are inconsistent"))
    ucsc_txtable
}

.fetch_UCSC_table <- function(genome, tablename, columns=NULL)
{
    message("Download the ", tablename, " table ... ", appendLF=FALSE)
    on.exit(message("OK"))
    if (tablename == "hgFixed.refLink")
        return(UCSC_dbselect("hgFixed", "refLink", columns=columns))
    UCSC_dbselect(genome, tablename, columns=columns)
}

### The 2 functions below must return a named list with 2 elements:
###   $genes: data.frame with tx_name and gene_id cols;
###   $gene_id_type: single string.
.fetch_txname2geneid_from_UCSC <- function(genome, Ltablename, mapdef)
{
    nlink <- length(mapdef$L2Rchain)
    for (i in seq_len(nlink)) {
        L2Rlink <- mapdef$L2Rchain[[i]]
        tablename <- L2Rlink[["tablename"]]
        Lcolname <- L2Rlink[["Lcolname"]]
        Rcolname <- L2Rlink[["Rcolname"]]
        ## The tables involved in the "left join" don't necessarily belong
        ## to the track of the leftmost table (e.g.
        ## "wgEncodeGencodeAttrsV17" table does NOT belong to the "GENCODE
        ## Genes V17" track) so we don't need the track information to fetch
        ## these tables.
        ucsc_table <- .fetch_UCSC_table(genome, tablename,
                                        columns=c(Lcolname, Rcolname))
        if (!all(has_col(ucsc_table, c(Lcolname, Rcolname))))
            stop("expected cols \"", Lcolname, "\" or/and \"",
                 Rcolname, "\" not found in table ", tablename)
        Lcol <- ucsc_table[[Lcolname]]
        Rcol <- ucsc_table[[Rcolname]]
        if (!is.character(Lcol))
            Lcol <- as.character(Lcol)
        if (!is.character(Rcol))
            Rcol <- as.character(Rcol)
        if (i == 1L) {
            tmp <- data.frame(Lcol=Lcol, Rcol=Rcol, stringsAsFactors=FALSE)
        } else {
            name2val <- Rcol
            names(name2val) <- Lcol
            tmp <- joinDataFrameWithName2Val(tmp, "Rcol", name2val, "Rcol")
        }
    }
    genes <- data.frame(tx_name=tmp$Lcol,
                        gene_id=tmp$Rcol,
                        stringsAsFactors=FALSE)
    gene_id_type <- mapdef$gene_id_type
    list(genes=genes, gene_id_type=gene_id_type)
}

.extract_txname2geneid_from_UCSC_txtable <- function(ucsc_txtable, mapdef)
{
    genes <- data.frame(tx_name=ucsc_txtable[["name"]],
                        gene_id=ucsc_txtable[[mapdef[["colname"]]]],
                        stringsAsFactors=FALSE)
    gene_id_type <- mapdef[["gene_id_type"]]
    list(genes=genes, gene_id_type=gene_id_type)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'transcripts' data frame from UCSC table.
###

.extract_transcripts_from_UCSC_txtable <- function(ucsc_txtable)
{
    message("Extract the 'transcripts' data frame ... ", appendLF=FALSE)
    on.exit(message("OK"))
    tx_id <- seq_len(nrow(ucsc_txtable))
    tx_name <- ucsc_txtable$name
    tx_chrom <- ucsc_txtable$chrom
    tx_strand <- ucsc_txtable$strand
    tx_start <- ucsc_txtable$txStart + 1L
    tx_end <- ucsc_txtable$txEnd
    data.frame(
        tx_id=tx_id,
        tx_name=tx_name,
        tx_chrom=tx_chrom,
        tx_strand=tx_strand,
        tx_start=tx_start,
        tx_end=tx_end,
        stringsAsFactors=FALSE
    )
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Extract the 'splicings' data frame from UCSC table.
###

### 'cdsStart0', 'cdsEnd1': single integers (resp. 0-based and 1-based).
### 'exon_start0', 'exon_end1': integer vectors of equal lengths (resp.
### 0-based and 1-based) and with no NAs.
### Returns a list with 2 elements, each of them being an integer vector of
### the same length as 'exon_start0' (or 'exon_end1') that may contain NAs.
### Notes (using genome="hg18"):
###   (1) In refGene table, transcript NM_001146685: cds cumulative length is
###       not a multiple of 3:
###                 name chrom strand txStart   txEnd cdsStart  cdsEnd
###         NM_001146685  chr1      + 1351370 1353029  1351370 1353029
###         exonCount       exonStarts         exonEnds id   name2
###                 2 1351370,1352796, 1351628,1353029,  0 TMEM88B
###         cdsStartStat cdsEndStat exonFrames
###                 cmpl     incmpl       0,0,
###       --> cds lengths: 1351628 - 1351370 -> 258
###                        1353029 - 1352796 -> 233
###       --> cds cum length: 491
###       Note that the cds end is marked as "incomplete" (see the cdsEndStat
###       col) which, according to UCSC, means that "the CDS is NOT completely
###       contained in the alignment at this end". See this post on the Genome
###       mailing list for more information:
###       https://lists.soe.ucsc.edu/pipermail/genome/2005-December/009184.html
###       Note that the post is about the Gencode Genes. Is it reasonable to
###       assume that this applies to RefSeq Genes too?
###   (2) Same thing in ensGene table, transcript ENST00000371841.
###   (3) All transcripts in the knownGene and ccdsGene tables have a cds
###       cumulative length that is a multiple of 3 (the former doesn't even
###       have the cdsStartStat/cdsStartEnd columns). For hg19 ccdsGene, this
###       is not true anymore :-/
### TODO: Investigate (1) and (2).
.extract_UCSC_cds_start_end <- function(cdsStart0, cdsEnd1,
                                        exon_start0, exon_end1, tx_name)
{
    cds_start0 <- cds_end1 <- integer(length(exon_start0))
    cds_start0[] <- NA_integer_
    cds_end1[] <- NA_integer_
    if (cdsStart0 >= cdsEnd1)
        return(list(cds_start0, cds_end1, FALSE))
    first_exon_with_cds <- which(exon_start0 <= cdsStart0
                                 & cdsStart0 < exon_end1)
    if (length(first_exon_with_cds) != 1L)
        stop("UCSC data ambiguity in transcript ", tx_name,
             ": cannot determine first exon with cds ('cdsStart' ",
             "falls in 0 or more than 1 exon)")
    last_exon_with_cds <- which(exon_start0 < cdsEnd1
                                & cdsEnd1 <= exon_end1)
    if (length(last_exon_with_cds) != 1L)
        stop("UCSC data ambiguity in transcript ", tx_name,
             ": cannot determine last exon with cds ('cdsEnd' ",
             "falls in 0 or more than 1 exon)")
    if (last_exon_with_cds < first_exon_with_cds)
        stop("UCSC data anomaly in transcript ", tx_name,
             ": last exon with cds occurs before first exon with cds")
    exons_with_cds <- first_exon_with_cds:last_exon_with_cds
    cds_start0[exons_with_cds] <- exon_start0[exons_with_cds]
    cds_end1[exons_with_cds] <- exon_end1[exons_with_cds]
    cds_start0[first_exon_with_cds] <- cdsStart0
    cds_end1[last_exon_with_cds] <- cdsEnd1
    ## NAs are OK in here since they indicate the absence of any CDS
    ## (which is common and nothing to write home about)
    ## changed from 50K to 19.3K ...   but the 'bad' ones are not present?
    bad <- sum(cds_end1 - cds_start0, na.rm=TRUE) %% 3L != 0L
    list(cds_start0, cds_end1, bad)
}

### Return a named list with 2 list elements, each of which is itself a list
### of integer vectors with eventually NAs. The 2 elements have the same
### "shape" as ucsc_txtable$exonStarts and ucsc_txtable$exonEnds and the NAs
### in them occur at the same places in the 2 elements.
.extract_cds_locs_from_UCSC_txtable <- function(ucsc_txtable)
{
    start_end <- Map(.extract_UCSC_cds_start_end,
                     ucsc_txtable$cdsStart, ucsc_txtable$cdsEnd,
                     ucsc_txtable$exonStarts, ucsc_txtable$exonEnds,
                     ucsc_txtable$name)
    bad <- sapply(start_end, "[[", 3L)
    if (any(bad)) {
        bad_cds <- ucsc_txtable$name[bad]
        msg <- sprintf("UCSC data anomaly in %d transcript(s):
            the cds cumulative length is not a multiple of 3
            for transcripts %s", length(bad_cds),
            paste(sQuote(bad_cds), collapse=" "))
        warning(paste(strwrap(msg, exdent=2L), collapse="\n"))
    }
    list(start=sapply(start_end, "[[", 1L),
         end=sapply(start_end, "[[", 2L))
}

.extract_splicings_from_UCSC_txtable <- function(ucsc_txtable,
                                                 transcripts_tx_id)
{
    message("Extract the 'splicings' data frame ... ", appendLF=FALSE)
    on.exit(message("OK"))
    exon_count <- ucsc_txtable$exonCount
    splicings_tx_id <- rep.int(transcripts_tx_id, exon_count)
    if (length(exon_count) == 0L) {
        exon_rank <- exon_start <- exon_end <-
            cds_start <- cds_end <- integer(0)
    } else {
        if (min(exon_count) <= 0L)
            stop("UCSC data anomaly: 'ucsc_txtable$exonCount' contains ",
                 "non-positive values")
        exon_rank <- make_exon_rank_col(exon_count, ucsc_txtable$strand)
        cds_locs <- .extract_cds_locs_from_UCSC_txtable(ucsc_txtable)
        exon_start <- unlist(ucsc_txtable$exonStarts) + 1L
        exon_end <- unlist(ucsc_txtable$exonEnds)
        cds_start <- unlist(cds_locs$start) + 1L
        cds_end <- unlist(cds_locs$end)
    }
    data.frame(
        tx_id=splicings_tx_id,
        exon_rank=exon_rank,
        exon_start=exon_start,
        exon_end=exon_end,
        cds_start=cds_start,
        cds_end=cds_end,
        stringsAsFactors=FALSE
    )
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Preprocess the 'genes' data frame.
###

.make_UCSC_genes <- function(genes, ucsc_txtable)
{
    #genes <- S4Vectors:::extract_data_frame_rows(genes,
    #                             genes$tx_name %in% ucsc_txtable$name)
    genes
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Download and preprocess the 'chrominfo' data frame.
###

.warn_about_circularity_guess <- function(genome, circ_seqs)
{
    if (length(circ_seqs) == 0L) {
        guess <- c("no sequence in ", genome , " is circular")
    } else if (length(circ_seqs) == 1L) {
        guess <- c(circ_seqs, " is circular")
    } else {
        in1string <- paste(circ_seqs, collapse=", ")
        guess <- c(length(circ_seqs), " sequences (",
                   in1string, ") are circular")
    }
    warning(wmsg("UCSC genome ", genome, " is not registered in ",
                 "GenomeInfoDb (see ?registered_UCSC_genomes), so ",
                 "we made the following guess about this assembly: ",
                 guess, ". You can see this by calling isCircular() ",
                 "on the TxDb object returned by makeTxDbFromUCSC(). "),
            "\n  ",
            wmsg("If you think this is incorrect, please let us know. ",
                 "In the meantime you can supply your own list of ",
                 "circular sequences (as a character vector) via ",
                 "the 'circ_seqs' argument."))
}

.make_UCSC_chrominfo <- function(genome, circ_seqs=NULL,
        goldenPath.url=getOption("UCSC.goldenPath.url"))
{
    message("Download and preprocess the 'chrominfo' data frame ... ",
            appendLF=FALSE)
    on.exit(message("OK"))
    chrom_info <- getChromInfoFromUCSC(genome, goldenPath.url=goldenPath.url)
    if (!is.null(circ_seqs)) {
        chrom_info[ , "circular"] <-
            GenomeInfoDb:::make_circ_flags_from_circ_seqs(
                                           chrom_info[ , "chrom"],
                                           circ_seqs=circ_seqs)
    } else {
        UCSC_genomes <- registered_UCSC_genomes()[ , "genome"]
        if (!(genome %in% UCSC_genomes)) {
            circ_seqs <- circ_seqs[circ_seqs[ , "circular"], "chrom"]
            .warn_about_circularity_guess(genome, circ_seqs)
        }
    }
    chrominfo <- data.frame(
        chrom=chrom_info[ , "chrom"],
        length=chrom_info[ , "size"],
        is_circular=chrom_info[ , "circular"],
        stringsAsFactors=FALSE
    )
    oo <- order(rankSeqlevels(chrominfo[ , "chrom"]))
    S4Vectors:::extract_data_frame_rows(chrominfo, oo)
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Prepare the 'metadata' data frame.
###

.prepare_UCSC_metadata <- function(genome, tablename, track, gene_id_type,
                                   full_dataset,
                                   taxonomyId=NA, miRBaseBuild=NA)
{
    message("Prepare the 'metadata' data frame ... ", appendLF=FALSE)
    on.exit(message("OK"))
    if (!isSingleStringOrNA(miRBaseBuild))
        stop("'miRBaseBuild' must be a a single string or NA")
    organism <- lookup_organism_by_UCSC_genome(genome)
    if (is.na(taxonomyId)) {
        taxonomyId <- GenomeInfoDb:::lookup_tax_id_by_organism(organism)
    } else {
        GenomeInfoDb:::check_tax_id(taxonomyId)
    }

    data.frame(
        name=c("Data source", "Genome", "Organism", "Taxonomy ID",
               "UCSC Table", "UCSC Track",
               "Resource URL", "Type of Gene ID",
               "Full dataset",
               "miRBase build ID"),
        value=c("UCSC", genome, organism, taxonomyId,
                tablename, track,
                "http://genome.ucsc.edu/", gene_id_type,
                ifelse(full_dataset, "yes", "no"),
                miRBaseBuild),
        stringsAsFactors=FALSE
    )
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### makeTxDbFromUCSC()
###

.make_TxDb_from_UCSC_txtable <- function(ucsc_txtable, genes,
        genome, tablename, track, gene_id_type,
        full_dataset,
        circ_seqs=NULL,
        goldenPath.url=getOption("UCSC.goldenPath.url"),
        taxonomyId=NA,
        miRBaseBuild=NA)
{
    strand_is_dot <- ucsc_txtable$strand == "."
    if (any(strand_is_dot)) {
        msg <- sprintf("dropped %d transcript(s) for which strand
            was not set (i.e. was set to '.')", sum(strand_is_dot))
        warning(wmsg(msg))
        keep_idx <- which(!strand_is_dot)
        ucsc_txtable <- S4Vectors:::extract_data_frame_rows(ucsc_txtable,
                                                            keep_idx)
    }

    transcripts <- .extract_transcripts_from_UCSC_txtable(ucsc_txtable)
    splicings <- .extract_splicings_from_UCSC_txtable(ucsc_txtable,
                                                      transcripts$tx_id)
    genes <- .make_UCSC_genes(genes, ucsc_txtable)
    chrominfo <- .make_UCSC_chrominfo(genome, circ_seqs, goldenPath.url)
    metadata <- .prepare_UCSC_metadata(genome, tablename, track, gene_id_type,
                                       full_dataset,
                                       taxonomyId,  miRBaseBuild)
    ## Jan 2019 -- The refGene tables in the hg19 and hg38 UCSC databases were
    ## last updated in Nov 2018 and now contain transcripts located on
    ## sequences that don't belong to the underlying genomes (GRCh37 and GRCh38
    ## respectively). More precisely some transcripts in these tables now
    ## belong to patched versions of these genomes: GRCh37.p13 for hg19 and
    ## GRCh38.p11 for hg38. This causes the makeTxDbFromUCSC() errors reported
    ## here:
    ##   https://github.com/Bioconductor/GenomicFeatures/issues/14
    ##   https://support.bioconductor.org/p/117265/
    ##   https://support.bioconductor.org/p/114901/
    ## The current fix is to drop these foreign transcripts with a warning.
    if (genome %in% c("hg19", "hg38") && tablename == "refGene")
        on.foreign.transcripts <- "drop"
    else
        on.foreign.transcripts <- "error"

    message("Make the TxDb object ... ", appendLF=FALSE)
    on.exit(message("OK"))
    makeTxDb(transcripts, splicings, genes=genes,
             chrominfo=chrominfo, metadata=metadata,
             reassign.ids=TRUE,
             on.foreign.transcripts=on.foreign.transcripts)
}

### Some timings (as of Jan 31, 2018, GenomicFeatures 1.31.7):
###          |             |    nb of    |
###   genome |   tablename | transcripts | time (s)
###   ---------------------------------------------
###     hg18 |   knownGene |       66803 |     37.2
###     hg18 |     refGene |       68178 |     42.6
###     hg19 |   knownGene |       82960 |     44.9
###     hg19 |     refGene |       69998 |     45.5
###     hg38 |     ensGene |      204940 |     63.7
###     hg19 |    ccdsGene |       28856 |     29.2
###     hg19 | xenoRefGene |      177746 |    114.1
###     hg38 |   knownGene |      197782 |     53.9
###     hg38 |     refGene |       74673 |     38.4
###      dm3 | flyBaseGene |       21236 |     28.9
###  sacCer2 |     sgdGene |        6717 |     22.6
###  sacCer3 |     ensGene |        7126 |     18.1
makeTxDbFromUCSC <- function(genome="hg19",
        tablename="knownGene",
        transcript_ids=NULL,
        circ_seqs=NULL,
        url="http://genome.ucsc.edu/cgi-bin/",
        goldenPath.url=getOption("UCSC.goldenPath.url"),
        taxonomyId=NA,
        miRBaseBuild=NA)
{
    if (!requireNamespace("RMariaDB", quietly=TRUE))
        stop(wmsg("Couldn't load the RMariaDB package. ",
                  "You need to install the RMariaDB package ",
                  "in order to use makeTxDbFromUCSC()."))

    if (!is.null(transcript_ids)) {
        if (!is.character(transcript_ids) || any(is.na(transcript_ids)))
            stop("'transcript_ids' must be a character vector with no NAs")
    }
    if (!isSingleString(url))
        stop("'url' must be a single string")
    if (!isSingleString(goldenPath.url))
        stop("'goldenPath.url' must be a single string")

    ## Create an UCSC Genome Browser session.
    session <- browserSession(url=url)
    genome(session) <- genome
    track <- .tablename2track(tablename, session)

    ## Download the transcript table.
    ucsc_txtable <- .fetch_UCSC_txtable(genome(session), tablename,
                                        transcript_ids=transcript_ids)

    ## Get the tx_name-to-gene_id mapping.
    mapdef <- .get_txname2geneid_mapdef(tablename)
    if (is.null(mapdef)) {
        txname2geneid <- list(genes=NULL, gene_id_type="no gene ids")
    } else if (is.list(mapdef)) {
        txname2geneid <- .fetch_txname2geneid_from_UCSC(
                                 genome(session),
                                 tablename, mapdef)
    } else if (is.character(mapdef)) {
        txname2geneid <- .extract_txname2geneid_from_UCSC_txtable(
                                 ucsc_txtable, mapdef)
    } else {
        stop("GenomicFeatures internal error: invalid 'mapdef'")
    }
    .make_TxDb_from_UCSC_txtable(ucsc_txtable, txname2geneid$genes,
                                 genome, tablename, track,
                                 txname2geneid$gene_id_type,
                                 full_dataset=is.null(transcript_ids),
                                 circ_seqs=circ_seqs,
                                 goldenPath.url=goldenPath.url,
                                 taxonomyId=taxonomyId,
                                 miRBaseBuild=miRBaseBuild)
}
Bioconductor/GenomicFeatures documentation built on Aug. 29, 2021, 1:03 p.m.