Tximeta: transcript quantification import with automatic metadata


The tximeta package [@Love2020] extends the tximport package [@Soneson2015] for import of transcript-level quantification data into R/Bioconductor. It automatically adds annotation metadata when the RNA-seq data has been quantified with Salmon [@Patro2017] or for scRNA-seq data quantified with alevin [@Srivastava2019]. To our knowledge, tximeta is the only package for RNA-seq data import that can automatically identify and attach transcriptome metadata based on the unique sequence of the reference transcripts. For more details on these packages -- including the motivation for tximeta and description of similar work -- consult the References below.

Note: tximeta requires that the entire output directory of Salmon / alevin is present and unmodified in order to identify the provenance of the reference transcripts. In general, it's a good idea to not modify or re-arrange the output directory of bioinformatic software as other downstream software rely on and assume a consistent directory structure. For sharing multiple samples, one can use, for example, tar -czf to bundle up a set of Salmon output directories, or to bundle one alevin output directory. For tips on using tximeta with other quantifiers see the other quantifiers section below.

Analysis starts with sample table

The first step using tximeta is to read in the sample table, which will become the column data, colData, of the final object, a SummarizedExperiment. The sample table should contain all the information we need to identify the Salmon quantification directories. For alevin quantification, one should point to the quants_mat.gz file that contains the counts for all of the cells.

Here we will use a Salmon quantification file in the tximportData package to demonstrate the usage of tximeta. We do not have a sample table, so we construct one in R. It is recommended to keep a sample table as a CSV or TSV file while working on an RNA-seq project with multiple samples.

dir <- system.file("extdata/salmon_dm", package="tximportData")
files <- file.path(dir, "SRR1197474", "quant.sf") 
coldata <- data.frame(files, names="SRR1197474", condition="A", stringsAsFactors=FALSE)

tximeta expects at least two columns in coldata:

  1. files - a pointer to the quant.sf files
  2. names - the unique names that should be used to identify samples

Running tximeta

Normally, we would just run tximeta like so:

se <- tximeta(coldata)

However, to avoid downloading remote GTF files during this vignette, we will point to a GTF file saved locally (in the tximportData package). We link the transcriptome of the Salmon index to its locally saved GTF. The standard recommended usage of tximeta would be the code chunk above, or to specify a remote GTF source, not a local one. This following code is therefore not recommended for a typically workflow, but is particular to the vignette code.

indexDir <- file.path(dir, "Dm.BDGP6.22.98_salmon-0.14.1")
fastaFTP <- c("ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/cdna/Drosophila_melanogaster.BDGP6.22.cdna.all.fa.gz",
gtfPath <- file.path(dir,"Drosophila_melanogaster.BDGP6.22.98.gtf.gz")
                organism="Drosophila melanogaster",
se <- tximeta(coldata)

What happened?

tximeta recognized the hashed checksum of the transcriptome that the files were quantified against, it accessed the GTF file of the transcriptome source, found and attached the transcript ranges, and added the appropriate transcriptome and genome metadata. A remote GTF is only downloaded once, and a local or remote GTF is only parsed to build a TxDb or EnsDb once: if tximeta recognizes that it has seen this Salmon index before, it will use a cached version of the metadata and transcript ranges.

Note the warning above that 5 of the transcripts are missing from the GTF file and so are dropped from the final output. This is a problem coming from the annotation source, and not easily avoided by tximeta.

TxDb, EnsDb, and AnnotationHub

tximeta makes use of Bioconductor packages for storing transcript databases as TxDb or EnsDb objects, which both are connected by default to sqlite backends. For GENCODE and RefSeq GTF files, tximeta uses the GenomicFeatures package [@granges] to parse the GTF and build a TxDb. For Ensembl GTF files, tximeta will first attempt to obtain the correct EnsDb object using AnnotationHub. The ensembldb package [@ensembldb] contains classes and methods for extracting relevant data from Ensembl files. If the EnsDb has already been made available on AnnotationHub, tximeta will download the database directly, which saves the user time parsing the GTF into a database (to avoid this, set useHub=FALSE). If the relevant EnsDb is not available on AnnotationHub, tximeta will build an EnsDb using ensembldb after downloading the GTF file. Again, the download/construction of a transcript database occurs only once, and upon subsequent usage of tximeta functions, the cached version will be used.

Pre-computed checksums

We plan to support a wide variety of sources and organisms for transcriptomes with pre-computed checksums, though for now the software focuses on predominantly human and mouse transcriptomes (see Next steps below for details). The following checksums are supported in this version of tximeta:

dir2 <- system.file("extdata", package="tximeta")
tab <- read.csv(file.path(dir2, "hashtable.csv"),
release.range <- function(tab, source, organism) {
  tab.idx <- tab$organism == organism & tab$source == source
  rels <- tab$release[tab.idx]
  if (organism == "Mus musculus" & source == "GENCODE") {
    paste0("M", range(as.numeric(sub("M","",rels))))
  } else if (source == "RefSeq") {
    paste0("p", range(as.numeric(sub(".*p","",rels))))
  } else {
dat <- data.frame(
  organism=c("Homo sapiens","Mus musculus",
             "Drosophila melanogaster")[c(1:2,1:3,1:2)]
rng <- t(sapply(seq_len(nrow(dat)), function(i)
  release.range(tab, dat[i,1], dat[i,2])))
dat$releases <- paste0(rng[,1], "-", rng[,2])

For Ensembl transcriptomes, we support the combined protein coding (cDNA) and non-coding (ncRNA) sequences, as well as the protein coding alone (although the former approach combining coding and non-coding transcripts is recommended for more accurate quantification).

tximeta also has functions to support linked transcriptomes, where one or more sources for transcript sequences have been combined or filtered. See the Linked transcriptome section below for a demonstration. (The makeLinkedTxome function was used above to avoid downloading the GTF during the vignette building process.)

SummarizedExperiment output

We have our coldata from before. Note that we've removed files.


Here we show the three matrices that were imported.


If there were inferential replicates (Gibbs samples or bootstrap samples), these would be imported as additional assays named "infRep1", "infRep2", ...

tximeta has imported the correct ranges for the transcripts:


We have appropriate genome information, which prevents us from making bioinformatic mistakes:


Retrieve the transcript database

The se object has associated metadata that allows tximeta to link to locally stored cached databases and other Bioconductor objects. In further sections, we will show examples functions that leverage this databases for adding exon information, summarize transcript-level data to the gene level, or add identifiers. However, first we mention that the user can easily access the cached database with the following helper function. In this case, tximeta has an associated EnsDb object that we can retrieve and use in our R session:

edb <- retrieveDb(se)

The database returned by retrieveDb is either a TxDb in the case of GENCODE or RefSeq GTF annotation file, or an EnsDb in the case of an Ensembl GTF annotation file. For further use of these two database objects, consult the GenomicFeatures vignettes and the ensembldb vignettes, respectively (both Bioconductor packages).

Add exons per transcript

Because the SummarizedExperiment maintains all the metadata of its creation, it also keeps a pointer to the necessary database for pulling out additional information, as demonstrated in the following sections.

If necessary, the tximeta package can pull down the remote source to build a TxDb, but given that we've already built a TxDb once, it simply loads the cached version. In order to remove the cached TxDb and regenerate, one can remove the relevant entry from the tximeta file cache that resides at the location given by getTximetaBFC().

The se object created by tximeta, has the start, end, and strand information for each transcript. Here, we swap out the transcript GRanges for exons-by-transcript GRangesList (it is a list of GRanges, where each element of the list gives the exons for a particular transcript).

se.exons <- addExons(se)

As with the transcript ranges, the exon ranges will be generated once and cached locally. As it takes a non-negligible amount of time to generate the exon-by-transcript GRangesList, this local caching offers substantial time savings for repeated usage of addExons with the same transcriptome.

We have implemented addExons to work only on the transcript-level SummarizedExperiment object. We provide some motivation for this choice in ?addExons. Briefly, if it is desired to know the exons associated with a particular gene, we feel that it makes more sense to pull out the relevant set of exons-by-transcript for the transcripts for this gene, rather than losing the hierarchical structure (exons to transcripts to genes) that would occur with a GRangesList of exons grouped per gene.

Easy summarization to gene-level

Likewise, the tximeta package can make use of the cached TxDb database for the purpose of summarizing transcript-level quantifications and bias corrections to the gene-level. After summarization, the rowRanges reflect the start and end position of the gene, which in Bioconductor are defined by the left-most and right-most genomic coordinates of all the transcripts. As with the transcript and exons, the gene ranges are cached locally for repeated usage. The transcript IDs are stored as a CharacterList column tx_ids.

gse <- summarizeToGene(se)

Add different identifiers

We would like to add support to easily map transcript or gene identifiers from one annotation to another. This is just a prototype function, but we show how we can easily add alternate IDs given that we know the organism and the source of the transcriptome. (This function currently only works for GENCODE and Ensembl gene or transcript IDs but could be extended to work for arbitrary sources.)

gse <- addIds(gse, "REFSEQ", gene=TRUE)

Differential expression analysis

The following code chunk demonstrates how to build a DESeqDataSet and begin a differential expression analysis.

# here there is a single sample so we use ~1.
# expect a warning that there is only a single sample...
suppressWarnings({dds <- DESeqDataSet(gse, ~1)})
# ... see DESeq2 vignette

We have a convenient wrapper function that will build a DGEList object for use with edgeR.

y <- makeDGEList(gse)
# ... see edgeR User's Guide for further steps

The following code chunk demonstrates the code inside of the above wrapper function, and produces the same output.

cts <- assays(gse)[["counts"]]
normMat <- assays(gse)[["length"]]
normMat <- normMat / exp(rowMeans(log(normMat)))
o <- log(calcNormFactors(cts/normMat)) + log(colSums(cts/normMat))
y <- DGEList(cts)
y <- scaleOffset(y, t(t(log(normMat)) + o))
# ... see edgeR User's Guide for further steps

The following code chunk demonstrates how one could use the Swish method in the fishpond Bioconductor package. Here we use the transcript-level object se. This dataset only has a single sample and no inferential replicates, but the analysis would begin with such code. See the Swish vignette in the fishpond package for a complete example:

y <- se # rename the object to 'y'
# if inferential replicates existed in the data,
# analysis would begin with:
# y <- scaleInfReps(y)
# ... see Swish vignette in the fishpond package

For limma with voom transformation we recommend, as in the tximport vignette to generate counts-from-abundance instead of providing an offset for average transcript length.

gse <- summarizeToGene(se, countsFromAbundance="lengthScaledTPM")
y <- DGEList(assays(gse)[["counts"]])
# see limma User's Guide for further steps

Above we generated counts-from-abundance when calling summarizeToGene. The counts-from-abundance status is then stored in the metadata:


Additional metadata

The following information is attached to the SummarizedExperiment by tximeta:


Errors connecting to a database

tximeta makes use of BiocFileCache to store transcript and other databases, so saving the relevant databases in a centralized location used by other Bioconductor packages as well. It is possible that an error can occur in connecting to these databases, either if the files were accidentally removed from the file system, or if there was an error generating or writing the database to the cache location. In each of these cases, it is easy to remove the entry in the BiocFileCache so that tximeta will know to regenerate the transcript database or any other missing database.

If you have used the default cache location, then you can obtain access to your BiocFileCache with:

bfc <- BiocFileCache()

Otherwise, you can recall your particular tximeta cache location with getTximetaBFC().

You can then inspect the entries in your BiocFileCache using bfcinfo and remove the entry associated with the missing database with bfcremove. See the BiocFileCache vignette for more details on finding and removing entries from a BiocFileCache.

Note that there may be many entries in the BiocFileCache location, including .sqlite database files and serialized .rds files. You should only remove the entry associated with the missing database, e.g. if R gave an error when trying to connect to the TxDb associated with GENCODE v99 human transcripts, you should look for the rid of the entry associated with the human v99 GTF from GENCODE.

What if checksum isn't known?

tximeta automatically imports relevant metadata when the transcriptome matches a known source -- known in the sense that it is in the set of pre-computed hashed checksums in tximeta (GENCODE, Ensembl, and RefSeq for human and mouse). tximeta also facilitates the linking of transcriptomes used in building the Salmon index with relevant public sources, in the case that these are not part of this pre-computed set known to tximeta. The linking of the transcriptome source with the quantification files is important in the case that the transcript sequence no longer matches a known source (uniquely combined or filtered FASTA files), or if the source is not known to tximeta. Combinations of coding and non-coding human, mouse, and fruit fly Ensembl transcripts should be automatically recognized by tximeta and does not require making a linkedTxome. As the package is further developed, we plan to roll out support for all common transcriptomes, from all sources. Below we demonstrate how to make a linkedTxome and how to share and load a linkedTxome.

We point to a Salmon quantification file which was quantified against a transcriptome that included the coding and non-coding Drosophila melanogaster transcripts, as well as an artificial transcript of 960 bp (for demonstration purposes only).

file <- file.path(dir, "SRR1197474.plus", "quant.sf")
coldata <- data.frame(files=file, names="SRR1197474", sample="1",

Trying to import the files gives a message that tximeta couldn't find a matching transcriptome, so it returns an non-ranged SummarizedExperiment.

se <- tximeta(coldata)

Linked transcriptomes

If the transcriptome used to generate the Salmon index does not match any transcriptomes from known sources (e.g. from combining or filtering known transcriptome files), there is not much that can be done to automatically populate the metadata during quantification import. However, we can facilitate the following two cases:

1) the transcriptome was created locally and has been linked to its public source(s) 2) the transcriptome was produced by another group, and they have produced and shared a file that links the transcriptome to public source(s)

tximeta offers functionality to assist reproducible analysis in both of these cases.

To make this quantification reproducible, we make a linkedTxome which records key information about the sources of the transcript FASTA files, and the location of the relevant GTF file. It also records the checksum of the transcriptome that was computed by Salmon during the index step.

Multiple GTF/GFF files: linkedTxome and tximeta do not currently support multiple GTF/GFF files, which is a more complicated case than multiple FASTA, which is supported. Currently, we recommend that users should add or combine GTF/GFF files themselves to create a single GTF/GFF file that contains all features used in quantification, and then upload such a file to Zenodo, which can then be linked as shown below. Feel free to contact the developers on the Bioconductor support site or GitHub Issue page for further details or feature requests.

By default, linkedTxome will write out a JSON file which can be shared with others, linking the checksum of the index with the other metadata, including FASTA and GTF sources. By default, it will write out to a file with the same name as the indexDir, but with a .json extension added. This can be prevented with write=FALSE, and the file location can be changed with jsonFile.

First we specify the path where the Salmon index is located.

Typically you would not use system.file and file.path to locate this directory, but simply define indexDir to be the path of the Salmon directory on your machine. Here we use system.file and file.path because we have included parts of a Salmon index directory in the tximeta package itself for demonstration of functionality in this vignette.

indexDir <- file.path(dir, "Dm.BDGP6.22.98.plus_salmon-0.14.1")

Now we provide the location of the FASTA files and the GTF file for this transcriptome.

Note: the basename for the GTF file is used as a unique identifier for the cached versions of the TxDb and the transcript ranges, which are stored on the user's behalf via BiocFileCache. This is not an issue, as GENCODE, Ensembl, and RefSeq all provide GTF files which are uniquely identified by their filename, e.g. Drosophila_melanogaster.BDGP6.22.98.gtf.gz.

The recommended usage of tximeta would be to specify a remote GTF source, as seen in the commented-out line below:

fastaFTP <- c("ftp://ftp.ensembl.org/pub/release-98/fasta/drosophila_melanogaster/cdna/Drosophila_melanogaster.BDGP6.22.cdna.all.fa.gz",
#gtfFTP <- "ftp://path/to/custom/Drosophila_melanogaster.BDGP6.22.98.plus.gtf.gz"

Instead of the above commented-out FTP location for the GTF file, we specify a location within an R package. This step is just to avoid downloading from a remote FTP during vignette building. This use of file.path to point to a file in an R package is specific to this vignette and should not be used in a typical workflow. The following GTF file is a modified version of the release 98 from Ensembl, which includes description of a one transcript, one exon artificial gene which was inserted into the transcriptome (for demonstration purposes only).

gtfPath <- file.path(dir,"Drosophila_melanogaster.BDGP6.22.98.plus.gtf.gz")

Finally, we create a linkedTxome. In this vignette, we point to a temporary directory for the JSON file, but a more typical workflow would write the JSON file to the same location as the Salmon index by not specifying jsonFile.

makeLinkedTxome performs two operation: (1) it creates a new entry in an internal table that links the transcriptome used in the Salmon index to its sources, and (2) it creates a JSON file such that this linkedTxome can be shared.

tmp <- tempdir()
jsonFile <- file.path(tmp, paste0(basename(indexDir), ".json"))
                source="Ensembl", organism="Drosophila melanogaster",
                release="98", genome="BDGP6.22",
                fasta=fastaFTP, gtf=gtfPath,

After running makeLinkedTxome, the connection between this Salmon index (and its checksum) with the sources is saved for persistent usage. Note that because we added a single transcript of 960bp to the FASTA file used for quantification, tximeta could tell that this was not quantified against release 98 of the Ensembl transcripts for Drosophila melanogaster. Only when the correct set of transcripts were specified does tximeta recognize and import the correct metadata.

With use of tximeta and a linkedTxome, the software figures out if the remote GTF has been accessed and compiled into a TxDb before, and on future calls, it will simply load the pre-computed metadata and transcript ranges.

Note the warning that 5 of the transcripts are missing from the GTF file and so are dropped from the final output. This is a problem coming from the annotation source, and not easily avoided by tximeta.

se <- tximeta(coldata)

We can see that the appropriate metadata and transcript ranges are attached.


Clear linkedTxomes

The following code removes the entire table with information about the linkedTxomes. This is just for demonstration, so that we can show how to load a JSON file below.

Note: Running this code will clear any information about linkedTxomes. Don't run this unless you really want to clear this table!

if (interactive()) {
  bfcloc <- getTximetaBFC()
} else {
  bfcloc <- tempdir()
bfc <- BiocFileCache(bfcloc)
bfcremove(bfc, bfcquery(bfc, "linkedTxomeTbl")$rid)

Loading linkedTxome JSON files

If a collaborator or the Suppmentary Files for a publication shares a linkedTxome JSON file, we can likewise use tximeta to automatically assemble the relevant metadata and transcript ranges. This implies that the other person has used tximeta with the function makeLinkedTxome demonstrated above, pointing to their Salmon index and to the FASTA and GTF source(s).

We point to the JSON file and use loadLinkedTxome and then the relevant metadata is saved for persistent usage. In this case, we saved the JSON file in a temporary directory.

jsonFile <- file.path(tmp, paste0(basename(indexDir), ".json"))

Again, using tximeta figures out whether it needs to access the remote GTF or not, and assembles the appropriate object on the user's behalf.

se <- tximeta(coldata)

Clear linkedTxomes again

Finally, we clear the linkedTxomes table again so that the above examples will work. This is just for the vignette code and not part of a typical workflow.

Note: Running this code will clear any information about linkedTxomes. Don't run this unless you really want to clear this table!

if (interactive()) {
  bfcloc <- getTximetaBFC()
} else {
  bfcloc <- tempdir()
bfc <- BiocFileCache(bfcloc)
bfcremove(bfc, bfcquery(bfc, "linkedTxomeTbl")$rid)

Other quantifiers

tximeta can import the output from any quantifiers that are supported by tximport, and if these are not Salmon, alevin, or Sailfish output, it will simply return a non-ranged SummarizedExperiment by default.

An alternative solution is to wrap other quantifiers in workflows that include metadata information JSON files along with each quantification file. One can place these files in aux_info/meta_info.json or any relative location specified by customMetaInfo, for example customMetaInfo="meta_info.json". This JSON file is located relative to the quantification file and should contain a tag index_seq_hash with an associated value of the SHA-256 hash of the reference transcripts. For computing the hash value of the reference transcripts, see the FastaDigest python package. The hash value used by Salmon is the SHA-256 hash value of the reference sequences stripped of the header lines, and concatenated together with the empty string (so only cDNA sequences combined without any new line characters). FastaDigest can be installed with pip install fasta_digest.

Automated analysis with ARMOR

This vignette described the use of tximeta to import quantification data into R/Bioconductor with automatic detection and addition of metadata. The SummarizedExperiment produced by tximeta can then be provided to downstream statistical analysis packages as described above. The tximeta package does not contain any functionality for automated differential analysis.

The ARMOR workflow does automate a variety of differential analyses, and make use of tximeta for creation of a SummarizedExperiment with attached annotation metadata. ARMOR stands for ``An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data'' and is described in more detail in the article by @Orjuelag2019.


The development of tximeta has benefited from suggestions from these and other individuals in the community:

Next steps

Integration with GA4GH / refget API

Facilitate plots and summaries

Extended functionality

Session info



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tximeta documentation built on April 13, 2021, 6:01 p.m.