Each tool has a set of Common options for input/output, profiling and debugging.
count_tab - Count reads per gene from flatfile using UMIs
Purpose
The purpose of this command is to count the number of reads per gene based on the read’s gene assignment and UMI. See the count command if you want to perform per-cell counting using a BAM file input.
The input must be in the following format (tab separated), where the first column is the read identifier (including UMI) and the second column is the assigned gene. The input must be sorted by the gene identifier.
Input template:
read_id[SEP]_UMI gene
Example:
NS500668:144:H5FCJBGXY:2:22309:18356:15843_TCTAA ENSG00000279457.3
NS500668:144:H5FCJBGXY:3:23405:39715:19716_CGATG ENSG00000225972.1
You can perform any required file transformation and pipe the output directly to count_tab. For example to pipe output from featureCounts with the ‘-R CORE’ option you can do the following:
awk '$2=="Assigned" {print $1" "$4}' my.bam.featureCounts | sort -k2 |
umi_tools count_tab -S gene_counts.tsv -L count.log
The tab file is assumed to contain each read id once only. For paired end reads with featureCounts you must include the “-p” option so each read id is included once only.
Per-cell counting can be enable with --per-cell. For per-cell
counting, the input must be in the following format (tab separated),
where the first column is the read identifier (including UMI and Cell
Barcode) and the second column is the assigned gene. The input must be
sorted by the gene identifier:
Input template:
read_id[SEP]_CB_UMI gene
Example:
NS500668:144:H5FCJBGXY:2:22309:18356:15843_AGTCGA_TCTAA ENSG00000279457.3
NS500668:144:H5FCJBGXY:3:23405:39715:19716_GGAGAA_CGATG ENSG00000225972.1
Extracting barcodes
It is assumed that the FASTQ files were processed with umi_tools extract before mapping and thus the UMI is the last word of the read name. e.g:
@HISEQ:87:00000000_AATT
where AATT is the UMI sequeuence.
If you have used an alternative method which does not separate the
read id and UMI with a “_”, such as bcl2fastq which uses “:”, you can
specify the separator with the option --umi-separator=<sep>,
replacing <sep> with e.g “:”.
Alternatively, if your UMIs are encoded in a tag, you can specify this
by setting the option –extract-umi-method=tag and set the tag name
with the –umi-tag option. For example, if your UMIs are encoded in
the ‘UM’ tag, provide the following options:
--extract-umi-method=tag --umi-tag=UM
Finally, if you have used umis to extract the UMI +/- cell barcode,
you can specify --extract-umi-method=umis
The start position of a read is considered to be the start of its alignment minus any soft clipped bases. A read aligned at position 500 with cigar 2S98M will be assumed to start at position 498.
--extract-umi-method
How are the barcodes encoded in the read?
Options are:
- read_id (default)
Barcodes are contained at the end of the read separated as specified with
--umi-separatoroption
- tag
Barcodes contained in a tag(s), see
--umi-tag/--cell-tagoptions
- umis
Barcodes were extracted using umis (https://github.com/vals/umis)
--umi-separator=[SEPARATOR]
Separator between read id and UMI. See
--extract-umi-methodabove. Default=``_``
--umi-tag=[TAG]
Tag which contains UMI. See
--extract-umi-methodabove
--umi-tag-split=[SPLIT]
Separate the UMI in tag by SPLIT and take the first element
--umi-tag-delimiter=[DELIMITER]
Separate the UMI in by DELIMITER and concatenate the elements
--cell-tag=[TAG]
Tag which contains cell barcode. See –extract-umi-method above
--cell-tag-split=[SPLIT]
Separate the cell barcode in tag by SPLIT and take the first element
--cell-tag-delimiter=[DELIMITER]
Separate the cell barcode in by DELIMITER and concatenate the elements
UMI grouping options
--method
What method to use to identify group of reads with the same (or similar) UMI(s)?
All methods start by identifying the reads with the same mapping position.
The simplest methods, unique and percentile, group reads with the exact same UMI. The network-based methods, cluster, adjacency and directional, build networks where nodes are UMIs and edges connect UMIs with an edit distance <= threshold (usually 1). The groups of reads are then defined from the network in a method-specific manner. For all the network-based methods, each read group is equivalent to one read count for the gene.
- unique
Reads group share the exact same UMI
- percentile
Reads group share the exact same UMI. UMIs with counts < 1% of the median counts for UMIs at the same position are ignored.
- cluster
Identify clusters of connected UMIs (based on hamming distance threshold). Each network is a read group
- adjacency
Cluster UMIs as above. For each cluster, select the node (UMI) with the highest counts. Visit all nodes one edge away. If all nodes have been visited, stop. Otherwise, repeat with remaining nodes until all nodes have been visted. Each step defines a read group.
- directional (default)
Identify clusters of connected UMIs (based on hamming distance threshold) and umi A counts >= (2* umi B counts) - 1. Each network is a read group.
--edit-distance-threshold
For the adjacency and cluster methods the threshold for the edit distance to connect two UMIs in the network can be increased. The default value of 1 works best unless the UMI is very long (>14bp).
--spliced-is-unique
Causes two reads that start in the same position on the same strand and having the same UMI to be considered unique if one is spliced and the other is not. (Uses the ‘N’ cigar operation to test for splicing).
--soft-clip-threshold
Mappers that soft clip will sometimes do so rather than mapping a spliced read if there is only a small overhang over the exon junction. By setting this option, you can treat reads with at least this many bases soft-clipped at the 3’ end as spliced. Default=4.
--multimapping-detection-method=[NH/X0/XT]
If the sam/bam contains tags to identify multimapping reads, you can specify for use when selecting the best read at a given loci. Supported tags are “NH”, “X0” and “XT”. If not specified, the read with the highest mapping quality will be selected.
--read-length
Use the read length as a criteria when deduping, for e.g sRNA-Seq.
Single-cell RNA-Seq options
--per-gene
Reads will be grouped together if they have the same gene. This is useful if your library prep generates PCR duplicates with non identical alignment positions such as CEL-Seq. Note this option is hardcoded to be on with the count command. I.e counting is always performed per-gene. Must be combined with either
--gene-tagor--per-contigoption.
--gene-tag
Deduplicate per gene. The gene information is encoded in the bam read tag specified
--assigned-status-tag
BAM tag which describes whether a read is assigned to a gene. Defaults to the same value as given for
--gene-tag
--per-contig
Deduplicate per contig (field 3 in BAM; RNAME). All reads with the same contig will be considered to have the same alignment position. This is useful if you have aligned to a reference transcriptome with one transcript per gene. If you have aligned to a transcriptome with more than one transcript per gene, you can supply a map between transcripts and gene using the
--gene-transcript-mapoption
--gene-transcript-map
File mapping genes to transcripts (tab separated), e.g:
gene1 transcript1 gene1 transcript2 gene2 transcript3
--per-cell
Reads will only be grouped together if they have the same cell barcode. Can be combined with
--per-gene.
SAM/BAM Options
--mapping-quality
Minimium mapping quality (MAPQ) for a read to be retained. Default is 0.
--unmapped-reads
- How should unmapped reads be handled. Options are:
- discard (default)
Discard all unmapped reads
- use
If read2 is unmapped, deduplicate using read1 and output read1 only. Note that if read1 is unmapped, read2 will always be descarded irrepsective of whether it is mapped. WARNING: May lead to unpaired reads in output. Requires
--paired
- output
Output unmapped reads/read pairs without UMI grouping/deduplication. Only available in umi_tools group
--chimeric-pairs
- How should chimeric read pairs be handled. Options are:
- discard
Discard all chimeric read pairs
- use (default)
Deduplicate using read1 information only. Both read1 and read2 should still be output, as long as Read2 is actaully found. Can lead to unpaired reads in output if read1 is marked as having a mapped mate, but read2 is never found.
- output
Output chimeric read pairs without UMI grouping/deduplication. Only available in umi_tools group
--unpaired-reads
- How should unpaired reads be handled. Options are:
- discard
Discard all unpaired reads. Note: Can still lead to unpaired reads in the output if a read1 is marked as having a mapped mate, but the mate is never found.
- use (default)
Deduplicate unpaired reads using read1 only. Note, unpaired read2s will still be discarded.
- output
Output unpaired reads without UMI grouping/deduplication. Only available in umi_tools group
--ignore-umi
Ignore the UMI and group reads using mapping coordinates only
--subset
Only consider a fraction of the reads, chosen at random. This is useful for doing saturation analyses.
--chrom
Only consider a single chromosome. This is useful for debugging/testing purposes
--paired
BAM is paired end - output both read pairs. This will also force the use of the template length to determine reads with the same mapping coordinates.
Input/Output Format Options
The following options deal with input and output format, and are useful for
outputting CRAM format. In general UMI-tools will attempt to guess the input
and output formats from the file names, but thing can be over-written using
the out-format and input-format options. The location of CRAM
reference files will be taken from the either the an input CRAM file
(if present) or from the --reference-filename option. Otherwise
the reference will be embedded in the file.
--in-format=IN_FORMAT
File format of the input file. Format is usually implied from the extension of the filename, but maybe overridden with this option. Default=bam
--input-options=INPUT_OPTIONS
Format string provided to htslib for reading. Mostly useful for CRAM formatted files. See samtools documentation
--in-sam
[DEPRECATED] USE
--in-format. By default, inputs are assumed to be in BAM format. Use this option to specify the use of SAM format for input.
--reference-filename=REFERENCE_FILENAME
File path or URL to the genome reference to be used when reading or writing CRAM files. Can be a path or a URL. By default, when reading a CRAM file, the reference recorded in the input file will be used unless this is specified. URL references cannot be read from input files, however. When writing, specifying a reference location is required unless specified in input.