Each tool has a set of Common options for input/output, profiling and debugging.

Group - Group reads based on their UMI and mapping coordinates

Identify groups of reads based on their genomic coordinate and UMI

The group command can be used to create two types of outfile: a tagged BAM or a flatfile describing the read groups

To generate the tagged-BAM file, use the option --output-bam and provide a filename with the --stdout/-S option. Alternatively, if you do not provide a filename, the bam file will be outputted to the stdout. If you have provided the --log/-L option to send the logging output elsewhere, you can pipe the output from the group command directly to e.g samtools view like so:

umi_tools group -I inf.bam --group-out=grouped.tsv --output-bam
--log=group.log --paired | samtools view - |less

The tagged-BAM file will have two tagged per read:

• UG Unique_id. 0-indexed unique id number for each group of reads with the same genomic position and UMI or UMIs inferred to be from the same true UMI + errors
• BX Final UMI. The inferred true UMI for the group

To generate the flatfile describing the read groups, include the --group-out=<filename> option. The columns of the read groups file are below. The first five columns relate to the read. The final 3 columns relate to the group.

• read_id

  read identifier

• contig

  alignment contig

• position

  Alignment position. Note that this position is not the start position of the read in the BAM file but the start of the read taking into account the read strand and cigar

• gene

  The gene assignment for the read. Note, this will be NA unless the –per-gene option is specified

• umi

  The read UMI

• umi_count

  The number of times this UMI is observed for reads at the same position

• final_umi

  The inferred true UMI for the group

• final_umi_count

  The total number of reads within the group

• unique_id

  The unique id for the group

group-specific options

–group-out

Outfile name for file mapping read id to read group

–out-bam

Output a bam file with read groups tagged using the UG tag

–umi-group-tag

BAM tag for the error corrected UMI selected for the group. Default=BX

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-separator option

• tag

  Barcodes contained in a tag(s), see --umi-tag/--cell-tag options

• umis

  Barcodes were extracted using umis (https://github.com/vals/umis)

--umi-separator=[SEPARATOR]

Separator between read id and UMI. See --extract-umi-method above. Default=``_``

--umi-tag=[TAG]

Tag which contains UMI. See --extract-umi-method above

--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-tag or --per-contig option.

--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

--skip-tags-regex

Use in conjunction with the --assigned-status-tag option to skip any reads where the tag matches this regex. Default ("^[__|Unassigned]") matches anything which starts with “__” or “Unassigned”:

--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-map option

--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 only. 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 only

• 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

• use (default)

  Deduplicate using read1 only

• 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

Input/Output Options

--in-sam, --out-sam

By default, inputs are assumed to be in BAM format and outputs are written in BAM format. Use these options to specify the use of SAM format for input or output.

--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.

Group/Dedup options

--no-sort-output

By default, output is sorted. This involves the use of a temporary unsorted file since reads are considered in the order of their start position which may not be the same as their alignment coordinate due to soft-clipping and reverse alignments. The temp file will be saved (in --temp-dir) and deleted when it has been sorted to the outfile. Use this option to turn off sorting.

--buffer-whole-contig

forces dedup to parse an entire contig before yielding any reads for deduplication. This is the only way to absolutely guarantee that all reads with the same start position are grouped together for deduplication since dedup uses the start position of the read, not the alignment coordinate on which the reads are sorted. However, by default, dedup reads for another 1000bp before outputting read groups which will avoid any reads being missed with short read sequencing (<1000bp).