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TRUmiCount

v 0.9.13

February 12, 2019

User & Installation Manual
1 Installing TRUmiCount
1.1 Installation via Conda (Recommended) . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Manual Installation on linux or mac OS . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Running TRUmiCount . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3
4

2 Using TRUmiCount
2.1 Input formats & options .
2.2 Output formats & options
2.3 Strand UMIs . . . . . . .
2.4 Single-cell Data . . . . .

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3 Parameter Reference

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4 Examples
10
4.1 Single-End Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Paired-End Data with strand UMIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3 Single-cell Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
5 License

13

1 Installing TRUmiCount

2

1.1 Installation via Conda (Recommended)
Installing Conda

Conda1 allows easy installation of a large range of software packages. See the Conda user guide for
installation instructions. Briefly, (on 64-bit Linux) do2
INSTALLER=Miniconda2-latest-Linux-x86_64.sh
curl -O https://repo.continuum.io/miniconda/$INSTALLER
bash $INSTALLER -p ~/conda

Creating an environment

Conda allows creating multiple environments, each containing a different collection of packages. We
create a separate environment for TRUmiCount
~/conda/bin/conda create -n trc

This environment is now activated to make it the target of further conda commands, and to make
the installed software visible. This must be done every time you open a new terminal window!
source ~/conda/bin/activate trc

Installing BioConda

Conda packages are organized into so-called channels. We add the BioConda3 channel which contains
many common bioinformatics tools, amongst which is also TRUmiCount.
conda config --env --add channels defaults
conda config --env --add channels conda-forge
conda config --env --add channels bioconda

Installing TRUmiCount

With Conda installed and the Bioconda channels added, TRUmiCount is installed with
conda install trumicount

Installing SAMtools and UMI-Tools (Optional)

TRUmiCount relies on UMI-Tools4 to extract the UMIs associated with a gene (or other genomic
feature) from the mapped reads in a BAM file. We provide a modified version of UMI-Tools that
improves the handling of paired-end reads and single-cell data in conjunction with TRUmiCount.
You can install our modified version of UMI-Tools with
conda install -c cibiv umi_tools_tuc

You can also use the unmodified version of UMI-Tools together with TRUmiCount, but will have
to manually post-process the output of umi_tools group before feeding it to TRUmiCount when
working with single-cell data. To install the unmodified version, do
conda install umi_tools

While TRUmiCount does not directly require SAMtools, some of the examples in this manual rely
on SAMtools being installed, so we suggest installing them with
conda install samtools

1 See http://conda.io for details
2 This will install Conda into the directory conda

in your home directory. You can replace ~/conda with a directory of your
choice, but remember to do so everywhere
3 See http://bioconda.github.io/ for details
4 Smith, T.S. et al. UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research 27, 491-499 (2017). Software available at http://github.com/CGATOxford/UMI-tools

1 Installing TRUmiCount

3

1.2 Manual Installation on linux or mac OS
Installing TRUmiCount

First, make sure you have R5 (at least version 3.1) installed. First, we’ll install a few required
packages from CRAN. Amogst those is gwpcR6 which contains the implementation of the model
that TRUmiCount relies on. Start R (either on the command line, or open RStudio) and type
install.packages(c('statmod', 'akima', 'data.table', 'docopt', 'devtools'))
devtools::install_github("Cibiv/gwpcR", ref="latest-release")

Note that unless you have write privileges to your system’s R installation, the packages will be
installed only for your user.
With all prerequisites now available, TRUmiCount can now be installed. The commands below
install TRUmiCount into your home directory. To install TRUmiCount system-wide (assuming you
have the necessary privileges), don’t specify --prefix ~/.local.
git clone https://github.com/Cibiv/trumicount.git trumicount
cd trumicount/
mkdir -p ~/.local/bin
./install.R --prefix ~/.local

To be able to run TRUmiCount, you must add ~/.local/bin to your PATH. This must be done
every time you open a new terminal window!
export PATH=~/.local/bin:$PATH

Installing UMI-Tools

TRUmiCount uses UMI-Tools7 to extract UMIs from mapped reads and to correct for sequencing
errors within the UMIs. We provide a modified version of UMI-Tools that improves the handling of
paired-end reads and single-cell data in conjunction with TRUmiCount. You can install our modified
version of UMI-Tools with
URL=https://github.com/Cibiv/UMI-tools.git
git clone -b tuc_patches $URL umi_tools
cd umi_tools
python setup.py install --user

To install the original version, instead do
URL=https://github.com/CGATOxford/UMI-tools
git clone $URL umi_tools
cd umi_tools
python setup.py install --user

Note that you will then have to manually post-process the output of umi_tools group before
feeding it to TRUmiCount when working with single-cell data.

5R

Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna,
Austria (2017). http://www.R-project.org/
6 Pflug, F.P. & von Haeseler, Arndt. TRUmiCount: Correctly counting absolute numbers of molecules using unique molecular identifiers. Bioinformatics (2017). DOI: http://doi.org/10.1093/bioinformatics/bty283
7 Smith, T.S. et al. UMI-tools: Modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Research 27, 491-499 (2017). Software available at http://github.com/CGATOxford/UMI-tools

1 Installing TRUmiCount

4

1.3 Running TRUmiCount
After installing TRUmiCount, you can display a list of options with
trumicount --help

For a detailed explanation of TRUmiCount’s various modes of operation and supported file format,
see Using TRUmiCount and Parameter Reference.
You can download an example input file8 in BAM format and index it (necessary for reading it
with TRUmiCount) with
curl -O https://cibiv.github.io/trumicount/kv_1000g.bam
samtools index kv_1000g.bam

From the indexed BAM file, you can let TRUmiCount compute a table containing the bias-corrected
number of transcript molecules for each gene and write it to kv_1000g.tab with
trumicount --input-bam kv_1000g.bam --umi-sep ':' \
--molecules 2 --threshold 2 --genewise-min-umis 3 \
--output-counts kv_1000g.tab

For more examples of how to use TRUmiCount and a discussion of the various options used here,
see Examples.

8 containing

a reduced version, restricted to the first 1000 genes and sub-sampled to 50%, of RNA-Seq data published in
Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods 9, 72-74
(2011)

2 Using TRUmiCount

5

2.1 Input formats & options
To be able to separate true UMIs from phantoms and to estimate and correct for the percentage
of true UMIs that are lost during library preparation or data processing, TRUmiCount analyses
the distribution of read counts per UMI for each gene (or any other type of genomic feature).
TRUmiCount by default assumes that
• Each UMI initially had two copies. This is e.g. the case if molecules before amplification were
double-stranded and the copies produced from both strands are identical. This number can be
changed with “--molecules”
• UMIs must be supported by at least two reads to be assumed to be a true UMI and not a
phantom. This threshold can be changed with “--threshold”.
Reading UMIs from a BAM File

If a mapped BAM File is provided as input with “--input-bam”, TRUmiCount uses UMI-Tools’
“group” tool to extract a list of UMIs and their read counts from the specified BAM file. Sequencing
errors within the UMIs are corrected by UMI-Tools by merging similar UMIs into one. When reading
BAM file, on top of the defaults mentioned above, UMI-Tools assumes that
• The BAM file must have a corresponding index. A suitable index can be created with
“samtools index bamfile”.
• Sequences names are gene names. With the “--gene-tag” option, gene names can be read
from a tag instead, whose name is specified with “--umitools-option --gene-tag=tagname”.
• The UMI was appended to the read name, separated by ‘_’. A different separator can be
specified with “--umi-sep”
• The BAM file contains single-end reads (read2 is ignored). To take the mapping position of
both mates into account when grouping reads by UMI, specify “--paired”.
• Reads with a mapping quality below 20 should be ignored. This threshold can be changed
with “--mapping-quality”.
Reading unfiltered UMIs from a tab-separated file

Instead of using UMI-Tools to extract UMI and their read counts from a BAM file, TRUmiCount is
also able to read a previously computed table of UMIs with “--input-umis” (see Output formats &
options for how to generate such a table). The table must be tab-separated with one row per UMI
and contain at least the columns “sample”, “gene”, “reads”. Similar UMIs are assumed to already
have been merged, but filters are assumed not to have been applied yet. When dealing with strand
UMIs (see section Strand UMIs), TRUmiCount also requires the columns “pos” and “end”, which
contain the mapping position of read1 respectively read2.
The UMI-Tools and BAM-related options “--umi-sep”, “--umitools”, “--umitools-option”,
“--paired” and “--mapping-quality” are ignored if “--input-umis” is used.

2.2 Output formats & options
Writing corrected per-gene molecule counts to a tab-separated file

The main output of TRUmiCount is a table, written to a tab-separated file specified with
“---output-counts” containing for each gene the columns
sample

: The sample identifier (e.g cell barcode)

gene

: The gene identifier (see discussion in Reading UMIs from a BAM File)

n.umis

: The number of true UMIs (i.e. after applying filters) detected

n.tot

: The estimated total number of molecules, i.e. “n.umis” / (1 - “loss”).

efficiency : The estimated gene-specific amplification efficiency
depth

: The gene-specific sequencing depth in reads/molecule.

2 Using TRUmiCount

6

loss

: The estimated gene-specific loss, i.e. the probability of not detecting or filtering a true
UMI.

n.obs

: The number of observations used to estimate “efficiency”, “depth” and “loss”. If
“--combine-strand-umis” is used, this number can be larger than “n.umis”, because in
this case “n.umis” counts UMI pairs while “n.obs” counts strand UMIs.

Writing unfiltered UMIs to a tab-separated file

The option “--output-umis” (together with “--input-bam”) writes the UMIs reported by UMI-Tools
(after merging similar UMIs) and their read counts to a tab-separated file, suitable for being later
read with “--input-umis”. This can be used to avoid the overhead of running UMI-Tools multiple
times if the same input BAM file is processed multiple times with TRUmiCount, e.g. to test different
read count thresholds or initial molecule counts.

Writing final, filtered & strand-combined UMIs to a tab-separated file

The final list of UMIs used when fitting the model and computing corrected molecule counts can
be output with “--output-final-umis”. The format of the generated file depends on whether one
of “--filter-strand-umis” or “--combine-strand-umis” is used. In the former case, the table
contains one line per strand UMI, listing the UMI’s read count in the column “reads” and it’s partner
UMI’s read count in “reads.other”. In the latter case, the table contains one line per UMI pair,
listing the read count of the plus-strand partner UMI (arbitrarily chosed to be the one where read1’s
mapping position is smaller than read2’s) in “reads.plus” and the read count of the minus-strand
partner UMI in “reads.minus”.

Diagnostic Plots

When the “--output-plots” option is used, TRUmiCount generates diagnostic plots as part of the
analysis and writes them to the specified PDF file. The plots produced are
Distribution of reads per UMI. This plot shows the observed library-wide distribution of per-

UMI read counts and the distribution predicted by TRUmiCount’s model of amplification and
sequencing.
Variance of the gene-specific loss estimate. Shows the observed variance of the gene-specific

loss estimates as a function of the number of UMIs per gene (nobs ) and compares it to the
interpolation curve (s + u/nobs ).

2.3 Strand UMIs
Some UMI-based library preparation protocols produce strand UMIs where the two strands of an
initial double-stranded template molecule produce distinct (but related) UMIs. Filtering out UMIs for
which the partner UMI corresponding to the second strand is not detected offers a second possibility
(besides the read count threshold) for filtering our phantom UMIs.
TRUmiCount supports stranded UMIs as produced by the protocol of Shiroguchi et al.9 . With
this protocol, both read1 and read2 carry a separate molecular barcode. UMI pairs belonging to the
same double-stranded template molecule are found by looking for pairs of UMIs whose read1 and
read2 barcodes and mapping positions are swapped (mapping position here refers to the genomic
coordinate of the first mapped base in read direction, i.e. for reverse-mapped reads this differes from
the mapping position stated in the BAM file).
When working with strand UMIs, the initial molecule count should usually be set to 1 (the default
is 2!), i.e. “--molecules 1” should be used.
9 Shiroguchi,

K., Jia, T. Z., Sims, P. A. & Xie, X. S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proceedings of the National Academy of Sciences of the United States
of America 109, 1347-1352 (2012).

2 Using TRUmiCount

7

Filtering out incomplete strand UMI pairs

With the option “--filter-strand-umis”, UMIs are filtered out if their partner UMI cannot be
detected (these UMIs are also not counted as “phantom UMIs” in the diagnostic plots!). This may
happen occasionally even for true UMIs, if their partner UMI happens not to have been sequenced.
The actual loss in this mode is not simply the probability P(C < T ) of an UMI having fewer than T
reads, but is instead 1 − (1 − P(C < T )) · (1 − P(C = 0)). TRUmiCount adjust the loss computation
accordingly, and states the corrected loss also in the diagnostic plots. The plotted model distribution,
however, does not take this adjustment into account, so that the stated loss is no longer simply the
sum of the model probabilities for read counts less than the threshold.
Combining strand UMIs into pairs

With the option “--combine-strand-umis”, pairs of partner UMIs stemming from the two strands
of a single initial template molecules are combined, and the read count threshold is applied to both
partners simultaneously (UMIs without a partner are again not shown as “phantom UMIs” in the
diagnostic plots). The actual loss in this mode is 1 − (1 − P(C < T ))2 , because pairs are dropped
if either of the partner UMIs has a read count below the threshold. TRUmiCount adjust the loss
computation accordingly, and states the corrected loss also in the diagnostic plots. The plotted model
distribution, however, does not take this adjustment into account, so that the stated loss is no longer
simply the sum of the model probabilities for read counts less than T.

2.4 Single-cell Data
TRUmiCount can handle single-cell data where the same library contains data from multiple individual cells, distinguishable by an additional cell barcode that is parts of each read in addition to
the UMI. To compute corrected UMI counts per cell (i.e. for the total number of UMIs within each
individual cell), specify “--group-per cell”. To compute corrected UMI counts per gene within
each cell, specify “--group-per cell,gene”. If the grouping key contains ‘cell’, and “--input-bam”
is used to read UMI directly from a BAM file with the help of UMI-Tools, the option “--per-cell”
is passed to UMI-Tools.
Note that even when the “--per-cell” option is specified, the “group” command of the stock
version of UMI-Tools (at least up to version 0.5.3) does not output the cell information in a format
usable by TRUmiCount. We provide a patched version of UMI-Tools that amends the output by
an additional column containing the cell barcode extracted from each read to make it possible for
TRUmiCount to use that information. If you followed the installation instructions, you probably
have our patched version installed already, if not see Installing TRUmiCount for how to obtain it.
Currently, the grouping key specified with “--group-per” is used to defined both the level on
which the percentage of lost UMIs is estimated, and the level on which UMI counts are output. We
plan to allow two separate grouping keys to be specified, so that the loss estimation can be done
on a higher level (where more data to compute reliable estimates is available), will still outputting
counts for each gene within each individual cell. Currently, if you desire to e.g. estimate the loss on
a per-cell level, and use those estimated to computed corrected per-gene counts, you have to run
TRUmiCount twice, and then use your own script to use the per-cell correction loss estimates to
corrected the counts for all genes within the cell.

3 Parameter Reference

8

--input-bam file: read UMIs from file (uses «umi_tools group»)
--input-umitools-group-out file: read UMIs from file produced by «umi_tools group»
--input-umis file: read UMIs from file (previously produced by --output-umis)
--output-counts file: write bias-corrected per-group counts and models to file
--output-umis file: write UMIs reported by «umi_tools group» to file
--output-final-umis file: write strand-combined and filtered UMIs to file
--output-readdist file: write global reads/UMI distribution (before and after filtering) to file
--output-plots plot: write diagnostic plots in PDF format to plot
--output-groupwise-fits file: write group-wise model details to file
--output-genewise-fits file: obsolete name for --output-groupwise-fits
--umitools umitools: use executable umitools to run «umi_tools group» (Default: ‘umi_tools’)
--umitools-option umitoolsopt: pass umitoolsopt to «umi_tools group» (see «umi_tools group
--help»)
--umi-sep umisep: assume umisep separates read name and UMI (passed to umi_tools) (Default:

‘_’)
--umipair-sep umipairsep: assume umipairsep separates read1 and read2 UMI (see Strand UMIs)

(Default: ‘’)
--paired : assume BAM file contains paired reads (passed to umi_tools) (Default: No)
--mapping-quality mapq: ignored read with mapping quality below mapq (passed to umi_tools)

(Default: 20)
--filter-strand-umis : filtes UMIs where only one strands was observed (Default: No)
--combine-strand-umis : combine UMIs strand pairs (implies --filter-strand-umis) (Default:

No)
--threshold threshold: remove UMIs with fewer than threshold reads
--threshold-quantile Q: use quantile Q of the raw read-count distribution for threshold (Default:

‘0.2’)
--molecules molecules: assume UMIs are initially represented by molecules copies (strands) (De-

fault: 2)
--group-per key1,key2,...: counts UMIs per distinct key(s), can be "cell" and/or "gene", "cell"
implies --umitools-option --per-cell (Default: ‘gene’)
--skip-groupwise-fits : skip group-wise model fitting, stop after plotting the global fit
--include-filter-statistics : add filtered and unfiltered read and UMI counts to count table

(Default: No)
--groupwise-min-umis minumis: use global estimates for groups with fewer than minumis (strand)

UMIs (Default: 5)
--genewise-min-umis minumis: obsolete name for --groupwise-min-umis
--cores cores: spread group-wise model fitting over cores cpus (Default: 1)
--variance-estimator varest: use varest to estimate variances, can be "lsq" or "mle" (Default: ‘lsq’)
--digits digits: number of digits to output (Default: 3)
--plot-hist-bin plotxbin: make read count histogram bins plotxbin wide
--plot-hist-xmax plotxmax: limit read count histogram plot to at most plotxmax reads per UMI

3 Parameter Reference

--plot-skip-phantoms : do not show phantom UMIs in histogram plot (Default: No)
--plot-var-bins plotvarbins: plot plotvarbins separate emprirical variances (Default: 10)
--plot-var-logy : use log scale for the variance (y) axis (Default: No)
--verbose : enable verbose output

9

4 Examples

10

The example data can be downloaded from https://cibiv.github.io/trumicount/. Before
TRUmiCount can process these BAM files, they need to be indexed by running
samtools index sg_100g.bam
samtools index kv_1000g.bam
samtools index 10xn9k_10c.bam

4.1 Single-End Data
The file kv_1000g.bam contains a reduced (restricted to the first 1000 genes, and subsamples to 50%)
version of data published by Kivioja et al.10 .
trumicount --input-bam kv_1000g.bam --umi-sep ':' \
--molecules 2 --threshold 2 --genewise-min-umis 3 \
--output-plots kv_1000g.pdf --plot-hist-bin 1 \
--plot-var-bins 20 --plot-var-logy \
--output-counts kv_1000g.tab \
--cores 4

This command uses UMI-Tools to read (error-corrected) UMIs and their read counts from

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Putative true UMIs
Putative phantom UMIs
Predicted True UMIs
Predicted Loss

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Empirical
Prediction s + u/n

0.020

Efficiency = 56%
Depth = 5.4 reads/UMI

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0.005

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0.001

Th.=2

Total Variance of Loss Estimates

Loss=8%

#UMIs with
x reads

1.9e+04

kv_1000g.bam (--input-bam), removes UMIs with fewer than 2 reads (--threshold), compute
gene-specific loss estimates for genes with at least 3 surviving UMIs (--genewise-min-umis)
assuming two initial copies of each UMI (--molecules), writes the bias-corrected counts to
kv_1000g.tab (--output-counts), and outputs diagnostic plots to kv_1000g.pdf (--output-plots).
The gene-specific parameter estimation is spread over 4 cores (--cores). For the plots, the bin size
for the read-count distribution plot is set to 1 (--plot-hist-bin), the number of bins for which the
empirical variance is plotted is increased to 15 (--plot-var-bins), and the y-axis of the variance
plot is log-scaled (--plot-var-logy).

28
1

x (Reads)

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10

50

100

500

# UMIs / Gene

kv_1000g.pdf. kv_1000g.bam processed in single-end mode.

4.2 Paired-End Data with strand UMIs
The file sg_100g.bam contains a reduced (restricted to the first 100 genes, and subsamples to 25%)
version of data published by Shiroguchi et al.11 which uses strand UMIs (see Strand UMIs for details).
Filtering out incomplete strand UMI pairs
trumicount --input-bam sg_100g.bam --umi-sep ':' --umipair-sep '-'\
--paired --filter-strand-umis --molecules 1 --threshold 24 \
--output-plots sg_100g.pdf --plot-hist-bin 3 \
--output-counts sg_100g.tab

This command uses UMI-Tools to read (error-corrected) UMIs and their read counts from
sg_100g.bam (--input-bam) containing paired-end reads (--paired), removes UMIs whose partner
corresponding to the initial molecule’s other strand was not detected (--filter-strand-umis,
--umipair-sep) or who have fewer than 24 reads (--threshold), compute gene-specific loss
estimates assuming a single initial copy of each UMI (--molecules), writes the bias-corrected counts
10 Kivioja,

T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature Methods 9, 72-74
(2011)
11 Shiroguchi, K., Jia, T. Z., Sims, P. A. & Xie, X. S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proceedings of the National Academy of Sciences of the United States
of America 109, 1347-1352 (2012).

4 Examples

11

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Putative true UMIs
Putative phantom UMIs
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Predicted Loss

Empirical
Prediction s + u/n

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Efficiency = 82%
Depth = 72 reads/UMI

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#UMIs with
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to sg_100g.tab (--output-counts), and outputs diagnostic plots to sg_100g.pdf (--output-plots).
For the plots, the bin size for the read-count distribution plot is set to 3 (--plot-hist-bin).

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# Strand UMIs / Gene

sg_100g.pdf. sg_100g.bam processed in filter strand UMIs mode.

Combining strand UMIs into pairs
trumicount --input-bam sg_100g.bam --umi-sep ':' --umipair-sep '-' \
--paired --combine-strand-umis --molecules 1 --threshold 24 \
--output-plots sg_100g_comb.pdf --plot-hist-bin 3 \
--output-counts sg_100g_comb.tab

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0−2
6−8
12−14
18−20
24−26
30−32
36−38
42−44
48−50
54−56
60−62
66−68
72−74
78−80
84−86
90−92
96−98
102−104
108−110
114−116
120−122
126−128
132−134
138−140
144−146

0

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●

x (Reads)

Putative true UMIs
Putative phantom UMIs
Predicted True UMIs
Predicted Loss

0.030

Empirical
Prediction s + u/n

0.020

#UMIs with
x reads

●
●

●

0.010

● ● ● ●

Total Variance of Loss Estimates

Efficiency = 82%
Depth = 73 reads/UMI
●
●

0.000

Th.=24

Loss=5%

1.5e+02

This command uses UMI-Tools to read (error-corrected) UMIs and their read counts from
sg_100g.bam (--input-bam) containing paired-end reads (--paired), combines strand UMIs into
UMI pairs (--combine-strand-umis, --umipair-sep), filters UMI pairs with fewer than 24 reads
of either strand UMI(--threshold), computes gene-specific loss estimates assuming a single
initial copy of each strand UMI (--molecules), writes the bias-corrected counts to sg_100g.tab
(--output-counts), and outputs diagnostic plots to sg_100g.pdf (--output-plots). For the plots,
the bin size for the read-count distribution plot is set to 3 (--plot-hist-bin).

1

2

5

10

20

50

100

200

# Strand UMIs / Gene

sg_100g_comb.pdf. sg_100g.bam processed in combine strand UMIs mode. Plots show individual

strand UMIs, not UMI pairs. Phantoms now appear even beyond the threshold, because strand
UMIs are also considered phantoms if their partner UMI has a read count below the threshold.

4.3 Single-cell Data
The file 10xn9k_10c.bam contains a reduced (restricted to 10 individual cells) version of the 9k Brain
Cells from an E18 Mouse12 public dataset provided by 10x Genomics.
trumicount --input-bam 10xn9k_10c.bam \
--molecules 1 --threshold 2 --group-per cell,gene \
--umitools-option --per-gene --umitools-option --gene-tag=XF \
--output-plots 10xn9k_10c.pdf --output-counts 10xn9k_10c.tab

This command uses UMI-Tools to read (error-corrected) UMIs and their read counts from
10xn9k_10c.bam (--input-bam), removes UMIs with fewer than 2 reads (--threshold), compute
gene-specific loss estimates for each combination of gene and cell (--group-per) assuming a
single initial copy of each UMI (--molecules), writes the bias-corrected counts to 10xn9k_10c.tab
(--output-counts), and outputs diagnostic plots to 10xn9k_10c.pdf (--output-plots).

UMIs are group disregarding the mapping position, i.e. identical UMI sequences found
within the same gene are assumed to belong to the same original molecule (--umitools-option
--per-gene). The gene a particular read belongs to is read from the read’s XF tag in the BAM file
(--umitools-option --gene-tag=XF).

●

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0

●

0

1

2

3

4

5

6

7

●

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8

9

●

●

●

●

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●

10 11 12 13 14 15

Empirical
Prediction s + u/n

0.08

Putative true UMIs
Putative phantom UMIs
Predicted True UMIs
Predicted Loss

0.06

●

0.04

Efficiency = 53%
Depth = 2.4 reads/UMI

●

0.02

Th.=2

Total Variance of Loss Estimates

12

0.00

Loss=40%

#UMIs with
x reads

9e+03

4 Examples

1

x (Reads)

2

5

10

20

# UMIs / Gene

10xn9k_10c.pdf. 10xn9k_10c.bam processed with “--group-per cell,gene”.

5 License

13

TRUmiCount is free software: you can redistribute it and/or modify it under the terms of the
GNU Affero General Public License as published by the Free Software Foundation, either version 3
of the License, or (at your option) any later version.
TRUmiCount is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License along with TRUmiCount. If not, see http://www.gnu.org/licenses/.



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