Bo Li (bli28 at mgh dot harvard dot edu)

Table of Contents


RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides an user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. For visualization, It can generate BAM and Wiggle files in both transcript-coordinate and genomic-coordinate. Genomic-coordinate files can be visualized by both UCSC Genome browser and Broad Institute’s Integrative Genomics Viewer (IGV). Transcript-coordinate files can be visualized by IGV. RSEM also has its own scripts to generate transcript read depth plots in pdf format. The unique feature of RSEM is, the read depth plots can be stacked, with read depth contributed to unique reads shown in black and contributed to multi-reads shown in red. In addition, models learned from data can also be visualized. Last but not least, RSEM contains a simulator.

Compilation & Installation

To compile RSEM, simply run


For Cygwin users, run

make cygwin=true

To compile EBSeq, which is included in the RSEM package, run

make ebseq

To install RSEM, simply put the RSEM directory in your environment’s PATH variable. Alternatively, run

make install

By default, RSEM executables are installed to /usr/local/bin. You can change the installation location by setting DESTDIR and/or prefix variables. The RSEM executables will be installed to ${DESTDIR}${prefix}/bin. The default values of DESTDIR and prefix are DESTDIR= and prefix=/usr/local. For example,

make install DESTDIR=/home/my_name prefix=/software

will install RSEM executables to /home/my_name/software/bin.

Note that make install does not install EBSeq related scripts, such as rsem-generate-ngvector, rsem-run-ebseq, and rsem-control-fdr. But rsem-generate-data-matrix, which generates count matrix for differential expression analysis, is installed.


C++, Perl and R are required to be installed.

To use the --gff3 option of rsem-prepare-reference, Python is also required to be installed.

To take advantage of RSEM’s built-in support for the Bowtie/Bowtie 2/STAR/HISAT2 alignment program, you must have Bowtie/Bowtie2/STAR/HISAT2 installed.


I. Preparing Reference Sequences

RSEM can extract reference transcripts from a genome if you provide it with gene annotations in a GTF/GFF3 file. Alternatively, you can provide RSEM with transcript sequences directly.

Please note that GTF files generated from the UCSC Table Browser do not contain isoform-gene relationship information. However, if you use the UCSC Genes annotation track, this information can be recovered by downloading the knownIsoforms.txt file for the appropriate genome.

To prepare the reference sequences, you should run the rsem-prepare-reference program. Run

rsem-prepare-reference --help

to get usage information or visit the rsem-prepare-reference documentation page.

Build RSEM references using RefSeq, Ensembl, or GENCODE annotations

RefSeq and Ensembl are two frequently used annotations. For human and mouse, GENCODE annotaions are also available. In this section, we show how to build RSEM references using these annotations. Note that it is important to pair the genome with the annotation file for each annotation source. In addition, we recommend users to use the primary assemblies of genomes. Without loss of generality, we use human genome as an example and in addition build Bowtie indices.

For RefSeq, the genome and annotation file in GFF3 format can be found at RefSeq genomes FTP:

For example, the human genome and GFF3 file locate at the subdirectory vertebrate_mammalian/Homo_sapiens/all_assembly_versions/GCF_000001405.31_GRCh38.p5. GCF_000001405.31_GRCh38.p5 is the latest annotation version when this section was written.

Download and decompress the genome and annotation files to your working directory:

GCF_000001405.31_GRCh38.p5_genomic.fna contains all top level sequences, including patches and haplotypes. To obtain the primary assembly, run the following RSEM python script:

rsem-refseq-extract-primary-assembly GCF_000001405.31_GRCh38.p5_genomic.fna GCF_000001405.31_GRCh38.p5_genomic.primary_assembly.fna

Then type the following command to build RSEM references:

rsem-prepare-reference --gff3 GCF_000001405.31_GRCh38.p5_genomic.gff \
               --trusted-sources BestRefSeq,Curated\ Genomic \
               --bowtie \
               GCF_000001405.31_GRCh38.p5_genomic.primary_assembly.fna \

In the above command, --trusted-sources tells RSEM to only extract transcripts from RefSeq sources like BestRefSeq or Curated Genomic. By default, RSEM trust all sources. There is also an --gff3-RNA-patterns option and its default is mRNA. Setting --gff3-RNA-patterns mRNA,rRNA will allow RSEM to extract all mRNAs and rRNAs from the genome. Visit here for more details.

Because the gene and transcript IDs (e.g. gene1000, rna28655) extracted from RefSeq GFF3 files are hard to understand, it is recommended to turn on the --append-names option in rsem-calculate-expression for better interpretation of quantification results.

For Ensembl, the genome and annotation files can be found at Ensembl FTP.

Download and decompress the human genome and GTF files:

Then use the following command to build RSEM references:

rsem-prepare-reference --gtf Homo_sapiens.GRCh38.83.gtf \
               --bowtie \
               Homo_sapiens.GRCh38.dna.primary_assembly.fa \

If you want to use GFF3 file instead, which is unnecessary and not recommended, you should add option --gff3-RNA-patterns transcript because mRNA is replaced by transcript in Ensembl GFF3 files.

GENCODE only provides human and mouse annotations. The genome and annotation files can be found from GENCODE website.

Download and decompress the human genome and GTF files:

Then type the following command:

rsem-prepare-reference --gtf gencode.v24.annotation.gtf \
               --bowtie \
               GRCh38.primary_assembly.genome.fa \

Similar to Ensembl annotation, if you want to use GFF3 files (not recommended), add option --gff3-RNA-patterns transcript.

Build RSEM references for untypical organisms

For untypical organisms, such as viruses, you may only have a GFF3 file that containing only genes but not any transcripts. You need to turn on --gff3-genes-as-transcripts so that RSEM will make each gene as a unique transcript.

Here is an example command:

rsem-prepare-reference --gff3 virus.gff \
               --gff3-genes-as-transcripts \
               --bowtie \
               virus.genome.fa \

II. Calculating Expression Values

To calculate expression values, you should run the rsem-calculate-expression program. Run

rsem-calculate-expression --help

to get usage information or visit the rsem-calculate-expression documentation page.

Calculating expression values from single-end data

For single-end models, users have the option of providing a fragment length distribution via the --fragment-length-mean and --fragment-length-sd options. The specification of an accurate fragment length distribution is important for the accuracy of expression level estimates from single-end data. If the fragment length mean and sd are not provided, RSEM will not take a fragment length distribution into consideration.

Using an alternative aligner

By default, RSEM automates the alignment of reads to reference transcripts using the Bowtie aligner. Turn on --bowtie2 for rsem-prepare-reference and rsem-calculate-expression will allow RSEM to use the Bowtie 2 alignment program instead. Please note that indel alignments, local alignments and discordant alignments are disallowed when RSEM uses Bowtie 2 since RSEM currently cannot handle them. See the description of --bowtie2 option in rsem-calculate-expression for more details. Similarly, turn on --star will allow RSEM to use the STAR aligner. Turn on --hisat2-hca will allow RSEM to use the HISAT2 aligner according to Human Cell Atals SMART-Seq2 pipeline. To use an alternative alignment program, align the input reads against the file reference_name.idx.fa generated by rsem-prepare-reference, and format the alignment output in SAM/BAM/CRAM format. Then, instead of providing reads to rsem-calculate-expression, specify the --alignments option and provide the SAM/BAM/CRAM file as an argument.

RSEM requires the alignments of a read to be adjacent. For paired-end reads, RSEM also requires the two mates of any alignment be adjacent. To check if your SAM/BAM/CRAM file satisfy the requirements, run

rsem-sam-validator <input.sam/input.bam/input.cram>

If your file does not satisfy the requirements, you can use convert-sam-for-rsem to convert it into a BAM file which RSEM can process. Run

convert-sam-for-rsem --help

to get usage information or visit the convert-sam-for-rsem documentation page.

Note that RSEM does ** not ** support gapped alignments. So make sure that your aligner does not produce alignments with intersions/deletions. In addition, you should make sure that you use reference_name.idx.fa, which is generated by RSEM, to build your aligner’s indices.

III. Visualization

RSEM includes a copy of SAMtools. When --no-bam-output is not specified and --sort-bam-by-coordinate is specified, RSEM will produce these three files:sample_name.transcript.bam, the unsorted BAM file, sample_name.transcript.sorted.bam and sample_name.transcript.sorted.bam.bai the sorted BAM file and indices generated by the SAMtools included. All three files are in transcript coordinates. When users in addition specify the --output-genome-bam option, RSEM will produce three more files: sample_name.genome.bam, the unsorted BAM file, sample_name.genome.sorted.bam and sample_name.genome.sorted.bam.bai the sorted BAM file and indices. All these files are in genomic coordinates.

a) Converting transcript BAM file into genome BAM file

Normally, RSEM will do this for you via --output-genome-bam option of rsem-calculate-expression. However, if you have run rsem-prepare-reference and use reference_name.idx.fa to build indices for your aligner, you can use rsem-tbam2gbam to convert your transcript coordinate BAM alignments file into a genomic coordinate BAM alignments file without the need to run the whole RSEM pipeline.


rsem-tbam2gbam reference_name unsorted_transcript_bam_input genome_bam_output

reference_name : The name of reference built by rsem-prepare-reference
unsorted_transcript_bam_input : This file should satisfy: 1) the alignments of a same read are grouped together, 2) for any paired-end alignment, the two mates should be adjacent to each other, 3) this file should not be sorted by samtools genome_bam_output : The output genomic coordinate BAM file’s name

b) Generating a Wiggle file

A wiggle plot representing the expected number of reads overlapping each position in the genome/transcript set can be generated from the sorted genome/transcript BAM file output. To generate the wiggle plot, run the rsem-bam2wig program on the sample_name.genome.sorted.bam/sample_name.transcript.sorted.bam file.


rsem-bam2wig sorted_bam_input wig_output wiggle_name [--no-fractional-weight]

sorted_bam_input : Input BAM format file, must be sorted
wig_output : Output wiggle file’s name, e.g. output.wig
wiggle_name : The name of this wiggle plot
–no-fractional-weight : If this is set, RSEM will not look for “ZW” tag and each alignment appeared in the BAM file has weight 1. Set this if your BAM file is not generated by RSEM. Please note that this option must be at the end of the command line

c) Loading a BAM and/or Wiggle file into the UCSC Genome Browser or Integrative Genomics Viewer(IGV)

For UCSC genome browser, please refer to the UCSC custom track help page.

For integrative genomics viewer, please refer to the IGV home page. Note: Although IGV can generate read depth plot from the BAM file given, it cannot recognize “ZW” tag RSEM puts. Therefore IGV counts each alignment as weight 1 instead of the expected weight for the plot it generates. So we recommend to use the wiggle file generated by RSEM for read depth visualization.

Here are some guidance for visualizing transcript coordinate files using IGV:

  1. Import the transcript sequences as a genome

Select File -> Import Genome, then fill in ID, Name and Fasta file. Fasta file should be reference_name.idx.fa. After that, click Save button. Suppose ID is filled as reference_name, a file called reference_name.genome will be generated. Next time, we can use: File -> Load Genome, then select reference_name.genome.

  1. Load visualization files

Select File -> Load from File, then choose one transcript coordinate visualization file generated by RSEM. IGV might require you to convert wiggle file to tdf file. You should use igvtools to perform this task. One way to perform the conversion is to use the following command:

igvtools tile reference_name.transcript.wig reference_name.transcript.tdf reference_name.genome   

d) Generating Transcript Wiggle Plots

To generate transcript wiggle plots, you should run the rsem-plot-transcript-wiggles program. Run

rsem-plot-transcript-wiggles --help

to get usage information or visit the rsem-plot-transcript-wiggles documentation page.

e) Visualize the model learned by RSEM

RSEM provides an R script, rsem-plot-model, for visulazing the model learned.


rsem-plot-model sample_name output_plot_file

sample_name: the name of the sample analyzed
output_plot_file: the file name for plots generated from the model. It is a pdf file

The plots generated depends on read type and user configuration. It may include fragment length distribution, mate length distribution, read start position distribution (RSPD), quality score vs observed quality given a reference base, position vs percentage of sequencing error given a reference base and alignment statistics.

fragment length distribution and mate length distribution: x-axis is fragment/mate length, y axis is the probability of generating a fragment/mate with the associated length

RSPD: Read Start Position Distribution. x-axis is bin number, y-axis is the probability of each bin. RSPD can be used as an indicator of 3’ bias

Quality score vs. observed quality given a reference base: x-axis is Phred quality scores associated with data, y-axis is the “observed quality”, Phred quality scores learned by RSEM from the data. Q = -10log_10(P), where Q is Phred quality score and P is the probability of sequencing error for a particular base

Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error

Alignment statistics: It includes a histogram and a pie chart. For the histogram, x-axis shows the number of isoform-level alignments a read has and y-axis provides the number of reads with that many alignments. The inf in x-axis means number of reads filtered due to too many alignments. For the pie chart, four categories of reads — unalignable, unique, isoform-levelmulti-mapping, filtered – are plotted and their percentages are noted. In both the histogram and the piechart, numbers belong to unalignable, unique, multi-mapping, and filtered are colored as green, blue, gray and red.


Suppose we download the mouse genome from UCSC Genome Browser. We do not add poly(A) tails and use /ref/mouse_0 as the reference name. We have a FASTQ-formatted file, mmliver.fq, containing single-end reads from one sample, which we call mmliver_single_quals. We want to estimate expression values by using the single-end model with a fragment length distribution. We know that the fragment length distribution is approximated by a normal distribution with a mean of 150 and a standard deviation of 35. We wish to generate 95% credibility intervals in addition to maximum likelihood estimates. RSEM will be allowed 1G of memory for the credibility interval calculation. We will visualize the probabilistic read mappings generated by RSEM on UCSC genome browser. We will generate a list of transcript wiggle plots (output.pdf) for the genes provided in gene_ids.txt. We will visualize the models learned in mmliver_single_quals.models.pdf

The commands for this scenario are as follows:

rsem-prepare-reference --gtf mm9.gtf --transcript-to-gene-map knownIsoforms.txt --bowtie --bowtie-path /sw/bowtie /data/mm9 /ref/mouse_0
rsem-calculate-expression --bowtie-path /sw/bowtie --phred64-quals --fragment-length-mean 150.0 --fragment-length-sd 35.0 -p 8 --output-genome-bam --calc-ci --ci-memory 1024 /data/mmliver.fq /ref/mouse_0 mmliver_single_quals
rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf 
rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf


RSEM provides users the rsem-simulate-reads program to simulate RNA-Seq data based on parameters learned from real data sets. Run


to get usage information or read the following subsections.


rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]

reference_name: The name of RSEM references, which should be already generated by rsem-prepare-reference

estimated_model_file: This file describes how the RNA-Seq reads will be sequenced given the expression levels. It determines what kind of reads will be simulated (single-end/paired-end, w/o quality score) and includes parameters for fragment length distribution, read start position distribution, sequencing error models, etc. Normally, this file should be learned from real data using rsem-calculate-expression. The file can be found under the sample_name.stat folder with the name of sample_name.model. model_file_description.txt provides the format and meanings of this file.

estimated_isoform_results: This file contains expression levels for all isoforms recorded in the reference. It can be learned using rsem-calculate-expression from real data. The corresponding file users want to use is sample_name.isoforms.results. If simulating from user-designed expression profile is desired, start from a learned sample_name.isoforms.results file and only modify the TPM column. The simulator only reads the TPM column. But keeping the file format the same is required. If the RSEM references built are aware of allele-specific transcripts, sample_name.alleles.results should be used instead.

theta0: This parameter determines the fraction of reads that are coming from background “noise” (instead of from a transcript). It can also be estimated using rsem-calculate-expression from real data. Users can find it as the first value of the third line of the file sample_name.stat/sample_name.theta.

N: The total number of reads to be simulated. If rsem-calculate-expression is executed on a real data set, the total number of reads can be found as the 4th number of the first line of the file sample_name.stat/sample_name.cnt.

output_name: Prefix for all output files.

–seed seed: Set seed for the random number generator used in simulation. The seed should be a 32-bit unsigned integer.

-q: Set it will stop outputting intermediate information.


output_name.sim.isoforms.results, output_name.sim.genes.results: Expression levels estimated by counting where each simulated read comes from. output_name.sim.alleles.results: Allele-specific expression levels estimated by counting where each simulated read comes from.

output_name.fa if single-end without quality score;
output_name.fq if single-end with quality score;
output_name_1.fa & output_name_2.fa if paired-end without quality score;
output_name_1.fq & output_name_2.fq if paired-end with quality score.

Format of the header line: Each simulated read’s header line encodes where it comes from. The header line has the format:


{>/@}: Either ‘>’ or ‘@’ must appear. ‘>’ appears if FASTA files are generated and ‘@’ appears if FASTQ files are generated

rid: Simulated read’s index, numbered from 0

dir: The direction of the simulated read. 0 refers to forward strand (‘+’) and 1 refers to reverse strand (‘-’)

sid: Represent which transcript this read is simulated from. It ranges between 0 and M, where M is the total number of transcripts. If sid=0, the read is simulated from the background noise. Otherwise, the read is simulated from a transcript with index sid. Transcript sid’s transcript name can be found in the transcript_id column of the sample_name.isoforms.results file (at line sid + 1, line 1 is for column names)

pos: The start position of the simulated read in strand dir of transcript sid. It is numbered from 0

insertL: Only appear for paired-end reads. It gives the insert length of the simulated read.


Suppose we want to simulate 50 millon single-end reads with quality scores and use the parameters learned from Example. In addition, we set theta0 as 0.2 and output_name as simulated_reads. The command is:

rsem-simulate-reads /ref/mouse_0 mmliver_single_quals.stat/mmliver_single_quals.model mmliver_single_quals.isoforms.results 0.2 50000000 simulated_reads

Generate Transcript-to-Gene-Map from Trinity Output

For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.


extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file

trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
map_file: transcript-to-gene-map file’s name.

Differential Expression Analysis

Popular differential expression (DE) analysis tools such as edgeR and DESeq do not take variance due to read mapping uncertainty into consideration. Because read mapping ambiguity is prevalent among isoforms and de novo assembled transcripts, these tools are not ideal for DE detection in such conditions.

EBSeq, an empirical Bayesian DE analysis tool developed in UW-Madison, can take variance due to read mapping ambiguity into consideration by grouping isoforms with parent gene’s number of isoforms. In addition, it is more robust to outliers. For more information about EBSeq (including the paper describing their method), please visit EBSeq’s website.

RSEM includes EBSeq in its folder named EBSeq. To use it, first type

make ebseq

to compile the EBSeq related codes.

EBSeq requires gene-isoform relationship for its isoform DE detection. However, for de novo assembled transcriptome, it is hard to obtain an accurate gene-isoform relationship. Instead, RSEM provides a script rsem-generate-ngvector, which clusters transcripts based on measures directly relating to read mappaing ambiguity. First, it calculates the ‘unmappability’ of each transcript. The ‘unmappability’ of a transcript is the ratio between the number of k mers with at least one perfect match to other transcripts and the total number of k mers of this transcript, where k is a parameter. Then, Ng vector is generated by applying Kmeans algorithm to the ‘unmappability’ values with number of clusters set as 3. This program will make sure the mean ‘unmappability’ scores for clusters are in ascending order. All transcripts whose lengths are less than k are assigned to cluster 3. Run

rsem-generate-ngvector --help

to get usage information or visit the rsem-generate-ngvector documentation page.

If your reference is a de novo assembled transcript set, you should run rsem-generate-ngvector first. Then load the resulting output_name.ngvec into R. For example, you can use

NgVec <- scan(file="output_name.ngvec", what=0, sep="\n")

. After that, set “NgVector = NgVec” for your differential expression test (either EBTest or EBMultiTest).

For users’ convenience, RSEM also provides a script rsem-generate-data-matrix to extract input matrix from expression results:

rsem-generate-data-matrix sampleA.[genes/isoforms].results sampleB.[genes/isoforms].results ... > output_name.counts.matrix

The results files are required to be either all gene level results or all isoform level results. You can load the matrix into R by

IsoMat <- data.matrix(read.table(file="output_name.counts.matrix"))

before running either EBTest or EBMultiTest.

Lastly, RSEM provides two scripts, rsem-run-ebseq and rsem-control-fdr, to help users find differential expressed genes/transcripts. First, rsem-run-ebseq calls EBSeq to calculate related statistics for all genes/transcripts. Run

rsem-run-ebseq --help

to get usage information or visit the rsem-run-ebseq documentation page. Second, rsem-control-fdr takes rsem-run-ebseq ’s result and reports called differentially expressed genes/transcripts by controlling the false discovery rate. Run

rsem-control-fdr --help

to get usage information or visit the rsem-control-fdr documentation page. These two scripts can perform DE analysis on either 2 conditions or multiple conditions.

Please note that rsem-run-ebseq and rsem-control-fdr use EBSeq’s default parameters. For advanced use of EBSeq or information about how EBSeq works, please refer to EBSeq’s manual.

Questions related to EBSeq should be sent to Ning Leng.

Prior-Enhanced RSEM (pRSEM)

I. Overview

Prior-enhanced RSEM (pRSEM) uses complementary information (e.g. ChIP-seq data) to allocate RNA-seq multi-mapping fragments. We included pRSEM code in the subfolder pRSEM/ as well as in RSEM’s scripts rsem-prepare-reference and rsem-calculate-expression.

II. Demo

To get a quick idea on how to use pRSEM, you can try this demo. It provides a single script, named, which allows you to run all pRSEM’s functions. It also contains detailed descriptions of pRSEM’s workflow, input and output files.

III. Installation

To compile pRSEM, type

make pRSEM

Note that you need to first compile RSEM before compiling pRSEM. Currently, pRSEM has only been tested on Linux.

IV. Example

To run pRSEM on the RSEM example above, you need to provide: - ChIP-seq sequencing file(s) in FASTQ format or a ChIP-seq peak file in BED format. They will be used by pRSEM to obtain complementatry information for allocating RNA-seq multi-mapping fragments. - a genome mappability file in bigWig format to let pRSEM build a training set of isoforms to learn prior. Mappability can be obtained from UCSC’s ENCODE composite track for human hg19 and mouse mm9. For other genomes, you can generate the mappability file by following [this tutorial] (

Assuming you would like to use RNA Pol II’s ChIP-seq sequencing files /data/mmliver_PolIIRep1.fq.gz and /data/mmliver_PolIIRep2.fq.gz, with ChIP-seq control /data/mmliver_ChIPseqCtrl.fq.gz. Also, assuming the mappability file for mouse genome is /data/mm9.bigWig and you prefer to use STAR located at /sw/STAR to align RNA-seq fragments and use Bowtie to align ChIP-seq reads. Then, you can use the following commands to run pRSEM:

rsem-prepare-reference --gtf mm9.gtf \
                       --star \
                       --star-path /sw/STAR \
                       -p 8 \
                       --prep-pRSEM \
                       --bowtie-path /sw/bowtie \
                       --mappability-bigwig-file /data/mm9.bigWig \
                       /data/mm9 \

rsem-calculate-expression --star \
                          --star-path /sw/STAR \
                          --calc-pme \
                          --run-pRSEM \
                          --chipseq-target-read-files /data/mmliver_PolIIRep1.fq.gz,/data/mmliver_PolIIRep2.fq.gz \
                          --chipseq-control-read-files /data/mmliver_ChIPseqCtrl.fq.gz \
                          --bowtie-path /sw/bowtie \
                          -p 8 \
                          /data/mmliver.fq \
                          /ref/mouse_0 \

To find out more about pRSEM options and examples, you can use the commands:

rsem-prepare-reference --help


rsem-calculate-expression --help

V. System Requirements

VI. Required External Packages

All the following packages will be automatically installed when compiling pRSEM. - data.table 1.9.6: an extension of R’s data.frame, heavily used by pRSEM. - GenomicRanges 1.24.3: efficient representing and manipulating genomic intervals, heavily used by pRSEM. - ShortRead 1.30.0: guessing the encoding of ChIP-seq FASTQ file’s quality score. - caTools 1.17.1: used for SPP Peak Caller. - SPP Peak Caller: ChIP-seq peak caller. Source code was slightly modified in terms of included headers in order to be compiled under R v3.3.1. - IDR: calculating Irreproducible Discovery Rate to call peaks from multiple ChIP-seq replicates.


Bo Li and Colin Dewey designed the RSEM algorithm. Bo Li implemented the RSEM software. Peng Liu contributed the STAR aligner options and prior-enhanced RSEM (pRSEM).


RSEM uses the Boost C++ and SAMtools libraries. RSEM includes EBSeq for differential expression analysis.

We thank earonesty, Dr. Samuel Arvidsson, John Marshall, and Michael R. Crusoe for contributing patches.

We thank Han Lin, j.miller, Joël Fillon, Dr. Samuel G. Younkin, Malcolm Cook, Christina Wells, Uroš Šipetić, outpaddling, rekado, and Josh Richer for suggesting possible fixes.

Note that bam_sort.c of SAMtools is slightly modified so that samtools sort -n will not move the two mates of paired-end alignments apart. In addition, we turn on the --without-curses option when configuring SAMtools and thus SAMtools’ curses-based tview subcommand is not built.


RSEM is licensed under the GNU General Public License v3.