* [Usage](#usage)
* [Example](#example)
* [Simulation](#simulation)
+* [Generate Transcript-to-Gene-Map from Trinity Output](#gen_trinity)
+* [Differential Expression Analysis](#de)
+* [Authors](#authors)
* [Acknowledgements](#acknowledgements)
* [License](#license)
## <a name="introduction"></a> Introduction
RSEM is a software package for estimating gene and isoform expression
-levels from RNA-Seq data. The new RSEM package (rsem-1.x) 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. It can also generate
-genomic-coordinate BAM files and UCSC wiggle files for visualization. In
-addition, it provides posterior mean and 95% credibility interval
-estimates for expression levels.
+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.
## <a name="compilation"></a> Compilation & Installation
make
+For cygwin users, please uncomment the 3rd and 7th line in
+'sam/Makefile' before you run 'make'.
+
+To compile EBSeq, which is included in the RSEM package, run
+
+ make ebseq
+
To install, simply put the rsem directory in your environment's PATH
variable.
### Prerequisites
+C++, Perl and R are required to be installed.
+
To take advantage of RSEM's built-in support for the Bowtie alignment
program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
#### 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 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
By default, RSEM automates the alignment of reads to reference
transcripts using the Bowtie alignment program. 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 or BAM format. Then, instead of providing
-reads to rsem-calculate-expression, specify the --sam or --bam option
-and provide the SAM or BAM file as an argument. When using an
-alternative aligner, you may also want to provide the --no-bowtie option
-to rsem-prepare-reference so that the Bowtie indices are not built.
+'reference_name.idx.fa' generated by 'rsem-prepare-reference', and
+format the alignment output in SAM or BAM format. Then, instead of
+providing reads to 'rsem-calculate-expression', specify the '--sam' or
+'--bam' option and provide the SAM or BAM file as an argument. When
+using an alternative aligner, you may also want to provide the
+'--no-bowtie' option to 'rsem-prepare-reference' so that the Bowtie
+indices are not built.
+
+RSEM requires all alignments of the same read group together. For
+vpaired-end reads, RSEM also requires the two mates of any alignment be
+adjacent. To check if your SAM/BAM file satisfy the requirements,
+please run
+
+ rsem-sam-validator <input.sam/input.bam>
+
+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. Please run
+
+ convert-sam-for-rsem --help
+
+to get usage information or visit the [convert-sam-for-rsem
+documentation
+page](http://deweylab.biostat.wisc.edu/rsem/convert-sam-for-rsem.html).
+
+However, please note that RSEM does ** not ** support gapped
+alignments. So make sure that your aligner does not produce alignments
+with intersions/deletions. Also, please make sure that you use
+'reference_name.idx.fa' , which is generated by RSEM, to build your
+aligner's indices.
### III. Visualization
-RSEM contains a version of samtools in the 'sam' subdirectory. When
-users specify the --out-bam option RSEM will produce three files:
-'sample_name.bam', the unsorted BAM file, 'sample_name.sorted.bam' and
-'sample_name.sorted.bam.bai' the sorted BAM file and indices generated
-by the samtools included.
+RSEM contains a version of samtools in the 'sam' subdirectory. RSEM
+will always produce 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 specify the --output-genome-bam
+option RSEM will produce three 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
+generated by the samtools included. 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. Please note that 'rsem-prepare-reference' will convert all
+'N' into 'G' by default for 'reference_name.idx.fa'. If you do not
+want this to happen, please use '--no-ntog' option.
-#### a) Generating a UCSC Wiggle file
+Usage:
+
+ 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 can be generated from the sorted BAM file
-output. To generate the wiggle plot, run the 'rsem-bam2wig' program on
-the 'sample_name.sorted.bam' file.
+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.
Usage:
- rsem-bam2wig bam_input wig_output wiggle_name
+ 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](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
+
+For integrative genomics viewer, please refer to the [IGV home page](http://www.broadinstitute.org/software/igv/home). 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.
-bam_input: sorted bam file
-wig_output: output file name, e.g. output.wig
-wiggle_name: the name the user wants to use for this wiggle plot
+Here are some guidance for visualizing transcript coordinate files using IGV:
-#### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser
+1) Import the transcript sequences as a genome
-Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
+Select File -> Import Genome, then fill in ID, Name and Fasta file. Fasta file should be 'reference_name.transcripts.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'.
-#### c) Visualize the model learned by RSEM
+2) 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](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
+
+#### e) Visualize the model learned by RSEM
RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
Usage:
- rsem-plot-model modelF outF
+ rsem-plot-model sample_name output_plot_file
-modelF: the sample_name.model file generated by RSEM
-outF: the file name for plots generated from the model. It is a pdf 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.
+error given a reference base and histogram of reads with different
+number of alignments.
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
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
+
+Histogram of reads with different number of alignments: x-axis is the number of alignments a read has and y-axis is the number of such reads. The inf in x-axis means number of reads filtered due to too many alignments
## <a name="example"></a> Example
-Suppose we download the mouse genome from UCSC Genome Browser. We will
-use a reference_name of 'mm9'. 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.
+Suppose we download the mouse genome from UCSC Genome Browser. We
+will use a reference_name of 'mm9'. 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 genes' transcript wiggle plots in 'output.pdf'. The
+list is '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 --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
- rsem-calculate-expression --bowtie-path /sw/bowtie --phred64-quals --fragment-length-mean 150.0 --fragment-length-sd 35.0 -p 8 --out-bam --calc-ci --memory-allocate 1024 /data/mmliver.fq /ref/mm9 mmliver_single_quals
+ 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 --memory-allocate 1024 /data/mmliver.fq /ref/mm9 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
## <a name="simulation"></a> Simulation
rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
-estimated_model_file: File containing model parameters. Generated by
+estimated_model_file: file containing model parameters. Generated by
rsem-calculate-expression.
-estimated_isoform_results: File containing isoform expression levels.
+estimated_isoform_results: file containing isoform expression levels.
Generated by rsem-calculate-expression.
theta0: fraction of reads that are "noise" (not derived from a transcript).
N: number of reads to simulate.
output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
+## <a name="gen_trinity"></a> 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.
+
+### Usage:
+
+ 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.
+
+## <a name="de"></a> 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](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package).
+
+
+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
+calcualtes 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](http://deweylab.biostat.wisc.edu/rsem/rsem-generate-ngvector.html).
+
+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](http://deweylab.biostat.wisc.edu/rsem/rsem-run-ebseq.html). 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](http://deweylab.biostat.wisc.edu/rsem/rsem-control-fdr.html). 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](http://www.bioconductor.org/packages/devel/bioc/vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf).
+
+Questions related to EBSeq should
+be sent to <a href="mailto:nleng@wisc.edu">Ning Leng</a>.
+
+## <a name="authors"></a> Authors
+
+RSEM is developed by Bo Li, with substaintial technical input from Colin Dewey.
+
## <a name="acknowledgements"></a> Acknowledgements
-RSEM uses randomc.h and mersenne.cpp from
-<http://lxnt.info/rng/randomc.htm> for random number generation. RSEM
-also uses the [Boost C++](http://www.boost.org) and
-[samtools](http://samtools.sourceforge.net) libraries.
+RSEM uses the [Boost C++](http://www.boost.org) and
+[samtools](http://samtools.sourceforge.net) libraries. RSEM includes
+[EBSeq](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/) for
+differential expression analysis.
+
+We thank earonesty for contributing patches.
+
+We thank Han Lin for suggesting possible fixes.
## <a name="license"></a> License
-RSEM is licensed under the [GNU General Public License v3](http://www.gnu.org/licenses/gpl-3.0.html).
+RSEM is licensed under the [GNU General Public License
+v3](http://www.gnu.org/licenses/gpl-3.0.html).
projects / rsem.git / blobdiff