* [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. For
-visualization, it can also generate transcript-coordinate BAM files
-and visualize them and also models learned.
+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++ and Perl are required to be installed.
+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.
-If you want to plot model learned by RSEM, you should also install R.
-
## <a name="usage"></a> Usage
### I. Preparing Reference Sequences
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.
-
-Some aligners' (other than Bowtie) output might need to be converted
-so that RSEM can use. For conversion, please run
+'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
+ convert-sam-for-rsem --help
to get usage information or visit the [convert-sam-for-rsem
documentation
generated by the samtools included. All these files are in genomic
coordinates.
-#### a) Generating a UCSC Wiggle file
+#### 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.
+
+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 genome
-BAM file output. To generate the wiggle plot, run the 'rsem-bam2wig'
-program on the 'sample_name.genome.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).
-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
+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.
-#### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser
+Here are some guidance for visualizing transcript coordinate files using IGV:
-Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
+1) Import the transcript sequences as a genome
-#### c) Generating Transcript Wiggle Plots
+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'.
+
+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
to get usage information or visit the [rsem-plot-transcript-wiggles
documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
-#### d) Visualize the model learned by RSEM
+#### e) Visualize the model learned by RSEM
RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
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 the [Boost C++](http://www.boost.org) and
-[samtools](http://samtools.sourceforge.net) libraries.
+[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