4 [Bo Li](http://pages.cs.wisc.edu/~bli) \(bli at cs dot wisc dot edu\)
11 * [Introduction](#introduction)
12 * [Compilation & Installation](#compilation)
15 * [Simulation](#simulation)
16 * [Generate Transcript-to-Gene-Map from Trinity Output](#gen_trinity)
17 * [Differential Expression Analysis](#de)
19 * [Acknowledgements](#acknowledgements)
24 ## <a name="introduction"></a> Introduction
26 RSEM is a software package for estimating gene and isoform expression
27 levels from RNA-Seq data. The RSEM package provides an user-friendly
28 interface, supports threads for parallel computation of the EM
29 algorithm, single-end and paired-end read data, quality scores,
30 variable-length reads and RSPD estimation. In addition, it provides
31 posterior mean and 95% credibility interval estimates for expression
32 levels. For visualization, It can generate BAM and Wiggle files in
33 both transcript-coordinate and genomic-coordinate. Genomic-coordinate
34 files can be visualized by both UCSC Genome browser and Broad
35 Institute's Integrative Genomics Viewer (IGV). Transcript-coordinate
36 files can be visualized by IGV. RSEM also has its own scripts to
37 generate transcript read depth plots in pdf format. The unique feature
38 of RSEM is, the read depth plots can be stacked, with read depth
39 contributed to unique reads shown in black and contributed to
40 multi-reads shown in red. In addition, models learned from data can
41 also be visualized. Last but not least, RSEM contains a simulator.
43 ## <a name="compilation"></a> Compilation & Installation
45 To compile RSEM, simply run
49 To install, simply put the rsem directory in your environment's PATH
54 C++, Perl and R are required to be installed.
56 To take advantage of RSEM's built-in support for the Bowtie alignment
57 program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
59 ## <a name="usage"></a> Usage
61 ### I. Preparing Reference Sequences
63 RSEM can extract reference transcripts from a genome if you provide it
64 with gene annotations in a GTF file. Alternatively, you can provide
65 RSEM with transcript sequences directly.
67 Please note that GTF files generated from the UCSC Table Browser do not
68 contain isoform-gene relationship information. However, if you use the
69 UCSC Genes annotation track, this information can be recovered by
70 downloading the knownIsoforms.txt file for the appropriate genome.
72 To prepare the reference sequences, you should run the
73 'rsem-prepare-reference' program. Run
75 rsem-prepare-reference --help
77 to get usage information or visit the [rsem-prepare-reference
78 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html).
80 ### II. Calculating Expression Values
82 To calculate expression values, you should run the
83 'rsem-calculate-expression' program. Run
85 rsem-calculate-expression --help
87 to get usage information or visit the [rsem-calculate-expression
88 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html).
90 #### Calculating expression values from single-end data
92 For single-end models, users have the option of providing a fragment
93 length distribution via the '--fragment-length-mean' and
94 '--fragment-length-sd' options. The specification of an accurate fragment
95 length distribution is important for the accuracy of expression level
96 estimates from single-end data. If the fragment length mean and sd are
97 not provided, RSEM will not take a fragment length distribution into
100 #### Using an alternative aligner
102 By default, RSEM automates the alignment of reads to reference
103 transcripts using the Bowtie alignment program. To use an alternative
104 alignment program, align the input reads against the file
105 'reference_name.idx.fa' generated by 'rsem-prepare-reference', and
106 format the alignment output in SAM or BAM format. Then, instead of
107 providing reads to 'rsem-calculate-expression', specify the '--sam' or
108 '--bam' option and provide the SAM or BAM file as an argument. When
109 using an alternative aligner, you may also want to provide the
110 '--no-bowtie' option to 'rsem-prepare-reference' so that the Bowtie
111 indices are not built.
113 RSEM requires all alignments of the same read group together. For
114 paired-end reads, RSEM also requires the two mates of any alignment be
115 adjacent. To check if your SAM/BAM file satisfy the requirements,
118 rsem-sam-validator <input.sam/input.bam>
120 If your file does not satisfy the requirements, you can use
121 'convert-sam-for-rsem' to convert it into a BAM file which RSEM can
124 convert-sam-for-rsem --help
126 to get usage information or visit the [convert-sam-for-rsem
128 page](http://deweylab.biostat.wisc.edu/rsem/convert-sam-for-rsem.html).
130 However, please note that RSEM does ** not ** support gapped
131 alignments. So make sure that your aligner does not produce alignments
132 with intersions/deletions. Also, please make sure that you use
133 'reference_name.idx.fa' , which is generated by RSEM, to build your
136 ### III. Visualization
138 RSEM contains a version of samtools in the 'sam' subdirectory. RSEM
139 will always produce three files:'sample_name.transcript.bam', the
140 unsorted BAM file, 'sample_name.transcript.sorted.bam' and
141 'sample_name.transcript.sorted.bam.bai' the sorted BAM file and
142 indices generated by the samtools included. All three files are in
143 transcript coordinates. When users specify the --output-genome-bam
144 option RSEM will produce three files: 'sample_name.genome.bam', the
145 unsorted BAM file, 'sample_name.genome.sorted.bam' and
146 'sample_name.genome.sorted.bam.bai' the sorted BAM file and indices
147 generated by the samtools included. All these files are in genomic
150 #### a) Generating a Wiggle file
152 A wiggle plot representing the expected number of reads overlapping
153 each position in the genome/transcript set can be generated from the
154 sorted genome/transcript BAM file output. To generate the wiggle
155 plot, run the 'rsem-bam2wig' program on the
156 'sample_name.genome.sorted.bam'/'sample_name.transcript.sorted.bam' file.
160 rsem-bam2wig sorted_bam_input wig_output wiggle_name [--no-fractional-weight]
162 sorted_bam_input : Input BAM format file, must be sorted
163 wig_output : Output wiggle file's name, e.g. output.wig
164 wiggle_name : the name of this wiggle plot
165 --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.
167 #### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser or Integrative Genomics Viewer(IGV)
169 For UCSC genome browser, please refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
171 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.
173 Here are some guidance for visualizing transcript coordinate files using IGV:
175 1) Import the transcript sequences as a genome
177 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'.
179 2) Load visualization files
181 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:
183 igvtools tile reference_name.transcript.wig reference_name.transcript.tdf reference_name.genome
185 #### c) Generating Transcript Wiggle Plots
187 To generate transcript wiggle plots, you should run the
188 'rsem-plot-transcript-wiggles' program. Run
190 rsem-plot-transcript-wiggles --help
192 to get usage information or visit the [rsem-plot-transcript-wiggles
193 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
195 #### d) Visualize the model learned by RSEM
197 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
201 rsem-plot-model sample_name output_plot_file
203 sample_name: the name of the sample analyzed
204 output_plot_file: the file name for plots generated from the model. It is a pdf file
206 The plots generated depends on read type and user configuration. It
207 may include fragment length distribution, mate length distribution,
208 read start position distribution (RSPD), quality score vs observed
209 quality given a reference base, position vs percentage of sequencing
210 error given a reference base and histogram of reads with different
211 number of alignments.
213 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
215 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
217 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
219 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
221 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
223 ## <a name="example"></a> Example
225 Suppose we download the mouse genome from UCSC Genome Browser. We
226 will use a reference_name of 'mm9'. We have a FASTQ-formatted file,
227 'mmliver.fq', containing single-end reads from one sample, which we
228 call 'mmliver_single_quals'. We want to estimate expression values by
229 using the single-end model with a fragment length distribution. We
230 know that the fragment length distribution is approximated by a normal
231 distribution with a mean of 150 and a standard deviation of 35. We
232 wish to generate 95% credibility intervals in addition to maximum
233 likelihood estimates. RSEM will be allowed 1G of memory for the
234 credibility interval calculation. We will visualize the probabilistic
235 read mappings generated by RSEM on UCSC genome browser. We will
236 generate a list of genes' transcript wiggle plots in 'output.pdf'. The
237 list is 'gene_ids.txt'. We will visualize the models learned in
238 'mmliver_single_quals.models.pdf'
240 The commands for this scenario are as follows:
242 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
243 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
244 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
245 rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf
246 rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf
248 ## <a name="simulation"></a> Simulation
252 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
254 estimated_model_file: file containing model parameters. Generated by
255 rsem-calculate-expression.
256 estimated_isoform_results: file containing isoform expression levels.
257 Generated by rsem-calculate-expression.
258 theta0: fraction of reads that are "noise" (not derived from a transcript).
259 N: number of reads to simulate.
260 output_name: prefix for all output files.
261 [-q] : set it will stop outputting intermediate information.
265 output_name.fa if single-end without quality score;
266 output_name.fq if single-end with quality score;
267 output_name_1.fa & output_name_2.fa if paired-end without quality
269 output_name_1.fq & output_name_2.fq if paired-end with quality score.
271 output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
273 ## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
275 For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
279 extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
281 trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
282 map_file: transcript-to-gene-map file's name.
284 ## <a name="de"></a> Differential Expression Analysis
286 Popular differential expression (DE) analysis tools such as edgeR and
287 DESeq do not take variance due to read mapping uncertainty into
288 consideration. Because read mapping ambiguity is prevalent among
289 isoforms and de novo assembled transcripts, these tools are not ideal
290 for DE detection in such conditions.
292 **EBSeq**, an empirical Bayesian DE analysis tool developed in
293 UW-Madison, can take variance due to read mapping ambiguity into
294 consideration by grouping isoforms with parent gene's number of
295 isoforms. In addition, it is more robust to outliers. For more
296 information about EBSeq (including the paper describing their method),
298 href="http://www.biostat.wisc.edu/~ningleng/EBSeq_Package">EBSeq
301 RSEM includes the newest version of EBSeq in its folder
302 named 'EBSeq'. To use it, first type
306 to compile the EBSeq related codes.
308 EBSeq requires gene-isoform relationship for its isoform DE
309 detection. However, for de novo assembled transcriptome, it is hard to
310 obtain an accurate gene-isoform relationship. Instead, RSEM provides a
311 script 'rsem-generate-ngvector', which clusters transcripts based on
312 measures directly relating to read mappaing ambiguity. First, it
313 calcualtes the 'unmappability' of each transcript. The 'unmappability'
314 of a transcript is the ratio between the number of k mers with at
315 least one perfect match to other transcripts and the total number of k
316 mers of this transcript, where k is a parameter. Then, Ng vector is
317 generated by applying Kmeans algorithm to the 'unmappability' values
318 with number of clusters set as 3. This program will make sure the mean
319 'unmappability' scores for clusters are in ascending order. All
320 transcripts whose lengths are less than k are assigned to cluster
323 rsem-generate-ngvector --help
325 to get usage information or visit the [rsem-generate-ngvector
327 page](http://deweylab.biostat.wisc.edu/rsem/rsem-generate-ngvector.html).
329 If your reference is a de novo assembled transcript set, you should
330 run 'rsem-generate-ngvector' first. Then load the resulting
331 'output_name.ngvec' into R. For example, you can use
333 NgVec <- scan(file="output_name.ngvec", what=0, sep="\n")
335 . After that, replace 'IsoNgTrun' with 'NgVec' in the second line of
336 section 3.2.5 (Page 10) of EBSeq's vignette:
338 IsoEBres=EBTest(Data=IsoMat, NgVector=NgVec, ...)
340 For users' convenience, RSEM also provides a script
341 'rsem-generate-data-matrix' to extract input matrix from expression
344 rsem-generate-data-matrix sampleA.[genes/isoforms].results sampleB.[genes/isoforms].results ... > output_name.counts.matrix
346 The results files are required to be either all gene level results or
347 all isoform level results. You can load the matrix into R by
349 IsoMat <- data.matrix(read.table(file="output_name.counts.matrix"))
351 before running function 'EBTest'.
353 At last, RSEM provides a R script, 'rsem-find-DE', which run EBSeq for
358 rsem-find-DE data_matrix_file [--ngvector ngvector_file] number_sample_condition1 FDR_rate output_file
360 This script calls EBSeq to find differentially expressed genes/transcripts in two conditions.
362 data_matrix_file: m by n matrix containing expected counts, m is the number of transcripts/genes, n is the number of total samples.
363 [--ngvector ngvector_file]: optional field. 'ngvector_file' is calculated by 'rsem-generate-ngvector'. Having this field is recommended for transcript data.
364 number_sample_condition1: the number of samples in condition 1. A condition's samples must be adjacent. The left group of samples are defined as condition 1.
365 FDR_rate: false discovery rate.
366 output_file: the output file.
368 The results are written as a matrix with row and column names. The row names are the differentially expressed transcripts'/genes' ids. The column names are 'PPEE', 'PPDE', 'PostFC' and 'RealFC'.
370 PPEE: posterior probability of being equally expressed.
371 PPDE: posterior probability of being differentially expressed.
372 PostFC: posterior fold change (condition 1 over condition2).
373 RealFC: real fold change (condition 1 over condition2).
375 To get the above usage information, type
379 Note: any wrong parameter setting will lead 'rsem-find-DE' to output
380 usage information and halt.
382 Questions related to EBSeq should
383 be sent to <a href="mailto:nleng@wisc.edu">Ning Leng</a>.
385 ## <a name="authors"></a> Authors
387 RSEM is developed by Bo Li, with substaintial technical input from Colin Dewey.
389 ## <a name="acknowledgements"></a> Acknowledgements
391 RSEM uses the [Boost C++](http://www.boost.org) and
392 [samtools](http://samtools.sourceforge.net) libraries. RSEM includes
393 [EBSeq](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/) for
394 differential expression analysis.
396 We thank earonesty for contributing patches.
398 ## <a name="license"></a> License
400 RSEM is licensed under the [GNU General Public License
401 v3](http://www.gnu.org/licenses/gpl-3.0.html).