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 For cygwin users, please uncomment the 3rd and 7th line in
50 'sam/Makefile' before you run 'make'.
52 To compile EBSeq, which is included in the RSEM package, run
56 To install, simply put the rsem directory in your environment's PATH
61 C++, Perl and R are required to be installed.
63 To take advantage of RSEM's built-in support for the Bowtie/Bowtie 2
64 alignment program, you must have
65 [Bowtie](http://bowtie-bio.sourceforge.net) and/or [Bowtie
66 2](http://bowtie-bio.sourceforge.net/bowtie2) installed.
68 ## <a name="usage"></a> Usage
70 ### I. Preparing Reference Sequences
72 RSEM can extract reference transcripts from a genome if you provide it
73 with gene annotations in a GTF file. Alternatively, you can provide
74 RSEM with transcript sequences directly.
76 Please note that GTF files generated from the UCSC Table Browser do not
77 contain isoform-gene relationship information. However, if you use the
78 UCSC Genes annotation track, this information can be recovered by
79 downloading the knownIsoforms.txt file for the appropriate genome.
81 To prepare the reference sequences, you should run the
82 'rsem-prepare-reference' program. Run
84 rsem-prepare-reference --help
86 to get usage information or visit the [rsem-prepare-reference
87 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html).
89 ### II. Calculating Expression Values
91 To calculate expression values, you should run the
92 'rsem-calculate-expression' program. Run
94 rsem-calculate-expression --help
96 to get usage information or visit the [rsem-calculate-expression
97 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html).
99 #### Calculating expression values from single-end data
101 For single-end models, users have the option of providing a fragment
102 length distribution via the '--fragment-length-mean' and
103 '--fragment-length-sd' options. The specification of an accurate fragment
104 length distribution is important for the accuracy of expression level
105 estimates from single-end data. If the fragment length mean and sd are
106 not provided, RSEM will not take a fragment length distribution into
109 #### Using an alternative aligner
111 By default, RSEM automates the alignment of reads to reference
112 transcripts using the Bowtie alignment program. Turn on '--bowtie2'
113 for 'rsem-prepare-reference' and 'rsem-calculate-expression' will
114 allow RSEM to use the Bowtie 2 alignment program instead. Please note
115 that indel alignments, local alignments and discordant alignments are
116 disallowed when RSEM uses Bowtie 2 since RSEM currently cannot handle
117 them. See the description of '--bowtie2' option in
118 'rsem-calculate-expression' for more details. To use an alternative
119 alignment program, align the input reads against the file
120 'reference_name.idx.fa' generated by 'rsem-prepare-reference', and
121 format the alignment output in SAM or BAM format. Then, instead of
122 providing reads to 'rsem-calculate-expression', specify the '--sam' or
123 '--bam' option and provide the SAM or BAM file as an argument. When
124 using an alternative aligner, you may also want to provide the
125 '--no-bowtie' option to 'rsem-prepare-reference' so that the Bowtie
126 indices are not built.
128 RSEM requires all alignments of the same read group together. For
129 paired-end reads, RSEM also requires the two mates of any alignment be
130 adjacent. To check if your SAM/BAM file satisfy the requirements,
133 rsem-sam-validator <input.sam/input.bam>
135 If your file does not satisfy the requirements, you can use
136 'convert-sam-for-rsem' to convert it into a BAM file which RSEM can
139 convert-sam-for-rsem --help
141 to get usage information or visit the [convert-sam-for-rsem
143 page](http://deweylab.biostat.wisc.edu/rsem/convert-sam-for-rsem.html).
145 However, please note that RSEM does ** not ** support gapped
146 alignments. So make sure that your aligner does not produce alignments
147 with intersions/deletions. Also, please make sure that you use
148 'reference_name.idx.fa' , which is generated by RSEM, to build your
151 ### III. Visualization
153 RSEM contains a version of samtools in the 'sam' subdirectory. RSEM
154 will always produce three files:'sample_name.transcript.bam', the
155 unsorted BAM file, 'sample_name.transcript.sorted.bam' and
156 'sample_name.transcript.sorted.bam.bai' the sorted BAM file and
157 indices generated by the samtools included. All three files are in
158 transcript coordinates. When users specify the --output-genome-bam
159 option RSEM will produce three files: 'sample_name.genome.bam', the
160 unsorted BAM file, 'sample_name.genome.sorted.bam' and
161 'sample_name.genome.sorted.bam.bai' the sorted BAM file and indices
162 generated by the samtools included. All these files are in genomic
165 #### a) Converting transcript BAM file into genome BAM file
167 Normally, RSEM will do this for you via '--output-genome-bam' option
168 of 'rsem-calculate-expression'. However, if you have run
169 'rsem-prepare-reference' and use 'reference_name.idx.fa' to build
170 indices for your aligner, you can use 'rsem-tbam2gbam' to convert your
171 transcript coordinate BAM alignments file into a genomic coordinate
172 BAM alignments file without the need to run the whole RSEM
173 pipeline. Please note that 'rsem-prepare-reference' will convert all
174 'N' into 'G' by default for 'reference_name.idx.fa'. If you do not
175 want this to happen, please use '--no-ntog' option.
179 rsem-tbam2gbam reference_name unsorted_transcript_bam_input genome_bam_output
181 reference_name : The name of reference built by 'rsem-prepare-reference'
182 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
183 genome_bam_output : The output genomic coordinate BAM file's name
185 #### b) Generating a Wiggle file
187 A wiggle plot representing the expected number of reads overlapping
188 each position in the genome/transcript set can be generated from the
189 sorted genome/transcript BAM file output. To generate the wiggle
190 plot, run the 'rsem-bam2wig' program on the
191 'sample_name.genome.sorted.bam'/'sample_name.transcript.sorted.bam' file.
195 rsem-bam2wig sorted_bam_input wig_output wiggle_name [--no-fractional-weight]
197 sorted_bam_input : Input BAM format file, must be sorted
198 wig_output : Output wiggle file's name, e.g. output.wig
199 wiggle_name : The name of this wiggle plot
200 --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
202 #### c) Loading a BAM and/or Wiggle file into the UCSC Genome Browser or Integrative Genomics Viewer(IGV)
204 For UCSC genome browser, please refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
206 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.
208 Here are some guidance for visualizing transcript coordinate files using IGV:
210 1) Import the transcript sequences as a genome
212 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'.
214 2) Load visualization files
216 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:
218 igvtools tile reference_name.transcript.wig reference_name.transcript.tdf reference_name.genome
220 #### d) Generating Transcript Wiggle Plots
222 To generate transcript wiggle plots, you should run the
223 'rsem-plot-transcript-wiggles' program. Run
225 rsem-plot-transcript-wiggles --help
227 to get usage information or visit the [rsem-plot-transcript-wiggles
228 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
230 #### e) Visualize the model learned by RSEM
232 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
236 rsem-plot-model sample_name output_plot_file
238 sample_name: the name of the sample analyzed
239 output_plot_file: the file name for plots generated from the model. It is a pdf file
241 The plots generated depends on read type and user configuration. It
242 may include fragment length distribution, mate length distribution,
243 read start position distribution (RSPD), quality score vs observed
244 quality given a reference base, position vs percentage of sequencing
245 error given a reference base and histogram of reads with different
246 number of alignments.
248 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
250 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
252 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
254 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
256 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
258 ## <a name="example"></a> Example
260 Suppose we download the mouse genome from UCSC Genome Browser. We
261 will use a reference_name of 'mouse_125'. We have a FASTQ-formatted file,
262 'mmliver.fq', containing single-end reads from one sample, which we
263 call 'mmliver_single_quals'. We want to estimate expression values by
264 using the single-end model with a fragment length distribution. We
265 know that the fragment length distribution is approximated by a normal
266 distribution with a mean of 150 and a standard deviation of 35. We
267 wish to generate 95% credibility intervals in addition to maximum
268 likelihood estimates. RSEM will be allowed 1G of memory for the
269 credibility interval calculation. We will visualize the probabilistic
270 read mappings generated by RSEM on UCSC genome browser. We will
271 generate a list of genes' transcript wiggle plots in 'output.pdf'. The
272 list is 'gene_ids.txt'. We will visualize the models learned in
273 'mmliver_single_quals.models.pdf'
275 The commands for this scenario are as follows:
277 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mouse_125
278 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/mouse_125 mmliver_single_quals
279 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
280 rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf
281 rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf
283 ## <a name="simulation"></a> Simulation
285 RSEM provides users the 'rsem-simulate-reads' program to simulate RNA-Seq data based on parameters learned from real data sets. Run
289 to get usage information or read the following subsections.
293 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
295 __reference_name:__ The name of RSEM references, which should be already generated by 'rsem-prepare-reference'
297 __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.
299 __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.
301 __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'.
303 __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'.
305 __output_name:__ Prefix for all output files.
307 __--seed seed:__ Set seed for the random number generator used in simulation. The seed should be a 32-bit unsigned integer.
309 __-q:__ Set it will stop outputting intermediate information.
313 output_name.sim.isoforms.results, output_name.sim.genes.results: Expression levels estimated by counting where each simulated read comes from.
314 output_name.sim.alleles.results: Allele-specific expression levels estimated by counting where each simulated read comes from.
316 output_name.fa if single-end without quality score;
317 output_name.fq if single-end with quality score;
318 output_name_1.fa & output_name_2.fa if paired-end without quality
320 output_name_1.fq & output_name_2.fq if paired-end with quality score.
322 **Format of the header line**: Each simulated read's header line encodes where it comes from. The header line has the format:
324 {>/@}_rid_dir_sid_pos[_insertL]
326 __{>/@}:__ Either '>' or '@' must appear. '>' appears if FASTA files are generated and '@' appears if FASTQ files are generated
328 __rid:__ Simulated read's index, numbered from 0
330 __dir:__ The direction of the simulated read. 0 refers to forward strand ('+') and 1 refers to reverse strand ('-')
332 __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)
334 __pos:__ The start position of the simulated read in strand dir of transcript sid. It is numbered from 0
336 __insertL:__ Only appear for paired-end reads. It gives the insert length of the simulated read.
340 Suppose we want to simulate 50 millon single-end reads with quality scores and use the parameters learned from [Example](#example). In addition, we set theta0 as 0.2 and output_name as 'simulated_reads'. The command is:
342 rsem-simulate-reads /ref/mouse_125 mmliver_single_quals.stat/mmliver_single_quals.model mmliver_single_quals.isoforms.results 0.2 50000000 simulated_reads
344 ## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
346 For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
350 extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
352 trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
353 map_file: transcript-to-gene-map file's name.
355 ## <a name="de"></a> Differential Expression Analysis
357 Popular differential expression (DE) analysis tools such as edgeR and
358 DESeq do not take variance due to read mapping uncertainty into
359 consideration. Because read mapping ambiguity is prevalent among
360 isoforms and de novo assembled transcripts, these tools are not ideal
361 for DE detection in such conditions.
363 EBSeq, an empirical Bayesian DE analysis tool developed in UW-Madison,
364 can take variance due to read mapping ambiguity into consideration by
365 grouping isoforms with parent gene's number of isoforms. In addition,
366 it is more robust to outliers. For more information about EBSeq
367 (including the paper describing their method), please visit [EBSeq's
368 website](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package).
371 RSEM includes EBSeq in its folder named 'EBSeq'. To use it, first type
375 to compile the EBSeq related codes.
377 EBSeq requires gene-isoform relationship for its isoform DE
378 detection. However, for de novo assembled transcriptome, it is hard to
379 obtain an accurate gene-isoform relationship. Instead, RSEM provides a
380 script 'rsem-generate-ngvector', which clusters transcripts based on
381 measures directly relating to read mappaing ambiguity. First, it
382 calcualtes the 'unmappability' of each transcript. The 'unmappability'
383 of a transcript is the ratio between the number of k mers with at
384 least one perfect match to other transcripts and the total number of k
385 mers of this transcript, where k is a parameter. Then, Ng vector is
386 generated by applying Kmeans algorithm to the 'unmappability' values
387 with number of clusters set as 3. This program will make sure the mean
388 'unmappability' scores for clusters are in ascending order. All
389 transcripts whose lengths are less than k are assigned to cluster
392 rsem-generate-ngvector --help
394 to get usage information or visit the [rsem-generate-ngvector
396 page](http://deweylab.biostat.wisc.edu/rsem/rsem-generate-ngvector.html).
398 If your reference is a de novo assembled transcript set, you should
399 run 'rsem-generate-ngvector' first. Then load the resulting
400 'output_name.ngvec' into R. For example, you can use
402 NgVec <- scan(file="output_name.ngvec", what=0, sep="\n")
404 . After that, set "NgVector = NgVec" for your differential expression
405 test (either 'EBTest' or 'EBMultiTest').
408 For users' convenience, RSEM also provides a script
409 'rsem-generate-data-matrix' to extract input matrix from expression
412 rsem-generate-data-matrix sampleA.[genes/isoforms].results sampleB.[genes/isoforms].results ... > output_name.counts.matrix
414 The results files are required to be either all gene level results or
415 all isoform level results. You can load the matrix into R by
417 IsoMat <- data.matrix(read.table(file="output_name.counts.matrix"))
419 before running either 'EBTest' or 'EBMultiTest'.
421 Lastly, RSEM provides two scripts, 'rsem-run-ebseq' and
422 'rsem-control-fdr', to help users find differential expressed
423 genes/transcripts. First, 'rsem-run-ebseq' calls EBSeq to calculate related statistics
424 for all genes/transcripts. Run
426 rsem-run-ebseq --help
428 to get usage information or visit the [rsem-run-ebseq documentation
429 page](http://deweylab.biostat.wisc.edu/rsem/rsem-run-ebseq.html). Second,
430 'rsem-control-fdr' takes 'rsem-run-ebseq' 's result and reports called
431 differentially expressed genes/transcripts by controlling the false
434 rsem-control-fdr --help
436 to get usage information or visit the [rsem-control-fdr documentation
437 page](http://deweylab.biostat.wisc.edu/rsem/rsem-control-fdr.html). These
438 two scripts can perform DE analysis on either 2 conditions or multiple
441 Please note that 'rsem-run-ebseq' and 'rsem-control-fdr' use EBSeq's
442 default parameters. For advanced use of EBSeq or information about how
443 EBSeq works, please refer to [EBSeq's
444 manual](http://www.bioconductor.org/packages/devel/bioc/vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf).
446 Questions related to EBSeq should
447 be sent to <a href="mailto:nleng@wisc.edu">Ning Leng</a>.
449 ## <a name="authors"></a> Authors
451 The RSEM algorithm is developed by Bo Li and Colin Dewey. The RSEM software is mainly implemented by Bo Li.
453 ## <a name="acknowledgements"></a> Acknowledgements
455 RSEM uses the [Boost C++](http://www.boost.org) and
456 [samtools](http://samtools.sourceforge.net) libraries. RSEM includes
457 [EBSeq](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/) for
458 differential expression analysis.
460 We thank earonesty and Dr. Samuel Arvidsson for contributing patches.
462 We thank Han Lin, j.miller, Joël Fillon and Dr. Samuel G. Younkin for suggesting possible fixes.
464 ## <a name="license"></a> License
466 RSEM is licensed under the [GNU General Public License
467 v3](http://www.gnu.org/licenses/gpl-3.0.html).