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 * [Acknowledgements](#acknowledgements)
22 ## <a name="introduction"></a> Introduction
24 RSEM is a software package for estimating gene and isoform expression
25 levels from RNA-Seq data. The RSEM package provides an user-friendly
26 interface, supports threads for parallel computation of the EM
27 algorithm, single-end and paired-end read data, quality scores,
28 variable-length reads and RSPD estimation. In addition, it provides
29 posterior mean and 95% credibility interval estimates for expression
30 levels. For visualization, It can generate BAM and Wiggle files in
31 both transcript-coordinate and genomic-coordinate. Genomic-coordinate
32 files can be visualized by both UCSC Genome browser and Broad
33 Institute's Integrative Genomics Viewer (IGV). Transcript-coordinate
34 files can be visualized by IGV. RSEM also has its own scripts to
35 generate transcript read depth plots in pdf format. The unique feature
36 of RSEM is, the read depth plots can be stacked, with read depth
37 contributed to unique reads shown in black and contributed to
38 multi-reads shown in red. In addition, models learned from data can
39 also be visualized. Last but not least, RSEM contains a simulator.
41 ## <a name="compilation"></a> Compilation & Installation
43 To compile RSEM, simply run
47 To install, simply put the rsem directory in your environment's PATH
52 C++ and Perl are required to be installed.
54 To take advantage of RSEM's built-in support for the Bowtie alignment
55 program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
57 If you want to plot model learned by RSEM, you should also install R.
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
162 sorted_bam_input: sorted bam file
163 wig_output: output file name, e.g. output.wig
164 wiggle_name: the name the user wants to use for this wiggle plot
166 #### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser or Integrative Genomics Viewer(IGV)
168 For UCSC genome browser, please refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
170 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.
172 Here are some guidance for visualizing transcript coordinate files:
174 1) Import the transcript sequences as a genome
176 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'.
178 2) Load visualization files
180 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
182 igvtools tile reference_name.transcript.wig reference_name.transcript.tdf reference_name.genome
184 #### c) Generating Transcript Wiggle Plots
186 To generate transcript wiggle plots, you should run the
187 'rsem-plot-transcript-wiggles' program. Run
189 rsem-plot-transcript-wiggles --help
191 to get usage information or visit the [rsem-plot-transcript-wiggles
192 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
194 #### d) Visualize the model learned by RSEM
196 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
200 rsem-plot-model sample_name output_plot_file
202 sample_name: the name of the sample analyzed
203 output_plot_file: the file name for plots generated from the model. It is a pdf file
205 The plots generated depends on read type and user configuration. It
206 may include fragment length distribution, mate length distribution,
207 read start position distribution (RSPD), quality score vs observed
208 quality given a reference base, position vs percentage of sequencing
209 error given a reference base and histogram of reads with different
210 number of alignments.
212 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
214 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
216 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
218 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
220 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
222 ## <a name="example"></a> Example
224 Suppose we download the mouse genome from UCSC Genome Browser. We
225 will use a reference_name of 'mm9'. We have a FASTQ-formatted file,
226 'mmliver.fq', containing single-end reads from one sample, which we
227 call 'mmliver_single_quals'. We want to estimate expression values by
228 using the single-end model with a fragment length distribution. We
229 know that the fragment length distribution is approximated by a normal
230 distribution with a mean of 150 and a standard deviation of 35. We
231 wish to generate 95% credibility intervals in addition to maximum
232 likelihood estimates. RSEM will be allowed 1G of memory for the
233 credibility interval calculation. We will visualize the probabilistic
234 read mappings generated by RSEM on UCSC genome browser. We will
235 generate a list of genes' transcript wiggle plots in 'output.pdf'. The
236 list is 'gene_ids.txt'. We will visualize the models learned in
237 'mmliver_single_quals.models.pdf'
239 The commands for this scenario are as follows:
241 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
242 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
243 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
244 rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf
245 rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf
247 ## <a name="simulation"></a> Simulation
251 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
253 estimated_model_file: file containing model parameters. Generated by
254 rsem-calculate-expression.
255 estimated_isoform_results: file containing isoform expression levels.
256 Generated by rsem-calculate-expression.
257 theta0: fraction of reads that are "noise" (not derived from a transcript).
258 N: number of reads to simulate.
259 output_name: prefix for all output files.
260 [-q] : set it will stop outputting intermediate information.
264 output_name.fa if single-end without quality score;
265 output_name.fq if single-end with quality score;
266 output_name_1.fa & output_name_2.fa if paired-end without quality
268 output_name_1.fq & output_name_2.fq if paired-end with quality score.
270 output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
272 ## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
274 For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
278 extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
280 trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
281 map_file: transcript-to-gene-map file's name.
283 ## <a name="acknowledgements"></a> Acknowledgements
285 RSEM uses the [Boost C++](http://www.boost.org) and
286 [samtools](http://samtools.sourceforge.net) libraries.
288 We thank earonesty for contributing patches.
290 ## <a name="license"></a> License
292 RSEM is licensed under the [GNU General Public License v3](http://www.gnu.org/licenses/gpl-3.0.html).