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 new RSEM package (rsem-1.x) provides an
26 user-friendly interface, supports threads for parallel computation of
27 the EM algorithm, single-end and paired-end read data, quality scores,
28 variable-length reads and RSPD estimation. It can also generate
29 genomic-coordinate BAM files and UCSC wiggle files for
30 visualization. In addition, it provides posterior mean and 95%
31 credibility interval estimates for expression levels. For
32 visualization, it can also generate transcript-coordinate BAM files
33 and visualize them and also models learned.
35 ## <a name="compilation"></a> Compilation & Installation
37 To compile RSEM, simply run
41 To install, simply put the rsem directory in your environment's PATH
46 C++ and Perl are required to be installed.
48 To take advantage of RSEM's built-in support for the Bowtie alignment
49 program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
51 If you want to plot model learned by RSEM, you should also install R.
53 ## <a name="usage"></a> Usage
55 ### I. Preparing Reference Sequences
57 RSEM can extract reference transcripts from a genome if you provide it
58 with gene annotations in a GTF file. Alternatively, you can provide
59 RSEM with transcript sequences directly.
61 Please note that GTF files generated from the UCSC Table Browser do not
62 contain isoform-gene relationship information. However, if you use the
63 UCSC Genes annotation track, this information can be recovered by
64 downloading the knownIsoforms.txt file for the appropriate genome.
66 To prepare the reference sequences, you should run the
67 'rsem-prepare-reference' program. Run
69 rsem-prepare-reference --help
71 to get usage information or visit the [rsem-prepare-reference
72 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html).
74 ### II. Calculating Expression Values
76 To calculate expression values, you should run the
77 'rsem-calculate-expression' program. Run
79 rsem-calculate-expression --help
81 to get usage information or visit the [rsem-calculate-expression
82 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html).
84 #### Calculating expression values from single-end data
86 For single-end models, users have the option of providing a fragment
87 length distribution via the '--fragment-length-mean' and
88 '--fragment-length-sd' options. The specification of an accurate fragment
89 length distribution is important for the accuracy of expression level
90 estimates from single-end data. If the fragment length mean and sd are
91 not provided, RSEM will not take a fragment length distribution into
94 #### Using an alternative aligner
96 By default, RSEM automates the alignment of reads to reference
97 transcripts using the Bowtie alignment program. To use an alternative
98 alignment program, align the input reads against the file
99 'reference_name.idx.fa' generated by 'rsem-prepare-reference', and format
100 the alignment output in SAM or BAM format. Then, instead of providing
101 reads to 'rsem-calculate-expression', specify the '--sam' or '--bam' option
102 and provide the SAM or BAM file as an argument. When using an
103 alternative aligner, you may also want to provide the '--no-bowtie' option
104 to 'rsem-prepare-reference' so that the Bowtie indices are not built.
106 Some aligners' (other than Bowtie) output might need to be converted
107 so that RSEM can use. For conversion, please run
109 convert-sam-for-rsem --help
111 to get usage information or visit the [convert-sam-for-rsem
113 page](http://deweylab.biostat.wisc.edu/rsem/convert-sam-for-rsem.html).
115 However, please note that RSEM does ** not ** support gapped
116 alignments. So make sure that your aligner does not produce alignments
117 with intersions/deletions. Also, please make sure that you use
118 'reference_name.idx.fa' , which is generated by RSEM, to build your
121 ### III. Visualization
123 RSEM contains a version of samtools in the 'sam' subdirectory. RSEM
124 will always produce three files:'sample_name.transcript.bam', the
125 unsorted BAM file, 'sample_name.transcript.sorted.bam' and
126 'sample_name.transcript.sorted.bam.bai' the sorted BAM file and
127 indices generated by the samtools included. All three files are in
128 transcript coordinates. When users specify the --output-genome-bam
129 option RSEM will produce three files: 'sample_name.genome.bam', the
130 unsorted BAM file, 'sample_name.genome.sorted.bam' and
131 'sample_name.genome.sorted.bam.bai' the sorted BAM file and indices
132 generated by the samtools included. All these files are in genomic
135 #### a) Generating a UCSC Wiggle file
137 A wiggle plot representing the expected number of reads overlapping
138 each position in the genome can be generated from the sorted genome
139 BAM file output. To generate the wiggle plot, run the 'rsem-bam2wig'
140 program on the 'sample_name.genome.sorted.bam' file.
144 rsem-bam2wig bam_input wig_output wiggle_name
146 bam_input: sorted bam file
147 wig_output: output file name, e.g. output.wig
148 wiggle_name: the name the user wants to use for this wiggle plot
150 #### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser
152 Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
154 #### c) Generating Transcript Wiggle Plots
156 To generate transcript wiggle plots, you should run the
157 'rsem-plot-transcript-wiggles' program. Run
159 rsem-plot-transcript-wiggles --help
161 to get usage information or visit the [rsem-plot-transcript-wiggles
162 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
164 #### d) Visualize the model learned by RSEM
166 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
170 rsem-plot-model sample_name output_plot_file
172 sample_name: the name of the sample analyzed
173 output_plot_file: the file name for plots generated from the model. It is a pdf file
175 The plots generated depends on read type and user configuration. It
176 may include fragment length distribution, mate length distribution,
177 read start position distribution (RSPD), quality score vs observed
178 quality given a reference base, position vs percentage of sequencing
179 error given a reference base and histogram of reads with different
180 number of alignments.
182 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
184 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
186 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
188 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
190 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
192 ## <a name="example"></a> Example
194 Suppose we download the mouse genome from UCSC Genome Browser. We
195 will use a reference_name of 'mm9'. We have a FASTQ-formatted file,
196 'mmliver.fq', containing single-end reads from one sample, which we
197 call 'mmliver_single_quals'. We want to estimate expression values by
198 using the single-end model with a fragment length distribution. We
199 know that the fragment length distribution is approximated by a normal
200 distribution with a mean of 150 and a standard deviation of 35. We
201 wish to generate 95% credibility intervals in addition to maximum
202 likelihood estimates. RSEM will be allowed 1G of memory for the
203 credibility interval calculation. We will visualize the probabilistic
204 read mappings generated by RSEM on UCSC genome browser. We will
205 generate a list of genes' transcript wiggle plots in 'output.pdf'. The
206 list is 'gene_ids.txt'. We will visualize the models learned in
207 'mmliver_single_quals.models.pdf'
209 The commands for this scenario are as follows:
211 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
212 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
213 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
214 rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf
215 rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf
217 ## <a name="simulation"></a> Simulation
221 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
223 estimated_model_file: file containing model parameters. Generated by
224 rsem-calculate-expression.
225 estimated_isoform_results: file containing isoform expression levels.
226 Generated by rsem-calculate-expression.
227 theta0: fraction of reads that are "noise" (not derived from a transcript).
228 N: number of reads to simulate.
229 output_name: prefix for all output files.
230 [-q] : set it will stop outputting intermediate information.
234 output_name.fa if single-end without quality score;
235 output_name.fq if single-end with quality score;
236 output_name_1.fa & output_name_2.fa if paired-end without quality
238 output_name_1.fq & output_name_2.fq if paired-end with quality score.
240 output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
242 ## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
244 For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
248 extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
250 trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
251 map_file: transcript-to-gene-map file's name.
253 ## <a name="acknowledgements"></a> Acknowledgements
255 RSEM uses the [Boost C++](http://www.boost.org) and
256 [samtools](http://samtools.sourceforge.net) libraries.
258 ## <a name="license"></a> License
260 RSEM is licensed under the [GNU General Public License v3](http://www.gnu.org/licenses/gpl-3.0.html).