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 visualization. In
30 addition, it provides posterior mean and 95% credibility interval
31 estimates for expression levels.
33 ## <a name="compilation"></a> Compilation & Installation
35 To compile RSEM, simply run
39 To install, simply put the rsem directory in your environment's PATH
44 C++ and Perl are required to be installed.
46 To take advantage of RSEM's built-in support for the Bowtie alignment
47 program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
49 If you want to plot model learned by RSEM, you should also install R.
51 ## <a name="usage"></a> Usage
53 ### I. Preparing Reference Sequences
55 RSEM can extract reference transcripts from a genome if you provide it
56 with gene annotations in a GTF file. Alternatively, you can provide
57 RSEM with transcript sequences directly.
59 Please note that GTF files generated from the UCSC Table Browser do not
60 contain isoform-gene relationship information. However, if you use the
61 UCSC Genes annotation track, this information can be recovered by
62 downloading the knownIsoforms.txt file for the appropriate genome.
64 To prepare the reference sequences, you should run the
65 'rsem-prepare-reference' program. Run
67 rsem-prepare-reference --help
69 to get usage information or visit the [rsem-prepare-reference
70 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html).
72 ### II. Calculating Expression Values
74 To calculate expression values, you should run the
75 'rsem-calculate-expression' program. Run
77 rsem-calculate-expression --help
79 to get usage information or visit the [rsem-calculate-expression
80 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html).
82 #### Calculating expression values from single-end data
84 For single-end models, users have the option of providing a fragment
85 length distribution via the --fragment-length-mean and
86 --fragment-length-sd options. The specification of an accurate fragment
87 length distribution is important for the accuracy of expression level
88 estimates from single-end data. If the fragment length mean and sd are
89 not provided, RSEM will not take a fragment length distribution into
92 #### Using an alternative aligner
94 By default, RSEM automates the alignment of reads to reference
95 transcripts using the Bowtie alignment program. To use an alternative
96 alignment program, align the input reads against the file
97 'reference_name.idx.fa' generated by rsem-prepare-reference, and format
98 the alignment output in SAM or BAM format. Then, instead of providing
99 reads to rsem-calculate-expression, specify the --sam or --bam option
100 and provide the SAM or BAM file as an argument. When using an
101 alternative aligner, you may also want to provide the --no-bowtie option
102 to rsem-prepare-reference so that the Bowtie indices are not built.
104 However, please note that RSEM does ** not ** support gapped
105 alignments. So make sure that your aligner does not produce alignments
106 with intersions/deletions. Also, please make sure that you use
107 'reference_name.idx.fa' , which is generated by RSEM, to build your
110 ### III. Visualization
112 RSEM contains a version of samtools in the 'sam' subdirectory. When
113 users specify the --out-bam option RSEM will produce three files:
114 'sample_name.bam', the unsorted BAM file, 'sample_name.sorted.bam' and
115 'sample_name.sorted.bam.bai' the sorted BAM file and indices generated
116 by the samtools included.
118 #### a) Generating a UCSC Wiggle file
120 A wiggle plot representing the expected number of reads overlapping
121 each position in the genome can be generated from the sorted BAM file
122 output. To generate the wiggle plot, run the 'rsem-bam2wig' program on
123 the 'sample_name.sorted.bam' file.
127 rsem-bam2wig bam_input wig_output wiggle_name
129 bam_input: sorted bam file
130 wig_output: output file name, e.g. output.wig
131 wiggle_name: the name the user wants to use for this wiggle plot
133 #### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser
135 Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
137 #### c) Visualize the model learned by RSEM
139 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
143 rsem-plot-model sample_name outF
145 sample_name: the name of the sample analyzed
146 outF: the file name for plots generated from the model. It is a pdf file
148 The plots generated depends on read type and user configuration. It
149 may include fragment length distribution, mate length distribution,
150 read start position distribution (RSPD), quality score vs observed
151 quality given a reference base, position vs percentage of sequencing
152 error given a reference base and histogram of reads with different
153 number of alignments.
155 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
157 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
159 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
161 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
163 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
165 ## <a name="example"></a> Example
167 Suppose we download the mouse genome from UCSC Genome Browser. We will
168 use a reference_name of 'mm9'. We have a FASTQ-formatted file,
169 'mmliver.fq', containing single-end reads from one sample, which we call
170 'mmliver_single_quals'. We want to estimate expression values by using
171 the single-end model with a fragment length distribution. We know that
172 the fragment length distribution is approximated by a normal
173 distribution with a mean of 150 and a standard deviation of 35. We wish
174 to generate 95% credibility intervals in addition to maximum likelihood
175 estimates. RSEM will be allowed 1G of memory for the credibility
176 interval calculation. We will visualize the probabilistic read mappings
179 The commands for this scenario are as follows:
181 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
182 rsem-calculate-expression --bowtie-path /sw/bowtie --phred64-quals --fragment-length-mean 150.0 --fragment-length-sd 35.0 -p 8 --out-bam --calc-ci --memory-allocate 1024 /data/mmliver.fq /ref/mm9 mmliver_single_quals
183 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
185 ## <a name="simulation"></a> Simulation
189 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
191 estimated_model_file: file containing model parameters. Generated by
192 rsem-calculate-expression.
193 estimated_isoform_results: file containing isoform expression levels.
194 Generated by rsem-calculate-expression.
195 theta0: fraction of reads that are "noise" (not derived from a transcript).
196 N: number of reads to simulate.
197 output_name: prefix for all output files.
198 [-q] : set it will stop outputting intermediate information.
202 output_name.fa if single-end without quality score;
203 output_name.fq if single-end with quality score;
204 output_name_1.fa & output_name_2.fa if paired-end without quality
206 output_name_1.fq & output_name_2.fq if paired-end with quality score.
208 output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
210 ## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
212 For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
216 extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
218 trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
219 map_file: transcript-to-gene-map file's name.
221 ## <a name="acknowledgements"></a> Acknowledgements
223 RSEM uses the [Boost C++](http://www.boost.org) and
224 [samtools](http://samtools.sourceforge.net) libraries.
226 ## <a name="license"></a> License
228 RSEM is licensed under the [GNU General Public License v3](http://www.gnu.org/licenses/gpl-3.0.html).