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 * [Acknowledgements](#acknowledgements)
21 ## <a name="introduction"></a> Introduction
23 RSEM is a software package for estimating gene and isoform expression
24 levels from RNA-Seq data. The new RSEM package (rsem-1.x) provides an
25 user-friendly interface, supports threads for parallel computation of
26 the EM algorithm, single-end and paired-end read data, quality scores,
27 variable-length reads and RSPD estimation. It can also generate
28 genomic-coordinate BAM files and UCSC wiggle files for visualization. In
29 addition, it provides posterior mean and 95% credibility interval
30 estimates for expression levels.
32 ## <a name="compilation"></a> Compilation & Installation
34 To compile RSEM, simply run
38 To install, simply put the rsem directory in your environment's PATH
43 To take advantage of RSEM's built-in support for the Bowtie alignment
44 program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
46 ## <a name="usage"></a> Usage
48 ### I. Preparing Reference Sequences
50 RSEM can extract reference transcripts from a genome if you provide it
51 with gene annotations in a GTF file. Alternatively, you can provide
52 RSEM with transcript sequences directly.
54 Please note that GTF files generated from the UCSC Table Browser do not
55 contain isoform-gene relationship information. However, if you use the
56 UCSC Genes annotation track, this information can be recovered by
57 downloading the knownIsoforms.txt file for the appropriate genome.
59 To prepare the reference sequences, you should run the
60 'rsem-prepare-reference' program. Run
62 rsem-prepare-reference --help
64 to get usage information or visit the [rsem-prepare-reference
65 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-prepare-reference.html).
67 ### II. Calculating Expression Values
69 To calculate expression values, you should run the
70 'rsem-calculate-expression' program. Run
72 rsem-calculate-expression --help
74 to get usage information or visit the [rsem-calculate-expression
75 documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html).
77 #### Calculating expression values from single-end data
79 For single-end models, users have the option of providing a fragment
80 length distribution via the --fragment-length-mean and
81 --fragment-length-sd options. The specification of an accurate fragment
82 length distribution is important for the accuracy of expression level
83 estimates from single-end data. If the fragment length mean and sd are
84 not provided, RSEM will not take a fragment length distribution into
87 #### Using an alternative aligner
89 By default, RSEM automates the alignment of reads to reference
90 transcripts using the Bowtie alignment program. To use an alternative
91 alignment program, align the input reads against the file
92 'reference_name.idx.fa' generated by rsem-prepare-reference, and format
93 the alignment output in SAM or BAM format. Then, instead of providing
94 reads to rsem-calculate-expression, specify the --sam or --bam option
95 and provide the SAM or BAM file as an argument. When using an
96 alternative aligner, you may also want to provide the --no-bowtie option
97 to rsem-prepare-reference so that the Bowtie indices are not built.
99 ### III. Visualization
101 RSEM contains a version of samtools in the 'sam' subdirectory. When
102 users specify the --out-bam option RSEM will produce three files:
103 'sample_name.bam', the unsorted BAM file, 'sample_name.sorted.bam' and
104 'sample_name.sorted.bam.bai' the sorted BAM file and indices generated
105 by the samtools included.
107 #### a) Generating a UCSC Wiggle file
109 A wiggle plot representing the expected number of reads overlapping
110 each position in the genome can be generated from the sorted BAM file
111 output. To generate the wiggle plot, run the 'rsem-bam2wig' program on
112 the 'sample_name.sorted.bam' file.
116 rsem-bam2wig bam_input wig_output wiggle_name
118 bam_input: sorted bam file
119 wig_output: output file name, e.g. output.wig
120 wiggle_name: the name the user wants to use for this wiggle plot
122 #### b) Loading a BAM and/or Wiggle file into the UCSC Genome Browser
124 Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
126 #### c) Visualize the model learned by RSEM
128 RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
132 rsem-plot-model modelF outF
134 modelF: the sample_name.model file generated by RSEM
135 outF: the file name for plots generated from the model. It is a pdf file
137 The plots generated depends on read type and user configuration. It
138 may include fragment length distribution, mate length distribution,
139 read start position distribution (RSPD), quality score vs observed
140 quality given a reference base, position vs percentage of sequencing
141 error given a reference base.
143 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
145 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
147 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
149 Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
151 ## <a name="example"></a> Example
153 Suppose we download the mouse genome from UCSC Genome Browser. We will
154 use a reference_name of 'mm9'. We have a FASTQ-formatted file,
155 'mmliver.fq', containing single-end reads from one sample, which we call
156 'mmliver_single_quals'. We want to estimate expression values by using
157 the single-end model with a fragment length distribution. We know that
158 the fragment length distribution is approximated by a normal
159 distribution with a mean of 150 and a standard deviation of 35. We wish
160 to generate 95% credibility intervals in addition to maximum likelihood
161 estimates. RSEM will be allowed 1G of memory for the credibility
162 interval calculation. We will visualize the probabilistic read mappings
165 The commands for this scenario are as follows:
167 rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
168 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
169 rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
171 ## <a name="simulation"></a> Simulation
175 rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
177 estimated_model_file: File containing model parameters. Generated by
178 rsem-calculate-expression.
179 estimated_isoform_results: File containing isoform expression levels.
180 Generated by rsem-calculate-expression.
181 theta0: fraction of reads that are "noise" (not derived from a transcript).
182 N: number of reads to simulate.
183 output_name: prefix for all output files.
184 [-q] : set it will stop outputting intermediate information.
188 output_name.fa if single-end without quality score;
189 output_name.fq if single-end with quality score;
190 output_name_1.fa & output_name_2.fa if paired-end without quality
192 output_name_1.fq & output_name_2.fq if paired-end with quality score.
194 output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
196 ## <a name="acknowledgements"></a> Acknowledgements
198 RSEM uses randomc.h and mersenne.cpp from
199 <http://lxnt.info/rng/randomc.htm> for random number generation. RSEM
200 also uses the [Boost C++](http://www.boost.org) and
201 [samtools](http://samtools.sourceforge.net) libraries.
203 ## <a name="license"></a> License
205 RSEM is licensed under the [GNU General Public License v3](http://www.gnu.org/licenses/gpl-3.0.html).