* [Usage](#usage)
* [Example](#example)
* [Simulation](#simulation)
+* [Generate Transcript-to-Gene-Map from Trinity Output](#gen_trinity)
* [Acknowledgements](#acknowledgements)
* [License](#license)
user-friendly interface, supports threads for parallel computation of
the EM algorithm, single-end and paired-end read data, quality scores,
variable-length reads and RSPD estimation. It can also generate
-genomic-coordinate BAM files and UCSC wiggle files for visualization. In
-addition, it provides posterior mean and 95% credibility interval
-estimates for expression levels.
+genomic-coordinate BAM files and UCSC wiggle files for
+visualization. In addition, it provides posterior mean and 95%
+credibility interval estimates for expression levels. For
+visualization, it can also generate transcript-coordinate BAM files
+and visualize them and also models learned.
## <a name="compilation"></a> Compilation & Installation
#### Calculating expression values from single-end data
For single-end models, users have the option of providing a fragment
-length distribution via the --fragment-length-mean and
---fragment-length-sd options. The specification of an accurate fragment
+length distribution via the '--fragment-length-mean' and
+'--fragment-length-sd' options. The specification of an accurate fragment
length distribution is important for the accuracy of expression level
estimates from single-end data. If the fragment length mean and sd are
not provided, RSEM will not take a fragment length distribution into
By default, RSEM automates the alignment of reads to reference
transcripts using the Bowtie alignment program. To use an alternative
alignment program, align the input reads against the file
-'reference_name.idx.fa' generated by rsem-prepare-reference, and format
+'reference_name.idx.fa' generated by 'rsem-prepare-reference', and format
the alignment output in SAM or BAM format. Then, instead of providing
-reads to rsem-calculate-expression, specify the --sam or --bam option
+reads to 'rsem-calculate-expression', specify the '--sam' or '--bam' option
and provide the SAM or BAM file as an argument. When using an
-alternative aligner, you may also want to provide the --no-bowtie option
-to rsem-prepare-reference so that the Bowtie indices are not built.
+alternative aligner, you may also want to provide the '--no-bowtie' option
+to 'rsem-prepare-reference' so that the Bowtie indices are not built.
+
+Some aligners' (other than Bowtie) output might need to be converted
+so that RSEM can use. For conversion, please run
+
+ convert-sam-for-rsem --help
+
+to get usage information or visit the [convert-sam-for-rsem
+documentation
+page](http://deweylab.biostat.wisc.edu/rsem/convert-sam-for-rsem.html).
+
+However, please note that RSEM does ** not ** support gapped
+alignments. So make sure that your aligner does not produce alignments
+with intersions/deletions. Also, please make sure that you use
+'reference_name.idx.fa' , which is generated by RSEM, to build your
+aligner's indices.
### III. Visualization
-RSEM contains a version of samtools in the 'sam' subdirectory. When
-users specify the --out-bam option RSEM will produce three files:
-'sample_name.bam', the unsorted BAM file, 'sample_name.sorted.bam' and
-'sample_name.sorted.bam.bai' the sorted BAM file and indices generated
-by the samtools included.
+RSEM contains a version of samtools in the 'sam' subdirectory. RSEM
+will always produce three files:'sample_name.transcript.bam', the
+unsorted BAM file, 'sample_name.transcript.sorted.bam' and
+'sample_name.transcript.sorted.bam.bai' the sorted BAM file and
+indices generated by the samtools included. All three files are in
+transcript coordinates. When users specify the --output-genome-bam
+option RSEM will produce three files: 'sample_name.genome.bam', the
+unsorted BAM file, 'sample_name.genome.sorted.bam' and
+'sample_name.genome.sorted.bam.bai' the sorted BAM file and indices
+generated by the samtools included. All these files are in genomic
+coordinates.
#### a) Generating a UCSC Wiggle file
A wiggle plot representing the expected number of reads overlapping
-each position in the genome can be generated from the sorted BAM file
-output. To generate the wiggle plot, run the 'rsem-bam2wig' program on
-the 'sample_name.sorted.bam' file.
+each position in the genome can be generated from the sorted genome
+BAM file output. To generate the wiggle plot, run the 'rsem-bam2wig'
+program on the 'sample_name.genome.sorted.bam' file.
Usage:
Refer to the [UCSC custom track help page](http://genome.ucsc.edu/goldenPath/help/customTrack.html).
-#### c) Visualize the model learned by RSEM
+#### c) Generating Transcript Wiggle Plots
+
+To generate transcript wiggle plots, you should run the
+'rsem-plot-transcript-wiggles' program. Run
+
+ rsem-plot-transcript-wiggles --help
+
+to get usage information or visit the [rsem-plot-transcript-wiggles
+documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-plot-transcript-wiggles.html).
+
+#### d) Visualize the model learned by RSEM
RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
Usage:
- rsem-plot-model modelF outF
+ rsem-plot-model sample_name output_plot_file
-modelF: the sample_name.model file generated by RSEM
-outF: the file name for plots generated from the model. It is a pdf file
+sample_name: the name of the sample analyzed
+output_plot_file: the file name for plots generated from the model. It is a pdf file
The plots generated depends on read type and user configuration. It
may include fragment length distribution, mate length distribution,
read start position distribution (RSPD), quality score vs observed
quality given a reference base, position vs percentage of sequencing
-error given a reference base.
+error given a reference base and histogram of reads with different
+number of alignments.
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
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
Position vs. percentage sequencing error given a reference base: x-axis is position and y-axis is percentage sequencing error
+
+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
## <a name="example"></a> Example
-Suppose we download the mouse genome from UCSC Genome Browser. We will
-use a reference_name of 'mm9'. We have a FASTQ-formatted file,
-'mmliver.fq', containing single-end reads from one sample, which we call
-'mmliver_single_quals'. We want to estimate expression values by using
-the single-end model with a fragment length distribution. We know that
-the fragment length distribution is approximated by a normal
-distribution with a mean of 150 and a standard deviation of 35. We wish
-to generate 95% credibility intervals in addition to maximum likelihood
-estimates. RSEM will be allowed 1G of memory for the credibility
-interval calculation. We will visualize the probabilistic read mappings
-generated by RSEM.
+Suppose we download the mouse genome from UCSC Genome Browser. We
+will use a reference_name of 'mm9'. We have a FASTQ-formatted file,
+'mmliver.fq', containing single-end reads from one sample, which we
+call 'mmliver_single_quals'. We want to estimate expression values by
+using the single-end model with a fragment length distribution. We
+know that the fragment length distribution is approximated by a normal
+distribution with a mean of 150 and a standard deviation of 35. We
+wish to generate 95% credibility intervals in addition to maximum
+likelihood estimates. RSEM will be allowed 1G of memory for the
+credibility interval calculation. We will visualize the probabilistic
+read mappings generated by RSEM on UCSC genome browser. We will
+generate a list of genes' transcript wiggle plots in 'output.pdf'. The
+list is 'gene_ids.txt'. We will visualize the models learned in
+'mmliver_single_quals.models.pdf'
The commands for this scenario are as follows:
rsem-prepare-reference --gtf mm9.gtf --mapping knownIsoforms.txt --bowtie-path /sw/bowtie /data/mm9 /ref/mm9
- 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
+ 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
rsem-bam2wig mmliver_single_quals.sorted.bam mmliver_single_quals.sorted.wig mmliver_single_quals
+ rsem-plot-transcript-wiggles --gene-list --show-unique mmliver_single_quals gene_ids.txt output.pdf
+ rsem-plot-model mmliver_single_quals mmliver_single_quals.models.pdf
## <a name="simulation"></a> Simulation
rsem-simulate-reads reference_name estimated_model_file estimated_isoform_results theta0 N output_name [-q]
-estimated_model_file: File containing model parameters. Generated by
+estimated_model_file: file containing model parameters. Generated by
rsem-calculate-expression.
-estimated_isoform_results: File containing isoform expression levels.
+estimated_isoform_results: file containing isoform expression levels.
Generated by rsem-calculate-expression.
theta0: fraction of reads that are "noise" (not derived from a transcript).
N: number of reads to simulate.
output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
+## <a name="gen_trinity"></a> Generate Transcript-to-Gene-Map from Trinity Output
+
+For Trinity users, RSEM provides a perl script to generate transcript-to-gene-map file from the fasta file produced by Trinity.
+
+### Usage:
+
+ extract-transcript-to-gene-map-from-trinity trinity_fasta_file map_file
+
+trinity_fasta_file: the fasta file produced by trinity, which contains all transcripts assembled.
+map_file: transcript-to-gene-map file's name.
+
## <a name="acknowledgements"></a> Acknowledgements
-RSEM uses randomc.h and mersenne.cpp from
-<http://lxnt.info/rng/randomc.htm> for random number generation. RSEM
-also uses the [Boost C++](http://www.boost.org) and
+RSEM uses the [Boost C++](http://www.boost.org) and
[samtools](http://samtools.sourceforge.net) libraries.
## <a name="license"></a> License
projects / rsem.git / blobdiff