X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=README.md;h=b46826a58209fda36bc59cff8e860a95707dfe25;hb=4a435fbee229af1dd5f76b4feb0ca71398ac8796;hp=0c900b725107ac55b3074fa6cc0fa5e1b628c978;hpb=18c158c73b8f7f0afe4db97ac51b520149d33c12;p=rsem.git
diff --git a/README.md b/README.md
index 0c900b7..b46826a 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,7 @@ Table of Contents
* [Usage](#usage)
* [Example](#example)
* [Simulation](#simulation)
+* [Generate Transcript-to-Gene-Map from Trinity Output](#gen_trinity)
* [Acknowledgements](#acknowledgements)
* [License](#license)
@@ -25,9 +26,11 @@ levels from RNA-Seq data. The new RSEM package (rsem-1.x) provides an
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.
## Compilation & Installation
@@ -40,9 +43,13 @@ variable.
### Prerequisites
+C++ and Perl are required to be installed.
+
To take advantage of RSEM's built-in support for the Bowtie alignment
program, you must have [Bowtie](http://bowtie-bio.sourceforge.net) installed.
+If you want to plot model learned by RSEM, you should also install R.
+
## Usage
### I. Preparing Reference Sequences
@@ -77,8 +84,8 @@ documentation page](http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-express
#### 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
@@ -89,27 +96,48 @@ consideration.
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:
@@ -123,42 +151,68 @@ wiggle_name: the name the user wants to use for this wiggle plot
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, plotModel.R, for visulazing the model learned.
+RSEM provides an R script, 'rsem-plot-model', for visulazing the model learned.
Usage:
- plotModel.R 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 percentage
-of sequecing error given the reference base, position vs percentage of
-sequencing errro given the reference base.
+read start position distribution (RSPD), quality score vs observed
+quality given a reference base, position vs percentage of sequencing
+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
+
+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
+
+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
+
## 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
## Simulation
@@ -166,9 +220,9 @@ The commands for this scenario are as follows:
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.
@@ -185,11 +239,20 @@ output_name_1.fq & output_name_2.fq if paired-end with quality score.
output_name.sim.isoforms.results, output_name.sim.genes.results : Results estimated based on sample values.
+## 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.
+
## Acknowledgements
-RSEM uses randomc.h and mersenne.cpp from
- 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.
## License