+**Format of the header line**: Each simulated read's header line encodes where it comes from. The header line has the format:
+
+ {>/@}_rid_dir_sid_pos[_insertL]
+
+__{>/@}:__ Either '>' or '@' must appear. '>' appears if FASTA files are generated and '@' appears if FASTQ files are generated
+
+__rid:__ Simulated read's index, numbered from 0
+
+__dir:__ The direction of the simulated read. 0 refers to forward strand ('+') and 1 refers to reverse strand ('-')
+
+__sid:__ Represent which transcript this read is simulated from. It ranges between 0 and M, where M is the total number of transcripts. If sid=0, the read is simulated from the background noise. Otherwise, the read is simulated from a transcript with index sid. Transcript sid's transcript name can be found in the 'transcript_id' column of the 'sample_name.isoforms.results' file (at line sid + 1, line 1 is for column names)
+
+__pos:__ The start position of the simulated read in strand dir of transcript sid. It is numbered from 0
+
+__insertL:__ Only appear for paired-end reads. It gives the insert length of the simulated read.
+
+### Example:
+
+Suppose we want to simulate 50 millon single-end reads with quality scores and use the parameters learned from [Example](#example). In addition, we set theta0 as 0.2 and output_name as 'simulated_reads'. The command is:
+
+ rsem-simulate-reads /ref/mouse_125 mmliver_single_quals.stat/mmliver_single_quals.model mmliver_single_quals.isoforms.results 0.2 50000000 simulated_reads
+
+## <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="de"></a> Differential Expression Analysis
+
+Popular differential expression (DE) analysis tools such as edgeR and
+DESeq do not take variance due to read mapping uncertainty into
+consideration. Because read mapping ambiguity is prevalent among
+isoforms and de novo assembled transcripts, these tools are not ideal
+for DE detection in such conditions.
+
+EBSeq, an empirical Bayesian DE analysis tool developed in UW-Madison,
+can take variance due to read mapping ambiguity into consideration by
+grouping isoforms with parent gene's number of isoforms. In addition,
+it is more robust to outliers. For more information about EBSeq
+(including the paper describing their method), please visit [EBSeq's
+website](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package).
+
+
+RSEM includes EBSeq in its folder named 'EBSeq'. To use it, first type
+
+ make ebseq
+
+to compile the EBSeq related codes.
+
+EBSeq requires gene-isoform relationship for its isoform DE
+detection. However, for de novo assembled transcriptome, it is hard to
+obtain an accurate gene-isoform relationship. Instead, RSEM provides a
+script 'rsem-generate-ngvector', which clusters transcripts based on
+measures directly relating to read mappaing ambiguity. First, it
+calcualtes the 'unmappability' of each transcript. The 'unmappability'
+of a transcript is the ratio between the number of k mers with at
+least one perfect match to other transcripts and the total number of k
+mers of this transcript, where k is a parameter. Then, Ng vector is
+generated by applying Kmeans algorithm to the 'unmappability' values
+with number of clusters set as 3. This program will make sure the mean
+'unmappability' scores for clusters are in ascending order. All
+transcripts whose lengths are less than k are assigned to cluster
+3. Run
+
+ rsem-generate-ngvector --help
+
+to get usage information or visit the [rsem-generate-ngvector
+documentation
+page](http://deweylab.biostat.wisc.edu/rsem/rsem-generate-ngvector.html).
+
+If your reference is a de novo assembled transcript set, you should
+run 'rsem-generate-ngvector' first. Then load the resulting
+'output_name.ngvec' into R. For example, you can use
+
+ NgVec <- scan(file="output_name.ngvec", what=0, sep="\n")
+
+. After that, set "NgVector = NgVec" for your differential expression
+test (either 'EBTest' or 'EBMultiTest').
+
+
+For users' convenience, RSEM also provides a script
+'rsem-generate-data-matrix' to extract input matrix from expression
+results:
+
+ rsem-generate-data-matrix sampleA.[genes/isoforms].results sampleB.[genes/isoforms].results ... > output_name.counts.matrix
+
+The results files are required to be either all gene level results or
+all isoform level results. You can load the matrix into R by
+
+ IsoMat <- data.matrix(read.table(file="output_name.counts.matrix"))
+
+before running either 'EBTest' or 'EBMultiTest'.
+
+Lastly, RSEM provides two scripts, 'rsem-run-ebseq' and
+'rsem-control-fdr', to help users find differential expressed
+genes/transcripts. First, 'rsem-run-ebseq' calls EBSeq to calculate related statistics
+for all genes/transcripts. Run
+
+ rsem-run-ebseq --help
+
+to get usage information or visit the [rsem-run-ebseq documentation
+page](http://deweylab.biostat.wisc.edu/rsem/rsem-run-ebseq.html). Second,
+'rsem-control-fdr' takes 'rsem-run-ebseq' 's result and reports called
+differentially expressed genes/transcripts by controlling the false
+discovery rate. Run
+
+ rsem-control-fdr --help
+
+to get usage information or visit the [rsem-control-fdr documentation
+page](http://deweylab.biostat.wisc.edu/rsem/rsem-control-fdr.html). These
+two scripts can perform DE analysis on either 2 conditions or multiple
+conditions.
+
+Please note that 'rsem-run-ebseq' and 'rsem-control-fdr' use EBSeq's
+default parameters. For advanced use of EBSeq or information about how
+EBSeq works, please refer to [EBSeq's
+manual](http://www.bioconductor.org/packages/devel/bioc/vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf).
+
+Questions related to EBSeq should
+be sent to <a href="mailto:nleng@wisc.edu">Ning Leng</a>.
+
+## <a name="authors"></a> Authors
+
+The RSEM algorithm is developed by Bo Li and Colin Dewey. The RSEM software is mainly implemented by Bo Li.