X-Git-Url: https://git.donarmstrong.com/?p=rsem.git;a=blobdiff_plain;f=README.md;h=4184f5d44caf4d94ebb8c5445f25d531517931d8;hp=f6b5ec857f44861c1cd4d5d62b483738e511833d;hb=d13cdd443afbefff6f7c8c0be818e1edcbc9cb8d;hpb=4f7502168c3816ba3283385f093e599527e2b144

diff --git a/README.md b/README.md
index f6b5ec8..4184f5d 100644
--- a/README.md
+++ b/README.md
@@ -56,6 +56,11 @@ To compile EBSeq, which is included in the RSEM package, run
 To install, simply put the rsem directory in your environment's PATH
 variable.
 
+If you prefer to put all RSEM executables to a bin directory, please
+also remember to put 'rsem_perl_utils.pm' and 'WHAT_IS_NEW' to the
+same bin directory. 'rsem_perl_utils.pm' is required for most RSEM's
+perl scripts and 'WHAT_IS_NEW' contains the RSEM version information.
+
 ### Prerequisites
 
 C++, Perl and R are required to be installed. 
@@ -209,7 +214,7 @@ Here are some guidance for visualizing transcript coordinate files using IGV:
 
 1) Import the transcript sequences as a genome 
 
-Select File -> Import Genome, then fill in ID, Name and Fasta file. Fasta file should be 'reference_name.transcripts.fa'. After that, click Save button. Suppose ID is filled as 'reference_name', a file called 'reference_name.genome' will be generated. Next time, we can use: File -> Load Genome, then select 'reference_name.genome'.
+Select File -> Import Genome, then fill in ID, Name and Fasta file. Fasta file should be 'reference_name.idx.fa'. After that, click Save button. Suppose ID is filled as 'reference_name', a file called 'reference_name.genome' will be generated. Next time, we can use: File -> Load Genome, then select 'reference_name.genome'.
 
 2) Load visualization files
 
@@ -304,11 +309,14 @@ __N:__ The total number of reads to be simulated. If 'rsem-calculate-expression'
 
 __output_name:__ Prefix for all output files.   
 
+__--seed seed:__ Set seed for the random number generator used in simulation. The seed should be a 32-bit unsigned integer.
+
 __-q:__ Set it will stop outputting intermediate information.   
 
 ### Outputs:
 
 output_name.sim.isoforms.results, output_name.sim.genes.results: Expression levels estimated by counting where each simulated read comes from.
+output_name.sim.alleles.results: Allele-specific expression levels estimated by counting where each simulated read comes from.
 
 output_name.fa if single-end without quality score;   
 output_name.fq if single-end with quality score;   
@@ -454,9 +462,9 @@ RSEM uses the [Boost C++](http://www.boost.org) and
 [EBSeq](http://www.biostat.wisc.edu/~ningleng/EBSeq_Package/) for
 differential expression analysis.
 
-We thank earonesty, Dr. Samuel Arvidsson for contributing patches.
+We thank earonesty and Dr. Samuel Arvidsson for contributing patches.
 
-We thank Han Lin, j.miller for suggesting possible fixes. 
+We thank Han Lin, j.miller, Joël Fillon, Dr. Samuel G. Younkin and Malcolm Cook for suggesting possible fixes. 
 
 ## <a name="license"></a> License