-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'