Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

5.1 Tag-sequencing Data Processing Methods
the TagDust program [258] with a target false discovery rate (FDR) of 1%.
Tags matching ribosomal sequence were identified by using the Bowtie pro-
gram [257] to align against a database consisting of all rRNA genes, including
pseudogenes, from Ensembl 56 [147] and all ribosomal repeats in the UCSC
Genome Browser RepeatMasker track for genome assembly GRCh37 [152];
only perfect matches to the extended 21nt tag sequence, consisting of the
NlaIII site CATG followed by the observed 17nt tag, were accepted. Mito-
chondrial tags were similarly excluded by searching for perfect matches to the
mitochondrial chromosome sequence. The parameters used to run the Bowtie
program were the following:
-f parameter indicates that the query input files are FASTA files;
-n parameter set to 0 means that the alignments may have no more than n
mismatches. Since we are only looking for perfect matches, n=0.
-y parameter specifies to the program to try as hard as possible to find valid
alignments when they exist, which makes running this mode much slower;
-k 2 instructs the program to report up to two valid alignments;
-m 1 instructs the program to refrain from reporting any alignments for reads
having more than one reportable alignment. This option is useful when
the user wants to guarantee that reported alignments are unique, which
is our case.
To assign tags to genes, we employed a hierarchical strategy based on the ex-
pectation that tags are most likely to originate from the 3’-most NlaIII site in
known transcripts. Tags were assigned to transcripts using virtual tag data
from the SAGE Genie database [65] and virtual tags extracted from Ensembl
transcript models. The SAGE Genie annotation consisted of 105 sets of virtual
tags obtained by scanning for NlaIII sites in cDNAs from RefSeq, Mammalian
Gene Collection (MGC) and Genbank, then Expressed Sequence Tags (ESTs),
UniGene consensus sequences and transfrags. The virtual tag sets are further
classified based on the position of the tag relative to the 3’ end of the transcript
and indicators of 3’ end reliability such as polyadenylation signal and poly-A
tail. Since SAGE Genie does not cover Ensembl transcripts, we also extracted
virtual tags from the 3’-most cut site in each Ensembl transcript. We used the
CATG recognition sequence as a "signal" that indicated where to start count-
ing 17nt ahead. This allowed us to extract that 17nt tag and know exactly from
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