Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

6.2 Tag mapping Results
Table 6.1: Summary of the available clinical data for our GNS cell lines (M=Male,
F=Female, n.a=not applicable).
Type of Name of Tissue Type Sex IDH1 IDH2 Patient
cell line cell line mutation mutation age (years)
GNS G144 GBM Primary M No No 51
GNS G144ED GBM Primary M No No 51
GNS G166 GBM Primary F No No 74
GNS G179 Giant cell GBM Primary M No No 56
NS CB541 Fetal forebrain n.a. n.a. n.a. n.a. n.a.
NS CB660 Fetal forebrain n.a. n.a. n.a. n.a. n.a.
6.2 Tag mapping
We created one Tag-seq library per cell line and obtained between 6 and 13
million sequence reads from each (Table 6.2). Every read was formed by a first
sequencing primer, the 17nt tag, and a second sequencing primer in this order
(Fig 6.1).
Table 6.2: Summary of reads per cell line library.
Type of cell line Cell line Number of reads
GNS G144 7,133,520
GNS G144ED 6,383,175
GNS G166 13,415,402
GNS G179 11,610,415
NS CB541 12,103,066
NS CB660 10,043,561
Figure 6.1: Diagram of the construct generated by the longSAGE protocol sent to
the Illumina sequencer for the final step of Tag-seq.
Once the read sequences were received as FASTA files, we first extracted the
17nt tags they contained, then filtered these tags and, finally, aligned them
to the genome. The entire process is summarised as a diagram in figure 6.2.
Firstly, the 17nt tags were extracted out of each read, separating the primer
sequences from the tag sequences that actually interested us. Secondly, each
tag sequence was counted and the resulting counts adjusted for sequencing and
library preparation errors that might have caused highly expressed transcripts
to give rise to a significant number of tags differing from the expected tag se-
quence by just one base. Secondly, the reads that were not going to map onto
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