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Lecture Note - TBCB - week 2

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Course
Tumor Biology and Clinical Behaviour

Institution
Vrije Universiteit Amsterdam (VU)

Lectures included: bioinformatics, genomic data analysis (class discovery), tumor metabolism, tumor-stroma interaction, tumor angiogenesis & hypoxia, glioma & angiogenesis, CT colonography, cancer dissemination, circulating tumor cells, MRD detection (hematological malignancy), liquid biopsy,

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Uploaded on January 9, 2020
Number of pages 38
Written in 2018/2019
Type Class notes
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Contains All classes

tumor biology clinical behavior
mandatory courses master oncology vu amsterdam

Institution
Vrije Universiteit Amsterdam (VU)
Education
Oncology
Course
Tumor Biology and Clinical Behaviour

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1. Class notes - Lecture note - tbcb - week 3
2. Class notes - Lecture note - tbcb - week 2
3. Class notes - Lecture notes - tbcb - week 1
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Week 2: Tumor Biology and Clinical Behaviour

LECTURE 11: BIOINFORMATICS INTRO TO PRACTICAL WORK (D. Sie) Monday, 05/11/2018

Illumina sequencing workflow

Fragmenting DNA (100-500 bp fragments)  sonication (creating frayed DNA ends)  ligate adapters to each end of the A-
tailed DNA fragment  electropherogram interpretation  cluster generation  bridge formation  bridge amplification
(isothermal)  create millions dense cluster of single strand DNA in each channel of flow cells (primer still attached) 
sequencing (adding terminator & DNA polymerase)  base calling  sequencing by synthesis 

Paired end reads sequencing: require bridge amplification, followed by a flip of the template (for reads that’s too long to read
as a whole)

, Week 2: Tumor Biology and Clinical Behaviour

FASTQ file

1st line: specific cluster being analyzed in the flow cell

a. @Machine name_ f. Cluster X coordinate:
b. Run number_ g. Cluster Y coordinate#
c. Flow cell ID: h. Multiplex barcode/
d. Lane: i. Read number
e. Tile:
2nd line: sequence of 50 nucleotide (example  25-150 nt depending on the machine; limitation: relatively short reads)

3rd line:

a. Quality record indicator (+)
b. Description (a-i of first line)

4th line: ASCII representation of Phred score  probability of the base call being wrong

3 (B) to 40 (H)  B = 3 = 0.40, H = 40 = 0.000 1 (good result: dominant H)

Phred score: (see slide)  determines accuracy of the base called

FASTQC for quality control

%GC (G & C bases added in the sequence)

Plots:

 X-axis: cycle number (1-150 for example)

 Y-axis: Phred score (0-40)  good quality: most of the bases in the sequence are closer to 40 (top most area)

 Less quality data: bad sample

Other QC measurement:

 Top right: GC %age  blue: ideal/normal distribution (general representation of human samples), red: result from
experiment (exact composition of nucleotides in the reads)

 Bottom left: amplicon assay, analyzing each nucleotide (Y axis: GC%, X axis: …)  significant difference in percentage:
overrepresentation of certain sequence

 Bottom right: …

More likely to get the accurate data from smaller molecule, that’s why fragmentation is required

Data processing

1. Remove adapter sequence (not informative for the experiment) & primers
2. Trim low quality reads from the ends  low Phred score, …? (listen to recording)

Chopping off the adapter/low quality reads would somehow affect result, albeit not significantly

Mapping reads to the reference

, Week 2: Tumor Biology and Clinical Behaviour

Aim: find where their sequence occurs in the genome (map against reference genome sequence)  Burrows Wheeler
transform as data compression algorithm, allows for searching large genome & incorporation of many queries/reads in short
time

SAM file: sequence alignment map  contains info about how sequence reads map to a reference genome (used in all NGS
tools)

Format

CIGAR line (bottom box)  9M = 9 matches to the reference, NM: non-matching (wrong base when mapped against the
reference)

I = inserted, D = deleted, N = …

BAM: binary SAM/compressed SAM

CRAM: doesn’t store sequenced data  relies on reference

Grey bar: reads completing sequencing process  allow analysis of reads that don’t agree with the reference sequence (actual
error vs. artefact; events located in the actual read)

a. wrong base: A > T
b. polymorphisms
c. deletion: sequenced read don’t have certain genomes present in reference sequence
d. insertion: extra piece of nucleotide normally absent in reference sequence)

Events located not in the actual read  detect with PET …?

 Distance between 2 tags/ends should be fixed  longer/shorter distance: insertion/deletion

, Week 2: Tumor Biology and Clinical Behaviour

LECTURE 12: BIOINFORMATICS ANALYSIS & WORKFLOW (S.Abeln) Monday, 05/11/2018

Tumor sample  sequencing & data
processing 1 (finding
events/mutations/etc)  data processing
2 (which specific mutation is acting as
driving/passenger mutation)

Most of the mutations: passenger
mutations  driver mutation is more
fundamental for tumor biology studies,
thus it has to be determined; driver v.
passenger mutations need to be
distinguished by comparing to reference
sequence (external data source: other
cohort, genome reference, etc)

TUMORS: hyper-mutate!

a. NGS
Massive parallel sequencing  huge amount of reads, more cost effective;
disadvantage: fragmented sequence, difficult to determine the order
Computational solution:
1. Read mapping (against reference genome)
Input: sequenced fragments, reference genome sequence
Reference genome: based on multiple individuals, to allow variations being
examined/taken into account
Process: string matching of sequence  reference & sequenced organism need
to be closely related (same species)
Output: reference alignment/BAM  fluctuations in the alignment: variation
of individual’s sequence being read
Mismatches in alignment caused by: polymorphism (SNP – patient specific),
artefact/read mistake (1% frequency – high, more nucleotide involved =
higher chance of read mistake), actual mutation (tumor-specific)
Depth of coverage:
Depth = average number of reads per base (over the whole sequencing
sample)
Coverage = number of reads per base (specific region in sequence)
2. De novo assembly (advantage: need no reference sequence)
Input: millions of sequenced fragments
Process: cut reads in k-mers  string matching (to determine read overlap)
Output: alignment & sequence of a new strain
b. Single molecule sequencing

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