Experimental Designs in Next Generation Sequencing Introduction Types of experimental designs Basic NGS chemistry Tools used in NGS Good and Bad experimental designs

1. Presented by
Aishwarya Mohorikar
Pawan Gutti
Msc -1 Biochemistry
The Institute of Science Mumbai
Presented by
Presented
by
Aishwarya Mohorikar
pavan Gutti
Msc -1 Biochemistry
The Institute of Science
Mumbai
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Experimental Designs in Next Generation
Sequencing

2. • Introduction
• Types of experimental designs
• Basic NGS chemistry
• Tools used in NGS
• Good and Bad experimental designs
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Sequencing

3. • Next generation sequencing (NGS), massively
parallel or deep sequencing are related terms
that describe a DNA sequencing technology
which has revolutionized genomic research.
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4. • There are 3 types of experimental designs in
NGS.
• Paired v/s single
• Multiple
• Mate
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5. Principle
The Basics of NGS Chemistry In principle, the
concept behind NGS technology is similar to
CE sequencing. DNApolymerase catalyzes the
incorporation of fluorescently labeled deoxy
ribonucleotide triphosphates (dNTPs) in to a
DNA template strand during sequential cycles
of DNA synthesis.
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6. Contd
Library Preparation
The sequencing library is prepared by random
fragmentation of the DNA or Cdna sample, followed
by5′and 3′adapterligation.
Cluster Generation
For cluster generation, the library is loaded in to a flow
cell where fragment are captured on a lawn of
surface-bound oligoscomplementary to the library
adapters.
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Sequencing

7. Contd
Sequencing
Illumina SBS technology uses a proprietary
reversible terminator–based method that detects
single bases as they are incorporated in to DNA
template strands.
DataAnalysis
During data analysis and alignment, the newly
identified sequence read are aligned to a
reference genome . Following alignment, many
variation s of analysis are possible, such as single
nucleotide polymorphism (SNP)or insertion-
deletion(indel) identification, read counting for
RNA methods, phylogenetic or meta genomic
analysis, and more.
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9. • Integrated Genome Browser
Integrated Genome Browser (IGB) is an open
source genome browser, a visualization tool used to observe
biologically-interesting patterns in genomic data sets,
including sequence data, gene models, alignments, and data
from DNA microarrays.
IGB reads data in dozens of formats, including BAM,
BED, Affymetrix CHP, FASTA, GFF, GTF, PSL, SGR, and WIG.
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10. Contd
• Galaxy
Galaxy was originally written for biological data
analysis, particularly genomics. The set of available
tools has been greatly expanded over the years and
Galaxy is now also used
for geneexpression, genomeassembly, proteomics, e
pigenomics, transcriptomics and host of other
disciplines in the life sciences.
https://usegalaxy.org is the URL.
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Sequencing

11. • the better approach for the above example is “not to” sequence all
samples from an experimental group in a single lane, but make sure
each lane contains samples from both the control and experimental
groups.
• That is where randomization comes in. One good NGS design is to
randomly
• pick three samples from control and experimental groups and
sequence them in a lane. And sequencing the remaining six samples
in the second lane.
In this design even if one lane goes rogue that affects both the control
and
• experimental group samples equally and we still have one more
“well
• behaved” lane with both the groups. In statistical parlance, now the
lane effect
• and the experimental group effect is no longer confounded.
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12. • A naive design is to put all six samples from the same
group in a single lane (or dosequencing in one day).
For example, multiplex all six control samples and
sequence
• them in one lane and multiplex all six experimental
group samples and sequence in
• the other lane. This is a bad design despite the fact
that there are six biological
• replicates and it is multiplexed.
The reason why it is bad design is simply the same as
the good old saying, “Don’t put
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14. A survey of tools for variant analysis of next-
generation genome sequencing data
BRIEFINGS IN BIOINFORMATICS. VOL 15. NO 2.
256 ^278 doi:10.1093/bib/bbs086 Advance
Access published on 21 January 2013
Next-generation sequencing data interpretation:
enhancing reproducibility and accessibility
NGS analyses by visualization with Trackster
Published in final edited form as: Nat Biotechnol.
2012 November ; 30(11): 1036–1039.
doi:10.1038/nbt.2404. NIH Public Access
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15. Thank you…..
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