First part of the training session 'RNA-seq for Differential expression' analysis. We explain how we can detect differential expression based on RNA-seq data. Interested in following this session? Please contact http://www.jakonix.be/contact.html

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RNA-seq for DE analysis training
Defining the goal of RNA-seq
analysis for differential
expression
Joachim Jacob
22 and 24 April 2014

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Great power comes with great responsibility
You can't do all
RNA-seq is powerful, we have
to aim for a certain goal.
Our goal is to detect
differential expression
on the gene level.

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With great power comes great responsibility
RNA-seq enables one to
1) get an idea which are all active genes
2) quantify expression of each transcript
3) quantify alternative splicing
… (use your imagination)
Principles of transcriptome analysis and gene expression quantification: an RNA-seq
tutorial. http://onlinelibrary.wiley.com/doi/10.1111/1755-0998.12109/abstract

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Differential expression: useful?
What are we looking for?
Explanations of observed phenotypes
yeast
GDA
Yeast mutant
GDA + vit C
why?

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The central dogma
<What?>
yeast
GDA
Yeast mutant
GDA + vit C
?
causes the phenotypic differences

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The central dogma
yeast
GDA
Yeast mutant
GDA + vit C
Difference in protein activity
causes the phenotypic differences

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The central dogma
yeast
GDA
Yeast mutant
GDA + vit C
Presence/concentration of proteins in a cell
causes the phenotypic differences

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The central dogma
yeast
GDA
Yeast mutant
GDA + vit C
?
Different regulation of protein production
causes the phenotypic differences

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The central dogma
yeast
GDA
Yeast mutant
GDA + vit C
?
Level of templates for protein production
causes the phenotypic differences

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The central dogma
yeast
GDA
Yeast mutant
GDA + vit C
?
Level of mRNA copies
causes the phenotypic differences

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Does the reason for measuring DE make sense?
Difference in protein activity
Level of mRNA copies
Level of templates for protein production
Level of protein production
Presence/concentration of proteins in a cell
Phenotype

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Problem reduction
We can measure mRNA levels (much easier
than protein levels).
So we measure mRNA.
The level of mRNA is a proxy of the level of
protein activity causing the aberrant
phenotype.

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How to measure mRNA levels
1. Q-PCR (real-time)
2. Microarray
3. RNA-seq
A lot of work to measure few
genes, in a relatively wide array
of tissues. Very accurate.
Easier way to measure many
predefined genes in a relatively
wide array of tissues. Robust.

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Sequence-based measuring: high level view
● Get your sample
● Lyse the cells and extract RNA
● Convert the RNA to cDNA
● The cDNA pool get sequenced
The result is sequence information from
scratch. No prior information is needed.
Yeast sample
Comprehensive comparative analysis of strand-specific RNA sequencing methods
http://www.nature.com/nmeth/journal/v7/n9/full/nmeth.1491.html
Comparative analysis of RNA sequencing methods for degraded or low-input samples
http://www.nature.com/nmeth/journal/v10/n7/full/nmeth.2483.html

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RNA-seq is not a new idea
● ESTs: expressed
sequence tags, ideal for
discovery of new genes.
● SAGE: serial analysis of
gene expression,
measurement of
number of copies of
mRNA
http://www.montana.edu/observatory/people/mcdermottlab.htm

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RNA-seq is not a new idea
● ESTs: expressed
sequence tags, ideal for
discovery of new genes.
● SAGE: serial analysis of
gene expression,
measurement of
number of copies of
mRNA
http://www.sagenet.org/findings/index.html

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RNA-seq is not a new idea
● ESTs: expressed sequence tags
● SAGE: serial analysis of gene expression
Low throughput: long sequence
information, but for only ~thousands of
genes.

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Concept of measuring with RNA-seq
Extract mRNA
and turn into
cDNA
Fragment, ligate
Adaptor, amplify,
size selection.
Put a fraction of
the pool on a
high throughput
sequencer to
read fragments.
One template of protein
production, mRNA
Figure: All things must pass: contrasts and commonalities in eukaryotic and bacterial mRNA decay, Nature Reviews Molecular Cell Biology 11, 467–478
GeneA GeneB GeneC
cell
nucleus
DNA

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Every step means some loss
Yeast sample

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RNA-seq numbers might explain phenotype
Phenotype
Proteins
mRNA levels
cDNA pool
RNA-seq read numbers
Represent the cDNA pool we've created
Represent the RNA pool we've extracted
Are a proxy for protein activity
Define the phenotype

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So many steps must fail our assumption
Phenotype
Proteins
mRNA levels
cDNA pool
RNA-seq read numbers
Protein activity is regulated:
Fosforylation, ubiquitination,...
mRNA templates have
different speeds of protein pro-
Duction: availability of tRNAs,
rate of mRNA degration,
Alternative splicing events,...
Loss on RNA extraction, 90% of
RNA in cell is rRNA, ligation
of adapters, conversion to cDNA
not 100%
Fail to map reads to correct
gene, lane-specific biases on
reading cDNA fragments,...

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Consequence: focus on comparison
Phenotype A
Proteins
mRNA pool
cDNA pool
RNA-seq reads
Phenotype B
Proteins
mRNA pool
cDNA pool
RNA-seq reads
Possibly due
to differences in
expression

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Consequence: focus on comparison
Phenotype A
Proteins
mRNA pool
cDNA pool
RNA-seq reads
Phenotype B
Proteins
mRNA pool
cDNA pool
RNA-seq reads
DESIGN OF
EXPERIMENT

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Comparing read numbers per gene
GeneA GeneB GeneC
sample
RNA-seq
Obviously, the number of reads is dependent on:
1. the expression level of the gene
2. the total number of reads generated
3. the length of the transcript
OUR QUESTION

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Interpreting the counts for our goal
Our focus: which genes are differentially expressed
between different conditions?
Obviously, the number of reads is dependent on:
1. the expression level of the gene
2. the total number of reads generated
3. the length of the transcript

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Experimental design
Our focus: which genes are differentially expressed
between different conditions?
“How can we detect genes for which the counts of
reads change between conditions more
systematically than as expected by chance”
We must design an experiment in which we can test
this deviance from chance.
Oshlack et al. 2010. From RNA-seq reads to differential expression results. Genome Biology 2010,
11:220 http://genomebiology.com/2010/11/12/220

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How many reads to sequence?
In other words: how deep to sequence? What is the
required 'depth of sequencing'?
GeneA GeneB GeneC
sample
RNA-seq
RNA-seq
GeneA GeneB GeneC
The final test will look at ratios:
6 5 3
5 6 4
1,2 0,83 0,75
sample

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How many reads to sequence?
The difference between the lowest gene count and
the highest gene count is typically 105
. This is called
the dynamic range.
Linear scale is useless. The logarithmic scale is better.
Wait! Something's not correct here!

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Zero remains zero!
We are working with counts. A count is >=1. A gene
with zero counts can be not yet sequenced (not
deep enough) or is not expressed in that condition.
It is not a full logarithmic scale.
It starts at zero.
0

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How do the numbers change?
Assuming equal sequencing depth in the samples,
and these counts. Do all these genes differ in
expression? sample sample
GeneA 5 10 2
GeneB 15 30 2
GeneC 40 80 2
GeneD 100 200 2
GeneE 1000 2000 2
GeneZ 1 2 2
RATIO

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How do the numbers change?
sample sample
GeneA 11 10 0,91
GeneB 11 30 2,72
GeneC 60 80 1,33
GeneD 79 200 2,53
GeneE 1150 2000 1,74
GeneZ 5 1 0,20
RATIO
2?
Is there a trend in how
these numbers change?
Sequencing the result of the same steps again is
called a technical replicate.

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Technical replicates
sample
GeneA 11 5 4 4
GeneB 11 16 14 8
GeneC 60 45 32 38
GeneD 79 102 95 110
GeneE 1150 1023 987 1005
GeneZ 3 0 0 1
sample sample sample
We take the same cDNA pool and sequence it several
times: technical replicates.

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The poisson distribution
The counts of technical replicates follow a poisson
distribution (Marioni et al 2008). The Poisson distribution
can be applied to systems with a large number of possible
events, each of which is rare.
From Wikipedia. Can be 3
different genes, each with
their own poisson
distribution. Lambda is
the mean of the gene's
distribution, with a
certain number of reads.
Y=axis: chance to pick
that number of reads.

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The poisson distribution
So when we have 4 technical replicates sequenced up
to a big depth (say 10 M reads). We can get by
chance, these numbers for 3 different genes.
GeneA 0, 0, 1, 3
GeneB 2, 3, 4, 7
GeneC 8, 9, 11, 14

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Working the intuition
How many blue balls?
How many red balls?
Draw 10
Draw 10 more
Draw 10 more
Estimate how large the fraction is in the set?

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The intuition with the balls
Color 10 draws 20 draws 30 draws 40 draws
Blue
Red
No color

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Conclusion of the experiment
How bigger the fraction in the pool, how quicker (i.e.
with less sequencing depth) we are certain about the
estimate of that fraction.
For lower counts, the variance is
relatively bigger than the
variance for higher counts.
CV (coëfficient of variation) =
sqrt(count)/count
Genes with lower expression
need much deeper sequencing
than genes with higher
expression levels.
estimate=count; variance=count

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Comparing counts
“Here we show the overlap of Poisson
distributions of single measurements at
different read counts. Because relative
Poisson uncertainty is high at low read
counts, a count of 1 versus 2 has very
little power to discriminate a true 2X fold
change, though at higher counts a 2X fold
change becomes significant.
In an actual experiment, the width of the
distribution would be greater due to
additional biological and technical
uncertainty, but the uncertainty to the
mean expression would narrow with
each additional replicate.”
Scotty: a web tool for designing RNA-Seq experiments to measure differential gene expression.
Bioinformatics (2013) doi: 10.1093/bioinformatics/btt015

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Comparing technical replicates
Risso et al. “GC-Content Normalization for RNA-Seq Data”
BMC Bioinformatics 2011, 12:480
http://www.biomedcentral.com/1471-2105/12/480 - EDASeq package (R)
Correlation
between mean
and variance
according to Poisson
Lowess fit through
the data
(Log2 of the counts)
(Log2ofthecounts)

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But poisson does not seem to fit
Extending the samples to real biological samples, this
mean variance relationship does not hold...
Plotted using EDASeq
Package in R.

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But poisson does not seem to fit
Extending the samples to real biological samples, this
mean variance relationship does not hold!
Plotted using EDASeq
Package in R.
Reasonable fit
Something is going on!

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An extra source of variation
The Poisson distribution has an 'overdispersed'
variance: the variance is bigger than expected for
higher counts between biological replicates.
Plotted using EDASeq
Package in R.
Something is going on!

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An extra source of variation
Where Poisson: CV = std dev / mean => CV² = 1/μ
If an additional distribution is involved (also
dependent on π, the fraction of the gene in the cDNA
pool), we have a
mixture of
distributions:
CV² = 1/μ + φ
Low counts! dispersion
Generalization of Poisson
with this extra parameter:
the Negative Binomial
Model fits better!

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The negative binomial model
The NB model fits observed expression data of RNA-
seq better. It is a generalization of Poisson, and 2
parameters need to be estimated (μ and φ)
Counts (gene g in sample j) has a
Mean = μgj
Variance = μgj
+ φg
μgj
²
Biological CV² = φg
=> Biological CV = √φg
Methods differ in estimating this dispersion per gene:
Can only be measured with true biological replicates

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Variation summary, intuitively
Total CV² = Technical CV² + Biological CV²
For low counts, the Poisson (technical) variation or
the measurement error is dominant.
For higher counts, the Poisson variation gets smaller,
and another source of variation becomes dominant,
the dispersion or the biological variation. Biological
variation does not get smaller with higher counts.

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Beyond the NB model
It appears from analysis of many
biological replicates (#=69) that not
every gene can be modeled as NB:
the Poisson-Tweedie model
provides a further generalisation
and a better fit for many genes
(with an additional shape
parameter).
Left figure: raw data shows that about 26% of
the genes fit a NB model. Depending on the
estimated shape parameter, other
distributions fit better.
Esnaola et al. BMC Bioinformatics 2013, 14:254
http://www.biomedcentral.com/1471-2105/14/254

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Consequence for our design
● For low counts: the uncertainty is big due to
Poisson
● For high counts: the uncertainty is big due to
biological variation. (highly expressed genes differ
in their natural variation (regulated by cellular
processes) more than lowly expressed genes).
● If we focus on the ratios between the conditions:
is it reasonable to set a restriction of fold change?
Highly expressed genes can have a smaller and be
significant. Lowly expressed genes can exceed 2.

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Consequence on fold change
The readily applied cut-off in micro-array analysis
is in RNA-seq not of use.
Blue and red:
known DE genes
Volcanoplot
These cut-offs often
applied can prohibit
detecting DE genes

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Just remember...
We need to estimate the model behind the count.
Never work without biological replicates.
Never work with 2 biological replicates.
Try avoiding working with 3 biological replicates.
Go for at least 4 biological replicates.

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Break?

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Overview
GeneA GeneB GeneC
Sample 1
RNA-seq
GeneA GeneB GeneC
Sample 2
RNA-seq
GeneA GeneB GeneC
Sample 3
RNA-seq
GeneA GeneB GeneC
Sample 4
RNA-seq
GeneA GeneB GeneC
Sample 5
RNA-seq
GeneA GeneB GeneC
Sample 6
RNA-seq
Condition X
Condition Y

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Factors influencing read count
Obviously, the number of reads is dependent on:
1. chance
→ Define the count model (NB) from replicates
2. the expression level of the gene
→ Compare the ratios with a test
3. the total number of reads generated
4. the length of the transcript

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Library size influences read counts
GeneA GeneB GeneC
sample
RNA-seq
The number of reads is dependent on the total
number of reads generated. If one library is
sequenced to 20M reads, and another one to
40M, most genes will ~double their counts.
GeneA GeneB GeneC
sample
More RNA-seq

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Normalization for library size
Naive approach: divide by total library size. Is not
applied anymore!
Why not? Composition matters!
2 things to remember:
- zero sum system or “every gene we measure takes up a part (at
least one read) of the total library”
- 5 orders of magnitude

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Normalization for library size
Every gene takes up at
least one read. But in
every sample, a lot of
reads are spend on few
extremely highly
expressed genes. Reason
unknown. Often different
between samples. This
fact biases average
based (naïve)
normalization attempts.
Average count (log2)
Comparing 2 samples
Countdifference(log2ratio)

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Normalization for library size
Schematically: when normalized on library size
(square represent number of reads).
Rest of the genes
Rest of the genes
Few genes with enormous counts
All counts for library A All counts for library B

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Normalization for library size
Better normalization would be as shown below.
DESeq2 and EdgeR apply such an approach (see
later).
Rest of the genesRest of the genes
100%
100%

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Gene length influences the count
“Longer transcripts generate more reads”
True! But the transcript length does not differ
between samples. Since we are concerned with
relative differences between samples, this needs
no normalization (this story changes in case of
absolute quantification).
Sample A Sample B
Gene A
Gene B
Gene A
Gene B

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The many flavours of sample variation
Some properties of libraries/samples can
effect the counts, and lead to variation. This is
called between-lane variation. Obvious ones:
library size (how many reads are sampled),
library composition.
Different libraries/samples differ sometimes in
how gene properties relate to gene counts. This
is called within-lane variation.

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GC-content of genes can influence counts
GC-content differs between genes. But it does
not change between samples, so there should
be no problem for relative expression
comparison.
We can visualize the
relationship between
counts and GC very
easily (see right). There is
some trend, and it is
equal for all samples.
EDAseq (R)

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GC-content of genes can influence counts
Sometimes, samples show different relationships
between GC-content of the genes and the counts.
This within-lane variation
(or intra-sample) variation
needs to be corrected for,
so that in one sample not
all differentially expressed
genes are also the GC-
riched ones.
Length can have also this
effect.

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Putting our experiment together
We want to detect differentially expressed genes
between 2 or more conditions.
For this, we need to apply the conditions in a
controlled environment (randomisation,...).
For good testing, we need to have some biological
replicates per condition.
For cost effectiveness, we determine how deep we
will sequence from each sample.
We analyse the reads, get raw counts and do the test!

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On the sequencer
HiSeq2000: 24 single-index barcodes available. 1
lane gives 150-180 M reads. One lane of 50 bp SE
approx €1.500.

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Bioinformatics analysis of the output
Quality control (QC) of raw reads
Preprocessing: filtering of reads
and read parts, to help our goal
of differential detection.
QC of preprocessing Mapping to a reference genome
(alternative: to a transcriptome)
QC of the mapping
Count table extraction
QC of the count table
DE test
Biological insight

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Bioinformatics analysis will take most of your time
Quality control (QC) of raw reads
Preprocessing: filtering of reads
and read parts, to help our goal
of differential detection.
QC of preprocessing Mapping to a reference genome
(alternative: to a transcriptome)
QC of the mapping
Count table extraction
QC of the count table
DE test
Biological insight
1
2
3
4
5
6

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Overview
Anders et al. Count-based differential expression analysis of RNA
sequencing data using R and Bioconductor. 2013
http://www.nature.com/nprot/journal/v8/n9/full/nprot.2013.099.html

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The numbers get reduced with every step
20M
25M
15M
~16%
~5%
~10%
~30% decrease
from sequenced
reads to counted
reads.

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Deeper, or more replicates?
Variance will be lower with more reads: but
sequencing another biological replicate is
preferred over sequencing deeper, or technical reps.
Busby et al. Scotty: a web tool for designing RNA-Seq experiments to
measure differential gene expression. Doi: 10.1093/bioinformatics/btt015

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There is tool to help you set up

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Scotty: power analysis
'How many samples and how deep in order to
minimize false negatives?'
Power: the probability to reject the null hypothesis if
the alternative is true. A null hypothesis is always a
scenario in which there is no difference, hence no
differential expression.
Check the BITS wiki:
http://wiki.bits.vib.be/index.php/RNAseq_toolbox

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Help with design
http://wiki.bits.vib.be/index.php/RNAseq_toolbox

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How many samples to sequence?
→ Scotty exercise

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Keywords
A read count of a gene is dependent on:
1. chance
2. expression level
3. transcript length
4. depth of sequencing
5. GC-content
Poisson distribution
Negative binomial distribution
Condition
Sample
Normalization
Write in your own words what the terms mean

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Reads
All my references available at:
https://www.zotero.org/groups/dernaseq/items