I wanted to find all the occurrences of a subsequence in a genome assembly. To do this, I first tried using BLAT but it didn't find them for me (not sure why).
So I instead wrote a little Python function to print out all the positions of a subsequence in a sequence:
#====================================================================#
# find the positions of a subsequence in a sequence:
def find_positions_of_subsequence(seq, subsequence, seqname):
still_searching = True
start = 0
end = len(seq) - 1
while (still_searching == True):
position = seq.find(subsequence, start, end)
if position == -1:
still_searching = False
else:
actual_position = position + 1
format_string = "Found at %d in %s" % (actual_position, seqname)
print(format_string)
start = position + 1
return
#====================================================================#
Python saves the day!
Friday, 22 December 2017
Monday, 4 December 2017
Trimming adapter sequences
I wanted to trim adapter sequences, specifically the sequence 'GTTTTAGGTC'
Attempt 1: Trimmomatric
I first tried Trimmomatic, with a fasta file with the forward and reverse complement of my adapter sequences, and the 'ILLUMINACLIP:/lustre/scratch118/infgen/team133/alc/000_FUGI_PatrickCRISPR/OmegaGenewhiz/adaptor.fa:2:40:0' option. However, I found that this just removes the sequences with adapter, it doesn't trim them. This is also described here.
Attempt 2: Cutadapt
I then tried cutadapt. With the following fasta file:
>seq1
GTTTTAGGTCGTTATCGTGTA
>seq2
TACACGATAACGACCTAAAAC
I was able to trim adapters using:
% cutadapt -g GTTTTAGGTC my.fasta -a GACCTAAAAC
where -g cuts sequences off the 5' end, and -a off the 3' end.
It cuts off adapter with at most 10% errors in single-end read mode.
This gave me:
>seq1
GTTATCGTGTA
>seq2
TACACGATAAC
Hurray!
Something strange I noticed about cutadapt:
I have been using cutadapt to trim some adaptors, and noticed that it totally removed some of my sequences.
For example, with this fastq input: (in tmp.fastq)
@M03558:259:000000000-BH588:1:1101:4913:6565 2:N:0:TTTGTA
ACTGACCCTCAGCAATCTTAAACTTCTTAGACGAATCACCAGAACGGAAAACATCCTTCATAGAAATTTCACGCGGCGGCAAGTTGCCATACAAAACAGGGTCGCCAGCAATATCGGTATAAGTCAAAGCACCTTTAGCGTTAAGGTACTGAATCTCTTTAGTCGCAGTAGGCGGAAAACGAACAAGCGCAAGAGTAAACATAGTGCCATGCTCAGGAACAAAGAAACGCGGCACAGAATGTTTATAGGTC
+
AAAAAFFFFCFCGGGGGGGGGGHHHHHHGFHHGEEGEHHFHHFFHGEGFGHFHHHHHHHHHHGHFHHHHHHHEHGCFGGGGGGHHHHHHHHHHHHHGHGHGHHGGGGGGHHGFHEGGCEFFHHGGGHHHEFGHEHHHGGGAE?EHHGDHHFGHHHHHEHHHH:C.A@DCGHBGA-?BGGGCGEGCFFFFFFFFEF;FFFEF/B/:BFEF;/:;BB;BFFFFFF/BFFFAAFA-;-B.FFBF;/BFBF/9/:
I then ran cutadapt like this:
% cutadapt -g GTTTTAGGTC -a GACCTAAAAC tmp.fastq
and it removed the whole input sequence.
I can't see any matches to the adaptors in that input though. When I ran blastn between that read and the NCBI database, the sequence was found to be PhiX174. Is that why cutadapt removed it, or was there some other reason?
Update: 7-Dec-2017: I sent an email about this to the cutadapt developer, Marcel Martin, and received a very helpful reply:
Attempt 1: Trimmomatric
I first tried Trimmomatic, with a fasta file with the forward and reverse complement of my adapter sequences, and the 'ILLUMINACLIP:/lustre/scratch118/infgen/team133/alc/000_FUGI_PatrickCRISPR/OmegaGenewhiz/adaptor.fa:2:40:0' option. However, I found that this just removes the sequences with adapter, it doesn't trim them. This is also described here.
Attempt 2: Cutadapt
I then tried cutadapt. With the following fasta file:
>seq1
GTTTTAGGTCGTTATCGTGTA
>seq2
TACACGATAACGACCTAAAAC
I was able to trim adapters using:
% cutadapt -g GTTTTAGGTC my.fasta -a GACCTAAAAC
where -g cuts sequences off the 5' end, and -a off the 3' end.
It cuts off adapter with at most 10% errors in single-end read mode.
This gave me:
>seq1
GTTATCGTGTA
>seq2
TACACGATAAC
Hurray!
Something strange I noticed about cutadapt:
I have been using cutadapt to trim some adaptors, and noticed that it totally removed some of my sequences.
For example, with this fastq input: (in tmp.fastq)
@M03558:259:000000000-BH588:1:1101:4913:6565 2:N:0:TTTGTA
ACTGACCCTCAGCAATCTTAAACTTCTTAGACGAATCACCAGAACGGAAAACATCCTTCATAGAAATTTCACGCGGCGGCAAGTTGCCATACAAAACAGGGTCGCCAGCAATATCGGTATAAGTCAAAGCACCTTTAGCGTTAAGGTACTGAATCTCTTTAGTCGCAGTAGGCGGAAAACGAACAAGCGCAAGAGTAAACATAGTGCCATGCTCAGGAACAAAGAAACGCGGCACAGAATGTTTATAGGTC
+
AAAAAFFFFCFCGGGGGGGGGGHHHHHHGFHHGEEGEHHFHHFFHGEGFGHFHHHHHHHHHHGHFHHHHHHHEHGCFGGGGGGHHHHHHHHHHHHHGHGHGHHGGGGGGHHGFHEGGCEFFHHGGGHHHEFGHEHHHGGGAE?EHHGDHHFGHHHHHEHHHH:C.A@DCGHBGA-?BGGGCGEGCFFFFFFFFEF;FFFEF/B/:BFEF;/:;BB;BFFFFFF/BFFFAAFA-;-B.FFBF;/BFBF/9/:
I then ran cutadapt like this:
% cutadapt -g GTTTTAGGTC -a GACCTAAAAC tmp.fastq
and it removed the whole input sequence.
I can't see any matches to the adaptors in that input though. When I ran blastn between that read and the NCBI database, the sequence was found to be PhiX174. Is that why cutadapt removed it, or was there some other reason?
Update: 7-Dec-2017: I sent an email about this to the cutadapt developer, Marcel Martin, and received a very helpful reply:
In short: The 5' adapter is found at the very end (3') of the read with one
error.
From the statistics report that cutadapt prints, you can see that it finds
the 5' adapter with one error. And if you run the above command without
specifying the 3' adapter with -a GACCTAAAAC, you get the same result, so
it must be the 5' adapter. When cutadapt finds a 5' adapter, it removes
the adapter sequnce itself and everything preceding it. And in your case,
the read ends with 'GTTTATAGGTC', which is the 5' adapter sequence with an
extra 'A' inserted between one of the 'T' nucleotides.
Although cutadapt doesn’t print it, the alignment likely looks like this:
...GTTTATAGGTC (read end)
GTTT-TAGGTC (adapter)
This is possibly not the result you were expecting, but cutadapt is doing
what it is supposed to.
You have a few options:
- Accept this result
- If you think allowing insertions is the problem, use --no-indels to
disallow insertions and deletions.
- If you want to prevent the 5' adapter from occurring within the read at
all, you can specify the adapter as -g XXGTTTTAGGTC. The 'X' is always
counted as a mismatch, so if you have more X than allowed mismatches,
the adapter is forced to occur at the 5' end (overlapping it).
Monday, 27 November 2017
Fastq format
I'm looking at a fastq file that has header lines like this and want to figure out what they mean:
@M03558:259:000000000-BH588:1:1101:16110:1341 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:16089:1342 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:15471:1344 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:15455:1333 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:14580:1411 2:N:0:CTTGTA
...
Luckily I found a nice wikipedia description of FASTQ, which tells me that probably:
M03558 = the unique instrument name
259 = the run id
000000000-BH588 = the flow cell id.
1 = the flow cell lane
1101 = the tile number within the flow cell lane (I get 28 different values for this)
16110 = x-coordinate of the cluster within the tile
1341 = y-coordinate of the cluster within the tile
2 = member of a pair (paired end reads only). (I only see '2' here so I think the reads in my fastq are single-end reads)
N = means the read is not filtered (I only see 'N' here)
0 = this will be '0' when none of the control bits are on (I only see '0' here)
NTTGTA = this is the index sequence. I see several different index sequences, with different frequencies.
Thanks wikipedia!
@M03558:259:000000000-BH588:1:1101:16110:1341 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:16089:1342 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:15471:1344 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:15455:1333 2:N:0:NTTGTA
@M03558:259:000000000-BH588:1:1101:14580:1411 2:N:0:CTTGTA
...
Luckily I found a nice wikipedia description of FASTQ, which tells me that probably:
M03558 = the unique instrument name
259 = the run id
000000000-BH588 = the flow cell id.
1 = the flow cell lane
1101 = the tile number within the flow cell lane (I get 28 different values for this)
16110 = x-coordinate of the cluster within the tile
1341 = y-coordinate of the cluster within the tile
2 = member of a pair (paired end reads only). (I only see '2' here so I think the reads in my fastq are single-end reads)
N = means the read is not filtered (I only see 'N' here)
0 = this will be '0' when none of the control bits are on (I only see '0' here)
NTTGTA = this is the index sequence. I see several different index sequences, with different frequencies.
Thanks wikipedia!
Friday, 3 November 2017
A stacked barplot in R
I wanted to make a stacked barplot in R. My input data looked like this:
plate barcode reads
1 1 3232
1 2 32232
1 4 23232
2 1 23322
2 2 2323
2 3 4343
2 4 23432
I wanted to make a barplot showing the 96 barcodes adjacent to each other, and for each barcode, a stack showing the number of reads for plate 1, 2, 3.
Getting the data into R (painful!)
The problem was getting my data into R. The input data did not have values for every plate-barcode combination, but I wanted to assume a value of 0 for combinations that were not in the input file. In the end I had to write some code to squeeze the data into R:
# input data has columns plate, barcode, number of input reads
MyData <- read.table("reads_in_inputs",header=TRUE)
plate <- MyData$plate
barcode <- MyData$barcode
reads <- MyData$reads
# put the input data in a matrix, for use in the barplot() command.
# The matrix will have three rows (plate 1,2,3) and 96 columns (barcode 1..96):
mymatrix <- matrix(, nrow=3, ncol=96)
for (platenum in 1:3)
{
for (barcodenum in 1:96)
{
# find the index (if any) in vector 'reads' for plate 'platenum' and barcode 'barcodenum'.
value <- intersect(which(plate==platenum),which(barcode==barcodenum))
if (length(value > 0))
{
# get the number of reads for plate 'platenum' and barcode 'barcodenum' from vector reads:
mymatrix[platenum,barcodenum] <- reads[value] / 1e+3 # in thousands of reads
}
else
{
mymatrix[platenum,barcodenum] <- 0
}
}
}
Plotting the data in R (ok!)
Plotting the data was not so hard. I used the example from http://www.r-graph-gallery.com/ to make a stacked barplot:
colnames <- seq(1,96)
rownames <- seq(1,3)
colnames(mymatrix) <- colnames
rownames(mymatrix) <- rownames
barplot(mymatrix, col=colors()[c(23,89,12)], border="white", space=0.04, font.axis=2, xlab="barcode", ylab="thousands of input reads", legend=rownames(mymatrix))
A little bit of the plot:
Some other little tricks I learnt:
To put some space around the plot I can type before the 'barplot' command:
par( mar=c(8, 4.7, 2.3, 0)) # last value is space on RHS, second last value is space at top, 2nd value is space on LHS, 1st value is space below
In the barplot command itself:
border="white": use white for the border of the bars
space=0.04 : leaves space before each bar. cex.names=0.5
makes the x-axis labels smallerlas=3 makes the labels perpendicular to the axis
plate barcode reads
1 1 3232
1 2 32232
1 4 23232
2 1 23322
2 2 2323
2 3 4343
2 4 23432
I wanted to make a barplot showing the 96 barcodes adjacent to each other, and for each barcode, a stack showing the number of reads for plate 1, 2, 3.
Getting the data into R (painful!)
The problem was getting my data into R. The input data did not have values for every plate-barcode combination, but I wanted to assume a value of 0 for combinations that were not in the input file. In the end I had to write some code to squeeze the data into R:
# input data has columns plate, barcode, number of input reads
MyData <- read.table("reads_in_inputs",header=TRUE)
plate <- MyData$plate
barcode <- MyData$barcode
reads <- MyData$reads
# put the input data in a matrix, for use in the barplot() command.
# The matrix will have three rows (plate 1,2,3) and 96 columns (barcode 1..96):
mymatrix <- matrix(, nrow=3, ncol=96)
for (platenum in 1:3)
{
for (barcodenum in 1:96)
{
# find the index (if any) in vector 'reads' for plate 'platenum' and barcode 'barcodenum'.
value <- intersect(which(plate==platenum),which(barcode==barcodenum))
if (length(value > 0))
{
# get the number of reads for plate 'platenum' and barcode 'barcodenum' from vector reads:
mymatrix[platenum,barcodenum] <- reads[value] / 1e+3 # in thousands of reads
}
else
{
mymatrix[platenum,barcodenum] <- 0
}
}
}
Plotting the data in R (ok!)
Plotting the data was not so hard. I used the example from http://www.r-graph-gallery.com/ to make a stacked barplot:
colnames <- seq(1,96)
rownames <- seq(1,3)
colnames(mymatrix) <- colnames
rownames(mymatrix) <- rownames
barplot(mymatrix, col=colors()[c(23,89,12)], border="white", space=0.04, font.axis=2, xlab="barcode", ylab="thousands of input reads", legend=rownames(mymatrix))
A little bit of the plot:
Some other little tricks I learnt:
To put some space around the plot I can type before the 'barplot' command:
par( mar=c(8, 4.7, 2.3, 0)) # last value is space on RHS, second last value is space at top, 2nd value is space on LHS, 1st value is space below
In the barplot command itself:
border="white": use white for the border of the bars
space=0.04 : leaves space before each bar. cex.names=0.5
makes the x-axis labels smallerlas=3 makes the labels perpendicular to the axis
Multivariate hypergeometric distribution in R
A hypergeometric distribution can be used where you are sampling coloured balls from an urn without replacement.
A univariate hypergeometric distribution can be used when there are two colours of balls in the urn, and a multivariate hypergeometric distribution can be used when there are more than two colours of balls.
The multivariate hypergeometric distribution can be used to ask questions such as: what is the probability that, if I have 80 distinct colours of balls in the urn, and sample 100 balls from the urn with replacement, that I will have at least one ball of each colour?
I recently have a similar problem in bioinformatics: if there are 6329 distinct copies of a gene in our eppendorph, and (after some PCRs) we sequence 10,000 distinct reads, what is the probability that we sequenced at least one read from each of the 6329 distinct copies of the gene? In other words, if there are 6329 distinct colours of balls in an urn, and we pick out 10,000 balls (without replacement), what is the probability that we chose at least one ball of each colour?
In R the dhyper function could be used in the case where there are two colours of ball in an urn, that is a univariate hypergeometric distribution. But what about where there are 6329 colours of ball, that is, a multivariate hypergeometric distribution? Then I came across the extraDistr R package, which has a multivariate hypergeometric distribution. The function is called "MultiHypergeometric".
I installed and load this library in R using:
> install.packages("extraDistr")
> library("extraDistr")
If two cards are drawn from a deck of 52 cards, what is the probability that one is a spade and one is a heart?
I found a nice example of using the MultiHypergeometric distribution here by Jonathan Fivelsdal, thank you! He asked the question: if two cards are drawn from a deck of 52 cards, what is the probability that one is a spade and one is a heart? In R:
> dmvhyper(x = c('heart' = 1, 'spade' = 1, 'other' = 0), n = c('heart' = 13, 'spade' = 13, 'other' = 26), k=2)
[1] 0.127451
Here x is a vector that has the frequency of hearts and spades and other cards in our sample of interest,
n is a vector that has the frequency of hearts and spades and other cards in the deck of cards,
k is the number of cards in our sample of interest.
[Note: the manual for this package says k is 'the number of balls drawn from the urn'.]
Given a huge bag containing scrabble letters, if know the number of each letter tile in the bag, what is the probability of drawing a particular word of length N if we select out N letter tiles without replacement?
This is another nice example that I found here by Herb Susmann.
The answer is that we can do something like this:
> dmvhyper(x = letterfreqs, n = freqs, k = sum(letterfreqs))
where x = letterfreqs is a vector of length 26, with the frequency of each letter in the word of interest,
n is a vector of length 26 with the frequency of each letter tile in the bag,
k is the number of letters in the word of interest (of length N), ie. it is N.
If I have 6329*2=12,658 balls of 6329 distinct colours in an urn (two of each colour), and pick out 10,000 balls (without replacement), what is the probability that I chose at least one ball of each colour?
> dmvhyper(x = rep(1, 6329), n = rep(2, 6329), k = 10000, log = FALSE)
[1] 0
x is an m-column matrix of quantiles. From the example above by Jonathan Fivelsdal using a deck of cards, I think that this gives the counts of each of the colours of ball in the sample that you're asking about. In our case we are asking about having at least one ball of each colour.
The vector n is an m-length vector and has the number of balls in each of the m different colours. In our case m is 6329, so it is a 6329-length vector. Here we just assume we have 2 balls of each colour in the urn.
In this case k is the number of balls drawn from the urn, which is 10,000 in our case.
Notes to myself:
- in the case of our sequencing problem, we need to take into account the number of sequences in the urn, ie. the total number of sequences after the PCR.
- is sequencing molecules a sampling without replacement problem? Need to check...
A univariate hypergeometric distribution can be used when there are two colours of balls in the urn, and a multivariate hypergeometric distribution can be used when there are more than two colours of balls.
The multivariate hypergeometric distribution can be used to ask questions such as: what is the probability that, if I have 80 distinct colours of balls in the urn, and sample 100 balls from the urn with replacement, that I will have at least one ball of each colour?
I recently have a similar problem in bioinformatics: if there are 6329 distinct copies of a gene in our eppendorph, and (after some PCRs) we sequence 10,000 distinct reads, what is the probability that we sequenced at least one read from each of the 6329 distinct copies of the gene? In other words, if there are 6329 distinct colours of balls in an urn, and we pick out 10,000 balls (without replacement), what is the probability that we chose at least one ball of each colour?
In R the dhyper function could be used in the case where there are two colours of ball in an urn, that is a univariate hypergeometric distribution. But what about where there are 6329 colours of ball, that is, a multivariate hypergeometric distribution? Then I came across the extraDistr R package, which has a multivariate hypergeometric distribution. The function is called "MultiHypergeometric".
I installed and load this library in R using:
> install.packages("extraDistr")
> library("extraDistr")
If two cards are drawn from a deck of 52 cards, what is the probability that one is a spade and one is a heart?
I found a nice example of using the MultiHypergeometric distribution here by Jonathan Fivelsdal, thank you! He asked the question: if two cards are drawn from a deck of 52 cards, what is the probability that one is a spade and one is a heart? In R:
> dmvhyper(x = c('heart' = 1, 'spade' = 1, 'other' = 0), n = c('heart' = 13, 'spade' = 13, 'other' = 26), k=2)
[1] 0.127451
Here x is a vector that has the frequency of hearts and spades and other cards in our sample of interest,
n is a vector that has the frequency of hearts and spades and other cards in the deck of cards,
k is the number of cards in our sample of interest.
[Note: the manual for this package says k is 'the number of balls drawn from the urn'.]
Given a huge bag containing scrabble letters, if know the number of each letter tile in the bag, what is the probability of drawing a particular word of length N if we select out N letter tiles without replacement?
This is another nice example that I found here by Herb Susmann.
The answer is that we can do something like this:
> dmvhyper(x = letterfreqs, n = freqs, k = sum(letterfreqs))
where x = letterfreqs is a vector of length 26, with the frequency of each letter in the word of interest,
n is a vector of length 26 with the frequency of each letter tile in the bag,
k is the number of letters in the word of interest (of length N), ie. it is N.
If I have 6329*2=12,658 balls of 6329 distinct colours in an urn (two of each colour), and pick out 10,000 balls (without replacement), what is the probability that I chose at least one ball of each colour?
> dmvhyper(x = rep(1, 6329), n = rep(2, 6329), k = 10000, log = FALSE)
[1] 0
x is an m-column matrix of quantiles. From the example above by Jonathan Fivelsdal using a deck of cards, I think that this gives the counts of each of the colours of ball in the sample that you're asking about. In our case we are asking about having at least one ball of each colour.
The vector n is an m-length vector and has the number of balls in each of the m different colours. In our case m is 6329, so it is a 6329-length vector. Here we just assume we have 2 balls of each colour in the urn.
In this case k is the number of balls drawn from the urn, which is 10,000 in our case.
Notes to myself:
- in the case of our sequencing problem, we need to take into account the number of sequences in the urn, ie. the total number of sequences after the PCR.
- is sequencing molecules a sampling without replacement problem? Need to check...
Thursday, 2 November 2017
'Learn Python Visually' book
I am doing some Python study, and found the book Learn Python Visually by Ivelin Demirov in my work library.
It has the subtitle 'An Accelerated Learning Method Which Uses Science and Creativity to Teach Right-Brained Non-Coders', which is intriguing. Also, the development of the book was funded by Kickstarter, which is cool to hear!
I'm thinking to make here some notes on the new things that I learnt from this book:
p. 14. Numbers are immutable. This page explains about numbers being immutable in Python. I always find this concept really confusing. The book gave me a little more information on it, but then I also found a nice article on it on the web.
There it is explained that when an object is initiated, it is assigned a unique object id. The state of the object can be changed later if the object is a mutable-type object. That is, the state of a mutable object can be changed after it is created, but the state of an immutable object can't be.
The book also tells about the id() function that tells you about the identity of an object as an integer, and the is() function that compares the identity of two objects.
Example:
>>> b = 11
>>> a = b
>>> c = 12
>>> d = 11
>>> id(a)
9084448
>>> id(b)
9084448
>>> id(c)
9084480
>>> id(d)
9084448
>>> a is b
True
>>> a is c
False
>>> a is d
True
p. 15 Assigning the same value to multiple variables at once. I didn't know you can assign a value to multiple variables at once:
>>> a = b = c = d = "Hello"
p. 15 Special words that can't be used as variable names. The book gives a list of special words that can't be used as variable names, I also found them on the web here.
p. 19 Using multi-line strings to store large text blocks, by enclosing them with triple quotes:
An example is:
>>> huidobro = """Al horitaña de la montazonte
La violondrina y el goloncelo
Descolgada esta mañana de la lunala
Se acerca a todo galope
"""
p. 21 Short forms of arithmetic operators.
I knew that it was possible to do:
>>> a += 1
but it turns out that you can also do:
>>> a -= 1
>>> a *= 1
>>> a /= 2
etc.
p. 28 Precedence of arithmetic operators.
Something I didn't know was that exponents have precedence before subtraction, which means that we get different answers for:
>>> abs(-2 ** 2 - 20)
24
which first finds 2**2 = 4, and then finds abs(-4 - 20), and
>>> abs((-2) ** 2 - 20)
16
which first finds (-2)**2 = 4, and then finds abs(4 - 20).
It has the subtitle 'An Accelerated Learning Method Which Uses Science and Creativity to Teach Right-Brained Non-Coders', which is intriguing. Also, the development of the book was funded by Kickstarter, which is cool to hear!
I'm thinking to make here some notes on the new things that I learnt from this book:
p. 14. Numbers are immutable. This page explains about numbers being immutable in Python. I always find this concept really confusing. The book gave me a little more information on it, but then I also found a nice article on it on the web.
There it is explained that when an object is initiated, it is assigned a unique object id. The state of the object can be changed later if the object is a mutable-type object. That is, the state of a mutable object can be changed after it is created, but the state of an immutable object can't be.
The book also tells about the id() function that tells you about the identity of an object as an integer, and the is() function that compares the identity of two objects.
Example:
>>> b = 11
>>> a = b
>>> c = 12
>>> d = 11
>>> id(a)
9084448
>>> id(b)
9084448
>>> id(c)
9084480
>>> id(d)
9084448
>>> a is b
True
>>> a is c
False
>>> a is d
True
p. 15 Assigning the same value to multiple variables at once. I didn't know you can assign a value to multiple variables at once:
>>> a = b = c = d = "Hello"
p. 15 Special words that can't be used as variable names. The book gives a list of special words that can't be used as variable names, I also found them on the web here.
p. 19 Using multi-line strings to store large text blocks, by enclosing them with triple quotes:
An example is:
>>> huidobro = """Al horitaña de la montazonte
La violondrina y el goloncelo
Descolgada esta mañana de la lunala
Se acerca a todo galope
"""
p. 21 Short forms of arithmetic operators.
I knew that it was possible to do:
>>> a += 1
but it turns out that you can also do:
>>> a -= 1
>>> a *= 1
>>> a /= 2
etc.
p. 28 Precedence of arithmetic operators.
Something I didn't know was that exponents have precedence before subtraction, which means that we get different answers for:
>>> abs(-2 ** 2 - 20)
24
which first finds 2**2 = 4, and then finds abs(-4 - 20), and
>>> abs((-2) ** 2 - 20)
16
which first finds (-2)**2 = 4, and then finds abs(4 - 20).
Thursday, 26 October 2017
CRISPresso software for analysing CRISPR-Cas9 data
I am learning to use the CRISPResso software for analysing CRISPR-Cas9 data, which was published by Pinello et al (2016). The supporting information for that paper contains a lovely user guide for the software.
CRISPR-Cas9
I am new to CRISPR-Cas9, so here is a summary to remind me!
The two key parts of this system are:
- the Cas9 enzyme, which cuts double-stranded DNA at a specific location.
- a guide RNA (gRNA), which is about 20 bp, and is located within a longer RNA scaffold. The scaffold binds to DNA and pre-designed gRNA guides Cas9 (Cas9 binds to the gRNA) to the right part of the genome. The gRNA has bases complementary to the target DNA sequence in the genome.
The PAM (protospacer adjacent motif) is a 2-6 bp DNA sequence immediately following the DNA sequence where the Cas9 cuts the DNA (ie. immediately following the gRNA sequence). Cas9 will not cleave the DNA sequence unless it is followed by the PAM sequence. The canonical PAM is 5'-
Details of the CRISPresso pipeline
The CRISPresso pipeline is described in the supporting information for the paper as including these steps:
1. Quality filtering
This allows the user to try to avoid false positives due to sequencing errors. CRISPResso allows reads to be filtered based (i) on a read's 'average quality score', defined as the average of the read's single-base quality scores, (ii) on the lowest base quality for all bases in a read. By default, the reads are not filtered.
2. Trimming
If the user provides the adaptor sequences used, this will trim them using Trimmomatic. By default, the reads are not trimmed.
3. Merging paired end reads
When paired end reads are used, it is possible to merge the reads since the amplicon sequence is usually shorter than twice the read length. This step mean that the sequence for the overlapping region is obtained with greater confidence. This step is performed using FLASH.
4. Alignment
To align the filtered reads to the reference amplicon, CRISPResso uses needle from the EMBOSS package, which performs Needleman-Wunsch alignment.
5. Quantification of mutation
HDR events: The sequence identity scores for alignment to the reference amplicon and expected HDR amplicon (if any) are considered. If the read aligns better to the expected HDR amplicon, the read is classified as HDR or mixed HDR-NHEJ. If the sequence identity is above 98%, the read is classified as HDR, otherwise as mixed HDR-NHEJ.
Unmodified versus NHEJ: Reads that align better to the reference amplicon than expected HDR amplicon are considered unmodified if they have 100% sequence identity with it. Otherwise, they are classified as NHEJ if they contain mutations in a window of w bp (w/2 bp on each side) about the gRNA's cleavage site.
Output plots: For all plots, all mutations on reads with a given classification are shown by default, even the ones outside the w bp window in the case of NHEJ that do not contribute to the classification.
Note that if there are deletion and substitutions in a read that are at the end of the reference amplicon, CRISPResso may ignore these (for quantification and in the Alleles_frequency_table.txt output file). This is because by default CRISPResso excludes 15bp from each side of the reads for quantification of indels, and only considers an indel if it overlaps the window around the cleavage site (ie. the window of size w). The value of 15 bp can be changed by the -exclude_bp_from_left and --exclude_bp_from_right options.
Input files
CRISPResso requires as input:
1. Paired-end reads in .fastq (or fastq.gz) format
2. A reference amplicon
Other optional inputs:
1. One or more gRNA sequences can be provided to compare the predicted cleavage position(s) to the position of the observed mutations.
2. For HDR quantification, the expected amplicon sequence after HDR must also be provided.
Command-line
First on the Sanger farm, I need to set some environmental variables to run CRISPresso:
% export PYTHONPATH=/nfs/team87/farm3_lims2_vms/software/python_local/lib/python/
% export PATH=/nfs/team87/farm3_lims2_vms/software/crispresso_dependencies/bin:$PATH
A simple job to run CRISPresso:
% /nfs/team87/farm3_lims2_vms/software/python_local/bin/CRISPResso -w 50 --hide_mutations_outside_window_NHEJ -o S1_expA -r1 Homo-sapiens_S1_L001_R1_001.fastq -a GAAAGTCCGGAAAGACAAGGAAGgaacacctccacttacaaaagaagataagacagttgtcagacaaagccctcgaaggattaagccagttaggattattccttcttcaaaaaggacagatgcaaccattgctaagcaactcttacagag -g CTCGAAGGATTAAGCCAGTT
where
/nfs/team87/farm3_lims2_vms/software/python_local/bin/CRISPResso is where CRISPResso is installed,
-w specifies the window(s) in bp around each sgRNA to quantify the indels. The window is centred on the predicted cleavage site specified by each sgRNA. By default this is 1 bp on the CRISPresso website,
--hide_mutations_outside_window_NHEJ allows the user to visualise only the mutations overlapping the window around the cleavage site and used to classify a read as NHEJ. By default this is set to False, and all mutations are visualised including those that do not overlap the window, even though they are not used to classify a read as NHEJ.
-o S1_expA specifies the output folder to use for the analysis (by default this is the current folder),
-r1 Homo-sapiens_S1_L001_R1_001.fastq specifies the first fastq file,
-a GAAAGTCCGGAAAGACAAGGAAGgaacacctccacttacaaaagaagataagacagttgtcagacaaagccctcgaaggattaagccagttaggattattccttcttcaaaaaggacagatgcaaccattgctaagcaactcttacagag specifies the amplicon sequence,
-g CTCGAAGGATTAAGCCAGTT specifies the sgRNA sequence. If more than one sequence is entered, they can be separated by commas.
Memory (RAM) and run-time requirements
On the Sanger compute farm, a colleague recommended to use 2000 Mbyte of RAM (in bsub: "-M2000 -R"select[mem>2000] rusage[mem=2000]")
I found that this ran fine for me, it actually used a maximum memory of ~300 Mbyte in my case, where I had about 550,000 forward reads and 550,000 reverse reads. It took 8 minutes to run.
Script to submit CRISPResso jobs to the Sanger farm
I also wrote a Python script to submit CRISPresso jobs to the Sanger farm, for lots of jobs. To run it I typed:
% export PYTHONPATH=/nfs/team87/farm3_lims2_vms/software/python_local/lib/python/% export PATH=/nfs/team87/farm3_lims2_vms/software/crispresso_dependencies/bin:$PATH
% python3 submit_crispresso_jobs.py amplicon_files guide_rna_files fastq_files/ 23528_1
Output files:
CRISPResso_RUNNING_LOG.txt
This gives the information on the CRISPresso run.
Alleles_frequency_table.txt
This looks like this:
Aligned_Sequence NHEJ UNMODIFIED HDR n_deleted n_inserted n_mutated #Reads %Reads
TCGTCAATCGACTAATTCATCTCAACAACAAACGGAAAAGGAAGCAATGTCAGCTAATTCGATGTTTCTTATTGCCGTATTGTCATACACATTGATAAGTCAATTGGGGATAACTACATCGGATTCATGCAAATATTGTCTACAATTGTACGATGAAACGTATGAGAGGGGT True False False 0.0 0.0 1 29716 55.876048287
TCGTCAATCGACTAATTCATCTCAACAACAAACGGAAAAAGAAGGAATGTCAGCTAATTCGATGTTTATTATTGCCGTATTGTCATACACATTGATAAGTCAATTGGGGATAACTACATCGGATTCATGCAAATATTGTCTACAATTGTACGATGAAACGTATGAGAGGGGT True False False 0.0 0.0 1 2110 3.96750780339
...
This is my interpretation:
- the first column 'Aligned_sequence' gives an aligned sequence present in some reads,
- the second column 'NHEJ' says whether reads with that sequence were classified as NHEJ reads,
- the third column 'UNMODIFIED' says whether those reads had any change (substitution or indel),
- the fourth column 'HDR' says whether they were classified as HDR reads,
- the fifth column 'n_deleted' - I think this is the number of deleted bases in reads with this sequence (???),
- the sixth column 'n_inserted' - I think this is the number of inserted bases in reads with this sequence (???),
- the seventh column 'n_mutated' - I think this is the number of mutated (substituted) bases in reads with this sequence (???),
- the eighth column '#Reads' says how many reads had that sequence,
- and the ninth column '%Reads' says what % of reads had that sequence.
Note: by default, this file only includes substitutions within the window of size w, and deletions that overlap that window of size w and indels that are at least 15 bp from the end of the reference amplicon.
cut_points.pickle
I think this is just a temporary file produced by the program (???).
deletion_histogram.txt
This seems to give the data for a histogram of deletion sizes, I'm guessing the first column is the deletion size in bp, and the second size is the %reads with a deletion of that size (???):
del_size fq
0 53175
-1 4
-2 3
-3 0
-4 0
-5 0
-6 0
-7 0
-8 0
-9 0
-10 0
-11 0
-12 0
-13 0
The supporting information for the paper says it is the data used to generate the deletion histogram in figure 3 in the output report.
effect_vector_combined.txt
This looks like this:
# amplicon position effect
1 4.964085592869768027e-01
2 2.519649505471776019e-01
3 2.450077093753525670e+00
4 1.147004625625211577e-01
5 2.425632732879545728e-01
...
According to the supporting information for the paper, this gives the location of mutations (deletions, insertions, substitutions) with respect to the reference amplicon. I'm guessing that the first column is the position in the reference amplicon, and the second column is the frequency of mutations at that position.
In an example I looked at, column 1 goes from 1...172, which I guess is the size of the amplicon (PCR product).
effect_vector_deletion_NHEJ.txt
According to the supporting information for the paper, this gives the location of deletions.
effect_vector_insertion_NHEJ.txt
According to the supporting information for the paper, this gives the location of insertions.
effect_vector_substitution_NHEJ.txt
According to the supporting information for the paper, this gives the location of substitutions.
indel_histogram.txt
The supporting information for the paper says it is the data used to generate figure 1 in the output report.
insertion_histogram.txt
The supporting information for the paper says it is the data used to generate the deletion histogram in figure 3 in the output report.
Mapping_statistics.txt
This seems to have the mapping information for a particular sample, eg.
READS IN INPUTS:135418
READS AFTER PREPROCESSING:117968
READS ALIGNED:53182
Note that in our case we had several different experiments (gRNA and amplicon combinations) sequenced in the same sequencing sample, so only some of the reads aligned (here 53182) to one of the amplicons.
The CRISPResso github page says the 'READS AFTER PREPROCESSING' is the number of reads left after merging reads (from paired-end read-pairs) and/or filtering based on base quality.
position_dependent_vector_avg_deletion_size.txt
This looks like this:
# amplicon position effect
1 0.000000000000000000e+00
2 0.000000000000000000e+00
3 0.000000000000000000e+00
4 0.000000000000000000e+00
...
The supporting information for the paper says it has the average length of the deletions for each position.
position_dependent_vector_avg_insertion_size.txt
The supporting information for the paper says it has the average length of the insertions for each position.
Quantification_of_editing_frequency.txt
This looks like this:
Quantification of editing frequency:
- Unmodified:1641 reads
- NHEJ:51541 reads (4 reads with insertions, 7 reads with deletions, 51540 reads with substitutions)
- HDR:0 reads (0 reads with insertions, 0 reads with deletions, 0 reads with substitutions)
- Mixed HDR-NHEJ:0 reads (0 reads with insertions, 0 reads with deletions, 0 reads with substitutions)
Total Aligned:53182 reads
sgRNA_intervals.pickle
I think this is just a temporary file produced by the program (???).
substitution_histogram.txt
The supporting information for the paper says it has the information used to make figure 3 in the output report. It looks like this:
sub_size fq
0 1642
1 47881
2 3408
3 189
4 43
5 10
6 5
7 2
8 0
9 0
10 1
11 1
12 0
13 0
This seems to have the substitution size (column 1) and frequency (column 2).
In Figure 3, it shows the x-axis of the right hand graph to be 'number of positions substituted', so I think this must be what is called the 'substitution size' (sub_size) in this file (???).
Figures:
1a.Indel_size_distribution_n_sequences.pdf
This is a histogram, which looks something like this:
1b.Indel_size_distribution_percentage.pdf
This is another histogram, which shows the y-axis as the % of sequences with a certain indel size, rather than the number of sequences (as in 1a above). It looks like this:
2.Unmodified_NHEJ_pie_chart.pdf
This is a pie chart, which shows the %reads with a modification (insertion, or deletion, or substitution):
3.Insertion_Deletion_Substitutions_size_hist.pdf
This looks like this:
4a.Combined_Insertion_Deletion_Substitution_Locations.pdf
This looks like this:
4b.Insertion_Deletion_Substitution_Locations_NHEJ.pdf
4e.Position_dependent_average_indel_size.pdf
Note: this picture does not seem to just include indels that overlap the window of size w around the Cas9 cut site. However, I think that this plot is excluding indels that occur in the last 15 bp of the reference amplicon.
Thank you for help
Patrick Driguez
Peter Keen
CRISPR-Cas9
I am new to CRISPR-Cas9, so here is a summary to remind me!
The two key parts of this system are:
- the Cas9 enzyme, which cuts double-stranded DNA at a specific location.
- a guide RNA (gRNA), which is about 20 bp, and is located within a longer RNA scaffold. The scaffold binds to DNA and pre-designed gRNA guides Cas9 (Cas9 binds to the gRNA) to the right part of the genome. The gRNA has bases complementary to the target DNA sequence in the genome.
The PAM (protospacer adjacent motif) is a 2-6 bp DNA sequence immediately following the DNA sequence where the Cas9 cuts the DNA (ie. immediately following the gRNA sequence). Cas9 will not cleave the DNA sequence unless it is followed by the PAM sequence. The canonical PAM is 5'-
Details of the CRISPresso pipeline
The CRISPresso pipeline is described in the supporting information for the paper as including these steps:
1. Quality filtering
This allows the user to try to avoid false positives due to sequencing errors. CRISPResso allows reads to be filtered based (i) on a read's 'average quality score', defined as the average of the read's single-base quality scores, (ii) on the lowest base quality for all bases in a read. By default, the reads are not filtered.
2. Trimming
If the user provides the adaptor sequences used, this will trim them using Trimmomatic. By default, the reads are not trimmed.
3. Merging paired end reads
When paired end reads are used, it is possible to merge the reads since the amplicon sequence is usually shorter than twice the read length. This step mean that the sequence for the overlapping region is obtained with greater confidence. This step is performed using FLASH.
4. Alignment
To align the filtered reads to the reference amplicon, CRISPResso uses needle from the EMBOSS package, which performs Needleman-Wunsch alignment.
5. Quantification of mutation
HDR events: The sequence identity scores for alignment to the reference amplicon and expected HDR amplicon (if any) are considered. If the read aligns better to the expected HDR amplicon, the read is classified as HDR or mixed HDR-NHEJ. If the sequence identity is above 98%, the read is classified as HDR, otherwise as mixed HDR-NHEJ.
Unmodified versus NHEJ: Reads that align better to the reference amplicon than expected HDR amplicon are considered unmodified if they have 100% sequence identity with it. Otherwise, they are classified as NHEJ if they contain mutations in a window of w bp (w/2 bp on each side) about the gRNA's cleavage site.
Output plots: For all plots, all mutations on reads with a given classification are shown by default, even the ones outside the w bp window in the case of NHEJ that do not contribute to the classification.
Note that if there are deletion and substitutions in a read that are at the end of the reference amplicon, CRISPResso may ignore these (for quantification and in the Alleles_frequency_table.txt output file). This is because by default CRISPResso excludes 15bp from each side of the reads for quantification of indels, and only considers an indel if it overlaps the window around the cleavage site (ie. the window of size w). The value of 15 bp can be changed by the -exclude_bp_from_left and --exclude_bp_from_right options.
Input files
CRISPResso requires as input:
1. Paired-end reads in .fastq (or fastq.gz) format
2. A reference amplicon
Other optional inputs:
1. One or more gRNA sequences can be provided to compare the predicted cleavage position(s) to the position of the observed mutations.
2. For HDR quantification, the expected amplicon sequence after HDR must also be provided.
Command-line
First on the Sanger farm, I need to set some environmental variables to run CRISPresso:
% export PYTHONPATH=/nfs/team87/farm3_lims2_vms/software/python_local/lib/python/
% export PATH=/nfs/team87/farm3_lims2_vms/software/crispresso_dependencies/bin:$PATH
A simple job to run CRISPresso:
% /nfs/team87/farm3_lims2_vms/software/python_local/bin/CRISPResso -w 50 --hide_mutations_outside_window_NHEJ -o S1_expA -r1 Homo-sapiens_S1_L001_R1_001.fastq -a GAAAGTCCGGAAAGACAAGGAAGgaacacctccacttacaaaagaagataagacagttgtcagacaaagccctcgaaggattaagccagttaggattattccttcttcaaaaaggacagatgcaaccattgctaagcaactcttacagag -g CTCGAAGGATTAAGCCAGTT
where
/nfs/team87/farm3_lims2_vms/software/python_local/bin/CRISPResso is where CRISPResso is installed,
-w specifies the window(s) in bp around each sgRNA to quantify the indels. The window is centred on the predicted cleavage site specified by each sgRNA. By default this is 1 bp on the CRISPresso website,
--hide_mutations_outside_window_NHEJ allows the user to visualise only the mutations overlapping the window around the cleavage site and used to classify a read as NHEJ. By default this is set to False, and all mutations are visualised including those that do not overlap the window, even though they are not used to classify a read as NHEJ.
-o S1_expA specifies the output folder to use for the analysis (by default this is the current folder),
-r1 Homo-sapiens_S1_L001_R1_001.fastq specifies the first fastq file,
-a GAAAGTCCGGAAAGACAAGGAAGgaacacctccacttacaaaagaagataagacagttgtcagacaaagccctcgaaggattaagccagttaggattattccttcttcaaaaaggacagatgcaaccattgctaagcaactcttacagag specifies the amplicon sequence,
-g CTCGAAGGATTAAGCCAGTT specifies the sgRNA sequence. If more than one sequence is entered, they can be separated by commas.
Memory (RAM) and run-time requirements
On the Sanger compute farm, a colleague recommended to use 2000 Mbyte of RAM (in bsub: "-M2000 -R"select[mem>2000] rusage[mem=2000]")
I found that this ran fine for me, it actually used a maximum memory of ~300 Mbyte in my case, where I had about 550,000 forward reads and 550,000 reverse reads. It took 8 minutes to run.
Script to submit CRISPResso jobs to the Sanger farm
I also wrote a Python script to submit CRISPresso jobs to the Sanger farm, for lots of jobs. To run it I typed:
% export PYTHONPATH=/nfs/team87/farm3_lims2_vms/software/python_local/lib/python/% export PATH=/nfs/team87/farm3_lims2_vms/software/crispresso_dependencies/bin:$PATH
% python3 submit_crispresso_jobs.py amplicon_files guide_rna_files fastq_files/ 23528_1
Output files:
CRISPResso_RUNNING_LOG.txt
This gives the information on the CRISPresso run.
Alleles_frequency_table.txt
This looks like this:
Aligned_Sequence NHEJ UNMODIFIED HDR n_deleted n_inserted n_mutated #Reads %Reads
TCGTCAATCGACTAATTCATCTCAACAACAAACGGAAAAGGAAGCAATGTCAGCTAATTCGATGTTTCTTATTGCCGTATTGTCATACACATTGATAAGTCAATTGGGGATAACTACATCGGATTCATGCAAATATTGTCTACAATTGTACGATGAAACGTATGAGAGGGGT True False False 0.0 0.0 1 29716 55.876048287
TCGTCAATCGACTAATTCATCTCAACAACAAACGGAAAAAGAAGGAATGTCAGCTAATTCGATGTTTATTATTGCCGTATTGTCATACACATTGATAAGTCAATTGGGGATAACTACATCGGATTCATGCAAATATTGTCTACAATTGTACGATGAAACGTATGAGAGGGGT True False False 0.0 0.0 1 2110 3.96750780339
...
This is my interpretation:
- the first column 'Aligned_sequence' gives an aligned sequence present in some reads,
- the second column 'NHEJ' says whether reads with that sequence were classified as NHEJ reads,
- the third column 'UNMODIFIED' says whether those reads had any change (substitution or indel),
- the fourth column 'HDR' says whether they were classified as HDR reads,
- the fifth column 'n_deleted' - I think this is the number of deleted bases in reads with this sequence (???),
- the sixth column 'n_inserted' - I think this is the number of inserted bases in reads with this sequence (???),
- the seventh column 'n_mutated' - I think this is the number of mutated (substituted) bases in reads with this sequence (???),
- the eighth column '#Reads' says how many reads had that sequence,
- and the ninth column '%Reads' says what % of reads had that sequence.
Note: by default, this file only includes substitutions within the window of size w, and deletions that overlap that window of size w and indels that are at least 15 bp from the end of the reference amplicon.
cut_points.pickle
I think this is just a temporary file produced by the program (???).
deletion_histogram.txt
This seems to give the data for a histogram of deletion sizes, I'm guessing the first column is the deletion size in bp, and the second size is the %reads with a deletion of that size (???):
del_size fq
0 53175
-1 4
-2 3
-3 0
-4 0
-5 0
-6 0
-7 0
-8 0
-9 0
-10 0
-11 0
-12 0
-13 0
The supporting information for the paper says it is the data used to generate the deletion histogram in figure 3 in the output report.
effect_vector_combined.txt
This looks like this:
# amplicon position effect
1 4.964085592869768027e-01
2 2.519649505471776019e-01
3 2.450077093753525670e+00
4 1.147004625625211577e-01
5 2.425632732879545728e-01
...
According to the supporting information for the paper, this gives the location of mutations (deletions, insertions, substitutions) with respect to the reference amplicon. I'm guessing that the first column is the position in the reference amplicon, and the second column is the frequency of mutations at that position.
In an example I looked at, column 1 goes from 1...172, which I guess is the size of the amplicon (PCR product).
effect_vector_deletion_NHEJ.txt
According to the supporting information for the paper, this gives the location of deletions.
effect_vector_insertion_NHEJ.txt
According to the supporting information for the paper, this gives the location of insertions.
effect_vector_substitution_NHEJ.txt
According to the supporting information for the paper, this gives the location of substitutions.
indel_histogram.txt
The supporting information for the paper says it is the data used to generate figure 1 in the output report.
insertion_histogram.txt
The supporting information for the paper says it is the data used to generate the deletion histogram in figure 3 in the output report.
Mapping_statistics.txt
This seems to have the mapping information for a particular sample, eg.
READS IN INPUTS:135418
READS AFTER PREPROCESSING:117968
READS ALIGNED:53182
Note that in our case we had several different experiments (gRNA and amplicon combinations) sequenced in the same sequencing sample, so only some of the reads aligned (here 53182) to one of the amplicons.
The CRISPResso github page says the 'READS AFTER PREPROCESSING' is the number of reads left after merging reads (from paired-end read-pairs) and/or filtering based on base quality.
position_dependent_vector_avg_deletion_size.txt
This looks like this:
# amplicon position effect
1 0.000000000000000000e+00
2 0.000000000000000000e+00
3 0.000000000000000000e+00
4 0.000000000000000000e+00
...
The supporting information for the paper says it has the average length of the deletions for each position.
position_dependent_vector_avg_insertion_size.txt
The supporting information for the paper says it has the average length of the insertions for each position.
Quantification_of_editing_frequency.txt
This looks like this:
Quantification of editing frequency:
- Unmodified:1641 reads
- NHEJ:51541 reads (4 reads with insertions, 7 reads with deletions, 51540 reads with substitutions)
- HDR:0 reads (0 reads with insertions, 0 reads with deletions, 0 reads with substitutions)
- Mixed HDR-NHEJ:0 reads (0 reads with insertions, 0 reads with deletions, 0 reads with substitutions)
Total Aligned:53182 reads
sgRNA_intervals.pickle
I think this is just a temporary file produced by the program (???).
substitution_histogram.txt
The supporting information for the paper says it has the information used to make figure 3 in the output report. It looks like this:
sub_size fq
0 1642
1 47881
2 3408
3 189
4 43
5 10
6 5
7 2
8 0
9 0
10 1
11 1
12 0
13 0
This seems to have the substitution size (column 1) and frequency (column 2).
In Figure 3, it shows the x-axis of the right hand graph to be 'number of positions substituted', so I think this must be what is called the 'substitution size' (sub_size) in this file (???).
Figures:
1a.Indel_size_distribution_n_sequences.pdf
This is a histogram, which looks something like this:
1b.Indel_size_distribution_percentage.pdf
This is another histogram, which shows the y-axis as the % of sequences with a certain indel size, rather than the number of sequences (as in 1a above). It looks like this:
2.Unmodified_NHEJ_pie_chart.pdf
This is a pie chart, which shows the %reads with a modification (insertion, or deletion, or substitution):
3.Insertion_Deletion_Substitutions_size_hist.pdf
This looks like this:
4a.Combined_Insertion_Deletion_Substitution_Locations.pdf
This looks like this:
4b.Insertion_Deletion_Substitution_Locations_NHEJ.pdf
4e.Position_dependent_average_indel_size.pdf
Note: this picture does not seem to just include indels that overlap the window of size w around the Cas9 cut site. However, I think that this plot is excluding indels that occur in the last 15 bp of the reference amplicon.
Thank you for help
Patrick Driguez
Peter Keen
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