README.md
In romunov/fishbone: Tools for Calling Alleles/Genotypes from Illumina NGS Sequences
Package fishbone will take a raw data from an NGS (Next Generation Sequencing) pipeline (de Barba et al., 2016) and output a called allele/stutter output. Based on these outputs, a genotype can be constructed. This problem will be tackled elsewhere.
Input might look something like this. Below is data for sample M0X0E, marker 03 and PCR reaction termed 8. Sequence is the raw sequence of the read(s) and Read_Count is the number of times this sequence has been detected for this particular combination of sample * plate * marker. TagCombo and Position are related, where former is the tag combination deposited into the position on a 96 well plate.
library(fishbone)
data(mt) # parameters used for filtering
data(smp1) # some sample data
smp1[Plate == 6 & Marker == "06", ]
Sample_Name Plate Read_Count Marker Run_Name length Position
1: M0X0E 6 76 06 DAB22 67 001
2: M0X0E 6 58 06 DAB22 83 001
3: M0X0E 6 26 06 DAB22 75 001
4: M0X0E 6 10 06 DAB22 79 001
5: M0X0E 6 7 06 DAB22 47 001
6: M0X0E 6 6 06 DAB22 63 001
7: M0X0E 6 6 06 DAB22 71 001
8: M0X0E 6 6 06 DAB22 70 001
9: M0X0E 6 6 06 DAB22 71 001
10: M0X0E 6 5 06 DAB22 71 001
11: M0X0E 6 4 06 DAB22 91 001
12: M0X0E 6 378 06 DAB22 71 001
13: M0X0E 6 209 06 DAB22 87 001
Sequence
1: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
2: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
3: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
6: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
7: aaggaaggaaggaaggaaggaaggaaggagggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
8: aggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
9: gaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
10: aaggaaggaaggaaggaaggaaggaaggaagggaggaaggaaggaaggaaggaaaagaagacagattgtaa
11: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
12: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
13: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
TagCombo
1: caggctaa:acaaccga
2: caggctaa:acaaccga
3: caggctaa:acaaccga
4: caggctaa:acaaccga
5: caggctaa:acaaccga
6: caggctaa:acaaccga
7: caggctaa:acaaccga
8: caggctaa:acaaccga
9: caggctaa:acaaccga
10: caggctaa:acaaccga
11: caggctaa:acaaccga
12: caggctaa:acaaccga
13: caggctaa:acaaccga
Below is an example of how you can call allele calling function on a specific combination Sample * Marker * Plate. Sequences not deemed alleles are removed.
data(mt) # parameters used to filter out (see algorithm description)
callAllele(smp1[Plate == 6 & Marker == "06", ], tbase = mt)
Sample_Name Plate Read_Count Marker Run_Name length Position called flag stutter
1: M0X0E 6 209 06 DAB22 87 001 TRUE FALSE
2: M0X0E 6 58 06 DAB22 83 001 FALSE TRUE
3: M0X0E 6 378 06 DAB22 71 001 TRUE FALSE
4: M0X0E 6 76 06 DAB22 67 001 TRUE DL TRUE
5: M0X0E 6 6 06 DAB22 63 001 FALSE TRUE
Sequence
1: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
2: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
3: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
TagCombo
1: caggctaa:acaaccga
2: caggctaa:acaaccga
3: caggctaa:acaaccga
4: caggctaa:acaaccga
5: caggctaa:acaaccga
Allele with count 76 got two flags (see below for possible flags) - L for low number of counts (as defined in the parameter file) and D because it is considered in disequilibrium with the highest allele (378 of length 71). Notice that this is an artefact because allele of length 63 is a stutter of allele of length 67 which is in turn stutter of allele of length 71. Stutter of a stutter then. This can be filtered out with appropriate parameters, which should be tailored according to your data.
To run allele calling on the entire dataset, split it by whichever combination you want and call the function.
xy <- split(smp1, f = list(smp1$Sample_Name, smp1$Marker, smp1$Plate, smp1$Run_Name))
out <- lapply(xy, FUN = callAllele, tbase = mt, verbose = FALSE)
out <- do.call(rbind, out)
Sample_Name Plate Read_Count Marker Run_Name length Position called flag stutter
1: M0X0E 3 233 03 DAB22 63 001 TRUE FALSE
2: M0X0E 3 284 03 DAB22 59 001 TRUE TRUE
3: M0X0E 3 27 03 DAB22 55 001 FALSE TRUE
4: M0X0E 3 96 06 DAB22 87 001 TRUE L FALSE
5: M0X0E 3 29 06 DAB22 83 001 FALSE TRUE
---
158: M0X0E 6 31 65 DAB22 89 001 FALSE TRUE
159: M0X0E 6 2036 67 DAB22 89 001 TRUE FALSE
160: M0X0E 6 96 67 DAB22 85 001 FALSE TRUE
161: M0X0E 6 489 68 DAB22 74 001 TRUE FALSE
162: M0X0E 6 56 68 DAB22 70 001 FALSE TRUE
Sequence
1: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctatctatctc
2: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctatctc
3: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctc
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
---
158: gaagcaacagggtatagatatatagagatagatagatagatagatagatagatagatagataaagagatttattataaggaattggctc
159: taattttaattttttaaaatttattttaaaatatttatttatttatttatttatttatttatttatttatttatttttgctcaccacac
160: taattttaattttttaaaatttattttaaaatatttatttatttatttatttatttatttatttatttatttttgctcaccacac
161: aacttaccaacaaactaatctatctatctatctatctatctatctatctatctatctatctatctacatatata
162: aacttaccaacaaactaatctatctatctatctatctatctatctatctatctatctatctacatatata
TagCombo
1: acacacac:ttacgcca
2: acacacac:ttacgcca
3: acacacac:ttacgcca
4: acacacac:ttacgcca
5: acacacac:ttacgcca
---
158: caggctaa:acaaccga
159: caggctaa:acaaccga
160: caggctaa:acaaccga
161: caggctaa:acaaccga
162: caggctaa:acaaccga
If you want to use data.table syntax, note that a set of columns will be duplicated.
system.time(out <- smp1[, callAllele(c(.BY, .SD), tbase = mt, verbose = TRUE),
by = .(Sample_Name, Marker, Plate, Run_Name)])
One duplicate can easily be removed (not shown).
Algorithm
Algorithm used to extract alleles from junk is as follows:
0. find max allele height
1. if allele has number of reads < L, flag it as "L"
2. see if allele has stutter
2a. if yes, mark as called
2aa. if A in disbalance (A < D), flag as "D"
2ab. mark stutter as such $stutter = TRUE
2b. if no, check AlleleWithNoStutterHeight
2ba. if x > AlleleWithNoStutterHeight, add flag "N"
2bb. if x < AlleleWithNoStutterHeight, ignore allele
3. if number of unflagged alleles is more than 2 (those marked with D are not counted), add flag "M" to all
Supported flags
- L = low amplification threshold (if for some reason, number of total reads is very low, alleles get a flag)
- N = no stutter (if there was enough reads but no stutter was present)
- D = disbalance - alleles not in balance (expecting 1:1 for heterozygotes, those out of balance flagged)
- M = multiple alleles (self explanatory)
Filtering parameters
mt is a parameter data.frame where stutter heights, absolute read count etc. are specified. Notice the addition of extra columns called, flag and stutter. This will depend on your experiment so modify or create a new one as needed.
> head(mt)
Marker Repeat Stutter Disbalance LowCount AlleleWithNoStutterHeight
1: 03 ctat 0.18 0.33 100 100
2: 06 aagg 0.18 0.33 100 100
3: 14 tttta 0.18 0.33 100 100
4: 16 cttt 0.18 0.33 100 100
5: 17 cttt 0.18 0.33 100 100
6: 25 cttt 0.18 0.33 100 100
References
De Barba M, Miquel C, Lobréaux S, et al (2016) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Molecular Ecology Resources 1–16. doi: 10.1111/1755-0998.12594
romunov/fishbone documentation built on June 17, 2019, 2:06 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Package fishbone will take a raw data from an NGS (Next Generation Sequencing) pipeline (de Barba et al., 2016) and output a called allele/stutter output. Based on these outputs, a genotype can be constructed. This problem will be tackled elsewhere.
Input might look something like this. Below is data for sample M0X0E, marker 03 and PCR reaction termed 8. Sequence is the raw sequence of the read(s) and Read_Count is the number of times this sequence has been detected for this particular combination of sample * plate * marker. TagCombo and Position are related, where former is the tag combination deposited into the position on a 96 well plate.
library(fishbone)
data(mt) # parameters used for filtering
data(smp1) # some sample data
smp1[Plate == 6 & Marker == "06", ]
Sample_Name Plate Read_Count Marker Run_Name length Position
1: M0X0E 6 76 06 DAB22 67 001
2: M0X0E 6 58 06 DAB22 83 001
3: M0X0E 6 26 06 DAB22 75 001
4: M0X0E 6 10 06 DAB22 79 001
5: M0X0E 6 7 06 DAB22 47 001
6: M0X0E 6 6 06 DAB22 63 001
7: M0X0E 6 6 06 DAB22 71 001
8: M0X0E 6 6 06 DAB22 70 001
9: M0X0E 6 6 06 DAB22 71 001
10: M0X0E 6 5 06 DAB22 71 001
11: M0X0E 6 4 06 DAB22 91 001
12: M0X0E 6 378 06 DAB22 71 001
13: M0X0E 6 209 06 DAB22 87 001
Sequence
1: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
2: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
3: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
6: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
7: aaggaaggaaggaaggaaggaaggaaggagggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
8: aggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
9: gaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
10: aaggaaggaaggaaggaaggaaggaaggaagggaggaaggaaggaaggaaggaaaagaagacagattgtaa
11: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
12: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
13: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
TagCombo
1: caggctaa:acaaccga
2: caggctaa:acaaccga
3: caggctaa:acaaccga
4: caggctaa:acaaccga
5: caggctaa:acaaccga
6: caggctaa:acaaccga
7: caggctaa:acaaccga
8: caggctaa:acaaccga
9: caggctaa:acaaccga
10: caggctaa:acaaccga
11: caggctaa:acaaccga
12: caggctaa:acaaccga
13: caggctaa:acaaccga
Below is an example of how you can call allele calling function on a specific combination Sample * Marker * Plate. Sequences not deemed alleles are removed.
data(mt) # parameters used to filter out (see algorithm description)
callAllele(smp1[Plate == 6 & Marker == "06", ], tbase = mt)
Sample_Name Plate Read_Count Marker Run_Name length Position called flag stutter
1: M0X0E 6 209 06 DAB22 87 001 TRUE FALSE
2: M0X0E 6 58 06 DAB22 83 001 FALSE TRUE
3: M0X0E 6 378 06 DAB22 71 001 TRUE FALSE
4: M0X0E 6 76 06 DAB22 67 001 TRUE DL TRUE
5: M0X0E 6 6 06 DAB22 63 001 FALSE TRUE
Sequence
1: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
2: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
3: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
TagCombo
1: caggctaa:acaaccga
2: caggctaa:acaaccga
3: caggctaa:acaaccga
4: caggctaa:acaaccga
5: caggctaa:acaaccga
Allele with count 76 got two flags (see below for possible flags) - L for low number of counts (as defined in the parameter file) and D because it is considered in disequilibrium with the highest allele (378 of length 71). Notice that this is an artefact because allele of length 63 is a stutter of allele of length 67 which is in turn stutter of allele of length 71. Stutter of a stutter then. This can be filtered out with appropriate parameters, which should be tailored according to your data.
To run allele calling on the entire dataset, split it by whichever combination you want and call the function.
xy <- split(smp1, f = list(smp1$Sample_Name, smp1$Marker, smp1$Plate, smp1$Run_Name))
out <- lapply(xy, FUN = callAllele, tbase = mt, verbose = FALSE)
out <- do.call(rbind, out)
Sample_Name Plate Read_Count Marker Run_Name length Position called flag stutter
1: M0X0E 3 233 03 DAB22 63 001 TRUE FALSE
2: M0X0E 3 284 03 DAB22 59 001 TRUE TRUE
3: M0X0E 3 27 03 DAB22 55 001 FALSE TRUE
4: M0X0E 3 96 06 DAB22 87 001 TRUE L FALSE
5: M0X0E 3 29 06 DAB22 83 001 FALSE TRUE
---
158: M0X0E 6 31 65 DAB22 89 001 FALSE TRUE
159: M0X0E 6 2036 67 DAB22 89 001 TRUE FALSE
160: M0X0E 6 96 67 DAB22 85 001 FALSE TRUE
161: M0X0E 6 489 68 DAB22 74 001 TRUE FALSE
162: M0X0E 6 56 68 DAB22 70 001 FALSE TRUE
Sequence
1: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctatctatctc
2: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctatctc
3: aaatcctgtaacaaatctatctatctatctatctatctatctatctatctatctc
4: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
5: aaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaggaaaagaagacagattgtaa
---
158: gaagcaacagggtatagatatatagagatagatagatagatagatagatagatagatagataaagagatttattataaggaattggctc
159: taattttaattttttaaaatttattttaaaatatttatttatttatttatttatttatttatttatttatttatttttgctcaccacac
160: taattttaattttttaaaatttattttaaaatatttatttatttatttatttatttatttatttatttatttttgctcaccacac
161: aacttaccaacaaactaatctatctatctatctatctatctatctatctatctatctatctatctacatatata
162: aacttaccaacaaactaatctatctatctatctatctatctatctatctatctatctatctacatatata
TagCombo
1: acacacac:ttacgcca
2: acacacac:ttacgcca
3: acacacac:ttacgcca
4: acacacac:ttacgcca
5: acacacac:ttacgcca
---
158: caggctaa:acaaccga
159: caggctaa:acaaccga
160: caggctaa:acaaccga
161: caggctaa:acaaccga
162: caggctaa:acaaccga
If you want to use data.table syntax, note that a set of columns will be duplicated.
system.time(out <- smp1[, callAllele(c(.BY, .SD), tbase = mt, verbose = TRUE),
by = .(Sample_Name, Marker, Plate, Run_Name)])
One duplicate can easily be removed (not shown).
Algorithm
Algorithm used to extract alleles from junk is as follows:
0. find max allele height
1. if allele has number of reads < L, flag it as "L"
2. see if allele has stutter
2a. if yes, mark as called
2aa. if A in disbalance (A < D), flag as "D"
2ab. mark stutter as such $stutter = TRUE
2b. if no, check AlleleWithNoStutterHeight
2ba. if x > AlleleWithNoStutterHeight, add flag "N"
2bb. if x < AlleleWithNoStutterHeight, ignore allele
3. if number of unflagged alleles is more than 2 (those marked with D are not counted), add flag "M" to all
Supported flags
- L = low amplification threshold (if for some reason, number of total reads is very low, alleles get a flag)
- N = no stutter (if there was enough reads but no stutter was present)
- D = disbalance - alleles not in balance (expecting 1:1 for heterozygotes, those out of balance flagged)
- M = multiple alleles (self explanatory)
Filtering parameters
mt is a parameter data.frame where stutter heights, absolute read count etc. are specified. Notice the addition of extra columns called, flag and stutter. This will depend on your experiment so modify or create a new one as needed.
> head(mt)
Marker Repeat Stutter Disbalance LowCount AlleleWithNoStutterHeight
1: 03 ctat 0.18 0.33 100 100
2: 06 aagg 0.18 0.33 100 100
3: 14 tttta 0.18 0.33 100 100
4: 16 cttt 0.18 0.33 100 100
5: 17 cttt 0.18 0.33 100 100
6: 25 cttt 0.18 0.33 100 100
References
De Barba M, Miquel C, Lobréaux S, et al (2016) High-throughput microsatellite genotyping in ecology: improved accuracy, efficiency, standardization and success with low-quantity and degraded DNA. Molecular Ecology Resources 1–16. doi: 10.1111/1755-0998.12594
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