Cutoffs for multimapping

Last updated: 2020-03-31

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Knit directory: Comparative_APA/analysis/

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Unstaged changes:
    Modified:   analysis/DiffUsedIntronic.Rmd
    Modified:   analysis/ExploredAPA.Rmd
    Modified:   analysis/ExploredAPA_DF.Rmd
    Modified:   analysis/MMExpreiment.Rmd
    Modified:   analysis/OppositeMap.Rmd
    Modified:   analysis/SpliceSiteStrength.Rmd
    Modified:   analysis/TotalVNuclearBothSpecies.Rmd
    Modified:   analysis/annotationInfo.Rmd
    Modified:   analysis/changeMisprimcut.Rmd
    Modified:   analysis/comp2apaQTLPAS.Rmd
    Modified:   analysis/correlationPhenos.Rmd
    Modified:   analysis/dAPAandapaQTL_DF.Rmd
    Modified:   analysis/establishCutoffs.Rmd
    Modified:   analysis/investigatePantro5.Rmd
    Modified:   analysis/multiMap.Rmd
    Modified:   analysis/orthoexonAnno.Rmd
    Modified:   analysis/speciesSpecific.Rmd
    Modified:   analysis/upsetter_DF.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	6de3b83	brimittleman	2020-03-31	add mismatch results
html	a70f2fe	brimittleman	2020-03-28	Build site.
Rmd	706fa0d	brimittleman	2020-03-28	add mm exp code
html	3d4dd47	brimittleman	2020-03-27	Build site.
Rmd	f92b89c	brimittleman	2020-03-26	add ortho exon and new mm

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
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✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

I want to look at the parameters for multimapping. I will look at the paremeters such as include a read when the first vs second map difference is 10%, 5% ect. I want to get to a cutoff where the PAS lost are not in the ortho exon.

I will look at the featurecounts documentation for this or I can look directly at the star info.

−−primary: If specified, only primary alignments will be counted. Primary and secondary alignments are identified using bit 0x100 in the Flag field of SAM/BAM files. All primary alignments in a dataset will be counted no matter they are from multi- mapping reads or not (ie. ‘-M’ is ignored).

For multi-mappers, all alignments except one are marked with 0x100 (secondary alignment) in the FLAG (column 2 of the SAM

I used options:

–outFilterMultimapNmax 10 –outSAMmultNmax 1

–outSAMmultNmax 1 will output exactly one SAM line for each mapped read

I only allowed for 1 read to be mapped. I want the secondary one as well. I want to mess with the secondary read.

Remap human and chimp reads with the new filter. Just to look first, don’t worry about misspriming.

mkdir ../data/TestMM2/
sbatch StarMM2.sh
sbatch ChimpStarMM2.sh

chimp_combined_18358_N.MM2.sort.bam is done. I can use this to look at the seondary mapped reads.

HI:i:i Query hit index, indicating the alignment record is the i-th one stored in SAM.

the HI:i:2 reads are the secondary maped reads.

NM:i:count number of mismatches

256 is the secondary allignment

I can use pysam to filter secondary reads.

samtools view chimp_combined_18358_N.MM2.sort.bam  | cut -f5 | sort -n | uniq -c

1860484 0 2727360 1 3182906 3 39284071 255

without multmap:

930242 0 1363680 1 1591453 3 39284071 255

I want to filter just secondary reads and get the scores- secondary alignment score is in column 2

samtools view chimp_combined_18358_N.MM2.sort.bam  | cut -f2 | sort -n | uniq -c

Primary (by strand) 23943403 0 19226043 16

seconday (by strand)
1571873 256 2313502 272

“AS” SAM tag express the “Alignment score generated by aligner” -scores between 53,85

get scores for primary and secondary

Mismatches (with secondary) 34622475 nM:i:0 7600018 nM:i:1 57259 nM:i:10 2337437 nM:i:2 1045189 nM:i:3 547184 nM:i:4 327976 nM:i:5 213004 nM:i:6 138913 nM:i:7 96974 nM:i:8 68392 nM:i:9

Primary

14717503 nM:i:0 3042728 nM:i:1 21887 nM:i:10 943180 nM:i:2 406973 nM:i:3 217371 nM:i:4 132942 nM:i:5 88071 nM:i:6 54904 nM:i:7 38671 nM:i:8 26097 nM:i:9

Seperate secondary, write code for any file:

mkdir ../data/TestMM2_SeondaryRead
mkdir ../data/TestMM2_PrimaryRead

python filterSecondaryread.py ../data/TestMM2/chimp_combined_18358_N.MM2.sort.bam  ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.bam


python filterPrimaryread.py ../data/TestMM2/chimp_combined_18358_N.MM2.sort.bam  ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.bam


samtools sort -o ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.sort.bam -O bam  ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.bam

samtools sort -o ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.sort.bam -O bam ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.bam

samtools index ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.sort.bam

samtools index ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.sort.bam

scores 53-84

2426470 nM:i:0 919015 nM:i:1 9997 nM:i:10 253038 nM:i:2 108368 nM:i:3 59802 nM:i:4 39210 nM:i:5 26476 nM:i:6 18892 nM:i:7 13544 nM:i:8 10563 nM:i:9

This means a lot of the secondary reads also have 0 mismatch. These are probably the reads I would want to filter.

Compare the porportion.

mkdir ../data/TestMM2_quality

samtools view chimp_combined_18358_N.MM2_secondary.sort.bam | cut -f15 | sort -n | uniq -c > ../TestMM2_quality/chimp18358_secondaryMismatch.txt

samtools view chimp_combined_18358_N.MM2.sort.bam | cut -f15 | sort -n | uniq -c > ../TestMM2_quality/chimp18358_PrimaryMismatch.txt

samtools view ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.sort.bam | cut -f15 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_PrimaryOnlyMismatch.txt

Compare this:

SecMismatch=read.table("../data/TestMM2_quality/chimp18358_secondaryMismatch.txt",col.names = c("Reads","Mismatch"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Secondary")
PrimaryOnlyMismatch= read.table("../data/TestMM2_quality/chimp18358_PrimaryOnlyMismatch.txt",col.names = c("Reads","Mismatch"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Primary")
PrimaryMismatch=read.table("../data/TestMM2_quality/chimp18358_PrimaryMismatch.txt",col.names = c("Reads","Mismatch"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="All")

BothMiss= SecMismatch %>% bind_rows(PrimaryOnlyMismatch)


ggplot(BothMiss, aes(x=Mismatch, y=PropReads, by=set, fill=set))+ geom_bar(stat="identity", position="dodge")+ labs(title="Primary v Seconday Mismatch Chimp 18358")

Version	Author	Date
a70f2fe	brimittleman	2020-03-28

Do this for scores as well

samtools view ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.sort.bam | cut -f14 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_secondaryScores.txt

samtools view ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.sort.bam | cut -f14 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_PrimaryOnlyScores.txt

SecScore=read.table("../data/TestMM2_quality/chimp18358_secondaryScores.txt",col.names = c("Reads","Score"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Secondary")
PrimaryOnlyScore= read.table("../data/TestMM2_quality/chimp18358_PrimaryOnlyScores.txt",col.names = c("Reads","Score"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Primary")

BothScore= SecScore %>% bind_rows(PrimaryOnlyScore)

ggplot(BothScore, aes(x=Score, y=PropReads, by=set, fill=set))+ geom_bar(stat="identity", position="dodge") +  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Primary v Seconday Scores Chimp 18358")

Version	Author	Date
a70f2fe	brimittleman	2020-03-28

Feature counts change the -Q for quality. what score is this?

may q scores?

samtools view ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.sort.bam | cut -f5 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_secondaryMAPScores.txt

samtools view ../data/TestMM2_PrimaryRead/chimp_combined_18358_N.MM2_primary.sort.bam | cut -f5 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_PrimaryOnlyMAPScores.txt

SecMAPScore=read.table("../data/TestMM2_quality/chimp18358_secondaryMAPScores.txt",col.names = c("Reads","Score"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Secondary")
PrimaryMAPOnlyScore= read.table("../data/TestMM2_quality/chimp18358_PrimaryOnlyMAPScores.txt",col.names = c("Reads","Score"),stringsAsFactors = F) %>% mutate(TotalRead=sum(Reads),PropReads=Reads/TotalRead,set="Primary")

BothMAPScore= SecMAPScore %>% bind_rows(PrimaryMAPOnlyScore)

ggplot(BothMAPScore, aes(x=Score, y=PropReads, by=set, fill=set))+ geom_bar(stat="identity", position="dodge") +  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Primary v Seconday Scores Chimp 18358")

Version	Author	Date
a70f2fe	brimittleman	2020-03-28

Mapping reads with the Fc.

18358:

Standard: 9195763 Primary: 9440757 Multimap: 9650073 9195763 for each score.

Mismatch scores for all primary and secondary

I want to compare the primary and secondary reads for all libraries. I can then look at the mismatch distribution.

First I need to wrap the primary and secondary read code.

sbatch FilterPrimSec.sh
mkdir ../data/TestMM2_mismatch
sbatch MismatchNumbers.sh

Load in results:

Chimp

Chimp_18358_primary=read.table("../data/TestMM2_mismatch/chimp_combined_18358_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp", line="18358")

Chimp_18358_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_18358_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp", line="18358")

Chimp_3622_primary=read.table("../data/TestMM2_mismatch/chimp_combined_3622_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp", line="3622")

Chimp_3622_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_3622_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp", line="3622")

Chimp_3659_primary=read.table("../data/TestMM2_mismatch/chimp_combined_3659_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp", line="3659")

Chimp_3659_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_3659_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp", line="3659")

Chimp_4973_primary=read.table("../data/TestMM2_mismatch/chimp_combined_4973_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp",line="4973")

Chimp_4973_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_4973_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp",line="4973")

Chimp_pt30_primary=read.table("../data/TestMM2_mismatch/chimp_combined_pt30_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp", line="pt30")

Chimp_pt30_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_pt30_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp",line="pt30")

Chimp_pt91_primary=read.table("../data/TestMM2_mismatch/chimp_combined_pt91_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Chimp",line="pt91")

Chimp_pt91_secondary=read.table("../data/TestMM2_mismatch/chimp_combined_pt91_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Chimp", line="pt91")

allChimpmismatch=Chimp_18358_primary %>% 
  bind_rows(Chimp_18358_secondary)  %>% 
  bind_rows(Chimp_3622_primary) %>% 
  bind_rows(Chimp_3622_secondary) %>% 
  bind_rows(Chimp_3659_primary) %>% 
  bind_rows(Chimp_3659_secondary) %>% 
  bind_rows(Chimp_4973_primary) %>% 
  bind_rows(Chimp_4973_secondary) %>% 
  bind_rows(Chimp_pt30_primary) %>% 
  bind_rows(Chimp_pt30_secondary) %>% 
  bind_rows(Chimp_pt91_primary) %>% 
  bind_rows(Chimp_pt91_secondary) %>% 
  group_by(line, set) %>% 
  mutate(TotalReads=sum(Reads)) %>%
  ungroup() %>% 
  mutate(PropReads=Reads/TotalReads)

allChimpmismatch$Mis=factor(allChimpmismatch$Mis, levels=c("nM:i:0",  "nM:i:1" , "nM:i:2", "nM:i:3" , "nM:i:4", "nM:i:5" , "nM:i:6",  "nM:i:7" , "nM:i:8" , "nM:i:9","nM:i:10"))


ggplot(allChimpmismatch, aes(x=Mis,y=PropReads,by=set, fill=set)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1))  +labs(title="Chimp Mismatch in primary and secondary")

ggplot(allChimpmismatch, aes(x=Mis,y=Reads,by=set, fill=set)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1))  +labs(title="Chimp Mismatch in primary and secondary")

Human:

Human_18498_primary=read.table("../data/TestMM2_mismatch/human_combined_18498_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18498")

Human_18498_secondary=read.table("../data/TestMM2_mismatch/human_combined_18498_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18498")


Human_18499_primary=read.table("../data/TestMM2_mismatch/human_combined_18499_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18499")

Human_18499_secondary=read.table("../data/TestMM2_mismatch/human_combined_18499_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18499")

Human_18502_primary=read.table("../data/TestMM2_mismatch/human_combined_18502_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18502")

Human_18502_secondary=read.table("../data/TestMM2_mismatch/human_combined_18502_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18502")

Human_18504_primary=read.table("../data/TestMM2_mismatch/human_combined_18504_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18504")

Human_18504_secondary=read.table("../data/TestMM2_mismatch/human_combined_18504_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18504")

Human_18510_primary=read.table("../data/TestMM2_mismatch/human_combined_18510_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18510")

Human_18510_secondary=read.table("../data/TestMM2_mismatch/human_combined_18510_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18510")

Human_18523_primary=read.table("../data/TestMM2_mismatch/human_combined_18523_N_primary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Primary", species="Human", line="18523")

Human_18523_secondary=read.table("../data/TestMM2_mismatch/human_combined_18523_N_secondary_mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Secondary", species="Human", line="18523")

All human:

allHumanmismatch=Human_18498_primary %>% 
  bind_rows(Human_18498_secondary)  %>% 
  bind_rows(Human_18499_primary) %>% 
  bind_rows(Human_18499_secondary) %>% 
  bind_rows(Human_18502_primary) %>% 
  bind_rows(Human_18502_secondary) %>% 
  bind_rows(Human_18504_primary) %>% 
  bind_rows(Human_18504_secondary) %>% 
  bind_rows(Human_18510_primary) %>% 
  bind_rows(Human_18510_secondary) %>% 
  bind_rows(Human_18523_primary) %>% 
  bind_rows(Human_18523_secondary)%>% 
  group_by(line, set) %>% 
  mutate(TotalReads=sum(Reads)) %>% 
  ungroup() %>%
  mutate(PropReads=Reads/TotalReads)
  
allHumanmismatch$Mis=factor(allHumanmismatch$Mis, levels=c("nM:i:0",  "nM:i:1" , "nM:i:2", "nM:i:3" , "nM:i:4", "nM:i:5" , "nM:i:6",  "nM:i:7" , "nM:i:8" , "nM:i:9","nM:i:10"))

Plot:

ggplot(allHumanmismatch, aes(x=Mis,y=PropReads,by=set, fill=set)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1))  +labs(title="Human Mismatch in primary and secondary")

ggplot(allHumanmismatch, aes(x=Mis,y=Reads,by=set, fill=set)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1))  +labs(title="Human Mismatch in primary and secondary")

This shows there is not an easy way to seperate the reads. Primary and secondary reads have mistmatches.

Mismatch score for unique reads.

Seperate the reads map uniq.

sbatch Filter255MM.sh

Look at the results:

Chimp_18358_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_18358_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp", line="18358")


Chimp_3622_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_3622_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp", line="3622")



Chimp_3659_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_3659_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp", line="3659")


Chimp_4973_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_4973_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp",line="4973")


Chimp_pt30_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_pt30_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp", line="pt30")


Chimp_pt91_Unique=read.table("../data/TestMM2_mismatch/chimp_combined_pt91_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Chimp",line="pt91")

Human_18498_Unique=read.table("../data/TestMM2_mismatch/human_combined_18498_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18498")


Human_18499_Unique=read.table("../data/TestMM2_mismatch/human_combined_18499_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18499")


Human_18502_Unique=read.table("../data/TestMM2_mismatch/human_combined_18502_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18502")


Human_18504_Unique=read.table("../data/TestMM2_mismatch/human_combined_18504_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18504")


Human_18510_Unique=read.table("../data/TestMM2_mismatch/human_combined_18510_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18510")


Human_18523_Unique=read.table("../data/TestMM2_mismatch/human_combined_18523_N_255mismatch.txt", stringsAsFactors = F, col.names = c("Reads", "Mis")) %>% mutate(set="Unique", species="Human", line="18523")

Look at these.

allUniqmismatch=Human_18498_Unique %>% 
  bind_rows(Human_18499_Unique)  %>% 
  bind_rows(Human_18502_Unique) %>% 
  bind_rows(Human_18504_Unique) %>% 
  bind_rows(Human_18510_Unique) %>% 
  bind_rows(Human_18523_Unique) %>% 
  bind_rows(Chimp_18358_Unique) %>% 
  bind_rows(Chimp_3622_Unique) %>% 
  bind_rows(Chimp_3659_Unique) %>% 
  bind_rows(Chimp_4973_Unique) %>% 
  bind_rows(Chimp_pt30_Unique)%>% 
  bind_rows(Chimp_pt91_Unique)%>% 
  group_by(line, set) %>% 
  mutate(TotalReads=sum(Reads)) %>% 
  ungroup() %>%
  mutate(PropReads=Reads/TotalReads)

allUniqmismatch$Mis=factor(allUniqmismatch$Mis, levels=c("nM:i:0",  "nM:i:1" , "nM:i:2", "nM:i:3" , "nM:i:4", "nM:i:5" , "nM:i:6",  "nM:i:7" , "nM:i:8" , "nM:i:9","nM:i:10"))

ggplot(allUniqmismatch, aes(x=Mis,y=Reads,by=species, fill=species)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Unique Read mismatch")

ggplot(allUniqmismatch, aes(x=Mis,y=PropReads,by=species, fill=species)) + geom_bar(stat="identity",position = "dodge") + facet_grid(~line)+  theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(title="Unique Read mismatch")

So even uniq reads have mismatches.

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2    
 [5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1  
 [9] tidyverse_1.2.1 workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 reshape2_1.4.3   haven_1.1.2      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] rlang_0.4.0      later_0.7.5      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0      
[45] lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1      
[49] rstudioapi_0.10  R6_2.3.0         nlme_3.1-137     git2r_0.26.1    
[53] compiler_3.5.1

Cutoffs for multimapping

Briana Mittleman

3/26/2020

Mismatch scores for all primary and secondary

Mismatch score for unique reads.