Cutoffs for multimapping

Last updated: 2020-03-28

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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
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    Modified:   analysis/SpliceSiteStrength.Rmd
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    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/speciesSpecific.Rmd
    Modified:   analysis/upsetter_DF.Rmd

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File	Version	Author	Date	Message
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 ──

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── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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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")

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")

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")

secondary score in column 2

samtools view ../data/TestMM2_SeondaryRead/chimp_combined_18358_N.MM2_secondary.sort.bam | cut -f2 | sort -n | uniq -c > ../data/TestMM2_quality/chimp18358_secondaryExtraScores.txt

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] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     later_0.7.5      git2r_0.26.1     tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] promises_1.0.1   htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0
[45] colorspace_1.3-2 httpuv_1.4.5     labeling_0.3     stringi_1.2.4   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4

Cutoffs for multimapping

Briana Mittleman

3/26/2020