Last updated: 2020-03-28
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Knit directory: Comparative_APA/analysis/
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library(workflowr)
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library(tidyverse)
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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