crash problem

[rson920]

When I started to run the function counts<-readCounts(features,bam,targets,readLength=100L,maxISize=50000). After a few minutes, the R session was abnormally terminated due to unexpected crash. Did you know how to solve this problem?

sessionInfo(): R version 3.4.2 (2017-09-28) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 14.04.5 LTS

Matrix products: default BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0 LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0

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

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

loaded via a namespace (and not attached): [1] compiler_3.4.2 tools_3.4.2

[estepi]

Hi! How much memory do you have? How many BAMs are you overlapping with your "features" object? Did you try with the "toy" dataset, as explained in the vignette/help? Thanks in advance!

[rson920]

Hi!

I tried the example data from the package. It works well. the memory is enough. I can load my bamfiles, but when start the readCounts, it is stopped after a while. There are only 6 bamfiles.

Best regards Renhua

At 2018-02-19 16:46:16, "Estefania Mancini" notifications@github.com wrote:

Hi! How many memory do you have? How many BAMs are you overlapping with your features object? Did you try with the "toy" dataset, as explained in the vignette/help? Thanks in advance!

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[estepi]

Hi Renhua, Great. The toy dataset is small, you are overlapping few reads against a small portion of the genome. I guess is something related to memory. Which organism are you using and how big are your original bams? It could be useful if you try to overlap only 1 bam file. Subset your bam object (it is a list) like this: bams[[1]] let me know how it goes Estefi

[bellenger-l]

Hello,

I encounter the same issue. I originally work on my local machine but when I used readCounts I had :

> counts <- readCounts(features, bams, targets, cores = 1, readLength = 50, maxISize = 50000)
Read summarization by gene completed
Read summarization by bin completed
Read summarization by ei1 region completed
Read summarization by ie2 region completed
Error: vector memory exhausted (limit reached?)

I have 4 bam files and I am currently working on the mm10 genome. So I tried on a server and in this time it's never end (the last print is Read summarization by ie2 region completed) and I eventually get a broken pipe.

When I tried to overlap only 1 bam file (as suggested) I have these errors : On my local machine :

> counts <- readCounts(features, bams[[1]], cores = 1, readLength = 50, maxISize = 50000)
Error: vector memory exhausted (limit reached?)

And on the Ubuntu server :

> counts <- readCounts(features, bams[[1]], cores = 1, readLength = 50, maxISize = 50000)
Error: C stack usage  7969844 is too close to the limit

I am using : ASpli_1.8.1 and GenomicFeatures_1.34.3.

What can I do to overcome this issue ?

Best, Lea

[estepi]

Hi all, thanks for the comments and really sorry for the inconvenience. The problem arises at the step of summarizing junctions. When your genome is big and has a lot of introns (~junctions) it is a high demanding step. At this point, known and new junctions are summarized and compared against annotation. We aim to fix the bug and include in the next release (April)

[CriticalPeriod]

Hi estepi, I also wanted to run your program ! I have the same error as bellenger above. Do you know if there is something I can do to overcome it ? I'm totally new in R, maybe there's some set-up I did wrong ? I have 16gb ram on mac mini (2 cores i7, 16gb ram, 500gb DD, OS mojave), and I want to run with 4 bam files (39,4 gb in total). For other programs, it works fine. Maybe there is a setup to do to allow more memory to R ?

bamFiles <- c("DATA_RNA_SEQ_P30/filtered_mapper_results_p30+--1.bam","DATA_RNA_SEQ_P30/filtered_mapper_results_p30+--2.bam","DATA_RNA_SEQ_P30/filtered_mapper_results_p30++-1.bam", "DATA_RNA_SEQ_P30/filtered_mapper_results_p30++-2.bam") targets <- data.frame( row.names = c("hetero_rep1", "hetero_rep2", "homo_rep1", "homo_rep2"),

• bam = bamFiles,
• genotype = c("hetero","hetero", "homo", "homo"),
• stringsAsFactors = FALSE ) targets bam genotype hetero_rep1 DATA_RNA_SEQ_P30/filtered_mapper_results_p30+--1.bam hetero hetero_rep2 DATA_RNA_SEQ_P30/filtered_mapper_results_p30+--2.bam hetero homo_rep1 DATA_RNA_SEQ_P30/filtered_mapper_results_p30++-1.bam homo homo_rep2 DATA_RNA_SEQ_P30/filtered_mapper_results_p30++-2.bam homo bam <- loadBAM(targets) counts <- readCounts (
• features,
• bam,
• targets,
• cores = 1,
• readLength = 50L,
• maxISize = 50000,
• minAnchor = 10 ) Read summarization by gene completed Read summarization by bin completed Read summarization by ei1 region completed Read summarization by ie2 region completed Erreur : vecteurs de mémoire épuisés (limite atteinte ?) sessionInfo() R version 3.5.3 (2019-03-11) Platform: x86_64-apple-darwin15.6.0 (64-bit) Running under: macOS Mojave 10.14.2

Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale: [1] fr_FR.UTF-8/fr_FR.UTF-8/fr_FR.UTF-8/C/fr_FR.UTF-8/fr_FR.UTF-8

attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets methods base

other attached packages: [1] ASpli_1.8.1 edgeR_3.24.3 limma_3.38.3 GenomicFeatures_1.34.6 [5] AnnotationDbi_1.44.0 Biobase_2.42.0 GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
[9] IRanges_2.16.0 S4Vectors_0.20.1 BiocGenerics_0.28.0

loaded via a namespace (and not attached): [1] ProtGenerics_1.14.0 bitops_1.0-6 matrixStats_0.54.0
[4] bit64_0.9-7 RColorBrewer_1.1-2 progress_1.2.0
[7] httr_1.4.0 tools_3.5.3 backports_1.1.3
[10] R6_2.4.0 rpart_4.1-13 Hmisc_4.2-0
[13] DBI_1.0.0 lazyeval_0.2.2 Gviz_1.26.5
[16] colorspace_1.4-1 nnet_7.3-12 gridExtra_2.3
[19] prettyunits_1.0.2 curl_3.3 bit_1.1-14
[22] compiler_3.5.3 htmlTable_1.13.1 DelayedArray_0.8.0
[25] rtracklayer_1.42.2 scales_1.0.0 checkmate_1.9.1
[28] stringr_1.4.0 digest_0.6.18 Rsamtools_1.34.1
[31] foreign_0.8-71 rmarkdown_1.12 XVector_0.22.0
[34] base64enc_0.1-3 dichromat_2.0-0 pkgconfig_2.0.2
[37] htmltools_0.3.6 ensembldb_2.6.7 BSgenome_1.50.0
[40] htmlwidgets_1.3 rlang_0.3.2 rstudioapi_0.10
[43] RSQLite_2.1.1 BiocParallel_1.16.6 acepack_1.4.1
[46] VariantAnnotation_1.28.13 RCurl_1.95-4.12 magrittr_1.5
[49] GenomeInfoDbData_1.2.0 Formula_1.2-3 Matrix_1.2-17
[52] Rcpp_1.0.1 munsell_0.5.0 stringi_1.4.3
[55] yaml_2.2.0 SummarizedExperiment_1.12.0 zlibbioc_1.28.0
[58] plyr_1.8.4 grid_3.5.3 blob_1.1.1
[61] crayon_1.3.4 lattice_0.20-38 Biostrings_2.50.2
[64] splines_3.5.3 hms_0.4.2 locfit_1.5-9.1
[67] knitr_1.22 pillar_1.3.1 biomaRt_2.38.0
[70] XML_3.98-1.19 evaluate_0.13 biovizBase_1.30.1
[73] latticeExtra_0.6-28 data.table_1.12.0 BiocManager_1.30.4
[76] gtable_0.2.0 assertthat_0.2.1 ggplot2_3.1.0
[79] xfun_0.5 AnnotationFilter_1.6.0 survival_2.43-3
[82] tibble_2.1.1 GenomicAlignments_1.18.1 memoise_1.1.0
[85] cluster_2.0.7-1 BiocStyle_2.10.0

Thanks a lot ! David

[estepi]

Hi, thanks for your comment. Maybe memory is not enough. I used to work with more 64Gb. The problem arises at the step of overall summarization of junctions, which is a high demanding step.We know and we are trying to fix it. Are you able to run readCounts in 1 bam ?

[CriticalPeriod]

Ah, 64gb of ram will be hard to find in my lab. I will try with 1, thanks ! Otherwise, I will try with 32gb computer... thanks !

Le mer. 27 mars 2019 à 11:57, Estefania Mancini notifications@github.com a écrit :

Hi, thanks for your comment. Maybe memory is not enough. I used to work with more 64Gb. The problem arises at the step of overall summarization of junctions, which is a high demanding step.We know and we are trying to fix it. Are you able to run readCounts in 1 bam ?

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[CriticalPeriod]

Hi again Estefania, I reset the memory limit of R and it seems to work

counts <- readCounts(features, bam, targets, cores = 2, readLength = 51L, maxISize = 50000, minAnchor = 10 ) Read summarization by gene completed Read summarization by bin completed Read summarization by ei1 region completed Read summarization by ie2 region completed Junction summarization completed

but then, i tried to follow the workflow :

GeneCounts <- countsg(counts) GeneRd <- rdsg(counts) BinCounts <- countsb(counts) BinRd <- rdsb(counts) JunctionCounts <- countsj(counts) writeCounts(counts=counts, output.dir = "partial_results_p30") writeRds(counts=counts, output.dir = "partial_results_p30") e1iCounts <- countse1i(counts) ie2Counts <- countsie2(counts) View(JunctionCounts) View(targets) View(e1iCounts) View(counts) as <- AsDiscover( counts, targets, features, bam, readLength=51L,

• threshold = 5) Error in matrix(unlist(strsplit(jnames, "[.]")), byrow = TRUE, ncol = 3) : 'data' doit être de type vecteur, il était 'NULL'

so the AsDiscover won't work. and there is no data in junctionCounts (it says 0 obs of 12 variables) Do you think there is something to do ? thanks

Le mer. 27 mars 2019 à 12:08, David Benacom david.benacom@gmail.com a écrit :

Ah, 64gb of ram will be hard to find in my lab. I will try with 1, thanks ! Otherwise, I will try with 32gb computer... thanks !

Le mer. 27 mars 2019 à 11:57, Estefania Mancini notifications@github.com a écrit :

Hi, thanks for your comment. Maybe memory is not enough. I used to work with more 64Gb. The problem arises at the step of overall summarization of junctions, which is a high demanding step.We know and we are trying to fix it. Are you able to run readCounts in 1 bam ?

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[estepi]

Hi! Great! (at least 1 issue solved :P ) In the meantime you can export your counts and start analyzing with many tools. I'm curious about which aligner did you use. It is a "splice aware aligner"? Do you have "junctions" in your BAM? You can check with samtools or visually with a sashimi plot. Let me know

Thanks!

[CriticalPeriod]

It is preprocessed data from a genomic facility(2013), so I don't know how they analyzed it ! I will look it up. My goal is really to perform splicing analysis, since the genomic facility already did all the differential analysis etc. But I still have the original fastq, maybe I can try to perform myself the alignement & mapping ? In this case do you have a program that works well ?

In an other hand, do you know the origin of the matrix error ?

Thanks, David

Le mer. 27 mars 2019 à 17:26, Estefania Mancini notifications@github.com a écrit :

Hi! Great! (at least 1 issue solved :P ) In the meantime you can export your counts and start analyzing with many tools. I'm curious about which aligner did you use. It is a "splice aware aligner"? Do you have "junctions" in your BAM? You can check with samtools or visually with a sashimi plot. Let me know

Thanks!

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[estepi]

First, just check if you have junctions in your BAMs. Use simple sashimi plot in one of your BAM, your preferred gene. If dont, for the analysis of alternatitve splicing you should re align, yes. I use STAR

[CriticalPeriod]

Hi estefania, i'm currently trying to re align the reads following your advice. I'm at the mapping step. do you have any special parameters that are required for Aspli ? I'm using : ./STAR --runThreadN 1 --genomeDir ~/Genome_anno --readFilesIn ~/Downloads/reads_p30++1.fastq --outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMattributes Standard --outSAMunmapped Within --outSAMtype BAM SortedByCoordinate

Le mer. 27 mars 2019 à 17:40, Estefania Mancini notifications@github.com a écrit :

First, just check if you have junctions in your BAMs. Use simple sashimi plot in one of your BAM, your preferred gene. If dont, for the analysis of alternatitve splicing you should re align, yes. I use STAR

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[estepi]

Hi, Im sorry for the delay. here are my parameters, I put OK if they are the same, in other case, I put my values:

--outFilterType BySJout OK --outFilterMultimapNmax 20 OK --alignSJoverhangMin 8 OK --alignSJDBoverhangMin 1 -> 5 --alignIntronMin 20 OK --alignIntronMax 1000000 OK --alignMatesGapMax 1000000 Ok --outSAMattributes Standard OK --outSAMunmapped Within Ok --outSAMtype BAM SortedByCoordinate OK cheers Estefi

[CriticalPeriod]

Hi again Estefania :) Thanks for your STAR settings, it worked well and now I have junctions in my bam files ! It is already great. For your program, i get this error at asdiscover just after readcounts Error in data.frame(matrix(unlist(strsplit(jnames, "[.]")), byrow = TRUE, : row names supplied are of the wrong length De plus : Warning message: In matrix(unlist(strsplit(jnames, "[.]")), byrow = TRUE, ncol = 3) : la longueur des données [584270] n'est pas un diviseur ni un multiple du nombre de lignes [194757] Indeed, I changed the name of CT / Mut for the name of my conditions. Maybe it is the problem ? Thanks again, David

Le jeu. 4 avr. 2019 à 13:38, Estefania Mancini notifications@github.com a écrit :

Hi, Im sorry for the delay. here are my parameters, I put OK if they are the same, in other case, I put my values:

--outFilterType BySJout OK --outFilterMultimapNmax 20 OK --alignSJoverhangMin 8 OK --alignSJDBoverhangMin 1 -> 5 --alignIntronMin 20 OK --alignIntronMax 1000000 OK --alignMatesGapMax 1000000 Ok --outSAMattributes Standard OK --outSAMunmapped Within Ok --outSAMtype BAM SortedByCoordinate OK cheers Estefi

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[qicaibiology]

Hello,

I encounter the same issue. I originally work on my local machine but when I used readCounts I had :

> counts <- readCounts(features, bams, targets, cores = 1, readLength = 50, maxISize = 50000)
Read summarization by gene completed
Read summarization by bin completed
Read summarization by ei1 region completed
Read summarization by ie2 region completed
Error: vector memory exhausted (limit reached?)

I have 4 bam files and I am currently working on the mm10 genome. So I tried on a server and in this time it's never end (the last print is Read summarization by ie2 region completed) and I eventually get a broken pipe.

When I tried to overlap only 1 bam file (as suggested) I have these errors : On my local machine :

> counts <- readCounts(features, bams[[1]], cores = 1, readLength = 50, maxISize = 50000)
Error: vector memory exhausted (limit reached?)

And on the Ubuntu server :

> counts <- readCounts(features, bams[[1]], cores = 1, readLength = 50, maxISize = 50000)
Error: C stack usage  7969844 is too close to the limit

I am using : ASpli_1.8.1 and GenomicFeatures_1.34.3.

What can I do to overcome this issue ?

Best, Lea

Hi: I used mm10.gtf also takes a lot of memory and did not finish (I ran it on server and used 400G). Later I tried to use "TxDb.Mmusculus.UCSC.mm10.knownGene" from bioconductor as TxDb object and it went through.

Hope it will help,

[estepi]

Hi all. Can you confirm you can reads 50bp long? From my experience, recently I did run ASpli with Mmu standard annotation, using less than 100Gb RAm, 12 samples and it was possible. I need this 2 infos:

-Can you confirm you have junctions in your bam? I see your reads are 50pb. I never did an alignment with reads shorter than 100. Which aligner did you use and against which file?

-Can you repeat this command, using this notation:

counts <- readCounts(features, bams[1], cores = 1, readLength = 50, maxISize = 50000)

(I changed bam[[1]] by bam[1])

Did you run the example code?

Thanks in advance

[qicaibiology]

The example code works well. For my situation, I did 75bp paired-end sequencing so in my command the readLength =75. I used STAR to align my fastq files using mm10 reference genome from illunima website (https://support.illumina.com/sequencing/sequencing_software/igenome.html)

Thanks,