Hi, I get the following error when running squire count

squire Count -r 76 -b hg19 -p 16 -v -e 10

Warning: gene "CDY1" (on chrY) has reference transcripts on both strands?
Warning: gene "TTTY3" (on chrY) has reference transcripts on both strands?
Warning: gene "TTTY3B" (on chrY) has reference transcripts on both strands?
Creating temporary files2019-06-27 08:42:57.385653

Creating unique and multiple alignment bedfiles 2019-06-27 08:42:57.385925

Identifying properly paired reads 2019-06-27 08:42:57.385953

Intersecting bam files with TE bedfile 2019-06-27 08:43:39.311783

Splitting into read1 and read 2 2019-06-27 08:44:24.749062

Combining adjacent TEs with same read alignment 2019-06-27 08:44:26.709173

Getting genomic coordinates of read2019-06-27 08:44:49.983114

Identifying and labeling unique and multi reads2019-06-27 08:44:57.855092

Matching paired-end mates and merging coordinates2019-06-27 08:45:03.586699

join: multi-character tab ‘$\t’
Traceback (most recent call last):
File "/mnt/RNA-SEQ/miniconda3/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/mnt/RNA-SEQ/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/mnt/RNA-SEQ/SQuIRE/squire/Count.py", line 1743, in main
match_reads(paired_tempfile1_ulabeled,paired_tempfile2_ulabeled,strandedness,paired_matched_tempfile,paired_unmatched1, paired_unmatched2,debug) #match pairs between paired files
File "/mnt/RNA-SEQ/SQuIRE/squire/Count.py", line 494, in match_reads
sp.check_call(["/bin/sh","-c",joincommand])
File "/mnt/RNA-SEQ/miniconda3/envs/squire/lib/python2.7/subprocess.py", line 186, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/bin/sh', '-c', "join -j 12 -t $'\t' -o 1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,1.10,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,2.10 /mnt/RNA-SEQ/MCF-7/squire_count/CTRL_1.fastq_paired_ulabeled_1.tmpx3ba1x_newread_v1 /mnt/RNA-SEQ/MCF-7/squire_count/CTRL_1.fastq_paired_ulabeled_2.tmpke3IRJ_newread_v1 > /mnt/RNA-SEQ/MCF-7/squire_count/CTRL_1.fastq_paired_matched.tmpeea9kD_10k_v1"]' returned non-zero exit status 1

I want to find out the differential expressed retrotransposons among two groups of samples. So I firstly ran SQuIRE pipeline on each sample. However, I found the rows in the squire_count/*_TEcounts.txt are not alignable, and neither column (tx_chr, tx_start, tx_stop) nor TE_ID is unique. Just according to my naive understanding, I'm going to do the downstream analysis as below:

1. On each *_TEcounts.txt file, group by the "TE_ID" column and sum up the "tot_counts" (or shall I use "tot_reads" instead? what's the difference?) in every group. So I can get a table uniq_TEcounts.txt with unique TE_ID and the tot_counts_sum for each TE_ID;

2. Merge uniq_TEcounts.txt of all samples together, aligned based on the unique TE_ID. If TE_ID are missed in any files, fill with 0;

3. Using DESeq to compare the tot_counts_sum among two condition groups, assigning the sizeFactors with the total refGenecounts (sum up the 7th column of *_refGenecounts.txt).

Do you think is the analysis correct?

I'm trying to process the public sample (https://www.ncbi.nlm.nih.gov/sra/?term=SRR6325536). The Count process never ends (now the subprocess stringtie is running more than 80 hours). I tried to redo the process for 3 times (in distinct machines) but the issue persists. No log messages was reported by SQuiRE. Thanks.

Hi there,

It seems that this tool is mainly though to use with already published genomes and uses these fetch and clean in published datasets before the mapping and counting, however I have a custom genome with a custom TE de novo annotation (together with an annotation made with maker) and I was wondering If I could use this tool and just fake the fetch and clean step or if I will face some incompatibilities in downstream analysis.

Thank you!

Finished running expectation-maximization calculation after iteration:38 2018-06-22 16:50:47.094679

Writing counts 2018-06-22 16:50:47.094714

Traceback (most recent call last):
File "/users/cpacyna/miniconda3/envs/squire_git/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/users/cpacyna/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/users/cpacyna/SQuIRE/squire/Count.py", line 2171, in main
RepClass.writeRep(aligned_libsize,counts_temp,basename,strandedness,iteration)
File "/users/cpacyna/SQuIRE/squire/Count.py", line 1205, in writeRep
self.fpkm += outline.fpkm
UnboundLocalError: local variable 'outline' referenced before assignment

Hi, I want to find TE expression of novel TE insertions detected in WES data. Do you have any suggestions on how to look for those specific TE insertions expression?

Hello,

I am trying to apply SQuiRE on my data but I have access on a Slurm scheduler and not on an SGE.
So, I have changed the scheduler commands but I am getting the following error:

sbatch: error: Invalid numeric value "wd" for cpus-per-task.

Which apparently is the SGE command for cpus.

Here are my sbatch commands:

##!/bin/bash --login #$ -cwd #SBATCH --job-name=fetch #SBATCH -p compute #SBATCH -n 1 --core-spec=40 #SBATCH --tasks-per-node=40

BTW, I am trying to run Fetch.

Thank you very much in advance,
Vasilis.

Hello,

I'm doing an RNA-seq analysis on 80 files taken from CARNES 2015 paper. At the mapping phase, 4 files fail, with the following error message:

Script Arguments

fetch_folder=squire_fetch
name=False
extra=None
read_length=115
verbosity=True
pthreads=4
read1=/beegfs/data/eugloh/CARNES/bug_files/B1_W1_F2_TCGAAG_L002_fp.fastq
read2=None
build=dm6
func=<function main at 0x7f1c979f7a28>
trim3=0
map_folder=squire_map_115

Aligning FastQ files 2019-04-24 16:04:36.165775

Apr 24 16:04:36 ..... started STAR run
Apr 24 16:04:36 ..... loading genome
Apr 24 16:06:16 ..... processing annotations GTF
Apr 24 16:06:23 ..... inserting junctions into the genome indices
Apr 24 16:07:26 ..... started 1st pass mapping
Apr 24 16:19:59 ..... finished 1st pass mapping
Apr 24 16:19:59 ..... inserting junctions into the genome indices
Apr 24 16:20:32 ..... started mapping
Apr 24 16:35:10 ..... finished successfully
[bam_sort_core] merging from 32 files...
Aborted
Traceback (most recent call last):
File "/beegfs/home/eugloh/miniconda3/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/beegfs/data/eugloh/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/beegfs/data/eugloh/SQuIRE/squire/Map.py", line 378, in main
align_unpaired(read1,pthreads,trim3,index,outfile,gtf,gzip,prefix, read_length,extra_fapath)
File "/beegfs/data/eugloh/SQuIRE/squire/Map.py", line 180, in align_unpaired
sp.check_call(["/bin/sh", "-c", sortcommand])
File "/beegfs/home/eugloh/miniconda3/envs/squire/lib/python2.7/subprocess.py", line 186, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/bin/sh', '-c', 'samtools sort -@ 4 squire_map_115/B1_W1_F2_TCGAAG_L002_fpAligned.out.bam squire_map_115/B1_W1_F2_TCGAAG_L002_fp']' returned non-zero exit status 134

I work on single-end data and reads of length 115. I don't believe the error is due to a RAM issue because I work on a powerful enough processing cluster.
Those 4 files are not particularly larger than the other 76, and I can't seem to find the root of the problem.

Have you ever encountered this issue, and if so, what was the cause, and how to fix it? I'm stuck here and would really appreciate your help.

Thanks in advance.

Eugenie

Hello all,
I was told to use the reference genome annotation with extra 5'-UTR and 3'-UTR records when I want to analyze the ref & TE expression out of the nuclear RNA-seq data set. I assume I should do the same thing using SQuIRE pipeline, shouldn't I?

I am asking because I observed slightly different marker gene expression between the SQuIRE and the other pipeline. Still trying to figure out what the real cause is.

Thanks in advance~
Maxwell

Hello, I would like to give you a question about the use of SQuIRE for the comparison of multiple RNA-seq data generated with different libraries.
I am performing the differential analysis of samples of data obtained with different libraries (first and unstranded). In the count step I indicate the type of specific library to correctly perform the TE quantification but when executing the differential analysis I obtain few significant differences due to the differences between both libraries.
When I remove the unstranded samples of the analysis it finish successfully and I have more consistent data. Is there a way to include this batch effect in squire?

Hello,

This may be a trivial one but I can't get the squire Call run with two groups of samples. Here is how my code looks like:

squire Call --group1 data_analysis/squire_count/Liv1_Hi272_Mut_* -2 data_analysis/squire_count/Liv2_Hi272_Cont_* -A mutant -B control -o data_analysis/squire_call/ -p 24 -v

The "squire_count" folder contains all the count file:

Then I get the following error:

Traceback (most recent call last): File "/scratch/gencore/conda3/envs/squire/bin/squire", line 11, in <module> load_entry_point('SQuIRE', 'console_scripts', 'squire')() File "/scratch/gencore/ma5877/TE/SQuIRE/squire/cli.py", line 156, in main subargs.func(args = subargs) File "/scratch/gencore/ma5877/TE/SQuIRE/squire/Call.py", line 270, in main group1_list=get_groupfiles(group1,gene_files,subF_files,TE_files,subfamily,count_folder) File "/scratch/gencore/ma5877/TE/SQuIRE/squire/Call.py", line 88, in get_groupfiles TE_files.append(find_file(count_folder,"_TEcounts.txt",sample,1,True)) File "/scratch/gencore/ma5877/TE/SQuIRE/squire/Call.py", line 43, in find_file raise Exception("No " + pattern + " file") Exception: No _TEcounts.txt file

But "_TEcounts.txt" file is there in the "squire_count" folder. Am I providing the wrong script? Please help!

As SQuIRE has been published, the repository is not seeing much change right now, and the latest release is several commits behind master, it would be nice to have a new release.

Hi,

I've tried to run 'squire fetch' in a slurm cluster but it never finished (run over 14 days and got killed). If I understand the code correctly, the 'squire fecth' command would download all the necessary fasta and bed files, but there was no output in the output directory in my run.

Below is the command I used and the log file.

Script:

_#!/bin/bash
#SBATCH -n 8
#SBATCH --time=14-24:00:00
#SBATCH -J fetch
#SBATCH -o fetch.%A.out
#SBATCH -e fetch.%A.err
#SBATCH --mem=320g

source activate squire

squire Fetch -c -b mm10 -f -r -g -k -p 8 -o ./ -v >squire_Fetch_mm10.log 2>squire_Fetch_mm10.err.log_

Log:
_start time is:2020-11-25 21:13:15.239034

Fetch.pyc

Script Arguments

index=False
fetch_folder=./
rmsk=True
verbosity=True
pthreads=8
keep=True
build=mm10
func=<function main at 0x7f0c84f4a578>
fasta=True
gene=True
chrom_info=True

Downloading Compressed Chromosome files..._

Any help would be appreciated.

Thanks!

Weida

Hi Wan,

I was wondering if there is a way (a parameter or a script inside the package) that it could allow us to "correct" the total number of reads in the counting phase, by taking account reads that are mapped across the intron-TE boundaries.

Thank you very much in advance,
Vasilis.

Hi ,

May I how many samples are simulated, if more than one samples are simulated, is it ok to use the avearge value to calc the true positive ratio and other ratios?

By the way, may I know if it's convenient to share the scripts of the data simulation?

Thanks a lot for your kind guidance!

Best,

Hi all,

I am using SQuIRE to look at TE expression in human fibroblast RNA-Seq data, but I'm slightly surprised by the results SQuIRE is producing. I am running squire Call with option -s to get subfamily level results as I do not need locus-level resolution. I find that ~81% tested TE subfamilies have mean counts > 10 and 60% > 100. With FPKM, this translates to 75% with an FPKM >2, 54% > 10 and 17% > 100. If I re-run the same data at a locus level, I find between 30-70% TE loci have counts > 10. Though I have been unable to find other data to compare to for what TE expression in fibroblasts should look like, this seems incredibly high and feels like something has gone wrong somewhere. Surely all of these TEs are not expressed in adult fibroblasts?

I am wondering whether other people see this with their data? is it to be expected with the way the read assignment algorithms work or have I made a mistake somewhere?

Is the issue with the cut-off value for when a TE is deemed 'expressed? The SQuIRE authors use a > 10 count cut-off at a locus level, so presumably it needs to be quite a lot higher than this at a subfamily level.

These are examples of the commands I am using:

squire Fetch -b 'hg38' -p 8 -r -f -c -x

squire Clean -b 'hg38'

squire Map -1 <fq file 1> -2 <fq file 2> -n -p 15 -r 75

squire Count -r 75 -n -p 15 -s 2

squire Call -1 TREATMENT1,TREATMENT2,TREATMENT3 -2 CONTROL1,CONTROL2,CONTROL3 -A Treatment -B Control -s -p 15 -N

Any advice or comments are appreciated; thanks in advance for your help.

Tom

Hi,
I was wondering if it will be possible to use the 'GENCODE comprehensive catalog' as an option to run SQuIRE.
Is RefSeq been chosen for a specific reason? Like shown here http://www.biomedcentral.com/1471-2164/16/S8/S2, the GENCODE comprehensive contains more annotation and covers more genomic regions which sounds ideal for TEs quantification purposes.

Would SQuIRE work with such annotation?

Thanks

Hi,
thanks for developing this cool tool!
I'm trying to use mm10 (obtained using fetch ) to quantify some RNA-seq libraries, then I noticed a weird warning at the Count procedure :

Warning: gene "Gm20747" (on chrY) has reference transcripts on both strands?

I figured out this was caused by the gene ID collision in the mm10_refGene.gtf

chrY    squire_fetch/mm10_refGene.genepred      transcript      21164554        21166898        .       +       .       gene_id "Gm20747"; transcript_id "NM_001025241"; gene_name "Gm20747";
chrY    squire_fetch/mm10_refGene.genepred      transcript      53415957        53418307        .       -       .       gene_id "Gm20747"; transcript_id "NM_001025241_2"; gene_name "Gm20747";
chrY    squire_fetch/mm10_refGene.genepred      transcript      73313695        73316051        .       -       .       gene_id "Gm20747"; transcript_id "NM_001025241_3"; gene_name "Gm20747";
chrY    squire_fetch/mm10_refGene.genepred      transcript      81799150        81801497        .       -       .       gene_id "Gm20747"; transcript_id "NM_001025241_4"; gene_name "Gm20747";

since there are quite a few such cases, wondering how does this affect the stranded libraries?
Possible to get a quick answer here before I go and check the code?

Another question is: do you have any descriptions of the input data preparation for each step somewhere ( such as the Clean procedure. too lazy to read the code, sorry :) )? Then I can quickly get some scripts to fix an Ensembl data converter.

Thanks!

I am running squire Map step for paired end data. However, when i list the files for each read (read 1 and 2), it doesn't work properly. It seems not to be picking up the files, and only picks one (the last one on the list). What could be wrong?
I separated the fastq files with comma (,) as directed.

Hi, I got the following errors when running squire Map and Call.
It looks like similar problems to #19 , but I am not sure how to fix it. Please would you let me know how to solve these problems?

• Map error.
  Traceback (most recent call last):
  File "/data02/home/usr/.pyenv/versions/miniconda3-latest/envs/squire/bin/squire", line 11, in
  load_entry_point('SQuIRE', 'console_scripts', 'squire')()
  File "/data02/home/usr/bio/SQuIRE/squire/cli.py", line 156, in main
  subargs.func(args = subargs)
  File "/data02/home/usr/bio/SQuIRE/squire/Map.py", line 387, in main
  align_paired(read1,read2,pthreads,trim3,index,outfile,gtf,gzip,prefix, read_length,extra_fapath)
  File "/data02/home/usr/bio/SQuIRE/squire/Map.py", line 138, in align_paired
  sp.check_call(["/bin/sh", "-c", sortcommand])
  File "/data02/home/usr/.pyenv/versions/miniconda3-latest/envs/squire/lib/python2.7/subprocess.py", line 190, in check_call
  raise CalledProcessError(retcode, cmd)
  subprocess.CalledProcessError: Command '['/bin/sh', '-c', 'samtools sort -@ 1 squire_map/01A_R1_trim.fastqAligned.out.bam squire_map/01A_R1_trim.fastq']' returned non-zero exit status 1

I got the above error, but I confirmed I could get bam file by squire map. So I tried squire call and got the following error.

• Call error
  Traceback (most recent call last):
  File "/data02/home/usr/.pyenv/versions/miniconda3-latest/envs/squire/bin/squire", line 11, in
  load_entry_point('SQuIRE', 'console_scripts', 'squire')()
  File "/data02/home/usr/bio/SQuIRE/squire/cli.py", line 156, in main
  subargs.func(args = subargs)
  File "/data02/home/usr/bio/SQuIRE/squire/Call.py", line 270, in main
  group1_list=get_groupfiles(group1,gene_files,subF_files,TE_files,subfamily,count_folder)
  File "/data02/home/usr/bio/SQuIRE/squire/Call.py", line 88, in get_groupfiles
  TE_files.append(find_file(count_folder,"_TEcounts.txt",sample,1,True))
  File "/data02/home/usr/bio/SQuIRE/squire/Call.py", line 43, in find_file
  raise Exception("No " + pattern + " file")

I have been working with SQuIRE a few months performing different analysis with the samples of my laboratory.
Now, I am going to start analyzing samples of different laboratories for increasing the number of samples and my statistical power.
I have been reading about it and the developers of DESEQ2 (the part of SQuIRE that performs the differential expression analysis) recommend to introduce the batch variable so the algorithm can take into account the differences between samples produced by its origin.
How can I introduce this variable if I am using SQuIRE? Is necessary to perform this after the mathematical correction performed by this sofware?

Hi!
Another question: in running squire Map on my paired-end RNA-Seq data, I'm getting a really large number of reads classified as Unmapped (about 70%, with 30% falling under "Unmapped other"). Is this expected? What are the possible reasons for these results, and how can I adjust?

The command I'm running is squire Map -1 $read1 -2 $read2 -f $squire_fetch -r $read_length -b hg19 -p 8 -v

Thanks!

Hi wyang17,
I am having an issue with running the fetch command as shown below.

Traceback (most recent call last): File "/home/svu/phaei/.conda/miniconda/4.9/envs/squire/bin/squire", line 11, in <module> load_entry_point('SQuIRE', 'console_scripts', 'squire')() File "/hpctmp/phaei/FACT/SQuIRE/squire/cli.py", line 156, in main subargs.func(args = subargs) File "/hpctmp/phaei/FACT/SQuIRE/squire/Fetch.py", line 231, in main raise Exception("Was not able to download chromosome file from UCSC" + "\n", file = sys.stderr) TypeError: exceptions.Exception does not take keyword arguments

I am not sure how to resolve this issue.

regards

I am trying to run SquiRE call. However, I am getting errors. I have attached the error below. Could anyone please help me fix the error.

Script Arguments

count_folder=/home/arpatil/TE_counts
projectname=TEs_T1D_Normal_Squire
output_format=pdf
pthreads=12
call_folder=/home/arpatil/dete
condition2=Normal
table_only=False
group1=T1D_*
func=<function main at 0x7fa278b34140>
group2=Normal_*
subfamily=False
verbosity=True
condition1=T1D

Traceback (most recent call last):
File "/home/arpatil/miniconda3/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/home/arpatil/Tools/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/home/arpatil/Tools/SQuIRE/squire/Call.py", line 286, in main
create_TE_dict(TE_combo,TE_dict,threshold)
File "/home/arpatil/Tools/SQuIRE/squire/Call.py", line 134, in create_TE_dict
milliDiv = int(line[12])
ValueError: invalid literal for int() with base 10: 'Sample'

I tried editing Call.py line 134 by changing milliDiv = int(line[12]) to milliDiv = int(float(line[12])). However, I still keep getting error.

Half of my count jobs are going through, half stop with this error:
I've come across this error before but it did not affect so many files as now.
Thanks!

Traceback (most recent call last):
File "/users/cpacyna/miniconda3/envs/squire_git/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/users/cpacyna/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/users/cpacyna/SQuIRE/squire/Count.py", line 1683, in main
reduce_reads(paired_bed_tempfile1_sorted, paired_reduced_tempfile1,debug)
File "/users/cpacyna/SQuIRE/squire/Count.py", line 378, in reduce_reads
prev.line_split[15] = prev_TE_ID
UnboundLocalError: local variable 'prev_TE_ID' referenced before assignment

Hi!

The "loop_map.sh" script has the following lines:

for fastq in $fastq_folder/${samplename}*$r1suffix
do
  read1+="${fastq},"

This concatenation means that squire map is called with an increasing number of libraries each time, and BAM files for the libraries have all the reads from all the libraries, instead only those from its respective FASTQ file.

Is there any reason for this?

Hi @wyang17

I have encountered the following ZeroDivisionError during SQuIRE Count stage. I have more than 20 different RNA-seq samples, and only this one has this issue every time I run this.

Could you please resolve this issue?

start time is:2020-07-02 13:33:52.016270

Count.pyc

Script Arguments
=================
count_folder=8_squire/squire_count
EM=auto
clean_folder=/scratch/twlab/hlee/genomes/danRer10/squire/squire_clean
fetch_folder=/scratch/twlab/hlee/genomes/danRer10/squire/squire_fetch
name=Kidney_rep1
tempfolder=False
verbosity=True
pthreads=8
strandedness=2
read_length=61
build=danRer10
func=<function main at 0x2b70404309b0>
map_folder=8_squire/squire_map


Quantifying Gene expression 2020-07-02 13:33:52.253733

Running Guided Stringtie on each bamfile Kidney_rep1 2020-07-02 13:33:52.385027

Warning: gene "adob" (on chr17) has reference transcripts on both strands?
Warning: gene "nr4a2a" (on chr9) has reference transcripts on both strands?
Creating temporary files2020-07-02 13:37:17.839948

Creating unique and multiple alignment bedfiles 2020-07-02 13:37:17.853702

Identifying properly paired reads 2020-07-02 13:37:17.853732

Intersecting bam files with TE bedfile 2020-07-02 13:59:53.159556

Splitting into read1 and read 2 2020-07-02 14:06:45.765105

Combining adjacent TEs with same read alignment 2020-07-02 14:07:18.157487

Getting genomic coordinates of read2020-07-02 14:11:31.226259

Identifying and labeling unique and multi reads2020-07-02 14:13:49.211829

Matching paired-end mates and merging coordinates2020-07-02 14:15:44.396913

Adding properly paired reads that have mates outside of TE into matched file2020-07-02 14:27:09.296185

Removing single-end reads that have matching paired-end mates at other alignment locations2020-07-02 14:27:26.819686

Identifying and labeling unique and multi fragments2020-07-02 14:27:43.282618

Identifying multi read pairs with one end unique2020-07-02 14:28:51.554478

counting unique alignments 2020-07-02 14:29:19.007752

counting multi alignments 2020-07-02 14:30:50.949737

Adding Tag information to aligned TEs 2020-07-02 14:31:48.145425

Calculating multialignment assignments 2020-07-02 14:31:49.172457

Running expectation-maximization calculation for iteration:1 2020-07-02 14:31:58.770414

Average change in TE count:0.0 2020-07-02 14:32:00.995930

Max change in TE count:0 2020-07-02 14:32:00.995979

Number changed TE:0 2020-07-02 14:32:00.995991

Number TEs changed by at least 1 count:0 2020-07-02 14:32:00.995997

Number TEs changed by at least 1 count with at least 10 counts:0 2020-07-02 14:32:00.996004

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:0 2020-07-02 14:32:00.996010

Expectation maximization changed the average likelihood by: 0.113478553779 2020-07-02 14:32:10.475061

Number of reads with average TE likelihoods changed by at least 0.1: 151086 2020-07-02 14:32:10.475121

Running expectation-maximization calculation for iteration:2 2020-07-02 14:32:10.475132

Traceback (most recent call last):
  File "/home/hyungjoo.lee/bin/miniconda3/envs/squire/bin/squire", line 11, in <module>
    load_entry_point('SQuIRE', 'console_scripts', 'squire')()
  File "/home/hyungjoo.lee/bin/SQuIRE/squire/cli.py", line 156, in main
    subargs.func(args = subargs)
  File "/home/hyungjoo.lee/bin/SQuIRE/squire/Count.py", line 1844, in main
    TE_changecount = RepClass.calcmultiRep(iteration)
  File "/home/hyungjoo.lee/bin/SQuIRE/squire/Count.py", line 948, in calcmultiRep
    self.oldmulti_plus_perkb = self.old_counts_plus/((self.length_plus - avg_fraglength +1)/1000)
ZeroDivisionError: float division by zero

I am getting errors while running the call step in SquIRE. The counts were generated successfully without any errors. However, in the call step, I am getting errors. I have attached the errors below:

The following object is masked from ‘package:base:

apply

Warning message:
multiple methods tables found for ‘pos’
[1] "/home/arpatil/dete/TEs_T1D_Normal_Squire_gene_TE_counttable.txt"
[2] "/home/arpatil/dete/TEs_T1D_Normal_Squire_coldata.txt"
[3] "/home/arpatil/dete"
[4] "TEs_T1D_Normal_Squire"
[5] "4"
[6] "T1D"
[7] "Normal"
[8] "20"
Error in read.table(file = file, header = header, sep = sep, quote = quote, :
duplicate 'row.names' are not allowed
Calls: as.matrix -> read.delim -> read.table
Execution halted
Traceback (most recent call last):
File "/home/arpatil/miniconda3/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/home/arpatil/Tools/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/home/arpatil/Tools/SQuIRE/squire/Call.py", line 337, in main
create_rscript(counttable,coldata,outfolder,output_format,projectname,verbosity,str(pthreads),prefilter,condition1,condition2,label_no)
File "/home/arpatil/Tools/SQuIRE/squire/Call.py", line 198, in create_rscript
sp.check_call(["/bin/sh","-c",Rcommand])
File "/home/arpatil/miniconda3/envs/squire/lib/python2.7/subprocess.py", line 186, in check_call
raise CalledProcessError(retcode, cmd)
subprocess.CalledProcessError: Command '['/bin/sh', '-c', 'Rscript /home/arpatil/dete/TEs_T1D_Normal_Squire_R_script.tmpOGNGUJ /home/arpatil/dete/TEs_T1D_Normal_Squire_gene_TE_counttable.txt /home/arpatil/dete/TEs_T1D_Normal_Squire_coldata.txt /home/arpatil/dete TEs_T1D_Normal_Squire 4 T1D Normal 20']' returned non-zero exit status 1

I have the count's table generated. For ex, I got the following files: TEs_T1D_Normal_Squire_coldata.txt, TEs_T1D_Normal_Squire _counttable.txt, TEs_T1D_Normal_Squire _combo.txt files generated successfully. But, it is not giving the differentially expressed TEs output.

Could anyone please help me in addressing this error? Thanks!

Hi,

I have been getting the following messages over and over again when I run squire count. It seems that it keeps counting but does not end...Any idea on what might be going on ?

Many thanks,

Salim.

Creating unique and multiple alignment bedfiles 2019-06-27 16:59:38.076091

Intersecting bam file with TE bedfile 2019-06-27 16:59:38.077534

Combining adjacent TEs with same read alignment 2019-06-27 17:19:13.274866

Getting genomic coordinates of read2019-06-27 18:16:52.544021

Identifying and labeling unique and multi reads2019-06-27 18:36:43.369293

counting unique alignments 2019-06-27 19:58:01.933640

counting multi alignments 2019-06-27 20:06:09.891890

Adding Tag information to aligned TEs 2019-06-27 20:21:03.918243

Calculating multialignment assignments 2019-06-27 20:21:05.750406

Running expectation-maximization calculation for iteration:1 2019-06-27 20:23:02.584813

Average change in TE count:0.0 2019-06-27 20:23:05.730731

Max change in TE count:0 2019-06-27 20:23:05.732306

Number changed TE:0 2019-06-27 20:23:05.733453

Number TEs changed by at least 1 count:0 2019-06-27 20:23:05.734695

Number TEs changed by at least 1 count with at least 10 counts:0 2019-06-27 20:23:05.735947

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:0 2019-06-27 20:23:05.737184

Expectation maximization changed the average likelihood by: 0.0511343014077 2019-06-27 20:24:45.524809

Number of reads with average TE likelihoods changed by at least 0.1: 1897485 2019-06-27 20:24:45.526482

Running expectation-maximization calculation for iteration:2 2019-06-27 20:24:45.527646

Average change in TE count:1.07411444581 2019-06-27 20:24:48.826892

Max change in TE count:95024.75679 2019-06-27 20:24:48.828276

Number changed TE:351560 2019-06-27 20:24:48.829507

Number TEs changed by at least 1 count:12190 2019-06-27 20:24:48.830639

Number TEs changed by at least 1 count with at least 10 counts:3828 2019-06-27 20:24:48.831712

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3773 2019-06-27 20:24:48.832848

Expectation maximization changed the average likelihood by: 0.0208707660624 2019-06-27 20:26:28.475871

Number of reads with average TE likelihoods changed by at least 0.1: 777028 2019-06-27 20:26:28.477260

Running expectation-maximization calculation for iteration:3 2019-06-27 20:26:28.478517

Average change in TE count:0.435416359278 2019-06-27 20:26:31.153396

Max change in TE count:72363.5924624 2019-06-27 20:26:31.154932

Number changed TE:47833 2019-06-27 20:26:31.156224

Number TEs changed by at least 1 count:1601 2019-06-27 20:26:31.157534

Number TEs changed by at least 1 count with at least 10 counts:953 2019-06-27 20:26:31.158797

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:892 2019-06-27 20:26:31.160008

Expectation maximization changed the average likelihood by: 0.00814132308952 2019-06-27 20:28:10.913557

Number of reads with average TE likelihoods changed by at least 0.1: 51618 2019-06-27 20:28:10.915097

Running expectation-maximization calculation for iteration:4 2019-06-27 20:28:10.916369

Average change in TE count:0.164178617785 2019-06-27 20:28:13.599662

Max change in TE count:30982.8882079 2019-06-27 20:28:13.601114

Number changed TE:45770 2019-06-27 20:28:13.602582

Number TEs changed by at least 1 count:874 2019-06-27 20:28:13.603975

Number TEs changed by at least 1 count with at least 10 counts:632 2019-06-27 20:28:13.605180

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:566 2019-06-27 20:28:13.606357

Expectation maximization changed the average likelihood by: 0.00429504488505 2019-06-27 20:29:53.296998

Number of reads with average TE likelihoods changed by at least 0.1: 50188 2019-06-27 20:29:53.298564

Running expectation-maximization calculation for iteration:5 2019-06-27 20:29:53.300400

Average change in TE count:0.0785211764923 2019-06-27 20:29:55.968532

Max change in TE count:11067.9082562 2019-06-27 20:29:55.970595

Number changed TE:44859 2019-06-27 20:29:55.972181

Number TEs changed by at least 1 count:599 2019-06-27 20:29:55.974156

Number TEs changed by at least 1 count with at least 10 counts:481 2019-06-27 20:29:55.976046

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:414 2019-06-27 20:29:55.977677

Expectation maximization changed the average likelihood by: 0.00311617217645 2019-06-27 20:31:35.842340

Number of reads with average TE likelihoods changed by at least 0.1: 45879 2019-06-27 20:31:35.843969

Running expectation-maximization calculation for iteration:6 2019-06-27 20:31:35.845344

Average change in TE count:0.0492869501621 2019-06-27 20:31:38.541818

Max change in TE count:6061.79276584 2019-06-27 20:31:38.543195

Number changed TE:44325 2019-06-27 20:31:38.544597

Number TEs changed by at least 1 count:441 2019-06-27 20:31:38.545872

Number TEs changed by at least 1 count with at least 10 counts:383 2019-06-27 20:31:38.547271

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:306 2019-06-27 20:31:38.548735

Expectation maximization changed the average likelihood by: 0.00275636485596 2019-06-27 20:33:17.999590

Number of reads with average TE likelihoods changed by at least 0.1: 45898 2019-06-27 20:33:18.001036

Running expectation-maximization calculation for iteration:7 2019-06-27 20:33:18.002219

Average change in TE count:0.038318265443 2019-06-27 20:33:20.678041

Max change in TE count:4859.82343395 2019-06-27 20:33:20.679473

Number changed TE:43975 2019-06-27 20:33:20.680943

Number TEs changed by at least 1 count:345 2019-06-27 20:33:20.682083

Number TEs changed by at least 1 count with at least 10 counts:303 2019-06-27 20:33:20.683332

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:233 2019-06-27 20:33:20.684473

Expectation maximization changed the average likelihood by: 0.00271953945072 2019-06-27 20:35:00.202770

Number of reads with average TE likelihoods changed by at least 0.1: 45971 2019-06-27 20:35:00.204421

Running expectation-maximization calculation for iteration:8 2019-06-27 20:35:00.205657

Average change in TE count:0.0359083173197 2019-06-27 20:35:02.892197

Max change in TE count:6683.84204686 2019-06-27 20:35:02.893611

Number changed TE:43736 2019-06-27 20:35:02.894816

Number TEs changed by at least 1 count:276 2019-06-27 20:35:02.896042

Number TEs changed by at least 1 count with at least 10 counts:251 2019-06-27 20:35:02.897082

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:181 2019-06-27 20:35:02.898140

Expectation maximization changed the average likelihood by: 0.0028531869672 2019-06-27 20:36:42.500407

Number of reads with average TE likelihoods changed by at least 0.1: 45993 2019-06-27 20:36:42.502106

Running expectation-maximization calculation for iteration:9 2019-06-27 20:36:42.503385

Average change in TE count:0.0382347414545 2019-06-27 20:36:45.166871

Max change in TE count:9054.59090785 2019-06-27 20:36:45.168059

Number changed TE:43516 2019-06-27 20:36:45.169250

Number TEs changed by at least 1 count:214 2019-06-27 20:36:45.170452

Number TEs changed by at least 1 count with at least 10 counts:195 2019-06-27 20:36:45.171551

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:141 2019-06-27 20:36:45.172629

Expectation maximization changed the average likelihood by: 0.00308161005019 2019-06-27 20:38:24.852120

Number of reads with average TE likelihoods changed by at least 0.1: 46036 2019-06-27 20:38:24.853556

Running expectation-maximization calculation for iteration:10 2019-06-27 20:38:24.854923

Average change in TE count:0.0433444850587 2019-06-27 20:38:27.540708

Max change in TE count:11700.2977783 2019-06-27 20:38:27.541917

Number changed TE:43326 2019-06-27 20:38:27.543153

Number TEs changed by at least 1 count:167 2019-06-27 20:38:27.544349

Number TEs changed by at least 1 count with at least 10 counts:153 2019-06-27 20:38:27.545431

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:104 2019-06-27 20:38:27.546703

Expectation maximization changed the average likelihood by: 0.00329804410289 2019-06-27 20:40:06.692700

Number of reads with average TE likelihoods changed by at least 0.1: 46075 2019-06-27 20:40:06.694309

Running expectation-maximization calculation for iteration:11 2019-06-27 20:40:06.695695

Average change in TE count:0.0499389090483 2019-06-27 20:40:09.365934

Max change in TE count:13930.1494273 2019-06-27 20:40:09.367182

Number changed TE:43146 2019-06-27 20:40:09.368407

Number TEs changed by at least 1 count:137 2019-06-27 20:40:09.369604

Number TEs changed by at least 1 count with at least 10 counts:124 2019-06-27 20:40:09.370739

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:83 2019-06-27 20:40:09.371919

Expectation maximization changed the average likelihood by: 0.00340465606123 2019-06-27 20:41:48.626337

Number of reads with average TE likelihoods changed by at least 0.1: 46110 2019-06-27 20:41:48.627810

Running expectation-maximization calculation for iteration:12 2019-06-27 20:41:48.629091

Average change in TE count:0.0537709260366 2019-06-27 20:41:51.292370

Max change in TE count:14858.1805188 2019-06-27 20:41:51.293685

Number changed TE:42968 2019-06-27 20:41:51.295011

Number TEs changed by at least 1 count:123 2019-06-27 20:41:51.296200

Number TEs changed by at least 1 count with at least 10 counts:115 2019-06-27 20:41:51.297567

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:73 2019-06-27 20:41:51.298779

Expectation maximization changed the average likelihood by: 0.00333866856605 2019-06-27 20:43:30.729615

Number of reads with average TE likelihoods changed by at least 0.1: 46139 2019-06-27 20:43:30.730967

Running expectation-maximization calculation for iteration:13 2019-06-27 20:43:30.732207

Average change in TE count:0.053363197281 2019-06-27 20:43:33.378818

Max change in TE count:14028.8332855 2019-06-27 20:43:33.380110

Number changed TE:42741 2019-06-27 20:43:33.381390

Number TEs changed by at least 1 count:111 2019-06-27 20:43:33.382869

Number TEs changed by at least 1 count with at least 10 counts:105 2019-06-27 20:43:33.384073

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:64 2019-06-27 20:43:33.385400

Expectation maximization changed the average likelihood by: 0.00312510886026 2019-06-27 20:45:12.833152

Number of reads with average TE likelihoods changed by at least 0.1: 46158 2019-06-27 20:45:12.834808

Running expectation-maximization calculation for iteration:14 2019-06-27 20:45:12.836113

Average change in TE count:0.0493298247059 2019-06-27 20:45:15.512704

Max change in TE count:11826.4783365 2019-06-27 20:45:15.514117

Number changed TE:42497 2019-06-27 20:45:15.515527

Number TEs changed by at least 1 count:98 2019-06-27 20:45:15.516983

Number TEs changed by at least 1 count with at least 10 counts:95 2019-06-27 20:45:15.518354

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:59 2019-06-27 20:45:15.519520

Expectation maximization changed the average likelihood by: 0.00284940382948 2019-06-27 20:46:54.466403

Number of reads with average TE likelihoods changed by at least 0.1: 46194 2019-06-27 20:46:54.468013

Running expectation-maximization calculation for iteration:15 2019-06-27 20:46:54.469371

Average change in TE count:0.0435483704919 2019-06-27 20:46:57.118977

Max change in TE count:9132.90551704 2019-06-27 20:46:57.120383

Number changed TE:42270 2019-06-27 20:46:57.121696

Number TEs changed by at least 1 count:94 2019-06-27 20:46:57.122968

Number TEs changed by at least 1 count with at least 10 counts:89 2019-06-27 20:46:57.124119

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:49 2019-06-27 20:46:57.125346

Expectation maximization changed the average likelihood by: 0.00258426128013 2019-06-27 20:48:36.133542

Number of reads with average TE likelihoods changed by at least 0.1: 46199 2019-06-27 20:48:36.134934

Running expectation-maximization calculation for iteration:16 2019-06-27 20:48:36.136223

Average change in TE count:0.0375320520522 2019-06-27 20:48:38.782909

Max change in TE count:6684.88294109 2019-06-27 20:48:38.784338

Number changed TE:41976 2019-06-27 20:48:38.785682

Number TEs changed by at least 1 count:88 2019-06-27 20:48:38.787236

Number TEs changed by at least 1 count with at least 10 counts:82 2019-06-27 20:48:38.788352

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:43 2019-06-27 20:48:38.789489

Expectation maximization changed the average likelihood by: 0.00236110482072 2019-06-27 20:50:17.968019

Number of reads with average TE likelihoods changed by at least 0.1: 46217 2019-06-27 20:50:17.969448

Running expectation-maximization calculation for iteration:17 2019-06-27 20:50:17.970551

Average change in TE count:0.0321293912345 2019-06-27 20:50:20.614179

Max change in TE count:5726.53391534 2019-06-27 20:50:20.615441

Number changed TE:41687 2019-06-27 20:50:20.616596

Number TEs changed by at least 1 count:83 2019-06-27 20:50:20.617772

Number TEs changed by at least 1 count with at least 10 counts:79 2019-06-27 20:50:20.619035

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:41 2019-06-27 20:50:20.620212

Expectation maximization changed the average likelihood by: 0.00218294079568 2019-06-27 20:51:59.762587

Number of reads with average TE likelihoods changed by at least 0.1: 46220 2019-06-27 20:51:59.764062

Running expectation-maximization calculation for iteration:18 2019-06-27 20:51:59.765339

Average change in TE count:0.0273788685319 2019-06-27 20:52:02.424787

Max change in TE count:5246.40423245 2019-06-27 20:52:02.426066

Number changed TE:41379 2019-06-27 20:52:02.427363

Number TEs changed by at least 1 count:74 2019-06-27 20:52:02.428502

Number TEs changed by at least 1 count with at least 10 counts:71 2019-06-27 20:52:02.429586

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:35 2019-06-27 20:52:02.430861

Expectation maximization changed the average likelihood by: 0.00204207345146 2019-06-27 20:53:40.322640

Number of reads with average TE likelihoods changed by at least 0.1: 46230 2019-06-27 20:53:40.324206

Running expectation-maximization calculation for iteration:19 2019-06-27 20:53:40.325536

Average change in TE count:0.0232583614708 2019-06-27 20:53:42.927558

Max change in TE count:4612.47756964 2019-06-27 20:53:42.928926

Number changed TE:41006 2019-06-27 20:53:42.930052

Number TEs changed by at least 1 count:66 2019-06-27 20:53:42.931286

Number TEs changed by at least 1 count with at least 10 counts:65 2019-06-27 20:53:42.932621

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:30 2019-06-27 20:53:42.933836

Expectation maximization changed the average likelihood by: 0.00192967916094 2019-06-27 20:55:20.763344

Number of reads with average TE likelihoods changed by at least 0.1: 46241 2019-06-27 20:55:20.764685

Running expectation-maximization calculation for iteration:20 2019-06-27 20:55:20.766005

Average change in TE count:0.0196431497373 2019-06-27 20:55:23.369147

Max change in TE count:3918.57418016 2019-06-27 20:55:23.370629

Number changed TE:40630 2019-06-27 20:55:23.371972

Number TEs changed by at least 1 count:55 2019-06-27 20:55:23.373450

Number TEs changed by at least 1 count with at least 10 counts:55 2019-06-27 20:55:23.374566

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:26 2019-06-27 20:55:23.375936

Expectation maximization changed the average likelihood by: 0.00183923556257 2019-06-27 20:57:01.474360

Number of reads with average TE likelihoods changed by at least 0.1: 46241 2019-06-27 20:57:01.475955

Running expectation-maximization calculation for iteration:21 2019-06-27 20:57:01.477240

Average change in TE count:0.0165341638683 2019-06-27 20:57:04.076442

Max change in TE count:3238.33391922 2019-06-27 20:57:04.077851

Number changed TE:40264 2019-06-27 20:57:04.079071

Number TEs changed by at least 1 count:55 2019-06-27 20:57:04.080064

Number TEs changed by at least 1 count with at least 10 counts:55 2019-06-27 20:57:04.081209

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:26 2019-06-27 20:57:04.082221

Expectation maximization changed the average likelihood by: 0.00176591813656 2019-06-27 20:58:41.822545

Number of reads with average TE likelihoods changed by at least 0.1: 46250 2019-06-27 20:58:41.824085

Running expectation-maximization calculation for iteration:22 2019-06-27 20:58:41.825459

Average change in TE count:0.0138651489821 2019-06-27 20:58:44.426406

Max change in TE count:2618.59973131 2019-06-27 20:58:44.427860

Number changed TE:39815 2019-06-27 20:58:44.429300

Number TEs changed by at least 1 count:55 2019-06-27 20:58:44.430579

Number TEs changed by at least 1 count with at least 10 counts:55 2019-06-27 20:58:44.431925

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:25 2019-06-27 20:58:44.433193

Expectation maximization changed the average likelihood by: 0.00170605754024 2019-06-27 21:00:21.903940

Number of reads with average TE likelihoods changed by at least 0.1: 46252 2019-06-27 21:00:21.905416

Running expectation-maximization calculation for iteration:23 2019-06-27 21:00:21.906577

Average change in TE count:0.01159880618 2019-06-27 21:00:24.503112

Max change in TE count:2082.2009462 2019-06-27 21:00:24.504509

Number changed TE:39347 2019-06-27 21:00:24.505776

Number TEs changed by at least 1 count:51 2019-06-27 21:00:24.506938

Number TEs changed by at least 1 count with at least 10 counts:51 2019-06-27 21:00:24.508277

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:23 2019-06-27 21:00:24.509553

Expectation maximization changed the average likelihood by: 0.00165694910525 2019-06-27 21:02:02.171758

Number of reads with average TE likelihoods changed by at least 0.1: 46248 2019-06-27 21:02:02.173264

Running expectation-maximization calculation for iteration:24 2019-06-27 21:02:02.174513

Average change in TE count:0.00970922054459 2019-06-27 21:02:04.768649

Max change in TE count:1634.66148864 2019-06-27 21:02:04.770092

Number changed TE:38801 2019-06-27 21:02:04.771515

Number TEs changed by at least 1 count:48 2019-06-27 21:02:04.772769

Number TEs changed by at least 1 count with at least 10 counts:47 2019-06-27 21:02:04.774048

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:20 2019-06-27 21:02:04.775345

Expectation maximization changed the average likelihood by: 0.00161711258005 2019-06-27 21:03:42.508227

Number of reads with average TE likelihoods changed by at least 0.1: 46257 2019-06-27 21:03:42.509565

Running expectation-maximization calculation for iteration:25 2019-06-27 21:03:42.510829

Average change in TE count:0.0081616192768 2019-06-27 21:03:45.128548

Max change in TE count:1271.04622332 2019-06-27 21:03:45.129995

Number changed TE:38336 2019-06-27 21:03:45.131224

Number TEs changed by at least 1 count:47 2019-06-27 21:03:45.132609

Number TEs changed by at least 1 count with at least 10 counts:46 2019-06-27 21:03:45.133893

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:17 2019-06-27 21:03:45.134903

Expectation maximization changed the average likelihood by: 0.0015844442798 2019-06-27 21:05:22.187435

Number of reads with average TE likelihoods changed by at least 0.1: 46269 2019-06-27 21:05:22.188765

Running expectation-maximization calculation for iteration:26 2019-06-27 21:05:22.190001

Average change in TE count:0.00688586579968 2019-06-27 21:05:24.790287

Max change in TE count:981.265316363 2019-06-27 21:05:24.791672

Number changed TE:37869 2019-06-27 21:05:24.792907

Number TEs changed by at least 1 count:43 2019-06-27 21:05:24.794107

Number TEs changed by at least 1 count with at least 10 counts:41 2019-06-27 21:05:24.795340

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:15 2019-06-27 21:05:24.796453

Expectation maximization changed the average likelihood by: 0.00155762887416 2019-06-27 21:07:01.908840

Number of reads with average TE likelihoods changed by at least 0.1: 46278 2019-06-27 21:07:01.910399

Running expectation-maximization calculation for iteration:27 2019-06-27 21:07:01.911900

Average change in TE count:0.00583103922745 2019-06-27 21:07:04.512607

Max change in TE count:823.981362897 2019-06-27 21:07:04.513862

Number changed TE:37456 2019-06-27 21:07:04.515010

Number TEs changed by at least 1 count:42 2019-06-27 21:07:04.516292

Number TEs changed by at least 1 count with at least 10 counts:42 2019-06-27 21:07:04.517547

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:15 2019-06-27 21:07:04.518693

Expectation maximization changed the average likelihood by: 0.00153577668836 2019-06-27 21:08:42.014963

Number of reads with average TE likelihoods changed by at least 0.1: 46280 2019-06-27 21:08:42.016686

Running expectation-maximization calculation for iteration:28 2019-06-27 21:08:42.018155

Average change in TE count:0.00499618689615 2019-06-27 21:08:44.614680

Max change in TE count:761.115133474 2019-06-27 21:08:44.616074

Number changed TE:36967 2019-06-27 21:08:44.617588

Number TEs changed by at least 1 count:39 2019-06-27 21:08:44.618847

Number TEs changed by at least 1 count with at least 10 counts:39 2019-06-27 21:08:44.620091

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:12 2019-06-27 21:08:44.621284

Expectation maximization changed the average likelihood by: 0.00151754606352 2019-06-27 21:10:21.590170

Number of reads with average TE likelihoods changed by at least 0.1: 46269 2019-06-27 21:10:21.591664

Running expectation-maximization calculation for iteration:29 2019-06-27 21:10:21.592839

Average change in TE count:0.00429722115661 2019-06-27 21:10:24.189884

Max change in TE count:702.23078489 2019-06-27 21:10:24.191362

Number changed TE:36586 2019-06-27 21:10:24.192623

Number TEs changed by at least 1 count:38 2019-06-27 21:10:24.194015

Number TEs changed by at least 1 count with at least 10 counts:38 2019-06-27 21:10:24.195130

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:11 2019-06-27 21:10:24.196402

Expectation maximization changed the average likelihood by: 0.00150227908722 2019-06-27 21:12:00.881477

Number of reads with average TE likelihoods changed by at least 0.1: 46272 2019-06-27 21:12:00.883071

Running expectation-maximization calculation for iteration:30 2019-06-27 21:12:00.884296

Average change in TE count:0.00374537930245 2019-06-27 21:12:03.483473

Max change in TE count:647.262833851 2019-06-27 21:12:03.484961

Number changed TE:36232 2019-06-27 21:12:03.486272

Number TEs changed by at least 1 count:38 2019-06-27 21:12:03.487640

Number TEs changed by at least 1 count with at least 10 counts:38 2019-06-27 21:12:03.488938

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:12 2019-06-27 21:12:03.490123

Expectation maximization changed the average likelihood by: 0.00148929562068 2019-06-27 21:13:40.612210

Number of reads with average TE likelihoods changed by at least 0.1: 46278 2019-06-27 21:13:40.613594

Running expectation-maximization calculation for iteration:31 2019-06-27 21:13:40.614695

Average change in TE count:0.00327274655353 2019-06-27 21:13:43.208198

Max change in TE count:596.091603337 2019-06-27 21:13:43.209662

Number changed TE:35861 2019-06-27 21:13:43.211016

Number TEs changed by at least 1 count:39 2019-06-27 21:13:43.212465

Number TEs changed by at least 1 count with at least 10 counts:38 2019-06-27 21:13:43.213860

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:11 2019-06-27 21:13:43.215073

Expectation maximization changed the average likelihood by: 0.00147823697945 2019-06-27 21:15:20.141891

Number of reads with average TE likelihoods changed by at least 0.1: 46272 2019-06-27 21:15:20.143444

Running expectation-maximization calculation for iteration:32 2019-06-27 21:15:20.144722

Average change in TE count:0.00287664556681 2019-06-27 21:15:22.761304

Max change in TE count:548.562483738 2019-06-27 21:15:22.762776

Number changed TE:35492 2019-06-27 21:15:22.764130

Number TEs changed by at least 1 count:38 2019-06-27 21:15:22.765285

Number TEs changed by at least 1 count with at least 10 counts:38 2019-06-27 21:15:22.766503

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:10 2019-06-27 21:15:22.767754

Expectation maximization changed the average likelihood by: 0.00146877295966 2019-06-27 21:16:59.012601

Number of reads with average TE likelihoods changed by at least 0.1: 46275 2019-06-27 21:16:59.014077

Running expectation-maximization calculation for iteration:33 2019-06-27 21:16:59.015336

Average change in TE count:0.00255849149255 2019-06-27 21:17:01.611622

Max change in TE count:504.499536169 2019-06-27 21:17:01.613099

Number changed TE:35219 2019-06-27 21:17:01.614275

Number TEs changed by at least 1 count:37 2019-06-27 21:17:01.615635

Number TEs changed by at least 1 count with at least 10 counts:34 2019-06-27 21:17:01.617011

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:9 2019-06-27 21:17:01.618155

Expectation maximization changed the average likelihood by: 0.00146086925031 2019-06-27 21:18:38.198824

Number of reads with average TE likelihoods changed by at least 0.1: 46278 2019-06-27 21:18:38.200441

Running expectation-maximization calculation for iteration:34 2019-06-27 21:18:38.201781

Average change in TE count:0.00228485720661 2019-06-27 21:18:40.787136

Max change in TE count:463.715127795 2019-06-27 21:18:40.788534

Number changed TE:34960 2019-06-27 21:18:40.790056

Number TEs changed by at least 1 count:30 2019-06-27 21:18:40.791405

Number TEs changed by at least 1 count with at least 10 counts:30 2019-06-27 21:18:40.792605

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:8 2019-06-27 21:18:40.793772

Expectation maximization changed the average likelihood by: 0.00145376151948 2019-06-27 21:20:17.578131

Number of reads with average TE likelihoods changed by at least 0.1: 46281 2019-06-27 21:20:17.579756

Running expectation-maximization calculation for iteration:35 2019-06-27 21:20:17.581020

Average change in TE count:0.00204286844238 2019-06-27 21:20:20.175911

Max change in TE count:426.0167221 2019-06-27 21:20:20.177406

Number changed TE:34694 2019-06-27 21:20:20.178878

Number TEs changed by at least 1 count:29 2019-06-27 21:20:20.180646

Number TEs changed by at least 1 count with at least 10 counts:29 2019-06-27 21:20:20.182240

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:8 2019-06-27 21:20:20.183842

Expectation maximization changed the average likelihood by: 0.00144761579982 2019-06-27 21:21:56.326294

Number of reads with average TE likelihoods changed by at least 0.1: 46281 2019-06-27 21:21:56.328044

Running expectation-maximization calculation for iteration:36 2019-06-27 21:21:56.329484

Average change in TE count:0.00184992582663 2019-06-27 21:21:58.929435

Max change in TE count:391.211709706 2019-06-27 21:21:58.930967

Number changed TE:34522 2019-06-27 21:21:58.932178

Number TEs changed by at least 1 count:29 2019-06-27 21:21:58.933564

Number TEs changed by at least 1 count with at least 10 counts:29 2019-06-27 21:21:58.934677

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:8 2019-06-27 21:21:58.935858

Expectation maximization changed the average likelihood by: 0.00144237608353 2019-06-27 21:23:35.015266

Number of reads with average TE likelihoods changed by at least 0.1: 46285 2019-06-27 21:23:35.016892

Running expectation-maximization calculation for iteration:37 2019-06-27 21:23:35.018193

Average change in TE count:0.00167405868438 2019-06-27 21:23:37.614310

Max change in TE count:359.110754416 2019-06-27 21:23:37.615647

Number changed TE:34279 2019-06-27 21:23:37.617111

Number TEs changed by at least 1 count:28 2019-06-27 21:23:37.618481

Number TEs changed by at least 1 count with at least 10 counts:28 2019-06-27 21:23:37.619823

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:7 2019-06-27 21:23:37.621102

Expectation maximization changed the average likelihood by: 0.00143757349531 2019-06-27 21:25:14.171650

Number of reads with average TE likelihoods changed by at least 0.1: 46287 2019-06-27 21:25:14.173085

Running expectation-maximization calculation for iteration:38 2019-06-27 21:25:14.174479

Average change in TE count:0.00151707123458 2019-06-27 21:25:16.756255

Max change in TE count:329.530127048 2019-06-27 21:25:16.757755

Number changed TE:34094 2019-06-27 21:25:16.759005

Number TEs changed by at least 1 count:27 2019-06-27 21:25:16.760180

Number TEs changed by at least 1 count with at least 10 counts:27 2019-06-27 21:25:16.761461

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:7 2019-06-27 21:25:16.762856

Expectation maximization changed the average likelihood by: 0.00143348606341 2019-06-27 21:26:52.637959

Number of reads with average TE likelihoods changed by at least 0.1: 46294 2019-06-27 21:26:52.639509

Running expectation-maximization calculation for iteration:39 2019-06-27 21:26:52.640830

Average change in TE count:0.00139494603957 2019-06-27 21:26:55.231428

Max change in TE count:302.293258583 2019-06-27 21:26:55.232736

Number changed TE:33854 2019-06-27 21:26:55.234004

Number TEs changed by at least 1 count:27 2019-06-27 21:26:55.235217

Number TEs changed by at least 1 count with at least 10 counts:27 2019-06-27 21:26:55.236214

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:8 2019-06-27 21:26:55.237649

Expectation maximization changed the average likelihood by: 0.00142969378107 2019-06-27 21:28:30.887263

Number of reads with average TE likelihoods changed by at least 0.1: 46295 2019-06-27 21:28:30.888909

Running expectation-maximization calculation for iteration:40 2019-06-27 21:28:30.890255

Average change in TE count:0.00127242305743 2019-06-27 21:28:33.478790

Max change in TE count:277.231740712 2019-06-27 21:28:33.480352

Number changed TE:33700 2019-06-27 21:28:33.481751

Number TEs changed by at least 1 count:24 2019-06-27 21:28:33.483030

Number TEs changed by at least 1 count with at least 10 counts:24 2019-06-27 21:28:33.484199

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:8 2019-06-27 21:28:33.485444

Expectation maximization changed the average likelihood by: 0.00142628373186 2019-06-27 21:30:09.711251

Number of reads with average TE likelihoods changed by at least 0.1: 46295 2019-06-27 21:30:09.712683

Running expectation-maximization calculation for iteration:41 2019-06-27 21:30:09.713898

Average change in TE count:0.00116724009883 2019-06-27 21:30:12.289737

Max change in TE count:254.185910391 2019-06-27 21:30:12.291183

Number changed TE:33517 2019-06-27 21:30:12.292622

Number TEs changed by at least 1 count:22 2019-06-27 21:30:12.293963

Number TEs changed by at least 1 count with at least 10 counts:22 2019-06-27 21:30:12.295178

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:30:12.296488

Expectation maximization changed the average likelihood by: 0.00142337798465 2019-06-27 21:31:47.928252

Number of reads with average TE likelihoods changed by at least 0.1: 46294 2019-06-27 21:31:47.929694

Running expectation-maximization calculation for iteration:42 2019-06-27 21:31:47.931117

Average change in TE count:0.00109452561911 2019-06-27 21:31:50.536766

Max change in TE count:233.005140503 2019-06-27 21:31:50.538262

Number changed TE:33376 2019-06-27 21:31:50.539589

Number TEs changed by at least 1 count:24 2019-06-27 21:31:50.540832

Number TEs changed by at least 1 count with at least 10 counts:23 2019-06-27 21:31:50.541951

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:6 2019-06-27 21:31:50.543186

Expectation maximization changed the average likelihood by: 0.00142065736362 2019-06-27 21:33:25.750015

Number of reads with average TE likelihoods changed by at least 0.1: 46297 2019-06-27 21:33:25.751474

Running expectation-maximization calculation for iteration:43 2019-06-27 21:33:25.752920

Average change in TE count:0.00099637559883 2019-06-27 21:33:28.335385

Max change in TE count:213.547891076 2019-06-27 21:33:28.336670

Number changed TE:33225 2019-06-27 21:33:28.337887

Number TEs changed by at least 1 count:22 2019-06-27 21:33:28.339108

Number TEs changed by at least 1 count with at least 10 counts:22 2019-06-27 21:33:28.340319

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:33:28.341507

Expectation maximization changed the average likelihood by: 0.00141819008395 2019-06-27 21:35:04.250397

Number of reads with average TE likelihoods changed by at least 0.1: 46301 2019-06-27 21:35:04.251953

Running expectation-maximization calculation for iteration:44 2019-06-27 21:35:04.253509

Average change in TE count:0.000926423704609 2019-06-27 21:35:06.826193

Max change in TE count:195.681613834 2019-06-27 21:35:06.827485

Number changed TE:33063 2019-06-27 21:35:06.828760

Number TEs changed by at least 1 count:21 2019-06-27 21:35:06.829895

Number TEs changed by at least 1 count with at least 10 counts:21 2019-06-27 21:35:06.831068

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:35:06.832260

Expectation maximization changed the average likelihood by: 0.00141595475478 2019-06-27 21:36:42.625183

Number of reads with average TE likelihoods changed by at least 0.1: 46300 2019-06-27 21:36:42.626480

Running expectation-maximization calculation for iteration:45 2019-06-27 21:36:42.627744

Average change in TE count:0.000857332461115 2019-06-27 21:36:45.235734

Max change in TE count:179.282531765 2019-06-27 21:36:45.237186

Number changed TE:32950 2019-06-27 21:36:45.238385

Number TEs changed by at least 1 count:20 2019-06-27 21:36:45.239725

Number TEs changed by at least 1 count with at least 10 counts:20 2019-06-27 21:36:45.240866

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:36:45.242041

Expectation maximization changed the average likelihood by: 0.00141389240555 2019-06-27 21:38:20.439304

Number of reads with average TE likelihoods changed by at least 0.1: 46299 2019-06-27 21:38:20.440875

Running expectation-maximization calculation for iteration:46 2019-06-27 21:38:20.442064

Average change in TE count:0.00079227159918 2019-06-27 21:38:23.032100

Max change in TE count:164.235342427 2019-06-27 21:38:23.033566

Number changed TE:32806 2019-06-27 21:38:23.034896

Number TEs changed by at least 1 count:20 2019-06-27 21:38:23.036262

Number TEs changed by at least 1 count with at least 10 counts:20 2019-06-27 21:38:23.037439

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:38:23.038813

Expectation maximization changed the average likelihood by: 0.00141202189043 2019-06-27 21:39:59.002635

Number of reads with average TE likelihoods changed by at least 0.1: 46299 2019-06-27 21:39:59.004148

Running expectation-maximization calculation for iteration:47 2019-06-27 21:39:59.005681

Average change in TE count:0.000737021598594 2019-06-27 21:40:01.585467

Max change in TE count:150.432858562 2019-06-27 21:40:01.587045

Number changed TE:32690 2019-06-27 21:40:01.588332

Number TEs changed by at least 1 count:19 2019-06-27 21:40:01.589623

Number TEs changed by at least 1 count with at least 10 counts:19 2019-06-27 21:40:01.590968

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:40:01.592212

Expectation maximization changed the average likelihood by: 0.0014105331083 2019-06-27 21:41:37.499622

Number of reads with average TE likelihoods changed by at least 0.1: 46301 2019-06-27 21:41:37.501094

Running expectation-maximization calculation for iteration:48 2019-06-27 21:41:37.502522

Average change in TE count:0.000691164155386 2019-06-27 21:41:40.099543

Max change in TE count:137.775616455 2019-06-27 21:41:40.100983

Number changed TE:32590 2019-06-27 21:41:40.102239

Number TEs changed by at least 1 count:18 2019-06-27 21:41:40.103668

Number TEs changed by at least 1 count with at least 10 counts:18 2019-06-27 21:41:40.104874

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:41:40.106228

Expectation maximization changed the average likelihood by: 0.00140902344907 2019-06-27 21:43:15.230267

Number of reads with average TE likelihoods changed by at least 0.1: 46301 2019-06-27 21:43:15.231965

Running expectation-maximization calculation for iteration:49 2019-06-27 21:43:15.233167

Average change in TE count:0.000641234525855 2019-06-27 21:43:17.826328

Max change in TE count:126.171469585 2019-06-27 21:43:17.827611

Number changed TE:32471 2019-06-27 21:43:17.828853

Number TEs changed by at least 1 count:18 2019-06-27 21:43:17.830155

Number TEs changed by at least 1 count with at least 10 counts:18 2019-06-27 21:43:17.831382

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:43:17.832524

Expectation maximization changed the average likelihood by: 0.00140759854425 2019-06-27 21:44:53.425303

Number of reads with average TE likelihoods changed by at least 0.1: 46301 2019-06-27 21:44:53.427046

Running expectation-maximization calculation for iteration:50 2019-06-27 21:44:53.428362

Average change in TE count:0.000600351862039 2019-06-27 21:44:56.006958

Max change in TE count:115.636994799 2019-06-27 21:44:56.008249

Number changed TE:32412 2019-06-27 21:44:56.009369

Number TEs changed by at least 1 count:18 2019-06-27 21:44:56.010648

Number TEs changed by at least 1 count with at least 10 counts:18 2019-06-27 21:44:56.011854

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:44:56.012960

Expectation maximization changed the average likelihood by: 0.00140642628317 2019-06-27 21:46:31.696950

Number of reads with average TE likelihoods changed by at least 0.1: 46305 2019-06-27 21:46:31.698427

Running expectation-maximization calculation for iteration:51 2019-06-27 21:46:31.699636

Average change in TE count:0.000573752666182 2019-06-27 21:46:34.289699

Max change in TE count:109.789331198 2019-06-27 21:46:34.291071

Number changed TE:32307 2019-06-27 21:46:34.292474

Number TEs changed by at least 1 count:15 2019-06-27 21:46:34.293680

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:46:34.295124

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:4 2019-06-27 21:46:34.296546

Expectation maximization changed the average likelihood by: 0.00140523894495 2019-06-27 21:48:09.237551

Number of reads with average TE likelihoods changed by at least 0.1: 46305 2019-06-27 21:48:09.239093

Running expectation-maximization calculation for iteration:52 2019-06-27 21:48:09.240193

Average change in TE count:0.000526706387801 2019-06-27 21:48:11.824855

Max change in TE count:104.227874967 2019-06-27 21:48:11.826166

Number changed TE:32203 2019-06-27 21:48:11.827426

Number TEs changed by at least 1 count:15 2019-06-27 21:48:11.828777

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:48:11.829995

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:4 2019-06-27 21:48:11.831208

Expectation maximization changed the average likelihood by: 0.00140419509835 2019-06-27 21:49:47.053050

Number of reads with average TE likelihoods changed by at least 0.1: 46305 2019-06-27 21:49:47.054662

Running expectation-maximization calculation for iteration:53 2019-06-27 21:49:47.056061

Average change in TE count:0.000504564376863 2019-06-27 21:49:49.643315

Max change in TE count:98.9397984083 2019-06-27 21:49:49.645005

Number changed TE:32122 2019-06-27 21:49:49.646208

Number TEs changed by at least 1 count:16 2019-06-27 21:49:49.647578

Number TEs changed by at least 1 count with at least 10 counts:16 2019-06-27 21:49:49.648741

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:5 2019-06-27 21:49:49.650101

Expectation maximization changed the average likelihood by: 0.0014032649676 2019-06-27 21:51:24.990779

Number of reads with average TE likelihoods changed by at least 0.1: 46309 2019-06-27 21:51:24.992293

Running expectation-maximization calculation for iteration:54 2019-06-27 21:51:24.993717

Average change in TE count:0.000466809371099 2019-06-27 21:51:27.581046

Max change in TE count:93.9127083258 2019-06-27 21:51:27.582298

Number changed TE:32040 2019-06-27 21:51:27.583673

Number TEs changed by at least 1 count:15 2019-06-27 21:51:27.584948

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:51:27.586126

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:51:27.587343

Expectation maximization changed the average likelihood by: 0.00140233818386 2019-06-27 21:53:02.233440

Number of reads with average TE likelihoods changed by at least 0.1: 46309 2019-06-27 21:53:02.235030

Running expectation-maximization calculation for iteration:55 2019-06-27 21:53:02.236210

Average change in TE count:0.000437849765234 2019-06-27 21:53:04.821629

Max change in TE count:89.1346483843 2019-06-27 21:53:04.822910

Number changed TE:31984 2019-06-27 21:53:04.824169

Number TEs changed by at least 1 count:15 2019-06-27 21:53:04.825346

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:53:04.826540

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:53:04.827724

Expectation maximization changed the average likelihood by: 0.00140153874811 2019-06-27 21:54:39.764457

Number of reads with average TE likelihoods changed by at least 0.1: 46309 2019-06-27 21:54:39.765956

Running expectation-maximization calculation for iteration:56 2019-06-27 21:54:39.767071

Average change in TE count:0.000415334713331 2019-06-27 21:54:42.345675

Max change in TE count:84.5940985575 2019-06-27 21:54:42.347076

Number changed TE:31912 2019-06-27 21:54:42.348339

Number TEs changed by at least 1 count:15 2019-06-27 21:54:42.349616

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:54:42.350881

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:54:42.352026

Expectation maximization changed the average likelihood by: 0.00140078299085 2019-06-27 21:56:17.404752

Number of reads with average TE likelihoods changed by at least 0.1: 46309 2019-06-27 21:56:17.406339

Running expectation-maximization calculation for iteration:57 2019-06-27 21:56:17.407591

Average change in TE count:0.000386093667688 2019-06-27 21:56:19.999590

Max change in TE count:80.2799716667 2019-06-27 21:56:20.001108

Number changed TE:31859 2019-06-27 21:56:20.002373

Number TEs changed by at least 1 count:15 2019-06-27 21:56:20.003596

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:56:20.004633

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:56:20.005852

Expectation maximization changed the average likelihood by: 0.00140014443752 2019-06-27 21:57:54.399653

Number of reads with average TE likelihoods changed by at least 0.1: 46310 2019-06-27 21:57:54.401133

Running expectation-maximization calculation for iteration:58 2019-06-27 21:57:54.402369

Average change in TE count:0.000364730718422 2019-06-27 21:57:56.981938

Max change in TE count:76.1816105884 2019-06-27 21:57:56.983213

Number changed TE:31799 2019-06-27 21:57:56.984396

Number TEs changed by at least 1 count:15 2019-06-27 21:57:56.985557

Number TEs changed by at least 1 count with at least 10 counts:15 2019-06-27 21:57:56.986643

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:57:56.987782

Expectation maximization changed the average likelihood by: 0.00139948292248 2019-06-27 21:59:31.709725

Number of reads with average TE likelihoods changed by at least 0.1: 46310 2019-06-27 21:59:31.711113

Running expectation-maximization calculation for iteration:59 2019-06-27 21:59:31.712369

Average change in TE count:0.000341869519353 2019-06-27 21:59:34.283456

Max change in TE count:72.2887815133 2019-06-27 21:59:34.284743

Number changed TE:31710 2019-06-27 21:59:34.285978

Number TEs changed by at least 1 count:14 2019-06-27 21:59:34.287090

Number TEs changed by at least 1 count with at least 10 counts:14 2019-06-27 21:59:34.288144

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 21:59:34.289216

Expectation maximization changed the average likelihood by: 0.00139893433134 2019-06-27 22:01:09.090080

Number of reads with average TE likelihoods changed by at least 0.1: 46310 2019-06-27 22:01:09.091645

Running expectation-maximization calculation for iteration:60 2019-06-27 22:01:09.092865

Average change in TE count:0.000326840366087 2019-06-27 22:01:11.676242

Max change in TE count:68.5916660654 2019-06-27 22:01:11.677653

Number changed TE:31637 2019-06-27 22:01:11.678886

Number TEs changed by at least 1 count:14 2019-06-27 22:01:11.680155

Number TEs changed by at least 1 count with at least 10 counts:14 2019-06-27 22:01:11.681417

Number TEs changed by at least 1 count with at least 10 counts and > 1pct total count:3 2019-06-27 22:01:11.682543

Hello,

I am really sorry for bothering you again, but I have a new problem when I am trying to run the fetch phase.
It looks like the arguments are fine, however, I am getting an error that has to do with the connection with UCSC.

`Downloading Compressed Chromosome files...

Traceback (most recent call last):
File "/scratch/x.v.l.01/yes/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/scratch/x.v.l.01/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/scratch/x.v.l.01/SQuIRE/squire/Fetch.py", line 212, in main
urllib.urlretrieve(chrom_loc1, filename=chrom_name_compressed)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/urllib.py", line 98, in urlretrieve
return opener.retrieve(url, filename, reporthook, data)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/urllib.py", line 245, in retrieve
fp = self.open(url, data)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/urllib.py", line 213, in open
return getattr(self, name)(url)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/urllib.py", line 350, in open_http
h.endheaders(data)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/httplib.py", line 1038, in endheaders
self._send_output(message_body)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/httplib.py", line 882, in _send_output
self.send(msg)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/httplib.py", line 844, in send
self.connect()
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/httplib.py", line 821, in connect
self.timeout, self.source_address)
File "/scratch/x.v.l.01/yes/envs/squire/lib/python2.7/socket.py", line 575, in create_connection
raise err
IOError: [Errno socket error] [Errno 101] Network is unreachable`

I have tested to download a single file from UCSC it works fine, meaning that my cluster is reachable from the internet.

Any possible explanation?
Many thanks in advance.

Hi,
What is the default method SQuIRE uses when multiple inputs are passed to "squire map"? I recently tried passing in three FASTQs and assumed the program would produce three BAM files corresponding to each of the inputs, but instead only one output BAM was produced (with the name I specified using the --name flag).
Thanks so much!

Dear all,

please would you let me know : would it be possible to have a docker container for SQuIRE ?

thanks !

bogdan

Hi,

I'm quite excited to use SQuIRE, but I work on a non-UCSC genome, with no files to "fetch". Would it be possible to make available the formats of the "fetch" files, but also potentially an option to upload our own well-formatted files? Otherwise, is it possible to maybe tweak the code to add my own files (#notabioinformaticienhere)?

Thank you very much
Rita

Hi,
I am trying to run the squire Fetch command but when it reaches the STAR index build it fails right away returning segmentation fault. It's not due to insufficient memory because it fails the moment it reaches the command. I am trying to build hg38 genome.

I installed squire in conda following the instructions.

Any idea why this is happening?

Thanks

I am getting this error in only 8 of 86 samples:

Traceback (most recent call last):
File "/home/alejandro.rubio/tools/miniconda3/envs/squire/bin/squire", line 11, in
srun: error: mulhacen9: task 0: Exited with exit code 1
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/home/alejandro.rubio/tools/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/home/alejandro.rubio/tools/SQuIRE/squire/Count.py", line 1560, in main
paired_end = is_paired(bamfile,basename,tempfolder,debug)
File "/home/alejandro.rubio/tools/SQuIRE/squire/Count.py", line 325, in is_paired
return paired
UnboundLocalError: local variable 'paired' referenced before assignment

Any idea?

Hi I wonder if you could add explanations about the format of the files generated by SQuIRE Count. This would be helpful for people who want to perform customized analysis besides differential expression on their own.

I think a good example of format explanations is from StringTie: http://ccb.jhu.edu/software/stringtie/index.shtml?t=manual#output

Specifically for me, I am wondering what each column in *_refGenecounts.txt means. For example,

chr17 26138685 26195811 Axin1 11.196273 + 264 NM_001159598,NM_009733

I think 11.196273 is the TPM, and 264 is the read count? And how did you deal with multiple mapped reads that align to genes?

Thanks.

Hello,
I am processing some fastq files coming out of trimmomatics trimming. They have different read lengths. What setting should I use for the squire Map and Count? The maximum or the minimum? Or shall I trim the reads to the minimum length and run it with a certain read length setting?

Mx

Hi @wyang17 ,

in line 1567 of Count.py has an issue on "genename_dict" : filter_tx(outgtf_ref, genename_dict,read_length,genecounts)

Thank you,

Traceback (most recent call last):
File "/users/cpacyna/miniconda3/envs/squire_git/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/users/cpacyna/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/users/cpacyna/SQuIRE/squire/Count.py", line 1564, in main
filter_abund(abund_ref,genename_dict,False, read_length)
TypeError: filter_abund() takes exactly 3 arguments (4 given)

Hello,

I was wondering if anyone else has the same problem with me, regarding the volcano and the PCA graphs. The pdf files that are generated are empty (4KB). This happens only in the volcano and PCA. All the other graphs are fine!

Thank you very much in advance,
Vasilis.

Hello, after performing some TE analysis in the mouse (analyzed against the mm39 version) I have obtained an erroneous call output in which several headers are missing, preventing the correct recognition of the data.
Anyone else has happened?

Hi,

I have been trying to run Fetch on my samples but I guess I must be doing something wrong in the argument.sh file.
Could you provide an example on how to fill out the argument file especially the sample names ? I keep getting "command not found" at the line 14 of the argument.sh file. Sorry, for that I am pretty new in the programming field but I find your tool pretty interesting for what I am doing.

best,

Salim.

Hello,
I am using SQuIRE for the TE expression analysis in Drosophila (dm6).
I find that there are at least three duplications: CG40439, CG46302, and CG17715.
Coincidentally (?), they were duplicated in chr2L and chr2R.
Here are the links for their documented info on FlyBase:
CG40439 http://flybase.org/reports/FBgn0058439
CG46302 http://flybase.org/reports/FBgn0284080
CG17715 http://flybase.org/reports/FBgn0041004
Any idea?

Hi,

I am looking into TE expression upon drug treatment using stranded RNA-seq of two samples.
Here are the commands I used
squire Count -c <clean_dir> -f <fetch_dir> -r 75 -n <sample fastq> -p 4 -b hg38 -s 2 squire Call -1 -2 -A treated -B control -i squire_count -o -p 4 -f pdf

The number of TE copies in "DESeq2_TE_only.txt" is not the same across samples while the number of genes in "DESeq2_RefSeq_only.txt" is the same. (To avoid the case where one TE copy/gene generates two transcripts in the opposite strand, I trimmed the transcript strand information in row names and count the number of TE copy and gene.)

• sample A: 28277 gene (from DESeq2_RefSeq_only.txt), 113262 TE copies (from DESeq2_TE_only.txt)
• sample B: 28277 gene (from DESeq2_RefSeq_only.txt), 159599 TE copies (from DESeq2_TE_only.txt)

I expect to see all TE copies in "DESeq2_TE_only.txt" but only a subset of TE copies are shown for each sample and the number of TE copies is discordant across samples. Is there a way for me to have SQuIRE output with all TE copies?

hello there,
After running successfully the tool until Squire Count, yay! I was going over the Count output tables and I noticed that the same individual (Same TE family, same Chrom:Start-END) was divided into several different TE_IDs based on the milliDiv parameter.
I went to the supplementary data from the paper and it states:

milliDiv = Base mismatches in parts per thousand (from RepeatMasker)

However, I do not understand why that parameter was chosen to be part of the ID and why it is used to separate individuals as "different". If anyone could explain more in detail that parameter and why use it as part of the unique identifier of a TE, that would be great.

Thank you!
Adrián

Hello,
I managed to arrive to mapping step, however it stops because of an error (please see below). Any help would be appreciated.
Thanks,
Martina

squire Map --read1 /data/MB998/HepC_Uwe/Uwe_Influenca/FASTqs/A549_DMSO_12h_dNS1_3_20141211_NoIndex_L001_R1_001.fastq.gz --read2 FALSE --read_length 100 --fetch_folder /data/MB998/SOFTWARE/SQuIRE/fetch_folder --build hg38 --verbosity
start time is:2019-04-08 18:52:14.682391

Map.pyc

Script Arguments

fetch_folder=/data/MB998/SOFTWARE/SQuIRE/fetch_folder
name=False
extra=None
read_length=100
verbosity=True
pthreads=1
read1=/data/MB998/HepC_Uwe/Uwe_Influenca/FASTqs/A549_DMSO_12h_dNS1_3_20141211_NoIndex_L001_R1_001.fastq.gz
read2=FALSE
build=hg38
func=<function main at 0x7f6a967c5578>
trim3=0
map_folder=squire_map

Traceback (most recent call last):
File "/home/mb998/miniconda/envs/squire/bin/squire", line 11, in
load_entry_point('SQuIRE', 'console_scripts', 'squire')()
File "/data/MB998/SOFTWARE/SQuIRE/squire/cli.py", line 156, in main
subargs.func(args = subargs)
File "/data/MB998/SOFTWARE/SQuIRE/squire/Map.py", line 301, in main
index = find_file(fetch_folder,"_STAR",build, 1,True)
File "/data/MB998/SOFTWARE/SQuIRE/squire/Map.py", line 95, in find_file
raise Exception("No " + pattern + " file")
Exception: No _STAR file

I'm not an expert in Python so forgive me if I'm wrong, but I've tried to decompose the Call.py function to understand how the counttables used in the DESeq analysis are generated.

Within the create_subfamily_dict function, the count assigned into the dictionary is the uniq_counts rather than the total counts used elsewhere:

def create_subfamily_dict(infilepath,count_dict):
TE_classes=["LTR","LINE","SINE","Retroposon","DNA","RC"]
with open(infilepath,'r') as infile:
for line in infile:
line = line.rstrip()
line = line.split("\t")
taxo = line[2]
count=line[6]
if any(x in taxo for x in TE_classes):
if count=="tot_counts":
continue
else:
count = str(int(round(float(line[5]))))
sample = line[0]
if taxo not in count_dict:
count_dict[taxo] = {sample:count}
else:
count_dict[taxo][sample]=count

Can you explain why this is the case?

Hi,

I am beginning to work with Squire to analyze L1 mobile elements in knockout cell lines. I am specifically interested in obtaining antisense expression on L1 (that should include transcripts from L1 antisense promoter and "run on" transcripts from nearby gene promoters).

I have some trouble in getting that information from strand output (tx_strand and TE_strand). According to SQuIRE Count output explanation, TE_strand is orientation of TE insertion and tx_strand the strand of TE transcription. My guess is that I can get antisense L1 transcripts by choosing counts where TE_strand is different from tx_strand (TE_strand != tx_strand). Am I right?

Thank you for your support and for this useful software,

Guille.

Hi,

May I have some guidance on how to use the TE counts result file to do DE analysis, as the rows differ for different samples, and if run the call step, it will show the error "list index out of range".

Thanks so much!