Software Installation – RepeatMasker v4.0.7 on Emu/Roadrunner Continued

After yesterday’s difficulties getting RMblast to compile, I deleted the folder and went through the build process again.

This time it worked, but it did not put rmblastn in the specified location (/home/shared/rmblast).

This fact took me a fair amount of time to figure out. Finally, after a couple of different re-builds, I ran find to see if rmblastn existed somewhere I wasn’t looking:

Additionally, I couldn’t find the location of the various BLAST executables. Some internet sleuthing led me to the NCBI page on installing BLAST+ from source, which indicates that the executables are stored in:

ncbi-blast-VERSION+-src/c++/ReleaseMT/bin/

How intuitive! /s

In order to improve readability and usability of the /home/shared/ directory, I renamed the /home/shared/rmblast directory to reflect the BLAST version and created a symbolic link in that directory to the rmlbastn executable:

Symbolic link to RMBLAST

Initiate RepeatMasker configuration


Confirm perl install location:


Confirm RepeatMasker install location:


Specify TRF install location:


Hmmm, TRF error. Looking for file called trf:


Renamed TRF file to trf and now it’s automatically found:


Set RMBlast as search engine:


Set RMBlast install location:


Set RMBlast as default search engine:


Confirmation of RMBlast as default search engine and successful installation of RepeatMasker:


Software Installation – RepeatMasker v4.0.7 on Emu/Roadrunner

Steven asked that I re-run some Olympia oyster transposable elements analysis using RepeatMasker and a newer version of our Olympia oyster genome assembly.

Installed the software on both of the Apple Xserves (Emu and Roadrunner) running Ubuntu 16.04.

Followed the instructions outlined here:

Starting with the prerequisites:

1. Download and install RMBlast

  • NCBI Blast 2.6.0 source

  • isb 2.6.0 patch

Unfortunately, the make command continually failed:

cd /home/shared/ncbi-blast-2.6.0+-src/c++
make

While trying to troubleshoot this issue, continued with the other prerequisites:

2. Downloaded Tandem Repeat Finder v.4.09

  • Saved file (trf409.linux64) to /home/shared/bin. NOTE: /home/shared/bin is part of the system PATH. See the /etc/environment file.

  • Changed permissions to be executable:

sudo chmod 775 trf409.linux64

3. Downloaded RepBase RepeatMasker Edition 20170127 (NOTE: This requires registration in order to obtain a username/password to download the file).

Installed RepeatMasker:

4. Downloaded RepeatMasker 4.0.7

  • Saved to /home/shared/RepeatMasker-4.0.7

5. Installed RepBase RepeatMasker Edition 20170127 in /home/shared//home/shared/RepeatMasker-4.0.7/Libraries

Currently re-building RMBlast and it takes forever… Will report back when I have it running.

TrimGalore/FastQC/MultiQC – TrimGalore! RRBS Geoduck BS-seq FASTQ data (directional)

Earlier this week, I ran TrimGalore!, but set the trimming, incorrectly – due to a copy/paste mistake, as --non-directional, so I re-ran with the correct settings.

Steven requested that I trim the Geoduck RRBS libraries that we have, in preparation to run them through Bismark.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

All analysis is documented in a Jupyter Notebook; see link below.

Overview of process:

  1. Run TrimGalore! with --paired and --rrbs settings.

  2. Run FastQC and MultiQC on trimmed files.

  3. Copy all data to owl (see Results below for link).

  4. Confirm data integrity via MD5 checksums.

Jupyter Notebook:


Results:
TrimGalore! output folder:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

FastQC – RRBS Geoduck BS-seq FASTQ data

Earlier today I finished trimming Hollie’s RRBS BS-seq FastQ data.

However, the original files were never analyzed with FastQC, so I ran it on the original files.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

FastQC was run, followed by MultiQC. Analysis was run on Roadrunner.

All analysis is documented in a Jupyter Notebook; see link below.

Jupyter Notebook:

Results:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

TrimGalore/FastQC/MultiQC – TrimGalore! RRBS Geoduck BS-seq FASTQ data


20180516 – UPDATE!!

THIS WAS RUN WITH THE INCORRECT SETTING IN TRIMGALORE! --non-directional

WILL RE-RUN


Steven requested that I trim the Geoduck RRBS libraries that we have, in preparation to run them through Bismark.

These libraries were originally created by Hollie Putnam using the TruSeq DNA Methylation Kit (Illumina):

All analysis is documented in a Jupyter Notebook; see link below.

Overview of process:

  1. Copy EPI* FastQ files from owl/P_generosa to roadrunner.

  2. Confirm data integrity via MD5 checksums.

  3. Run TrimGalore! with --paired, --rrbs, and --non-directional settings.

  4. Run FastQC and MultiQC on trimmed files.

  5. Copy all data to owl (see Results below for link).

  6. Confirm data integrity via MD5 checksums.

Jupyter Notebook:


Results:
TrimGalore! output folder:
FastQC output folder:
MultiQC output folder:
MultiQC report (HTML):

Data Management – Illumina NovaSeq Geoduck Genome Sequencing

As part of the Illumina collaborative geoduck genome sequencing project, their end goal has always been to sequence the genome in a single run.

They’ve finally attempted this by running 10x Genomics, Hi-C, Nextera, and TruSeq libraries in a single run of the NovaSeq.

I downloaded the data using the BaseSpace downloader using Chrome on a Windows 7 computer (this is not available on Ubuntu and the command line tools that are available from Illumina are too confusing for me to bother spending the time on to figure out how to use them just to download the data).

Data was saved here:

Generated MD5 checksums (using md5sum on Ubuntu) and appended to the checksums file:

Illumina was unable to provide MD5 checksums on their end, so I was unable to confirm data integrity post-download.

Illumina sample info is here:

Will add info to:

List of files received:

10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleA_S10_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleB_S11_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleC_S12_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x5-A3-MultipleD_S13_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleA_S14_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleB_S15_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleC_S16_L002_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L001_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L001_R2_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L002_R1_001.fastq.gz
10x-Genomics-Libraries-Geo10x6-B3-MultipleD_S17_L002_R2_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L001_R1_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L001_R2_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L002_R1_001.fastq.gz
HiC-Libraries-GeoHiC-C3-N701_S18_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP10-B2-AD013_S7_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP11-C2-AD014_S8_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP12-D2-AD015_S9_L002_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L001_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L001_R2_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L002_R1_001.fastq.gz
Nextera-Mate-Pair-Library-GeoNMP9-A2-AD002_S6_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA1-A1-NR006_S1_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA3-C1-NR012_S2_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA5-E1-NR005_S3_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA7-G1-NR019_S4_L002_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L001_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L001_R2_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L002_R1_001.fastq.gz
Trueseq-stranded-mRNA-libraries-GeoRNA8-H1-NR021_S5_L002_R2_001.fastq.gz

Assembly – Geoduck Hi-C Assembly Subsetting

Steven asked me to create a couple of subsets of our Phase Genomics Hi-C geoduck genome assembly (pga_02):

  • Contigs >10kbp

  • Contigs >30kbp

I used pyfaidx on Roadrunner and the following commands:

faidx --size-range 10000,100000000 PGA_assembly.fasta > PGA_assembly_10k_plus.fasta
faidx --size-range 30000,100000000 PGA_assembly.fasta > PGA_assembly_30k_plus.fasta

Ran Quast afterwards to get stats on the new FastA files just to confirm that the upper cutoff value was correct and didn’t get rid of the largest contig(s).

Results:

faidx Output folder: 20180512_geoduck_fasta_subsets/

10kbp contigs (FastA): 20180512_geoduck_fasta_subsets/PGA_assembly_10k_plus.fasta

30kbp contigs (FastA): 20180512_geoduck_fasta_subsets/PGA_assembly_30k_plus.fasta

Quast output folder: results_2018_05_14_06_26_26/

Quast report (HTML): results_2018_05_14_06_26_26/report.html

Everything looks good. The main thing I wanted to confirm by running Quast was that the largest contig in each subset was the same as the original PGA assembly (95,480,635bp.

Read Mapping – Mapping Illumina Data to Geoduck Genome Assemblies with Bowtie2

We have an upcoming meeting with Illumina to discuss how the geoduck genome project is coming along and to decide how we want to proceed.

So, we wanted to get a quick idea of how well our geoduck assemblies are by performing some quick alignments using Bowtie2.

Used the following assemblies as references:

  • sn_ph_01 : SuperNova assembly of 10x Genomics data

  • sparse_03 : SparseAssembler assembly of BGI and Illumina project data

  • pga_02 : Hi-C assembly of Phase Genomics data

The analysis is documented in a Jupyter Notebook.

Jupyter Notebook (GitHub):

NOTE: Due to large amount of stdout from first genome index command, the notebook does not render well on GitHub. I recommend downloading and opening notebook on a locally install version of Jupyter.

Here’s a brief overview of the process:

  1. Generate Bowtie2 indexes for each of the genome assemblies.
  2. Map 1,000,000 reads from the following Illumina NovaSeq FastQ files:

Results:

Bowtie2 Genome Indexes:

Bowtie2 sn_ph_01 alignment folder:

Bowtie2 sparse_03 alignment folder:

Bowtie2 pga_02 alignment folder:


MAPPING SUMMARY TABLE

All mapping data was pulled from the respective *.err file in the Bowtie2 alignment folders.

sequence_ID Assembler Alignment Rate (%)
sn_ph_01 SuperNova (10x) 79.89
sparse_03 SparseAssembler 85.83
pga_02 Hi-C (Phase Genomics) 79.90|

Mapping efficiency is similar for all assemblies. After speaking with Steven, we’ve decided we’ll begin exploring genome annotation pipelines.

BS-seq Mapping – Olympia oyster bisulfite sequencing: TrimGalore > FastQC > Bismark

Steven asked me to evaluate our methylation sequencing data sets for Olympia oyster.

According to our Olympia oyster genome wiki, we have the following two sets of BS-seq data:

All computing was conducted on our Apple Xserve: roadrunner.

All steps were documented in this Jupyter Notebook (GitHub): 20180503_emu_oly_methylation_mapping.ipynb

NOTE: The Jupyter Notebook linked above is very large in size. As such it will not render on GitHub. It will need to be downloaded to a computer that can run Jupyter Notebooks and viewed that way.

Here’s a brief overview of what was done.

Samples were trimmed with TrimGalore and then evaluated with FastQC. MultiQC was used to generate a nice visual summary report of all samples.

The Olympia oyster genome assembly, pbjelly_sjw_01, was used as the reference genome and was prepared for use in Bismark:


/home/shared/Bismark-0.19.1/bismark_genome_preparation \
--path_to_bowtie /home/shared/bowtie2-2.3.4.1-linux-x86_64/ \
--verbose /home/sam/data/oly_methylseq/oly_genome/ \
2> 20180507_bismark_genome_prep.err

Bismark was run on trimmed samples with the following command:


/home/shared/Bismark-0.19.1/bismark \
--path_to_bowtie /home/shared/bowtie2-2.3.4.1-linux-x86_64/ \
--genome /home/sam/data/oly_methylseq/oly_genome/ \
-u 1000000 \
-p 16 \
--non_directional \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/1_ATCACG_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/2_CGATGT_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/3_TTAGGC_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/4_TGACCA_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/5_ACAGTG_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/6_GCCAAT_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/7_CAGATC_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/8_ACTTGA_L001_R1_001_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_10_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_11_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_12_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_13_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_14_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_15_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_16_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_17_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_18_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_1_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_2_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_3_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_4_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_5_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_6_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_7_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_8_s456_trimmed.fq.gz \
/home/sam/analyses/20180503_oly_methylseq_trimgalore/zr1394_9_s456_trimmed.fq.gz \
2> 20180507_bismark_02.err

Results:

TrimGalore output folder:

FastQC output folder:

MultiQC output folder:

MultiQC Report (HTML):

Bismark genome folder: 20180503_oly_genome_pbjelly_sjw_01_bismark/

Bismark output folder:


Whole genome BS-seq (2015)

Prep overview
  • Library prep: Roberts Lab
  • Sequencing: Genewiz
Bismark Report Mapping Percentage
1_ATCACG_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 40.3%
2_CGATGT_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 39.9%
3_TTAGGC_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 40.2%
4_TGACCA_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 40.4%
5_ACAGTG_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 39.9%
6_GCCAAT_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 39.6%
7_CAGATC_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 39.9%
8_ACTTGA_L001_R1_001_trimmed_bismark_bt2_SE_report.txt 39.7%

MBD BS-seq (2015)

Prep overview
  • MBD: Roberts Lab
  • Library prep: ZymoResearch
  • Sequencing: ZymoResearch
Bismark Report Mapping Percentage
zr1394_1_s456_trimmed_bismark_bt2_SE_report.txt 33.0%
zr1394_2_s456_trimmed_bismark_bt2_SE_report.txt 34.1%
zr1394_3_s456_trimmed_bismark_bt2_SE_report.txt 32.5%
zr1394_4_s456_trimmed_bismark_bt2_SE_report.txt 32.8%
zr1394_5_s456_trimmed_bismark_bt2_SE_report.txt 35.2%
zr1394_6_s456_trimmed_bismark_bt2_SE_report.txt 35.5%
zr1394_7_s456_trimmed_bismark_bt2_SE_report.txt 32.8%
zr1394_8_s456_trimmed_bismark_bt2_SE_report.txt 33.0%
zr1394_9_s456_trimmed_bismark_bt2_SE_report.txt 34.7%
zr1394_10_s456_trimmed_bismark_bt2_SE_report.txt 34.9%
zr1394_11_s456_trimmed_bismark_bt2_SE_report.txt 30.5%
zr1394_12_s456_trimmed_bismark_bt2_SE_report.txt 35.8%
zr1394_13_s456_trimmed_bismark_bt2_SE_report.txt 32.5%
zr1394_14_s456_trimmed_bismark_bt2_SE_report.txt 30.8%
zr1394_15_s456_trimmed_bismark_bt2_SE_report.txt 31.3%
zr1394_16_s456_trimmed_bismark_bt2_SE_report.txt 30.7%
zr1394_17_s456_trimmed_bismark_bt2_SE_report.txt 32.4%
zr1394_18_s456_trimmed_bismark_bt2_SE_report.txt 34.9%