Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient’s GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression.For complete details on the use and execution of this protocol, please refer to   •Integration of whole-exome and RNA sequences to determine Germline Variants (GVs)•Whole-exome and RNA sequences from tumors resolve low coverage issue in normal samples•High correlation of GV allele frequencies between patient data and the GnomAD database•GVs predict patient cancer outcome Germline Variants (GVs) are effective in predicting cancer risk and may be relevant in predicting patient outcomes. Here we provide a bioinformatic pipeline to identify GVs from the TCGA lower grade glioma cohort in Genomics Data Commons. We integrate paired whole exome sequences from normal and tumor samples and RNA sequences from tumor samples to determine a patient’s GV status. We then identify the subset of GVs that are predictive of patient outcomes by Cox regression. Timing: 5 minThe scripts and test data sets required to run this protocol are available on the GitHub repositoryhttps://github.com/ds21uab/STAR_protocols_GV_calling.git.1.Open the Linux command-line interface. Copy and paste the command below to clone protocol directory from the GitHub repository:>git clonehttps://github.com/ds21uab/STAR_protocols_GV_calling.gitTiming: 1 min2.a.>pwdTo determine the exact location of the ‘STAR_protocols_GV_calling’ directory using command-line, type command pwd from the directory where “STAR_protocols_GV_calling” is locatedThe output should look something like thisThe output of the pwd command shows that user is in the project directory, which is in the /home/dsahu/ directory.b.>nano ∼/.bash_profileOpen and edit the bash profile using your favorite text editorc.>export protocol_dir="/home/dsahu/project"Add the output of the pwd command to the last line.d.Press ‘control’ + ‘x’ key to exit. You will be prompted to save the file. Press ‘Y’ for Yes to save the changes and ‘enter’.e.>source ∼/.bash_profileActivate the changes in the bash_profileSave path for the ‘STAR_protocols_GV_calling’ directory in a local or online Linux cluster.Timing: 1 min3.Navigate to STAR_protocols_GV_calling directory.>cd STAR_protocols_GV_calling/4.Navigate to ‘scripts’ directory and set executable permission to all the scripts executable using the following command>cd scripts/>chmod +x ∗This protocol describes a computational approach that is based on several Linux-based software including gdc-client, SAMtools , BCFtoolTiming: 20 min5.https://gdc.cancer.gov/about-data/gdc-data-processing/gdc-reference-files/.a.>cd data/reference_data/HSapiens/hg38/>tar xvzf GRCh38.d1.vd1.fa.tar.gzUnzip and place the reference human genome FASTA sequence in the ‘/data/reference_data/HSapiens/hg38/’ directory.b.>module load samtools/1.12SAMtools should be loaded in the system PATH. We used the command below to load samtools in our online Linux cluster.c.>samtools faidx GRCh38.d1.vd1.faIndex the FASTA sequence.Navigate to ‘data’ directory with three subdirectories ‘BAM’, ‘reference_data’ and ‘samples’. The reference_data directory contains gene annotation BED file from the GENCODE database. Download GDC reference genome (hg38) FASTA sequence (GRCh38.d1.vd1.fa.tar.gz) from 6.a.Download BAM files using the following commandNavigate to ‘BAM’ directory and download BAM files for wxs-normal samples, wxs-tumor samples, and rnaseq-tumor samples from the provided manifest files and place them in their respective directory. A GDC token is required to download BAMs from the GDC database.>cd STAR_protocols_GV_calling/data/BAM/wxs-normal/>gdc-client download -m gdc_manifest_wxs-normal.txt -t token.txt>cd STAR_protocols_GV_calling/data/BAM/wxs-tumor/>gdc-client download -m gdc_manifest_wxs-tumor.txt -t token.txt>cd STAR_protocols_GV_calling/data/BAM/rnaseq-tumor/>gdc-client download -m gdc_manifest_rnaseq-tumor.txt -t token.txtThis protocol requires whole-exome-sequences from the normal samples , whole-exome-sequences from the tumor samples (wxs-tumor) and RNA-sequences from the tumor samples (rnaseq-tumor) from The Cancer Genome Atlas (TCGA) patients available in the Genomics Data Commons (GDC) data portal . The aliTiming: 1 min7.a.>cd data/BAM/wxs-normal/>find $(pwd) -type d | awk '!/logs/' | sed '1d' > input.listRun the following command from the directory that contains only folders of BAM files and do not contain other information except previous logs. For example, let us create an input.list for wxs-normal BAMs.b.>mv input.list wxs-normal_input.listRename the input.list as you wish.Variant calling using VarDictJava requires an input.list that contain path to folders of BAM files. Prepare an input list for wxs-normal, wxs-tumor and rnaseq-tumor data types, separately.8.The default output for this protocol is within the following directory.>cd STAR_protocols_GV_calling/analysis/•data.table (v.1.14.0)•dplyr (v1.0.6)•forcats (v0.5.1)•stringr (v1.4.0)•purrr (v0.3.4)•readr (v1.4.0)•tidyr (v1.1.3)•tibble (v3.1.2)•ggplot2 (v3.3.3)•Tidyverse (v.1.3.1)•survival (v3.2-13) R software and required R packages: While newer versions of some of these packages are available, this protocol was developed with R v4.0.3, RStudio v4.1.1, and the following versions of R packages:•Operating system: GNU/Linux•Memory: 100 GB (memory requirement depends on size of the dataset)•Processors: 1 required, 5 recommendedHardware Recommendations:The script in this protocol follows Simple Linux Utility for Resource Management (SLURM)-based schema and requires submission of the job to an online Linux cluster. The steps discussed below loop over each BAM file or the downstream output files that were generated. Therefore, it is easier to submit similar jobs as job arrays, which will create a number of independent jobs (corresponding to the defined number of tasks) and execute them simultaneously. It is common to load pre-installed software as an environmental module available on the Linux cluster. User needs to load the relevant modules on their cluster. This protocol expects that R, VarDictJava, SAMtools and BCFtools are installed on your local or online Linux cluster and loaded in the system PATH.Note: Run parts 1-5 separately for wxs-normal samples, wxs-tumor samples, and rnaseq-tumor samples.A typical workflow to call germline variants from the wxs-normal samples, wxs-tumor samples, and rnaseq-tumor samples (the three ‘data types’ in this protocol) requires six parts. The first part is variant calling using VarDictJava. The second part is the preprocessing of VCFs and extraction of genotype status of each variant for each sample. We then perform union of all the unique variants from all three data types. The third part is to calculate the sequencing coverage of the union of unique variants. The fourth part is to merge genotype status file and sequencing coverage file of each sample to determine the status of each variant after correction for low sequence coverage. The variant status at positions with fewer than ten reads for a given sample is changed to unknown. The fifth part is to combine variant status file from each sample to create a large multi-sample VCF file. Finally, in the sixth part, at positions at which variant status is listed as unknown in the wxs-normal samples (because of low sequence coverage) we will insert variant calls made from the corresponding wxs-tumor samples. If the variant status is still unknown in a normal sample but is called in the corresponding rnaseq-tumor sample, then the rnaseq-derived variant is inserted to create the final combined wxs-rnaseq variant call set.Timing: 2 h 1.a.>nano VariantCallingFrom_VarDict.shNavigate to ‘scripts’ directory and open VariantCallingFrom_VarDict.sh script in your favorite text editor.b.>#SBATCH -N 1 ##number of nodes>#SBATCH --cpus-per-task=5 ##number of cpus per task>#SBATCH -mem=100Gb ##memory requested per node in GB>#SBATCH -t 02:00:00 ##time limit hrs:min:sec>#SBATCH -p partition ##partition requested in the cluster>#SBATCH -A account ## account to be charged>#SBATCH -e slurm-%j.err ##standard error>#SBATCH -output slurm-%j.out ##standard output>#SBATCH -array=1-5 ##number of array jobs is from 1 to 5Note: Set the number of nodes, number of cpus, requested memory, and time in the job script according to the number of samples to be processed and resources available on the user online Linux cluster. The numbers designating the array jobs correspond to the numbers of the jobs in the input list.As the script in this protocol follows SLURM based schema. The structure of the job script is described below.c.>input_list="$protocol_dir/STAR_protocols_GV_calling/data/BAM/wxs -normal/wxs-normal_input.list"As different data types are processed, set the path in the script to the input list that corresponds to the data type See . For exad.>cd STAR_protocols_GV_calling/analysis/VCFs_from_VarDict/The default output for this step is stored in the following directory.e.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create directory ‘wxs-normal’, ‘wxs-tumor’, and ‘rnaseq-tumor’ in the ‘VCFs_from_VarDict’ directory to store VCFs for respective data types.f.>out_VCF="$protocol_dir/STAR_protocols_GV_calling/analysis/VCFs_fr om_VarDict/wxs-normal"Set output path in the script accordingly See . For exag.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/VariantCallingFrom_VarDict.shRun script using the following commandh.i.>#PBS -k o ##keep the job output>#PBS -N JobName ## name of the job>#PBS -l nodes=1 ##number of nodes>#PBS -l ncpus=5 ## number of cpus>#PBS -l mem=100Gb ## memory requested per node in Gb>#PBS -l walltime=02:00:00 ## time limit hrs:min:sec>#PBS -q queue ##partition requested in the cluster>#PBS -e torque-%j.err ##standard error>#PBS -o torque-%j.out ##standard output>#PBS -t 1-5 ##number of array jobs is from 1 to 5Change the structure of the job script in the VariantCallingFrom_VarDict.sh as described belowii.>qsub$protocol_dir/STAR_protocols_GV_calling/scripts/VariantCallingFrom_VarDict.shRun script using the following commandIf the user online Linux cluster supports Terascale Open-source Resource and QUEue Manager (TORQUE) system,Variant calling on wxs-normal BAMs, wxs-tumor BAMs, and rnaseq-tumor BAMs using VarDictJava. The settings were set as default except for requiring mapping quality greater than 30, base quality greater than 25, a minimum of 3 reads supporting a variant, minimum allele frequency of 5%, no structural variant calling, and the removal of duplicate reads.Timing: 30 min2.a.>cd STAR_protocols_GV_calling/analysis/PASS_variants/The default output for this step is stored in the following directory.b.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create directory for each data type in the ‘PASS_variants’ directory to store outputs for respective data type.c.>nano extract_pass_variants.sh>out_VCF="$protocol_dir/STAR_protocols_GV_calling/analysis/PASS_va riants/wxs-normal"Set output path in the extract_pass_variants.sh script for each data type accordingly. For example, let’s set the output path to store indexed passed variants for wxs-normal samplesd.>$protocol_dir/STAR_protocols_GV_calling/scripts/extract_pass_variants.shRun the script from the directory where VCFs from VarDictJava are located.Index VCF file and extract variants that received a PASS (shown in the “filter” column) in the indexed VCF file.3.Remove header section from VCFs. Run the script from the directory where indexed pass variants are located.>$protocol_dir/STAR_protocols_GV_calling/scripts/remove_header_VCF.sh4.Extract unique variants from all the passed variants VCF files. Run script from the directory where VCF files without header are located. This script will output allVCF_variants.txt file , and unique_variants.txt file . User may rename the unique_variants.txt for each data type accordingly.>$protocol_dir/STAR_protocols_GV_calling/scripts/extract_uniqueVariants.shNote: This bash script extract_uniqueVariants.sh includes R script extract_uniqueVariants.R. See ‘Potential 5.a.>cd STAR_protocols_GV_calling/analysis/genotype_status/The default output for this step is in the following directory.b.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create separate directory in the ‘genotype_status’ directory to store outputs for each data types.c.>nano extract_genotype_from_passedVariants.sh>out_VCF="$protocol_dir/STAR_protocols_GV_calling/analysis/genotyp e_status/wxs-normal"Set output path in the extract_genotype_from_passedVariants.sh script for each data type. For example, set output path for wxs-normal samplesd.>$protocol_dir/STAR_protocols_GV_calling/scripts/extract_genotype_from_passedVariants.shRun script from the indexed passed variants VCFs directoryExtract genotype status for each variant from each sample. Here, we extract chromosome, position, reference allele, observed alternate allele, mutation status, total depth, and variant depth of each variant from each VCF file.6.a.>library (data.table)Load required R packageb.>wxs_normal=fread>wxs_tumor=fread>rnaseq_tumor=freadLoad data in Rc.>wxs_normal <- setDF >wxs_tumor <- setDF (wxs_tumor)>rnaseq_tumor <- setDF (rnaseq_tumor)Convert data.table to data.framed.>variant_list = listConvert into liste.>union_variants= Reduce merge, by.y = c, all.x=TRUE, all.y=TRUE),  variant_list)Merge list based on chromosome, position, reference allele, and alternate allelef.>drop <- c>union_variants<-union_variantsDrop unwanted columnsg.>fwriteSave union variants in the analysis directoryh.>quitTerminate the current R session. User will be prompted to save the workspace. Please type ‘no’ if you wish not to save the workspace.i.>mv union_wxs_rnaseq_variants.txt union_wxs_rnaseq_variants.bedNow we are outside R. Convert the union_wxs_rnaseq_variants.txt file into .bed file in the Linux command lineMerge unique variants obtained from the wxs-normal, wxs-tumor and rnaseq-tumor data type by chromosome, position, reference allele and observed alternate allele. Here, we will keep the union of unique variants obtained from all the three data types and save them as union_wxs_rnaseq_variants.bed file. User can use R to merge the unique variants obtained from the three data typesTiming: 2 h7.a.>cd STAR_protocols_GV_calling/analysis/variant_coverageThe default output for this step is in the following directory.b.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create separate directory for each data type in the ‘variant_coverage’ directory to store output for each data type.c.>nano UnionVariantCoverageFrom_Samtools_depth.sh>out_depth="$protocol_dir/STAR_protocols_GV_calling/analysis/varia   nt_coverage/wxs-normal"Set output path in the script accordingly. For example, set output path for wxs-normal samplesd.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/UnionVariantCoverageFrom_Samtools_depth.shCRITICAL: union_wxs_rnaseq_variants.bed file and input.list that contain the location of the BAM files are required as input files. Set path of input files in the script. Set array jobs with number of jobs in the input list.Run the script using the following commandIn this step, we will calculate the sequencing coverage of union variants (listed in the union_wxs_rnaseq_variants.bed file) on all BAMs requiring mapping quality greater than 30.Timing: 5 min8.a.Input files: prepare a tab separated input_genotype_samdepth.txt file as shown in b.>cd STAR_protocols_GV_calling/analysis/variant_statusThe default output for this step is in the following directory.c.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create separate directory for each data type in the ‘variant_status’ directory to save output for each data type.d.>STAR_protocols_GV_calling/analysis/variant_status/wxs-normal/Set path in the out_filename column in the input_genotype_samdepth.txt file to save results for each data type. For example, set output path for wxs-normal samplese.Place the input_genotype_samdepth.txt file in the analysis directory.f.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/determineVariantStatus.shNote: The bash script determineVariantStatus.sh includes R script determineVariantStatus.R. Set array jobs with number of jobs in the input list.Run the script using the following commands from the analysis folder.Determine status of each variant using the genotype status and sequencing coverage of each variant in each sample. The variant status at positions fewer than ten reads for a given patient is changed to unknown. If the sequencing coverage of the variant is more than ten reads and no alternate allele is reported for that variant, then the status of that given variant is changed to Homozygous reference.Timing: 1 h9.a.>cd STAR_protocols_GV_calling/analysis/combined_variant_statusThe default output for this step is in the following directory.b.>mkdir wxs-normal>mkdir wxs-tumor>mkdir rnaseq-tumorUser should create a separate directory in the ‘combined_variant_status’ to save output for each data type.c.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/combineAllSamplesVariantStatus.shCRITICAL: The bash script combineAllSamplesVariantStatus.sh includes the R script combineAllSamplesVariantStatus.R. In the R script set full path for input files  and the output path where combined variants from all samples to be saved. The memory and time may vary based on the number of samples to be processed.Run the script using the following commandsCombine variant status file of each sample to create a multi-sample VCF which stores the variant status from all the samples.10.>file=combinedVariantStatusFromAllSamples.txt>head -1 $file > header.txt>sed '1d' $file > combined_variants_without_header.txt>split -d -l 50000 combined_variants_without_header.txt -a 4 – additional-suffix=.txt segment_The script above outputs a large table which contains variant status from all the samples. However, we need to do a bit of legwork to get our data into reasonable shape. First, the combined variants status file contains millions of variants, and it will take too much of memory and time for data pre-processing. Therefore, we will first split the large-combined variant status file into chunks of small files keeping the same number of columns but split based on fixed number of rows. In this file, each line corresponds to one variant.a.-d #add numeric suffix to split fileb.-l #number of lines in each of the smaller filesc.-a #suffix lengthd.--additional-suffixe.segment_ #split file prefixWith the above commands we did the following:11.Prepare input list for pre-processing>find $(pwd) -type f -name "segment∗" > input.txt>sort -V input.txt > input.list12.a.Input files: input.list and header.txt fileb.Output files: processed combined variant status file for each split filec.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/processCombinedVariants.shNote: The bash script processCombinedVariants.sh includes R script processCombinedVariants.R. Set array jobs with number of jobs in the input list.Run script from the folder where input.list and header.txt is storedThe pre-processing step takes each split file from the input.list and first removes the unwanted columns. Then for each variant it extracts the chromosome, base-position, the reference allele, the observed alternate allele and the variant status for each sample. From the fifth column onwards, the column names change to the respective sample names from TCGA. Modify the script if you have different sample names.13.Once preprocessing of each split file is finished, the next step is to merge them. Run the command below from the command line interface>awk 'NR == 1 || FNR > 1' processed_CombinedVariants_∗.txt >processed_CombinedVariantsFromAllSamples.txt14.Remove variants which are either unknown or Homozygous reference across all samples from the processed_CombinedVariantsFromAllSamples.txt file. Run the script below from the directory where the processed_CombinedVariantsFromAllSamples.txt file is located. The script will output the processed_CombinedPotentialSNPs.txt file.>Rscript$protocol_dir/STAR_protocols_GV_calling/scripts/extractPotentialSNPsFromCombinedVariants.RTiming: 40 min15.a.i.processed_CombinedPotentialSNPs.txt file obtained for wxs-normal, wxs-tumor and rnaseq-tumor data types.ii.Patient list with Case_ID and Sample_Barcode. If a matched tumor /normal sample have whole exome sequencing and associated RNA sequencing file, then the different datasets from the patient will have the same Case_ID. Prepare separate patient list for wxs-normal, wxs-tumor and rnaseq tumor samples. Set the column names of each file as suggested: wxs-normal: Case_ID and normal wxs-tumor: Case_ID and tumor rnaseq-tumor: Case_ID and rnaseqInput files:b.>cd STAR_protocols_GV_calling/data/samplesPatient list for this protocol is provided in the sample directory and can be accessed by the following commandc.>sbatch$protocol_dir/STAR_protocols_GV_calling/scripts/fillUnknownVariantsInNormalSamples.shCRITICAL: The bash script fillUnknownVariantInNormalSamples.sh includes R script fillUnknownVariantInNormalSamples.R. Set full path for the patient files and the processed_CombinedPotentialSNPs.txt file in the script.Run the script using the following command. Run the script from the analysis folder.d.i.combined_normal_unknown_filled.txtii.combined_normal_unknown_filled_arranged.txtiii.final_merged_wxs_rnaseq_variants.txt iv.final_variantsForAnnovar.txt The output from step six includes four filesThe goal of this step is to insert variant status at positions listed as unknown in the wxs-normal sample from the corresponding tumor sample. If the variant status is still unknown in the normal sample, then we will insert variant status of the rnaseq-derived variant from the corresponding rnaseq-tumor sample. This step will allow us to create a final combined wxs-rnaseq variant call set.16.Use Annovar software to perform functional annotation of germline variants such as in which gene the variant is located, whether variant is in the exonic, intronic, UTR, promoter, or splicing region of a gene or is in the intergenic region.17.Use Annovar to determine the allele frequencies of germline variants in whole-genome sequencing data from various ethnic populations such as listed in the GnomAD database.18.Calculate the allele frequency of the germline variant in this study using the following formula:19.Calculate the correlation of the allele frequencies in the four variant call sets  with each other and with the allele frequency from GnomAD using GGally R package. In our study, the allele frequencies in the combined data set from ∼500 patients gave a correlation coefficient of >0.9 with the allele frequencies in GnomAD.20.Determine whether the germline variant calls separate patients based on self-reported race using the PLINK software.21.Determine the genetic linkage between germline variants at different loci by performing Linkage Disequilibrium analysis.22.a.>libraryLoad required R packageb.i.Homozygous_ref: 0ii.Heterozygous: 1iii.Homozygous_alt: 2Variant genotype is encoded as character vector. Model genotype in ordinal scalec.>labels <- variant_genotypeSave variant genotype  for each patientd.>data <- read.csvDownload the survival data to the working directory see  and loade.>Risk_survival= survfit∼ labels,  data=data)>pvalue_Risk_survival= survdiff∼   labels, data=data, rho=0)>p.val= 1 - pchisq - 1)Survival analysisf.>par, cex.axis=1, cex.lab=1, cex.main=1,font.axis=1, font.lab=1, par(font=1))>plot, col=c,mark.time=TRUE, xlab="Follow up in months", ylab="Overallsurvival", lwd=1)>text))Plot the Kaplan-Meier curves >par ∼ labels + Age + Percent_aneuploidy + Histology + Grade + IDH_status + Mutation_count + Chr7gainORChr10loss + MGMT_promoter_status + Chr1OR19q_codeletion + Treatment_site + PC3, data=data)>summary(variant.cox)Principal component 3 (PC3)Multivariate Cox regression analysis : ConvertDetermine for a given germline variant whether there is a significant difference in survival outcome in the minor allele compared to the reference major allele . Here, we have compared the overall survival outcome between LGG patients (n=507) with different rs1131397 (Chr1-154965759-G-C) genotypes. See 23.The output for multivariate Cox regression analysis includes coefficient, hazard ratio, z-score and p value for each variant. As multiple variants are tested, it is important to correct the p values after Cox regression. In our study, 196,022 variants were tested. False discovery was performed through Bonferroni Correction.24.Determine whether a given germline variant is predictive of increase or decrease in tumor mutation burden and responsiveness to immune checkpoint chemotherapy .25.For germline variants that are significantly associated with a phenotype , determine whether the variant is associated with differences in gene expression of nearby genes by performing expression quantitative trait loci (eQTLs) analysis. For more details on other downstream analyses that can be performed with germline variants, please refer to .In the previous steps, we have identified germline variants from the whole-exome sequences and rna-sequences in the TCGA-LGG cohort. Several downstream analyses can be performed to evaluate the quality and clinical relevance of the identified germline variants.This protocol presents instructions to integrate the whole-exome sequencing datasets from normal and tumor samples and RNA sequencing dataset from the tumor samples of the TCGA-LGG cohort to determine the germline variants.Germline variants from the TCGA datasets are typically called in wxs-normal blood samples. This approach captures exonic region of the genome, allowing one to find the variants within genes. However, sequence (and variants) outside the exonic region, such as the promoters, introns, intron-exon boundaries and UTRs are also captured as a byproduct of whole-exome capture and are present in wxs data . In thisThere were some choices we made in our protocol that may be changed by a user. We use a minimal sequencing coverage threshold of 10 before a variant status is called because that gives us enough confidence that if a variant was present at that locus, it should have been sampled by then (and sampled at least 3 times). This is an arbitrary threshold and other users can set other thresholds if they so desire, but one has to balance the need for finding sufficient patients with a minor allele with the need to be 100% confident that the variant call is correct. In addition, we have a filter that a minimum of three reads should support the reported variant, and this too can be changed by the user. Note that we also removed reads that may have come from PCR duplicates, so that only one read with the identical start position is kept among all the duplicates. Finally, we turned off the calling of structural variants in this analysis so that no insertions, deletions, duplications, inversions, or large copy number variations were called.Our method has the benefit of determining the genotype status of a variant with low sequencing coverage in wxs-normal sample, by taking the genotype status of that variant from the corresponding wxs-tumor sample, where the variant at that position may be supported with higher sequencing coverage. However, if we are still unable to determine the genotype status, we insert the genotype status from the rnaseq counterparts. This method has not significantly affected the accuracy of the variant call in our study because the allele frequency calculated from the rnaseq tumor datasets were significantly positively correlated (r > 0.98) with the allele frequencies from the wxs-normal, wxs-tumor and combined-wxs-rnaseq variant call set. Importantly, allele frequencies in our study were significantly positively correlated with the allele frequency of germline variants listed in the population gnomAD database, supporting the reliability of our calls. Specifically, the allele frequency in GnomAD correlated (Pearson’s correlation coefficient) with wxs-normal variant set (r > 0.963), wxs-tumor variant set (r > 0.964), rnaseq-tumor variant set (r > 0.937) and combined-wxs-rnaseq variant set (r > 0.947) .Once the genotype status for each variant in each patient is identified, the user can check the survival outcome of minor allele compared to major allele for each variant. The Kaplan-Meier survival analysis for the three genotype  in variaThis protocol performs variant calling, pre-processing VCFs, calculating sequencing coverage, determining variant status, merging variant status from multi-samples, determining genotype of variant with low sequencing coverage from the respective wxs-normal, wxs-tumor and rnaseq-tumor BAM files. As these calculations are computationally intensive, we recommend running the protocol on a high-performance cluster. The memory and number of CPUs required to run each step See  may varyhttps://gdc.cancer.gov/access-data/obtaining-access-controlled-data.A token is required for downloading controlled test dataset See  from theDownloading GDC datasets and germline variant calling consumes considerable amount of time and computational resources. We recommend processing a small number of wxs and rnaseq BAM files to guide the computational resources requirement for the future analyses. This protocol requires user to modify the time needed in the job script for each step accordingly.This protocol follows SLURM based schema and requires knowledge of the bash and R scripting language. Newer users may need to learn some basic Linux commands such as ls, echo, cd, rm, mv, mkdir, find, nano, awk, sed, grep, pwd, bash, and sbatch used in this protocol to execute it fully.A common warning may be displayed for the bam index: BAM index file is older than BAM file (corresponding protocol step: >samtools index ∗.bamIt is a warning message for bam index file. It can be ignored if you are sure that the index file is up to date. You can create the more recent index file of the bam file using the command below and the warning message should go away.A common error may be displayed: These module(s) or extension(s) exist but cannot be loaded as requested  < value: ‘names’ attribute must be the same length as the vector  and Anindya Dutta (duttaa@uab.edu).Further information and requests for resources should be directed to and will be fulfilled by the technical and lead contacts, Divya Sahu (This study did not generate new unique reagents.


This study aimed to assess the predictive ability of 18F-FDG PET/CT radiomic features for MYCN, 1p and 11q abnormalities in NB.One hundred and twenty-two pediatric patients  with NB were retrospectively enrolled. Significant features by multivariable logistic regression were retained to establish a clinical model (C_model), which included clinical characteristics. 18F-FDG PET/CT radiomic features were extracted by Computational Environment for Radiological Research. The least absolute shrinkage and selection operator (LASSO) regression was used to select radiomic features and build models (R-model). The predictive performance of models constructed by clinical characteristic (C_model), radiomic signature (R_model), and their combinations (CR_model) were compared using receiver operating curves (ROCs). Nomograms based on the radiomic score (rad-score) and clinical parameters were developed.n = 86) and a test set (n = 36). Accordingly, 6, 8, and 7 radiomic features were selected to establish R_models for predicting MYCN, 1p and 11q status. The R_models showed a strong power for identifying these aberrations, with area under ROC curves (AUCs) of 0.96, 0.89, and 0.89 in the training set and 0.92, 0.85, and 0.84 in the test set. When combining clinical characteristics and radiomic signature, the AUCs increased to 0.98, 0.91, and 0.93 in the training set and 0.96, 0.88, and 0.89 in the test set. The CR_models had the greatest performance for MYCN, 1p and 11q predictions (P < 0.05).The patients were classified into a training set (The pre-therapy 18F-FDG PET/CT radiomics is able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus aiding tumor stage, risk stratification and disease management in the clinical practice. Neuroblastoma (NB), the most common extracranial solid pediatric tumor, accounts for about 8–10% of all childhood cancer and 12–15% of childhood cancer mortality . It is t123I-Metaiodobenzylguanidine (123I-MIBG) scintigraphy is a standard practice in the diagnosis of NB  in the diagnosis and follow-up of NB patients. 18F-FDG PET imaging has been reported to be equal or superior to 123I-MIBG scan for delineating NB disease extent in the chest, abdomen, and pelvis  pathologically confirmed NB; (2) age ≤ 18 years at diagnosis; (3) complete PET/CT imaging data; (4) complete clinical information; (5) no cancer therapy before PET/CT imaging; (6) complete MYCN amplification and 1p and 11q aberrations data. Subsequently, 17 cases were excluded because of unavailable MYCN, 1p and 11q information, and 122 patients were included in this study. These patients were randomly divided into training set and test set with a ratio of 7:3. This retrospective study was approved by Institutional Review Board of our hospital and the requirement of written informed consent was waived.MYCN amplification and 1p and 11q aberrations were determined using FISH from paraffin-embedded tissue obtained by biopsy or surgery at initial diagnosis according to the previously published method . AccordiPatient gender, age, neuron-specific enolase (NSE), serum ferritin (SF), lactate dehydrogenase (LDH), vanillylmandelic acid (VMA), homovanillic acid (HVA), maximum tumor diameter (MTD) in Ultrasound, and MTD in CT and/or MRI.All patients underwent whole body scan on the PET/CT scanner  in accordance with EANM guidelines , 20 and Univariate analysis was performed to compare the differences in clinical characteristics. Based on the selected characteristics, a clinical model (C-model) was established.n = 18), shape features (n = 14), gray level co-occurrence matrix (GLCM) features (n = 24), gray level run length matrix (GLRLM) features (n = 16), gray level size zone matrix (GLSZM) features (n = 16), neighboring gray tone difference matrix (NGTDM) features (n = 5), and gray level dependence matrix (GLDM) features (n = 14) were extracted from the original and the pre-processed images. The following methods were used in the imaging processing: wavelet filtering, square, square root, logarithm, exponential and gradient filtering  were obtained to assess the reliability of variables using the features extracted from the two sets of ROIs portrayed separately by two different nuclear medicine physicians in 24 out of the 122 patients with NB after 2 months. Because of imbalanced datasets, synthetic minority oversampling technique (SMOTE) was used to improve random oversampling in the training set. Least absolute shrinkage and selection operator (LASSO) was applied for variable selection and regularization in the training set. Predictive R_models were built by logistic regression and the radiomic score (rad-score) for each patient was computed based on the selected radiomic features. Additionally, the selected clinical characteristics combined with radiomics features were used to construct the combination model (CR_model). All models were built and trained in the training set, and the prediction performance was evaluated in the training and test sets. Ten-fold cross-validation was applied to prevent model overfitting in the training process. Receiver operating characteristic (ROC) curve and area under curve (AUC) were employed for the evaluation of the diagnostic performance in the training and test sets.www.python.org) and R . The Python packages of “sklearn,” “numpy,” and “pandas” were used for LASSO binary logistic regression and ROC curve; the “scipy” was for analyzing statistical properties; the “imblearn” was for SMOTE. The R package “rms” was employed to create nomograms. The t-test or Mann-Whitney U-test was applied for univariate analysis, and p < 0.05 with a 95% confidence interval was considered as statistical significance. AUC-ROC curve was calculated for evaluating the diagnostic performance of models. AUC ranging from 0.5 to 1.0 is commonly used as a measure of classifier performance. A value of 0.5 is equal to random guessing, while 1.0 means a perfect classifier.Statistical analyses were performed with Python . Between 1p-positive and negative cases, NSE, LDH, VMA, MTD in Ultrasound and MTD in CT/MRI were distinct (All p < 0.05). Between 11q-positive and negative cases, age, SF, LDH, VMA, and HVA were distinct (All p < 0.05)  .The total of 2,632 radiomic features were extracted from PET/CT images using pyradiomics. After assessing the robustness, 1,623 out of 2,632 features retained for model building, with intraclass correlation coefficients (ICC) > 0.75. In respect of C-model  constructed by logistic regression and trained in the training set, 4 clinical characteristics  were selected for MYCN prediction, with 3 characteristics  for 1p prediction and 3 characteristics  for 11q prediction. As for R_model (radiomics signature) establishment, 6 radiomic features were chosen for MYCN prediction, with 8 features for 1p prediction and 7 features for 11q prediction .In regard to CR_model  construction, eight features were chosen for MYCN prediction, which included 4 clinical characteristics  and 2 PET, 2 CT features , 3. ElevRad-scores were calculated by the following formula:Rad_score_MYCN = −2.6446+ 0.17750 × PET_wavelet-LLH_glszm_GrayLevelNonUniformity+ 0.88251 × PET_wavelet-HHH_glszm_SizeZoneNonUniformity– 0.00069 × CT_exponential_glrlm_LongRunEmphasis– 0.02217 × CT_wavelet-HHL_firstorder_MaximumRad_score_1p = 2.9612– 115.24 × PET_squareroot_ngtdm_Contrast– 0.29673 × PET_logarithm_firstorder_Minimum+ 0.04218 × PET_wavelet-LLH_glrlm_LongRunLowGrayLevelEmphasis+ 2.1217 × PET_wavelet-HHH_glszm_SmallAreaHighGrayLevelEmphasis– 5.5262 × PET_wavelet-HHH_glszm_LowGrayLevelZoneEmphasis– 5.1213 × CT_exponential_glszm_SmallAreaEmphasisRad_score_11q = −2217.3– 147.63 × PET_wavelet-LHL_gldm_DependenceNonUniformityNormalized– 0.41560 × CT_wavelet-LLL_glrlm_RunVariance– 0.59915 × CT_wavelet-LHL_firstorder_Median+ 58.736 × CT_wavelet-LHL_glcm_Imc1– 14.536 × CT_wavelet-HLL_glrlm_LowGrayLevelRunEmphasis+ 2232.9 × CT_wavelet-HHH_firstorder_Entropy.p-values of radiomic features are shown in p < 0.001). NB with MYCN, 1p and 11q positive had higher Rad-score than those with negative in both the training and test sets.The Nomogram score (Nomo_score) was calculated by the following formula :Nomo_score_MYCN = −0.7569 + 0.0064 × LDH + 2.4857 × Rad_score_MYCNNomo_score_1p = −0.5175 + 0.0017 × LDH + 1.0476 × Rad_score_1pNomo_score_11q = −0.3897 – 0.0020 × LDH + 0.0088 × SF + 1.6657 × Rad_score_11qThe nomogram was created based on the training set, which represented individualized prediction and visualized proportion of each factor .To evaluate the performance in predicting MYCN, 1p and 11q status, C_model, R_model and CR_model were compared. The predictive abilities of models  were shown in 18F-FDG PET/CT-based radiomics had an extremely important role in predicting MYCN amplification and 1p and 11q aberrations. In particular, CR_model was suggested to be the best model for the prediction of MYCN, 1p and 11q status with the largest AUCs in the training and test sets.Considering the well-established role of MYCN, 1p and 11q abnormalities in the prognosis of NB, identifying these events are crucial for risk stratification. This study provided three distinct forms of predictive models  for identifying MYCN and chromosomal abnormalities in a non-invasive way, demonstrating that pre-therapy Recently, clinical variables (such as LDH and SF) have been demonstrated to be prognostic biomarkers in large-scale studies, which suggested to reconsider utilizing LDH and SF as NB risk stratification factors , 23. In In this study, radiomic features were selected to construct CR_model for predicting MYCN, 1p and 11q abnormalities, including: PET_wavelet-LLH_glszm_GrayLevelNonUniformity, PET_wavelet-HHH_glszm_SizeZoneNonUniformity, CT_exponential_glrlm_LongRunEmphasis, CT_wavelet-HHL_firstorder_Maximum, PET_squareroot_ngtdm_Contrast, PET_logarithm_firstorder_Minimum, PET_wavelet-LLH_glrlm_LongRunLowGrayLevelEmphasis, PET_wavelet-HHH_glszm_SmallAreaHighGrayLevelEmphasis, PET_wavelet-HHH_glszm_LowGrayLevelZoneEmphasis, CT_exponential_glszm_SmallAreaEmphasis, PET_wavelet-LHL_gldm_DependenceNonUniformityNormalized, CT_wavelet-LLL_glrlm_RunVariance, CT_wavelet-LHL_firstorder_Median, CT_wavelet-LHL_glcm_Imc1, CT_wavelet-HLL_glrlm_LowGrayLevelRunEmphasis, and CT_wavelet-HHH_firstorder_Entropy. The majority of these features (12/16) were not derived from the primary image but from wavelet decomposition images, possibly because wavelet transformed features contained high-order information that may be more helpful for MYCN, 1p and 11q prediction. Previous studies have revealed the potential value of wavelet features in histologic subtype prediction and prognostic assessment , 26. In 123I-MIBG scan is the most frequently used imaging modality and is regarded as standard of care in patients with NB. In comparison with 18F-FDG PET/CT, 123I-MIBG scan is carried out over 2 days and the image quality is less ideal that could post a challenge to inexperienced physicians  the status of MYCN, 1p and 11q can be used for risk stratification, therapy selection, therapy response monitor and prognosis prediction.The potential clinical significance of the present study included: (1) radiomics based on pre-therapy This study had limitations. Small size cohort from single center may influence the generalized ability, sensitivity and specify of the predictive models. Therefore, prospective larger cohort from multi-center is necessary to validate the results and improve the reliability of models for MYCN, 1p and 11q predictions in NB.The models developed by the pre-therapy 18F-FDG PET/CT radiomic signature and clinical parameters are able to predict MYCN amplification and 1p and 11 aberrations in pediatric NB, thus risk stratification, disease management and guiding personalized malignancy therapy in the clinical practice.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Beijing Friendship Hospital, Capital Medical University. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.LQ, SY, and SZ made substantial contributions to study design, image acquisition, data analysis and interpretation, and new software creation in this work. SZ, HQ, WW, YK, LL, JL, and HZ contributed writing and/or revising the manuscript. JY and JL approved all versions to be published and were responsible for all aspects of this study. All authors contributed to the article and approved the submitted version.This study was supported by Capital Health Development Research Project (No. 2020-2-2025), National Natural Science Foundation of China , and National Key Research and Development Plan (No. 2020YFC0122000).LL was employed by the company Sinounion Medical Technology (Beijing) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The Carbon Ore Resources Database (CORD) is a working collection of 399 data files associated with carbon ore resources in the United States. The collection includes spatial/non-spatial, filtered, processed, and secondary data files with original data acquisition efforts focused on domestic coal resources. All data were acquired via open-source, online sources from a combination of 18 national, state, and university entities. Datasets are categorized to represent aspects of carbon ore resources, to include: Geochemistry, Geology, Infrastructure, and Samples. Geospatial datasets are summarized and analyzed by record and dataset density or the number of records or datasets per 400 square kilometer grid cells. Additionally, the “CORD Platform,” an ArcGIS Online geospatial dashboard web application, enables users to interact and query with CORD datasets. The CORD provides a single database and location for data-driven analytical needs associated with the utilization of carbon ore resources. Specifications TableValue of the Data•The Carbon Ore Resources Database (CORD) enables broader understanding and data-driven analyses of in-situ-, supply chain-, and consumer- based carbon resources, by providing a single location to efficiently access carbon ore resource datasets for a range of applications and end users. The systematized database organizes carbon ore data so it can easily be retrieved and analyzed.•Increased accessibility to systematized carbon ore resource datasets benefits research and development scientists, analysts, developers, economists, and engineers from various organizations. These entities include coal mining companies; power plant operators; government agencies; non-governmental organizations (NGOs); and natural resource managers.•Access to integrated, comprehensive carbon ore resource data are necessary for a range of applications, including optimizing coal production and deliveries to existing and new markets; mitigating the impacts of coal ash disposal, acid mine drainage, and greenhouse gas emissions; increase beneficial use of coal and coal by-products; and extraction of specific coal sources for carbon-based products and rare earth elements.•Broader applications include decision support for carbon management and policy, identifying opportunities for the development of coal and carbon management technologies.•Geospatial datasets within the CORD facilitate mapping and analysis using GIS (Geographic Information Systems) software.1https://edx.netl.doe.gov/dataset/cord) as two separate zipped folders, one in a geodatabase format and the other in a folder file structure.The Carbon Ore Resources Database (CORD) is a collection of 399 individual data files associated with carbon ore resources. The original data acquisition efforts focused on coal resources in the United States. Supplementary File 1 provides descriptions for each individual data file organized by category. Supplementary File 2 lists each file by name, category, data type (secondary or processed), coal filter field , data format type , spatial (raster), or table), available formats, source organization, link to the data download source, and publication citation (if available). The CORD can be downloaded from the NETL's Energy Data eXchange website The “Infrastructure network” category consists of nine processed data files and 90,634 records associated with coal resource infrastructure . CurrentThe “Samples integrated” category consists of two processed data files and 64,776 records associated with coal samples . This inThe “Samples original” category consists of 17 processed and secondary data files and 36,216 records associated with coal samples . This in•Samples_All (tab “Samples_All”)•Coal_Delivery_Pathways_2011_2016 •Coal_Mine_Deliveries_2011_2016 •Coal_Mine_Production_2011_2016 •Coal_Source_Regions_Production_Deliveries_2011_2016 •Power_Plant_ByProductsType_2011_2016 (tab “PowerPlant_ByProdType_2011_2016”)•Power_Plant_Consumption_2011_2016 (tab “PowerPlant_Cons_2011_2016”)•Power_Plant_Deliveries_and_ByProducts_2011_2016 (tab “PowerPlant_Del_ByProd_2011_2016”)•Power_Plant_Deliveries_by_Coal_Source_Region_2011_2016 (tab “PowerPlant_Del_by_CSR_2011_2016”)•Power_Plant_Stockpiles_2011_2016 (tab “PowerPlant_Stock_2011_2016”)A data dictionary (field names and descriptions) is provided in an Excel workbook for datasets that required additional processing and integration steps (Supplementary File 3), where each tab refers the following 10 datasets:•CORD_Data_Script1.py - This script takes an input folder path with CSV files and exports the files into a new folder with updated and modified attribute names.•CORD_Data_Script2.py - This script takes an input folder path with CSV files and exports the combined files into a new folder with an updated schema and all empty rows removed.•CORD_Field_map.csv - This CSV file provides the Mapping of the input data fields to the output data fields for the data conversion script CORD_Data_Script1.py.•CORD_Schema_combined.csv - This CSV file contains the combined schema for the data that is converted from multiple input CSV files to a single output CSV. It is used with the CORD_Data_Script2.py python script.Additionally, two python scripts and two CSV files associated with field mapping of the “Samples_All” table are provided in Supplementary File 4:2All data processing was performed using ESRI's ArcGIS ArcMap 10.7 software Secondary data files that did not require any processing were directly imported into the database. If data required filtering for explicit coal records, the field name used to filter the records was recorded Supplementary File 2, within the “Coal_filter_field”. Files labelled as “processed” in Supplementary File 2, required additional modification before including into the database. These data files include those in the “Infrastructure network”, “Samples original”, and “Samples integrated” categories. Each processed data file involves a unique method before integrating into the CORD. These methods are described by category and for each processed data file as necessary:Infrastructure network:Coal_Source_Regions_Production_Deliveries_2011_2016: This dataset was created from the original secondary data file “Coal_fields_USGS” https://www.eia.gov/coal/data/browser/). This North Appalachian Basin Region was further split to separate the Pennsylvania Anthracite Region. In total, 109 separate coal source regions were developed. Coal production and delivery quantities information from 2011 through 2016 were added by spatially joining (point features closest to each region) and summing values from the “Coal_Mine_Production_2011_2016” and “Coal_Mine_Deliveries_2011_2016”, respectively.Coal_Delivery_Pathways_2011_2016: This dataset was extracted from the “Page 5 Fuel Receipts and Costs” tab in the EIA-923 excel files Coal_Mine_Deliveries_2011_2016: This dataset was created from the “Coal_Delivery_Pathways_2011_2016” dataset, by dissolving on the MSHA unique identifying number, latitude, and longitude fields to obtain a single unique record for each mine. Delivery quantities were aggregated and summed for each and all years, including total delivery count from each mine. Point features were then created to represent individual mines. To obtain the name of the coal source region associated with each mine , the “Coal_Source_Region_2011_2016” dataset was spatially joined to the mine point features . The mine point features were then joined to the “Coal_Delivery_Pathways_2011_2016” dataset” by a temporary “Delivery_ID” field (deleted after join), to obtain the coal source region names within the deliveries dataset.Coal_Mine_Production_2011_2016: This was extracted from the EIA-7A excel files Power_Plant_Deliveries_by_Coal_Source_Region_2011_2016: This dataset was extracted from the “Coal_Delivery_Pathways_2011_2016”dataset. First, the “Coal_Delivery_Pathways_2011_2016” dataset was dissolved on the unique identifying number for power plants (“Plant_code”) and “Coal_Source_Region” fields to obtain a single unique record for each unique combination of power plant and coal source region. Delivery quantities were aggregated and summed for each and all years. The “Coal_Source_Region” field was then pivoted to add fields for coal delivery quantity totals for each unique combination of region and year. Point features were then created to represent individual power plants.Power_Plant_Deliveries_and_ByProducts_2011_2016: This dataset was extracted from the “Coal_Delivery_Pathways_2011_2016” dataset and the “8A Annual Byproduct Disposition” tab in the EIA-923 excel files Power_Plant_Consumption_2011_2016: This dataset was extracted from the “Page 1 Generation and Fuel Data” tab within the EIA-923 excel files Power_Plant_Stockpiles_2011_2016: This dataset was extracted from the “Page 2 Coal Stocks Data” tab within the EIA-923 excel files Power_Plant_ByProductsType_2011_2016: This dataset was extracted from the “8A Annual Byproduct Disposition” tab in the EIA-923 excel files disposition type]_2011_2016”) and overall total .Samples original:AK_Coal_samples_AGDB_USGS: The original data were extracted from the “Main”, “Chemistry”, and “Parameters” tables within the Alaska Geochemical Database (geodatabase format) AK_Holitina_Basin_Coal_samples_ADGGS: The original data were filtered for the term “coal” from the “ri2015–3-rock-eval-toc” CSV file AK_Jarvis_creek_coal_samples_ADGGS: The original data were extracted from the “pir2018–2-jarvis-creek-coal-proximate-analysis”, “pir2018–2-jarvis-creek-coal-ultimate-analysis”, and “pir2018–2-jarvis-creek-coal-rock-eval-ro-toc” CSV files AR_Coal_samples_AGS: The original data were copied from “https://www.geology.arkansas.gov/energy/coal-in-arkansas.html under the “Chemical Analysis” heading. The entire table was pasted into an excel spreadsheet, converted to a CSV and file geodatabase table.Coal_Ash_samples_NETL: The original data were extracted from the “Results (whole Sample Conc.)” and “Results (Ash Based Conc.)” tables or tabs within the “collected-samples-spreadsheet-v051515” excel file Coal_CCP_samples_USGS: The original data Coal_samples_NaCQI_USGS: The original data were extracted from the “Descriptive data”, “Oxide analyses”, “Whole Coal – Remnant moisture”, “Whole Coal – Dry basis”, and “Proximate-Ultimate analyses” tables or tabs within the “NaCQI” excel file Coal_samples_NGDB_USGS: The original data were extracted from the “GEODATA”, “NAA”, “OTHER”, and “UNKNOWN” dbf files from the National Geochemical Database for Rocks Coal_samples_PSU_Energy_Institute_Coal_Bank: The original data were downloaded from the PSU Energy Institute Coal Sample Bank  COALQUAL_USGS: The original data were extracted from the “COALQUAL” point feature class, “Oxide”, “Proximate_Ultimate”, and “Trace_Elements” and file geodatabase tables within the “COALQUAL” geodatabase Fly_Ash_Samples_Taggart: The original data were downloaded from the supplementary data associated with the Taggart et al. (2016) journal publication IL_Coal_quality_samples_ISGS: The original data was directly converted from the “coal-quality-nonconf” excel file IN_Coal_quality_samples_DB_IGWS: The dataset KY_Coal_quality_samples_KGS: The original data were extracted from the “borehole_12,182,019_19,372”, “physicalPropsAnaly”, “proximateAnaly”, “ultimateAnaly”, “wholeTraceAnaly”, and “ashTraceAnaly” excel files from the KGS. All but the 2 “TraceAnaly” tables were joined using the “sample_number” field and saved out into a single CSV file OK_Coal_quality_samples_OGS: The original data were extracted from the “Data-Coal-Analytical-Header” and “Data-Coal-Analytical-Data” excel files from the OGS PRB_Trace_elements_Stratigraphy_USGS: The original data was directly converted from the “waptg” shapefile WY_Coal_samples_WSGS: The original data was extracted from the “Appendix 1”, “Appendix 2”, “Appendix 3” tabs within the “ri-71-ap” excel file from the WSGS Samples integrated:Samples_All: This working dataset was created by manually mapping the fields within the “Samples original” datasets (CSV files) to a new single table schema (Supplementary File 4) in Excel. With the field mapping complete, the new table (CSV file) was populated with the new schema using several python scripts (Supplementary File 4). The resulting CSV file was converted into a file geodatabase table. Additionally, fields with all null values were deleted from the table.Samples_spatial: This working dataset was created by converting the available latitude and longitude coordinates from the “Samples_All” dataset to a point feature class .The authors declare that the work described is original and has not been submitted elsewhere for publication. No conflict of interest exists in this submission.Devin Justman: Methodology, Software, Investigation, Data curation, Writing – original draft, Visualization. Michael Sabbatino: Software, Data curation, Methodology, Writing – review & editing. Scott Montross: Conceptualization, Writing – review & editing. Scott Pantaleone: Writing – review & editing. Andrew Bean: Writing – review & editing. Kelly Rose: Conceptualization, Supervision, Project administration, Funding acquisition. Randal B. Thomas: Conceptualization, Supervision, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships which have or could be perceived to have influenced the work reported in this article.


Periophthalmus modestus, is one of the mudskippers, which are the largest group of amphibious teleost fishes, which are uniquely adapted to live on mudflats. Because mudskippers can survive on land for extended periods by breathing through their skin and through the lining of the mouth and throat, they were evaluated as a model for the evolutionary sea-land transition of Devonian protoamphibians, ancestors of all present tetrapods.The shuttles hoppfish (mudskipper), Ab initio and homology-based gene prediction identified 30,505 genes, of which 94% had homology to the 14 Actinopterygii transcriptomes and 89% and 85% to Pfam familes and InterPro domains, respectively. Comparative genomics with 15 Actinopterygii species identified 59,448 gene families of which 12% were only in P. modestus.A total of 39.6, 80.2, 52.9, and 33.3 Gb of Illumina, Pacific Biosciences, 10X linked, and Hi-C data, respectively, was assembled into 1,419 scaffolds with an N50 length of 33 Mb and BUSCO score of 96.6%. The assembly covered 117% of the estimated genome size (729 Mb) and included 23 pseudo-chromosomes anchored by a Hi-C contact map, which corresponded to the top 23 longest scaffolds above 20 Mb and close to the estimated one. Of the genome, 43.8% were various repetitive elements such as DNAs, tandem repeats, long interspersed nuclear elements, and simple repeats. We present the high quality of the first genome assembly and gene annotation of the shuttles hoppfish. It will provide a valuable resource for further studies on sea-land transition, bimodal respiration, nitrogen excretion, osmoregulation, thermoregulation, vision, and mechanoreception. Mudskippers are of the subfamily Oxudercinae and the family Oxudercidae, which was recently separated from the family Gobiidae , and theBoleophthalmus pectinirostris is useful as a draft genome.The family Oxudercidae has 10 genera and 42 species in FishBase. Among them, 4 species have been sequenced for the draft genome [Periophthalmus modestus  using Pacific Biosciences (PacBio) long-read, Illumina short-read, 10X linked read, and Hi-C sequencing. P. modestus [P. modestus can reach a length of 10 cm , in May 2018. Total DNA was isolated from the muscle of P. modestus using the DNeasy Blood & Tissue kit , following the manufacturer’s protocol.RRID:SCR_018059) with the same PCR primer set. The sequence data were edited and aligned using the ATGC 4.0 software .For species identification, the mitochondrial DNA cytochrome b gene barcode region was amplified using PCR as described in . The PCROrgans of specimens collected in July 2019 were manually dissected for eye, brain, liver, gut, muscle, and fin tissues, and total RNA was extracted from the dissected organs using the RNeasy Mini Kit . The RNA preparation was repeated 3 times, and then 3-replicate RNA samples were mixed and processed for RNA sequencing (RNA-seq) and isoform sequencing (Iso-seq).RRID:SCR_016386). For long-read sequencing, a 20-kb SMRTbell library  was prepared and sequenced on a PacBio Sequel  using 11 cells. To increase continuity in the genome assembly, we further produced linked reads and Hi-C reads. For linked-read sequencing, a 10X Chromium genome v2 library  was constructed and sequenced on an Illumna NovaSeq 6000 instrument. For long-range scaffolding, a Dovetail Hi-C library was prepared with Dovetail Hi-C Library kit  and sequenced on an Illumina NovaSeq 6000 instrument .For short-read sequencing, a paired-end library with insert sizes of 550 bp was constructed using Illumina TruSeq DNA Nano Prep Kit  and sequenced on an Illumina HiSeq 4000 instrument . For PacBio Iso-seq, 3 libraries of length 1–2, 2–3, and 3–6 kb were prepared from polyadenylated RNA according to the PacBio Iso-seq protocol . Six SMRT cells were run on a PacBio RS II system .For RNA-seq, paired-end libraries with insert size of 150 bp were prepared with the Truseq mRNA Prep kit  from total messenger RNA (mRNA), which was subsequently sequenced on an Illumina HiSeq 2500  [RRID:SCR_005491) [RRID:SCR_017014) [Trimmomatic   was used_005491)  generateRRID:SCR_018550) [RRID:SCR_017642) [RRID:SCR_018550) using PacBio long reads, and further polished using Pilon  [RRID:SCR_010910) [RRID:SCR_007936) and PCR duplicates were marked using Novosort [RRID:SCR_001228) [RRID:SCR_015008) [RRID:SCR_021173) [MiniASM  assemble_018550)  using Pa_017642)  with the_014731)  with the_010910)  using Il_010910) . Dovetai_010910)  linked tNovosort . Then Hi_001228)  accessed_021173)  purged hRRID:SCR_012954) [de novo library built by RepeatModeler  [RRID:SCR_021169) [Repeats were predicted in 3 ways. Tandem Repeats Finder  identifi_012954)  identifi_015027)  and withde novo, RNA-based and homology-based methods to carry out protein-coding gene prediction. For the de novo and RNA-based gene prediction, Illumina RNA-seq and PacBio Iso-seq datasets were used to generate 2 hint files. Tophat  [RRID:SCR_008992) [RRID:SCR_008417) [RRID:SCR_018964) [RRID:SCR_015661) [RRID:SCR_008417). GeneMark-ET  predicts genes with unsupervised training, whereas AUGUSTUS  predicts genes with supervised training based on intron and protein hints.We combined _013035)  aligned _013035)  correcte_008417)  generate_018964)  predicte_015661)  and AUGUP. modestus was aligned against the genes of 14 Actinopterygii genomes (RRID:SCR_011980) using TBLASTN  [RRID:SCR_020951) [RRID:SCR_016088) [ab initio prediction only when there was no conflict. Then the merged genes were removed if their coding sequences contained premature stop codons or were not supported by hints. InterProScan  [RRID:SCR_007701) [RRID:SCR_004726) [RRID:SCR_003352) [RRID:SCR_003412) [RRID:SCR_006969) [RRID:SCR_003457) [RRID:SCR_007952) [RRID:SCR_005493) [For the homology-based gene prediction, the assembly of  genomes  and vert_011822)  with an _020951)  clustere_016088) . Finally_005829)  annotate_007701) , Pfam (P_004726) , PIRSF (_003352) , PRINTS _006969) , PROSITE_003457) , SUPERFA_007952) , and TIGRRID:SCR_011809) [RRID:SCR_017075) [RRID:SCR_010835) [To predict non-coding genes, Infernal  , RNAmmer_017075) , and tRN_010835)  were useRRID:SCR_007839) [RRID:SCR_002811) enrichment was performed using the Fisher exact test and false discovery rate correction to identify functionally enriched GO terms among gene families relative to the “genome background,” as annotated by Pfam.Chromeister  performe_007839)  identifiRRID:SCR_011812) [RRID:SCR_017334) [RRID:SCR_006086) [RRID:SCR_000667) [RRID:SCR_005983) [For phylogenetic analysis and divergence time estimation, MUSCLE   aligned _017334)  filtered_006086)  construc_000667)  calculat_005983)  with thehttp://www.ncbi.nlm.nih.gov/) showed >99% sequence identity to P. modestus (GenBank accession No. DQ901364.1), 89% to Periophthalmus argentilineatus (AP019359.1), and 85% to Periophthalmus barbarus (KF415633.1).Comparison of cytochrome b sequences against the NCBI GenBank database  of Illumina, PacBio, 10X linked, and Hi-C data, respectively, for genome sequencing . The genP. modestus with 16 Actinopterygii species for repeats  with 25 types . RNAmmerP. modestus and Periophthalmus magnuspinnatus had the lowest score, meaning the closest pair. The second and third lowest score corresponded to the pair of Boleophthalmus pectinirostris with P. magnuspinnatus and P. modestus, respectively. Note that the scores of Danio rerio and Lepisosteus oculatus with the others were >0.99 because of the evolutionary distances.The 17 Actinopterygii genomes  were comAstatotilapia calliptera, Anabas testudineus, B. pectinirostris, D. rerio, Esox lucius, Gastersteus aculeatus, Kryptolebias marmoratus, Lates calcarifer, L. oculatus, Oryzias latipes, P. magnuspinnatus, P. modestus, Scophthalmus maximus, Tetraodon nigroviridis, and Takifugu rubireps, respectively. As shown in Fig. P. modestus had more families than the others and the number of common families in ≥13 species were dominant. The unique gene families of P. modestus were enriched in negative regulation of RNA metabolic and biosynthetic process, nucleic acid-templated, transcription DNA-templated, nucleobase-containing, biosynthetic process, and cellular macromolecule , respectively.All genomes had 281 single-copy orthologous gene families, which were used to construct a phylogenetic tree and estimate divergence time. The TimeTree database  was usedP. modestus with its common ancestor were 411 and 225, while those of P. magnuspinnatus, the closest genome, were 257 and 442, respectively .J.H.C. and Y.Y. conceived the concept; H.Y.S., S.J., and S.H.J. collected and classified the sample; J.H.C. and Y.Y. designed the experiments; J.Y.Y., S.H.B., Y.Y., and J.H.C. analyzed the genomic data; S.H.B. and Y.Y. deposited the data into NCBI; and H.Y.S., Y.Y., and J.H.C. wrote the manuscript. All authors reviewed the manuscript.giab089_GIGA-D-21-00233_Original_SubmissionClick here for additional data file.giab089_GIGA-D-21-00233_Revision_1Click here for additional data file.giab089_GIGA-D-21-00233_Revision_2Click here for additional data file.giab089_GIGA-D-21-00233_Revision_3Click here for additional data file.giab089_Response_to_Reviewer_Comments_Revision_2Click here for additional data file.giab089_Reviewer_1_Report_Original_SubmissionJiang Hu -- 9/1/2021 ReviewedClick here for additional data file.giab089_Reviewer_2_Report_Original_SubmissionYunyun Lv -- 9/7/2021 ReviewedClick here for additional data file.giab089_Reviewer_2_Report_Revision_1Yunyun Lv -- 11/9/2021 ReviewedClick here for additional data file.giab089_Supplemental_Figures_and_TablesClick here for additional data file.


This also falsely reduces PVPI and GEF. One of these studies suggested a correction formula for femoral venous access that markedly reduced the bias for GEDV. Consequently, the last PiCCO-algorithm requires information about the CVC, and correction for femoral access has been shown. However, two recent studies demonstrated inconsistencies of the last PiCCO algorithm using incorrected GEDV for PVPI, but corrected GEDV for GEF. Nevertheless, these studies were based on mathematical analyses of data displayed in a total of 15 patients equipped with only a femoral, but not with a jugular CVC.Transpulmonary thermodilution (TPTD) is used to derive cardiac output CO, global end-diastolic volume GEDV and extravascular lung water EVLW. To facilitate interpretation of these data, several ratios have been developed, including pulmonary vascular permeability index (defined as EVLW/(0.25*GEDV)) and global ejection fraction ((4*stroke volume)/GEDV). PVPI and GEF have been associated to the aetiology of pulmonary oedema and systolic cardiac function, respectively. Several studies demonstrated that the use of Therefore, this study compared PVPI_fem and GEF_fem derived from femoral TPTD to values derived from jugular indicator injection in 25 patients with both jugular and femoral CVCs.54 datasets in 25 patients were recorded. Each dataset consisted of three triplicate TPTDs using the jugular venous access as the gold standard and the femoral access with (PVPI_fem_cor) and without (PVPI_fem_uncor) information about the femoral indicator injection to evaluate, if correction for femoral GEDV pertains to PVPI_fem and GEF_fem.PVPI_fem_uncor was significantly lower than PVPI_jug . Similarly, PVPI_fem_cor was significantly lower than PVPI_jug . This is explained by the finding that PVPI_fem_uncor was not different to PVPI_fem_cor . This clearly suggests that correction for femoral CVC does not pertain to PVPI.GEF_fem_uncor was significantly lower than GEF_jug . By contrast, GEF_fem_cor was not different to GEF_jug . Furthermore, GEF_fem_cor was significantly higher than GEF_fem_uncor . This finding emphasizes that an appropriate correction for femoral CVC is applied to GEF_fem_cor.2/821mL/m2; 129%). This further emphasizes that GEF, but not PVPI is corrected in case of femoral indicator injection.The extent of the correction  for GEF and the relation of PVPI_jug/PVPI_fem_uncor  are in the same range as the ratio of GEDVI_fem_uncor/GEDVI_fem_cor (1056ml/mFemoral indicator injection for TPTD results in significantly lower values for PVPI and GEF. While the last PiCCO algorithm appropriately corrects GEF, the correction is not applied to PVPI. Therefore, GEF-values can be used in case of femoral CVC, but PVPI-values are substantially underestimated. PVPI_fem_uncor_form was calculated by multiplying PVPI_fem_uncor with the ratio 0.25*GEDVuncorrected/0.25*GEDVcorrected.PVPI_fem_uncor_form was corrected using the formula suggested for correction of femoral indicator injection derived GEDVI: GEDVI(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.


Scientific Reports7: Article number: 4362310.1038/srep43623; published online: 03022017; updated: 05042017This Article contains errors in Reference 11 which was incorrectly given as:http://www.who.int/influenza/human_animal_interface/faq_H7N9/en/ (2014).World Health Organisation. Frequently Asked Questions on human infection caused by the avian influenza A(H7N9) virus. URL The correct reference is listed below:http://www.who.int/influenza/human_animal_interface/virology_laboratories_and_vaccines/influenza_virus_infections_humans_feb14.pdf (2014).World Health Organisation. Influenza virus infections in humans (February 2014). URL


Podoviridae family and vB_KpnM_BIS47 of the Myoviridae family, which act against animal-pathogenic Klebsiella pneumoniae strains, were isolated from sewage plants in Poland. They possess double-stranded DNA genomes of 41,697 bp, 41,335 bp, 40,605 bp, and 147,443 bp, respectively.Four lytic phages, vB_KpnP_BIS33, vB_KpnP_IL33, and vB_KpnP_PRA33 of the  Klebsiella pneumoniae is a facultative anaerobic Gram-negative bacterium that causes a wide range of diseases in humans and in domestic and farm animals. As a pathogen, it can acquire resistance to carbapenems, which are often considered to be drugs of last resort (K. pneumoniae infections might be phage therapy. Lytic bacteriophages (phages) and/or their gene products, such as lysins, can easily be used as therapeutic agents against bacteria, as they are host specific and generally show no side effects.t resort . One proKlebsiella phages, vB_KpnP_BIS33, vB_KpnP_IL33, vB_KpnP_PRA33, and vB_KpnM_BIS47, were isolated by a standard enrichment method , Iława (IL33), and Prabuty (PRA33) in Poland. Genomic DNA from the phages was isolated with a modified phenol-chloroform extraction method  were identified also for putative homing endonuclease, helicase, DNA ligase, and DNA polymerase. Additionally, CDSs for terminase, head-tail connector protein, collar protein, putative tail tubular proteins, and tail fiber protein were found. Bacteriophages vB_KpnP_BIS33 and vB_KpnP_IL33 possess their own RNA polymerase, suggesting that they are related to phage T7. The CDSs for holin and therapeutically desired endolysin were detected in all four genomes. Lysogenization genes, such as site-specific integrases and repressors, were not identified in any of the four genomes. Additionally, genome annotation of vB_KpnM_BIS47 revealed 18 tRNA genes.Whole-genome sequence alignments with BLASTn  and moleNucleotide sequence accession numbers are shown in


ACS Synthetic Biology abstracts (Volumes 1 to 6 and Volume 7 issue 1) was performed as well as extraction from the following databases: Bionemo v6.0  [1], RegTransbase r20120406  [2], RegulonDB v9.0  [3], RegPrecise v4.0  [4] and Sigmol v20180122  [5].The aim of this dataset is to identify and collect compounds that are known for being detectable by a living cell, through the action of a genetically encoded biosensor and is centred on bacterial transcription factors. Such a dataset should open the possibility to consider a wide range of applications in synthetic biology. The reader will find in this dataset the name of the compounds, their InChI (molecular structure), the publication where the detection was reported, the organism in which this was detected or engineered, the type of detection and experiment that was performed as well as the name of the biosensor. A comment field is also provided that explains why the compound was included in the dataset, based on quotes from the reference publication or the database it was extracted from. Manual curation of  Specifications TableValue of the data•This dataset provides a basis for the development of new biosensing circuits for synthetic biology and metabolic engineering applications, e.g. the design of whole-cell biosensor, high-throughput screening experiments, dynamic regulation of metabolic pathways, transcription factor engineering or creation of sensing-enabling pathways.•This dataset provides a unique source of a broad number of compounds that can be detected and acted upon by a cell, increasing the possibility of orthogonal circuit design from the few usual compounds used in those applications.•The manually curated section provides information on where the biosensor has been first reported and successfully used, enabling the reader to select trustworthy information for his application of choice.•Detectable compounds can be searched by both by name and chemical similarity.•This dataset is an update of [10.6084/m9.figshare.3144715.v1].1ACS Synthetic Biology abstracts (Volumes 1 to 6 and Volume 7 issue 1) was performed as well as extraction from the following databases: Bionemo v6.0 The aim of this dataset is to identify and collect compounds that are known for being detectable by a living cell, through the action of a genetically encoded biosensor and is centred on bacterial transcription factors. The dataset should allow the synthetic biology community to consider a wide range of applications. The reader will find in this dataset the name of the compounds, their InChI (molecular structure), the publication where the detection was reported, the organism in which this was detected or engineered, the type of detection and experiment that was performed as well as the name of the biosensor. A comment field is also provided that explains why the compound was included in the dataset, based on quotes from the reference publication or the database it was extracted from. Manual curation of This dataset is available online on GitHub to allow for further updates as well as community contributions.2•Manual curation of ACS Synthetic Biology (Volume 1–6 and Volume 7 issue 1):ACS Synthetic Biology (Volume 1–6 and Volume 7 issue 1) were read and information relevant to this dataset was extracted from those abstracts. The aim of this manual curation was to establish a list of detectable compounds whose detection method was already successfully implemented in a synthetic circuit, providing a good basis for further implementation for synthetic biologists.All abstracts of •Bionemo v6.0The SQL request used to create this dataset is:SELECT DISTINCT substrate.id_substrate, minesota_code, name FROM substrateINNER JOIN complex_substrate ON complex_substrate.id_substrate=substrate.id_substrateINNER JOIN complex ON complex.id_complex=complex_substrate.id_complexWHERE activity='REG';•RegTransbase r20120406The SQL request used to create this dataset is:SELECT DISTINCT a.pmid, e.name, r.nameFROM regulator2effectors AS reINNER JOIN exp2effectors AS ee ON ee.effector_guid=re.effector_guidINNER JOIN dict_effectors AS e ON e.effector_guid=ee.effector_guidINNER JOIN regulators AS r ON r.regulator_guid=re.regulator_guidINNER JOIN articles AS a ON a.art_guid=ee.art_guidORDER BY e.name;RegTransbase was not maintained anymore at the time of writing of this manuscript.•RegulonDB v9.0The SQL request used to create this dataset is:SELECT c.conformation_id, c.final_state, e.effector_id, e.effector_name, tf.transcription_factor_id, tf.transcription_factor_name, p.reference_id, xdb.external_db_nameFROM effector AS eINNER JOIN conformation_effector_link AS mm_ce ON mm_ce.effector_id=e.effector_idLEFT JOIN conformation AS c ON c.conformation_id=mm_ce.conformation_idLEFT JOIN transcription_factor AS tf ON tf.transcription_factor_id=c.transcription_factor_idLEFT JOIN object_ev_method_pub_link AS x ON x.object_id=c.conformation_id OR x.object_id=tf.transcription_factor_id OR x.object_id=e.effector_idLEFT JOIN publication AS p ON p.publication_id=x.publication_idLEFT JOIN external_db AS xdb ON xdb.external_db_id=p.external_db_idWHERE c.interaction_type IS Null OR c.interaction_type!='Covalent';•RegPrecise v4.0The RegPrecise website was accessed (version v4.0) and all relevant data was extracted from the effector pages of the website.•Sigmol v20170216Quorum Sensing Signaling Molecule page. In the “detected by” column, we provide the class of signaling compounds the compound belongs to. The comment field reads ‘Extracted from Sigmol v20170216 – Uniq_QSSM_“number”’.Sigmol was accessed on 16/02/2017 and all effector data was retrieved from the unique 2.1In in vivo, unspecified or other), as well as the repartition of Biosensor type  in the full dataset and the manually curated dataset from ACS Synthetic Biology.


The URL at the end of the Materials and Methods section directing readers to download the data is incorrect.https://figshare.com/articles/Survey_of_biologist_s_computational_needs/4643641The correct URL is: