Preparing the Input Data¶

The input files of DROP are:

BAM files from RNA-seq (and their respective index files)
VCF files from either WES or WGS (and their respective index files). Only used for the MAE module
a configuration file containing the different parameters
a sample annotation file
a gene annotation file (gtf)
a reference genome file (fasta, and its respective index)

For more details see the Materials section of the DROP manuscript.

Config file¶

The config file is in YAML format. It is composed of general and module-specific parameters. In YAML, a variable can be of the following types: boolean, string, numeric, list and dictionary. They are declared by writing the variable name followed by a colon, a space, and the value, for example:

# A boolean is binary and can be true of false
boolean_var: true    # or false

# A string is a text. Quotation marks are not needed.
string_var: whatever text

# A numeric can be an integer or a real number. A dot separates a decimal.
numeric_var: 0.05

# A list is a collection of elements of the same type.
list_var:
  - element_1   # elements are indented
  - element_2

# A dictionary contains key-value pairs. It is a collection of multiple
#    elements where the key is a string and the value any type.
dictionary_var:
  key_1: value_1
  key_2: value_2

Now we describe the different parameters needed in DROP. When providing a path to a file or directory, please provide the full system path.

Global parameters¶

These parameters are applied to multiple modules and as a result should be consistent throughout the data you are analyzing

Parameter	Type	Description	Default/Examples
projectTitle	character	Title of the project to be displayed on the rendered HTML output	`Project 1`
htmlOutputPath	character	Full path of the folder where the HTML files are rendered	`/data/project1/htmlOutput`
indexWithFolderName	boolean	If true, the basename of the project directory will be used as prefix for the index.html file	`true`
genomeAssembly	character	Either hg19/hs37d5 or hg38/GRCh38, depending on the genome assembly used for mapping	`/data/project1`
sampleAnnotation	character	Full path of the sample annotation table	`/data/project1/sample_annotation.tsv`
root	character	Full path of the folder where the sub-directories processed_data and processed_results will be created containing DROP’s output files.	`/data/project1`
genome	character	Full path of a human reference genome fasta file	`/path/to/hg19.fa`
genome	dictionary	(Optional) Multiple fasta files can be specified when RNA-seq BAM files belong to different genome. assemblies (eg, ncbi, ucsc).	`ncbi: /path/to/hg19_ncbi.fa` `ucsc: /path/to/hg19_ucsc.fa`
geneAnnotation	dictionary	A key-value list of the annotation name (key) and the full path to the GTF file (value). More than one annotation file can be provided.	`anno1: /path/to/gtf1.gtf` `anno2: /path/to/gtf2.gtf`
hpoFile	character	Full path of the file containing HPO terms. If `null` (default), it reads it from our webserver. Refer to files-to-download	`/path/to/hpo_file.tsv`
tools	dictionary	A key-value list of different commands (key) and the command (value) to run them	`gatkCmd: gatk` `bcftoolsCmd: bcftools` `samtoolsCmd: samtools`

Export counts dictionary¶

These parameters are directly used by the exportCounts snakemake command. This section is used to designate which aberrant expression and aberrant splicing groups should be exported into datasets that can be shared. To avoid sharing sensitive data, only the canonical annotations as described by geneAnnotations are exported. Only the groups excluded by excludeGroups are not exported.

Parameter	Type	Description	Default/Examples
geneAnnotations	list	key(s) from the `geneAnnotation` parameter, whose counts should be exported	`- gencode34`
excludeGroups	list	aberrant expression and aberrant splicing groups whose counts should not be exported. If `null` all groups are exported.	`- group1`

Aberrant expression dictionary¶

These parameters are directly used by the aberrantExpression snakemake command. Aberrant expression groups must have at least 10 samples per group. To use external counts please see the Using External Counts section.

Parameter	Type	Description	Default/Examples
run	boolean	If true, the module will be run. If false, it will be ignored.	`true`
groups	list	DROP groups that should be executed in this module. If not specified or `null` all groups are used.	`- group1` `- group2`
minIds	numeric	A positive number indicating the minimum number of samples that a group needs in order to be analyzed. We recommend at least 50.	`1`
fpkmCutoff	numeric	A positive number indicating the minimum FPKM per gene that 5% of the samples should have. If a gene has less it is filtered out.	`1 # suggested by OUTRIDER`
implementation	character	Either ‘autoencoder’, ‘pca’ or ‘peer’. Methods to remove sample covariation in OUTRIDER.	`autoencoder`
zScoreCutoff	numeric	A non-negative number. Z scores (in absolute value) greater than this cutoff are considered as outliers.	`0`
padjCutoff	numeric	A number between (0, 1] indicating the maximum FDR an event can have in order to be considered an outlier.	`0.05`
maxTestedDimensionProportion	numeric	An integer that controls the maximum value that the encoding dimension can take. Refer to advanced-options.	`3`
yieldSize	numeric	An integer that sets the batch size for counting reads within a bam file. If memory issues persist lower the yieldSize.	`2000000`
genesToTest	character	Full path to a yaml file specifying lists of candidate genes per sample to test during FDR correction. See the documentation for details on the structure of this file.	`/path/to/genes_to_test.yaml`

Aberrant splicing dictionary¶

These parameters are directly used by the aberrantSplicing snakemake command. Each group must have at least 10 samples. This module uses FRASER to detect aberrant splicing. We recently developed an improved version of FRASER that uses the Intron Jaccard Index instead of percent spliced in and splicing efficiency to call aberrant splicing. To use this improved version, set the FRASER_version parameter to ‘FRASER2’. To use external counts, refer to the Using External Counts section.

Parameter	Type	Description	Default/Examples
run	boolean	If true, the module will be run. If false, it will be ignored.	`true`
groups	list	Same as in aberrant expression.	`# see aberrant expression example`
minIds	numeric	Same as in aberrant expression.	`1`
recount	boolean	If true, it forces samples to be recounted.	`false`
longRead	boolean	Set to true only if counting Nanopore or PacBio long reads.	`false`
keepNonStandardChrs	boolean	Set to true if non standard chromosomes are to be kept for further analysis.	`false`
filter	boolean	If false, no filter is applied. We recommend filtering.	`true`
minExpressionInOneSample	numeric	The minimal read count in at least one sample required for an intron to pass the filter.	`20`
quantileMinExpression	numeric	The minimum total read count (N) an intron needs to have at the specified quantile across samples to pass the filter. See `quantileForFiltering`.	`10`
quantileForFiltering	numeric	Defines at which percentile the `quantileMinExpression` filter is applied. A value of 0.95 means that at least 5% of the samples need to have a total read count N >= `quantileMinExpression` to pass the filter.	`0.95`
minDeltaPsi	numeric	The minimal variation (in delta psi) required for an intron to pass the filter.	`0.05`
implementation	character	Either ‘PCA’ or ‘PCA-BB-Decoder’. Methods to remove sample covariation in FRASER.	`PCA`
FRASER_version	character	Either ‘FRASER’ or ‘FRASER2’.	`FRASER`
deltaPsiCutoff	numeric	A non-negative number. Delta psi values greater than this cutoff are considered as outliers. Set to 0.1 when using FRASER2.	`0.3 # suggested by FRASER`
padjCutoff	numeric	Same as in aberrant expression.	`0.1`
maxTestedDimensionProportion	numeric	Same as in aberrant expression.	`6`
genesToTest	character	Same as in aberrant expression.	`/path/to/genes_to_test.yaml`

Mono-allelic expression (MAE) dictionary¶

These parameters are directly used by the mae snakemake command. MAE groups are not bound by a minimum number of samples, but require additional information in the sample annotation table.

Parameter	Type	Description	Default/Examples
run	boolean	If true, the module will be run. If false, it will be ignored.	`true`
groups	list	Same as in aberrant expression.	`# see aberrant expression example`
gatkIgnoreHeaderCheck	boolean	If true (recommended), it ignores the header warnings of a VCF file when performing the allelic counts	`true`
padjCutoff	numeric	Same as in aberrant expression.	`0.05`
allelicRatioCutoff	numeric	A number between [0.5, 1) indicating the maximum allelic ratio allele1/(allele1+allele2) for the test to be significant.	`0.8`
addAF	boolean	Whether or not to add the allele frequencies from gnomAD	`true`
maxAF	numeric	Maximum allele frequency (of the minor allele) cut-off. Variants with AF equal or below this number are considered rare.	`0.001`
maxVarFreqCohort	numeric	Maximum variant frequency among the cohort.	`0.05`
qcVcf	character	Full path to the vcf file used for VCF-BAM matching. Refer to files-to-download.	`/path/to/qc_vcf.vcf.gz`
qcGroups	list	Same as “groups”, but for the VCF-BAM matching	`# see aberrant expression example`
dnaRnaMatchCutoff	numeric	fraction (0-1) used to seperate “matching” samples and “non-matching” samples comparing the DNA and RNA data during QC	`0.85`

RNA Variant Calling dictionary¶

Calling variants on RNA-seq data may be useful for researchers who do not have access to variant calls from genomic data. While variant calling from WES and WGS technologies may be more traditional (and reliable), variant calling from RNA-Seq data can provide additional evidence for the underlying causes of aberrant expression or splicing. The RNA variant calling process uses information from multiple samples (as designated by the groups variable) to improve the quality of the called variants. However, the larger the group size, the more costly the computation is in terms of time and resources. To prioritize accuracy, include many samples in each DROP_GROUP, and to prioritize speed up computation, separate samples into many groups. Additionally, certain vcf and bed files must be included to further boost the quality of the called variants (refer to files-to-download).

Parameter	Type	Description Default/Examples
run	boolean	If true, the module will be run. If false, it will be ignored.	`true`
groups	list	Same as in aberrant expression.	`# see aberrant expression example`
highQualityVCFs	list	File paths where each item is the path to a vcf file. Each vcf file describes known high quality variants, which are used to recalibrate sequencing scores. Refer to files-to-download	`- known_indels.vcf` `- known_SNPs.vcf`
dbSNP	character	Location of the dbSNP `.vcf` file. This improves both recalibrating sequencing scores, as well as variant calling precision. Refer to files-to-download	`path/to/dbSNP.vcf`
repeat_mask	character	Location of the RepeatMask `.bed` file. Refer to files-to-download	`path/to/RepeatMask.bed`
createSingleVCF	boolean	If `true`, splits the multi-sample VCF file into individual sample VCF files. This only subsets the larger vcf sample.	`true`
addAF	boolean	Whether or not to add the allele frequencies from gnomAD	`true`
maxAF	numeric	Maximum allele frequency (of the minor allele) cut-off. Variants with AF equal or below this number are considered rare.	`0.001`
maxVarFreqCohort	numeric	Maximum variant frequency among the cohort.	`0.05`
minAlt	numeric	Integer describing the minimum required reads that support the alternative allele. We recommend a minimum of 3 if further filtering on your own. 10 otherwise.	`3`
hcArgs	character	String describing additional arguments for GATK haplocaller. Refer to advanced-options.	`""`
yieldSize	numeric	An integer that sets the batch size for counting reads within a vcf file. If memory issues persist during `batch_data_table` lower the yieldSize.	`100000`

Modularization of DROP¶

DROP allows to control which modules to run via the run variable in the config file. By default, each module is set to run: true. Setting this value to false stops a particular module from being run. This will be noted as a warning at the beginning of the snakemake run, and the corresponding module will be renamed in the Scripts/ directory.

For example, if the AberrantExpression module is set to false, the Scripts/AberrantExpression/ directory will be renamed to Scripts/_AberrantExpression/ which tells DROP not to execute this module.

Creating the sample annotation table¶

For a detailed explanation of the columns of the sample annotation, please refer to Box 3 of the DROP manuscript. Although some information has been updated since puplication, please use this documentation as the preferred syntax/formatting.

Each row of the sample annotation table corresponds to a unique pair of RNA and DNA samples derived from the same individual. An RNA assay can belong to one or more DNA assays, and vice-versa. If so, they must be specified in different rows. The required columns are RNA_ID, RNA_BAM_FILE and DROP_GROUP, plus other module-specific ones (see DROP manuscript).

The following columns describe the RNA-seq experimental setup: PAIRED_END, STRAND, COUNT_MODE and COUNT_OVERLAPS. They affect the counting procedures of the aberrant expression and splicing modules. For a detailed explanation, refer to the documentation of HTSeq.

To run the MAE module, the columns DNA_ID and DNA_VCF_FILE are needed. MAE can not be run in samples using external counts as we need to use the RNA_BAM_FILE to count reads supporting each allele of the heterozygous variants found in the DNA_VCF_FILE.

In case RNA-seq BAM files belong to different genome assemblies (eg, ncbi, ucsc), multiple reference genome fasta files can be specified. Add a column called GENOME that contains, for each sample, the key from the genome parameter in the config file that matches its genome assembly (eg, ncbi or ucsc).

The sample annotation file must be saved in the tab-separated values (tsv) format. The column order does not matter. Also, it does not matter where it is stored, as the path is specified in the config file. Here we provide some examples on how to deal with certain situations. For simplicity, we do not include all possible columns in the examples.

Using External Counts¶

DROP can utilize external counts for the aberrantExpression and aberrantSplicing modules which can enhance the statistical power of these modules by providing more samples from which we can build a distribution of counts and detect outliers. However this process introduces some particular issues that need to be addressed to make sure it is a valuable addition to the experiment.

In case external counts are included, add a new row for each sample from those files (or a subset if not all samples are needed). Add the columns: GENE_COUNTS_FILE (for aberrant expression), GENE_ANNOTATON, and SPLICE_COUNTS_DIR (for aberrant splicing). These columns should remain empty for samples processed locally (from RNA_BAM).

Aberrant Expression¶

Using external counts for aberrant expression forces you to use the exact same gene annotation for each external sample as well as using the same gene annotation file specified in the config file Global parameters section. This is to avoid potential mismatching on counting, 2 different gene annotations could drastically affect which reads are counted in which region drastically skewing the results.

The user must also use special consideration when building the sample annotation table. Samples using external counts need only RNA_ID which must exactly match the column header in the external count file DROP_GROUP, GENE_COUNTS_FILE, and GENE_ANNOTATION which must contain the exact key specified in the config. The other columns should remain empty.

Using exportCounts generates the sharable GENE_COUNTS_FILE file in the appropriate ROOT_DIR/Output/processed_results/exported_counts/ sub-directory.

Aberrant Splicing¶

Using external counts for aberrant splicing reduces the number of introns processed to only those that are exactly the same between the local and external junctions. Because rare junctions may be personally identifiable the exportCounts command only exports regions canonically mentioned in the gtf file. As a result, when merging the external counts with the local counts we only match introns that are exact between the 2 sets, this is to ensure that if a region is missing we don’t introduce 0 counts into the distribution calculations.

The user must also use special consideration when building the sample annotation table. Samples using external counts need only RNA_ID which must exactly match the column header in the external count file DROP_GROUP, and SPLICE_COUNTS_DIR. SPLICE_COUNTS_DIR is the directory containing the set of 5 needed count files. The other columns should remain empty.

Using exportCounts generates the necessary files in the appropriate ROOT_DIR/Output/processed_results/exported_counts/ sub-directory

SPLICE_COUNTS_DIR should contain the following:

k_j_counts.tsv.gz
k_theta_counts.tsv.gz
n_psi3_counts.tsv.gz
n_psi5_counts.tsv.gz
n_theta_counts.tsv.gz

Publicly available DROP external counts¶

You can find different sets of publicly available external counts to add to your analysis on our github page

If you want to contribute with your own count matrices, please contact us: yepez at in.tum.de.

External count examples¶

In case counts from external matrices are to be integrated into the analysis, the sample annotation must be built in a particular way A new row must be added for each sample from the count matrix that should be included in the analysis. The RNA_ID must match the column header of the external files, the RNA_BAM_FILE must not be specified. The DROP_GROUP of the local and external samples that are to be analyzed together must be the same. For aberrant expression, the GENE_ANNOTATION of the external counts and the key of the geneAnnotation parameter from the config file must match.

This example will use the DROP_GROUP BLOOD_AE for the aberrant expression module (containing S10R, EXT-1R, EXT-2R) and the DROP_GROUP BLOOD_AS for the aberrant expression module (containing S10R, EXT-2R, EXT-3R)

RNA_ID	DNA_ID	DROP_GROUP	RNA_BAM_FILE	GENE_COUNTS_FILE	GENE_ANNOTATION	SPLICE_COUNTS_DIR
S10R	S10G	BLOOD_AE,BLOOD_AS	/path/to/S10R.BAM
EXT-1R		BLOOD_AE		/path/to/externalCounts.tsv.gz	gencode34
EXT-2R		BLOOD_AE,BLOOD_AS		/path/to/externalCounts.tsv.gz	gencode34	/path/to/externalCountDir
EXT-3R		BLOOD_AS				/path/to/externalCountDir

Limiting FDR correction to subsets of genes of interest¶

In addition to returning transcriptome-wide results, DROP provides the option to limit the FDR correction to user-provided genes of interest in the aberrantExpression and aberrantSplicing modules. These could e.g. be all OMIM genes. It is also possible to provide sample-specific genes such as all genes with a rare splice region variant for each sample. To use this feature, a YAML file containing the set(s) of genes to test (per sample or for all samples) needs to be specified in the genesToTest parameter of the aberrantExpression and aberrantSplicing modules in the config file. If no file is provided, only transcriptome-wide results will be reported. Otherwise, the results tables of the aberrantExpression and aberrantSplicing modules will additionally report aberrant events passing the cutoffs based on calculating the FDR with respect to the genes in the provided lists.

Creating the YAML file specifying subsets of genes to test¶

The file containing the list of genes (HGNC symbols) to be tested must be a YAML file, where the variable names specify the name of each set of tested genes. In the output of DROP, this name will be used to identify the set in the results table. Each set can either be a list of genes, in which case the set will be tested for all samples. Alternatively (and additionally), sample-specific sets can be created by giving the RNA_ID of the sample for which the set should be used as the name (see example below). This YAML file can be created in R using yaml::write_yaml(subsetList, filepath), where subsetList is a named list of named lists containing the sets of genes to test. In the following example, the name of the global set of genes is Genes_to_test_on_all_samples and the name of the sample-specific set is Genes_with_rare_splice_variants:

Example content of /path/to/genes_to_test.yaml:

Genes_to_test_on_all_samples:
  - BTG3
  - GATD3B
  - PKNOX1
  - APP
  - RRP1
  - WRB-SH3BGR
  - SLC19A1
Genes_with_rare_splice_variants:
  sample1:
  - ABCG1
  - MCOLN1
  - SLC45A1
  sample2:
  - CLIC6
  - ATP5PO
  - WRB
  - ETS2
  - HLCS

Files to download¶

The following files can be downloaded from our public repository.

1. VCF file containing different positions to be used to match DNA with RNA files. The file name is qc_vcf_1000G_{genome_build}.vcf.gz. One file is available for each genome build (hg19/hs37d5 and hg38/GRCh38). Download it together with the corresponding .tbi file. Indicate the full path to the vcf file in the qcVcf key in the mono-allelic expression dictionary. This file is only needed for the MAE module. Otherwise, write null in the qcVcf key.

2. Text file containing the relations between genes and phenotypes encoded as HPO terms. The file name is hpo_genes.tsv.gz. Download it and indicate the full path to it in the hpoFile key. The file is only needed in case HPO terms are specified in the sample annotation. Otherwise, write null in the hpoFile key.

3. For the rnaVariantCalling module known high quality variants are needed to calibrate variant and sequencing scores to be used in the rnaVariantCalling module in the highQualityVCF config parameter. These and the associated .tbi indexes can be downloaded for hg19 at our public repository and for hg38 through the Broad Institute’s resource bundle.

hg19

Mills_and_1000G_gold_standard.indels.hg19.sites.chrPrefix.vcf.gz
1000G_phase1.snps.high_confidence.hg19.sites.chrPrefix.vcf.gz

hg38

Mills_and_1000G_gold_standard.indels.hg38.vcf.gz
Homo_sapiens_assembly38.known_indels.vcf.gz

We also recommend using the variants from dbSNP which is quite large. You can download them and their associated .tbi indexes from NCBI

follow links for the current version (human_9606/VCF/00-All.vcf.gz) or older assemblies (eg. human_9606_b151_GRCh37p13/VCF/00-All.vcf.gz)

The repeat masker file is used to filter hard-to-call regions. In general, this removes false-positive calls, however, some targeted and known splicing defects lie within these repeat regions. Understand that this filter is labeled Mask in the result VCF files. You can download the repeat mask and associated .idx on our public repository. for the repeat_mask config parameter.

Example of RNA replicates¶

RNA_ID	DNA_ID	DROP_GROUP	RNA_BAM_FILE	DNA_VCF_FILE
S10R_B	S10G	BLOOD	/path/to/S10R_B.BAM	/path/to/S10G.vcf.gz
S10R_M	S10G	MUSCLE	/path/to/S10R_M.BAM	/path/to/S10G.vcf.gz

Example of DNA replicates¶

RNA_ID	DNA_ID	DROP_GROUP	RNA_BAM_FILE	DNA_VCF_FILE
S20R	S20E	WES	/path/to/S20R.BAM	/path/to/S20E.vcf.gz
S20R	S20G	WGS	/path/to/S20R.BAM	/path/to/S20G.vcf.gz

Example of a multi-sample vcf file¶

RNA_ID	DNA_ID	DROP_GROUP	RNA_BAM_FILE	DNA_VCF_FILE
S10R	S10G	WGS	/path/to/S10R.BAM	/path/to/multi_sample.vcf.gz
S20R	S20G	WGS	/path/to/S20R.BAM	/path/to/multi_sample.vcf.gz

Advanced options¶

A local copy of DROP can be edited and modified. For example, the user might want to add new plots to the Summary scripts, add additional columns to the results tables, or modify the number of threads allowed for a script.

Note

DROP needs to be installed from a local directory using pip install -e <path/to/drop-repo> so that any changes in the code will be available in the next pipeline run Any changes made to the R code need to be updated with drop update in the project directory.

The aberrant expression and splicing modules use a denoising autoencoder to correct for sample covariation. This process reduces the fitting space to a dimension smaller than the number of samples N. The encoding dimension is optimized. By default, the maximum value in the search space is N/3 for the aberrant expression, and N/6 for the aberrant splicing case. The user can specify the denominator with the parameter maxTestedDimensionProportion.

DROP allows that BAM files from RNA-seq from samples belonging to the same DROP_GROUP were aligned to different genome assemblies from the same build (e.g., some to ucsc and others to ncbi, but all to either hg19 or hg38). If so, for the aberrant expression and splicing modules, no special configuration is needed. For the MAE and rnaVariantCalling modules, the different fasta files must be specified as a dictionary in the genome parameter of the config file, and, for each sample, the corresponding key of the genome dictionary must be specified in the GENOME column of the sample annotation. In additon, DROP allows that BAM files from RNA-seq were aligned to one genome assembly (eg ucsc) and the corresponding VCF files from DNA sequencing to another genome assembly (eg ncbi). If so, the assembly of the reference genome fasta file must correspond to the one of the BAM file from RNA-seq.

Specific haplotype parameters can be denoted in the config file to further customize the RNA-seq variant calling. The different available parameters can be found in the HaplotypeCaller GATK documentation. One example for the value in the config file would be “–assembly-region-padding 100 –base-quality-score-threshold 18”.