iAnnotateSV: Annotation of structural variants detected from NGS

Author

Ronak H Shah

Contributors

Gowtham Jayakumaran and Ian Johonson

Contact

rons.shah@gmail.com

Source code

http://github.com/rhshah/iAnnotateSV

License

Apache License 2.0

iAnnotateSV is a Python library and command-line software toolkit to annotate and visualize structural variants detected from Next Generation DNA sequencing data. This works for majority is just re-writing of a tool called dRanger_annotate written in matlab by Mike Lawrence at Broad Institue. But it also has some additional functionality and control over the annotation w.r.t the what transcripts to be used for annotation. It is designed for use with hybrid capture, including both whole-exome and custom target panels, and short-read sequencing platforms such as Illumina.

Citation

We are in the process of publishing a manuscript describing iAnnotateSV as part of the Structural Variant Detection framework. If you use this software in a publication, for now, please cite our website iAnnotateSV.

Acknowledgements

I would like to thanks Mike Lawrence from Broad Institute for sharing his code and Michael Berger for his insights into the dRanger_annotate tool.

Required Packages

We require that you install:

python

v2.7.15

pandas

v0.24.2

biopython

v1.76

Pillow

v6.2.1

openpyxl

v2.6.4

reportlab

v3.5.2

coloredlogs

v14.0

Quick Usage

If you know python I have created a small test script in /iAnnotateSV/test directory it runs a test on existing code and compares the result with the output file.

Else To Run:

• If you want to run with default options:

python /path/to/iAnnotateSV.py -i svFile.txt -ofp outputfilePrefix -o /path/to/output/dir -r hg19 -d 3000

• If you want to run with your own transcripts:

python path/to/path/to/iAnnotateSV.py -i svFile.txt -ofp outputfilePrefix -o /path/to/output/dir -r hg19 -d 3000 -c canonicalTranscripts.txt

• If you want to run with your own transcripts & make plots (making plots is a test module only):

python path/to/iAnnotateSV.py -i svFile.txt -ofp outputfilePrefix -o /path/to/output/dir -r hg19 -d 3000 -c canonicalTranscripts.txt -u uniprot.txt -p

usage: iAnnotateSV.py [options]

Annotate SV based on a specific human reference

optional arguments:
-h, --help            show this help message and exit
-v, --verbose         make lots of noise [default]
-r hg19, --refFileVersion hg19
                        Which human reference file to be used, hg18,hg19 or
                        hg38
-rf hg19.sv.table.txt, --refFile hg19.sv.table.txt
                        Human reference file location to be used
-ofp test, --outputFilePrefix test
                        Prefix for the output file
-o /somedir, --outputDir /somedir
                        Full Path to the output dir
-i svfile.txt, --svFile svfile.txt
                        Location of the structural variants file to annotate
-d 3000, --distance 3000
                        Distance used to extend the promoter region
-a, --autoSelect      Auto Select which transcript to be used[default]
-c canonicalExons.txt, --canonicalTranscripts canonicalExons.txt
                        Location of canonical transcript list for each gene.
                        Use only if you want the output for specific
                        transcripts for each gene.
-p, --plotSV          Plot the structural variant in question (very primitive)
-u uniprot.txt, --uniprotFile uniprot.txt
                        Location of UniProt list contain information for
                        protein domains. Use only if you want to plot the
                        structural variant
-rr RepeatRegionFile.tsv, --repeatFile RepeatRegionFile.tsv
                        Location of the Repeat Region Bed File
-dgv DGvFile.tsv, --dgvFile DGvFile.tsv
                        Location of the Database of Genomic Variants Bed File
-cc CosmicConsensus.tsv, --cosmicConsensusFile CosmicConsensus.tsv
                        Location of the Cosmic Consensus TSV file
-cct cosmic_fusion_counts.tsv, --cosmicCountsFile cosmic_fusion_counts.tsv
                        Location of the Cosmic Counts TSV file

Input file format is a tab-delimited file containing:

chr1 pos1 str1 chr2 pos2 str2

as the header and where:

• chr1: Its the chromosome name for first break point [1,2,3,4,5,6,7 etc..],
• pos1: Its the chromosome loaction for first break point [1-based],
• str1: Its the read direction for the first break point [0=top/plus/reference, 1=bottom/minus/complement],
• chr2: Its the chromosome name for second break point [1,2,3,4,5,6,7 etc..],
• pos2: Its the chromosome loaction for second break point [1-based],
• str2: Its the read direction for the second break point [0=top/plus/reference, 1=bottom/minus/complement],

Output file will is a tab-delimited file containing:

chr1 pos1 str1 chr2 pos2 str2 gene1 transcript1 site1 gene2 transcript2 site2 fusion

as the header and where:

• chr1 : Its the chromosome name for first break point [1,2,3,4,5,6,7 etc..],
• pos1 : Its the chromosome loaction for first break point [1-based],
• str1 : Its the read direction for the first break point [0=top/plus/reference, 1=bottom/minus/complement],
• chr2 : Its the chromosome name for second break point [1,2,3,4,5,6,7 etc..],
• pos2 : Its the chromosome loaction for second break point [1-based],
• str2 : Its the read direction for the second break point [0=top/plus/reference, 1=bottom/minus/complement],
• gene1 : Gene for the first break point,
• transcript1 : Transcript used for the first breakpoint,
• site1 : Explanation of the site where the first breakpoint occurs [Example=>Intron of EWSR1(+):126bp after exon 10],
• kinasedomain1 : Explanation of the site where the first breapoint involves a Kinase Domain or not[Example=>Partial Kinase Domain Included]
• gene2 : Gene for the second break point,
• transcript2 : Transcript used for the second breakpoint,
• site2 : Explanation of the site where the second breakpoint occurs [Example=>Intron of ERG(-):393bp after exon 4],
• kinasedomain2 : Explanation of the site where the second breapoint involves a Kinase Domain or not[Example=>Partial Kinase Domain Included]
• fusion : Explanation if the evnet leads to fusion or not. [Example=>Protein Fusion: in frame {EWSR1:ERG}]
• Cosmic_Fusion_Counts : Number of Counts for the Events from Cosmic Fusion Results
• repName-repClass-repFamily:-site1 : Repeat Region Annotation for site 1
• repName-repClass-repFamily:-site2 : Repeat Region Annotation for site 2
• CC_Chr_Band : Cosmic Cancer Census Chromosome Band
• CC_Tumour_Types(Somatic) : Cosmic Cancer Census Tumor Type in Somatic Samples
• CC_Cancer_Syndrome : Cosmic Cancer Census Cancer Syndrome the genes are related to.
• CC_Mutation_Type : Cosmic Cancer Census Mutation Types the Genes are related to.
• CC_Translocation_Partner : Cosmic Cancer Census Translocation Partners for the gene.
• DGv_Name-DGv_VarType-site1 : Database of Genomic Variants annotation for site 1
• DGv_Name-DGv_VarType-site : Database of Genomic Variants annotation for site 2

Example Plot

The above plot shows the following:

• There are three tracks for each break point. The first three tracks belong to breakpoint 1 and second three tracks belong to breakpoint 2.

• Thre three tracks are:

  • Gene Model Track:

    • Displays Exons as brown and there direction with exons in arrow formation.
    • In orange it displays the breakpoint description.

  • Alignment Track:

    • Displays the direction of the reads for the breakpoint. Also displays the co-ordinate in Purple.
    • Read in Positive direction are Blue and Negative direction are Red

  • Protein Domain Track:

    • Displays the Protein Domain as green boxes with there names in green.

Output file name for plot is Gene1-Chromosome1_Position1_Gene2-Chromosome2_Position2_EventType.jpg All the Outputs are written into a folder called iAnnotateSVplots in the given output directory

Please look at examples of input and output files in /data/test directory where: /data/test/testData.txt is the input file /data/test/testResult.txt is the output file

The refFileVersion are automaticslly chosen from /data/references. But caution this is only tested on hg19. All these files are created using UCSC table browser.

The example for canonical transcripts can be also found in /data/canonicalInfo. In general the file is tab-delimited containing:

Gene Transcripts

as the headers where:

• Gene : Gene symobol should match the gene name from /data/references file.
• Transcripts : Transcripts is a particular transcript that you are interested in using instead of auto-selection.

The file for hg19 uniprot is created using UCSC table browser (Uniprot spAnnot track). The file for hg19 is in /data/UcscUniprotdomainInfo

iAnnotateSV package

Module iAnnotateSV contents

iAnnotateSV

Submodules

AnnotateEachBreakpoint module

iAnnotateSV.AnnotateEachBreakpoint

• This module will annotate each breakpoint taking in:

  • chr : chromosome for the event,
  • pos : position in the chromosome for the event,
  • str : direction of the reads for the event[either 0 or 1],

  * referenceDataframe : a pandas data-frame that will store reference information :Example:

  AnnotateEachBreakpoint(chr1,pos1,str1,refDF)

FindATranscript function

iAnnotateSV.FindTranscrpit.FindATranscript

• This module will automatically find the highest preference transcript based on input:

  • queryDF : Its a dataframe with

    • c = zone: 1=exon, 2=intron, 3=3'-UTR, 4=5'-UTR, 5=promoter
    • d,e = for exons: which one, and how far
    • d1,d2,e1,e2 = for introns: between which exons and how far?
    • f = for introns: how many bases in the partially completed codon?,

  * referenceDataframe : a pandas data-frame that will store reference information :Example:

  FindATranscript(queryDF,refDF)

FindAllTranscripts funtion

iAnnotateSV.FindTranscrpit.FindAllTranscripts

• This module will find all transcripts based on input:

  • queryDF : Its a dataframe with

    • c = zone: 1=exon, 2=intron, 3=3'-UTR, 4=5'-UTR, 5=promoter
    • d,e = for exons: which one, and how far
    • d1,d2,e1,e2 = for introns: between which exons and how far?
    • f = for introns: how many bases in the partially completed codon?,

  * referenceDataframe : a pandas data-frame that will store reference information :Example:

  FindAllTranscripts(queryDF,refDF)

FindCanonicalTranscript module

iAnnotateSV.FindCanonicalTranscript

• This module will Finad a canonical transcript based on the input for main function and output of FindAllTranscripts:

  • geneList : List of genes [this will normally be list with same names] for the Structural Variant in question,
  • transcriptList : List of transcripts for the Structural Variant in question,
  • siteList : direction of the site for the event[either 0 or 1],
  • zoneList : different zones in which the event can occur [zone: 1=exon, 2=intron, 3=3'-UTR, 4=5'-UTR, 5=promoter]
  • strandList : direction of the read for the event[either 0 or 1],
  • intronnumList : Which intron the event occurs if the event is in intron for each transcript,
  • intronframeList : What is the frame of the intron where the event is occuring for each transcript.

  * ctDict : a dictionary containing the canonical transcript information for each gene. [Gene=>Transcript] :Example:

  FindCT(geneList,transcriptList,siteList,zoneList,strandList,intronnumList,intronframeList,ctDict)

PredictFunction module

iAnnotateSV.PredictFunction

• This module will predict the function of each annotated breakpoint

• It takes two pandas series which has following information:

  • gene : Gene for the event,
  • transcript : Transcript used for the event,
  • site : Explanation for site where the event occurs,
  • zone : Where does the event occur [ 1=exon, 2=intron, 3=3'-UTR, 4=5'-UTR, 5=promoter ],
  • strand : Direction of the transcript,
  • str : Direction of the read,
  • intronnum : Which intron the event occurs if the event is in intron,

  * intronframe : What is the frame of the intron where the event is occuring. :Example:

  ann1S = pandas.Series([gene1,transcript1,site1,zone1,strand1,str1,intronnum1,intronframe1],index=['gene1', 'transcript1', 'site1', 'zone1', 'txstrand1', 'readstrand1', 'intronnum1','intronframe1'])

  ann2S = pandas.Series([gene2,transcript2,site2,zone2,strand2,str2,intronnum2,intronframe2],index=['gene2', 'transcript2', 'site2', 'zone2', 'txstrand2', 'readstrand2', 'intronnum2','intronframe2'])

  So ann1S & ann2S are series that will go to PredictFuntionForSV()

  PredictFunctionForSV(ann1S,ann2S)

AddExternalAnnotations module

iAnnotateSV.AddExternalAnnotations

• This module will annotate each breakpoint for Repeat Region, Database of Genomic Variants and Cosmic Census taking in:

  • repeat region file : Repeat Track from UCSC in tab-delimited format (see: /data/repeat_region/hg19_repeatRegion.tsv),
  • data base of genimic variant file : DGv in tab-delimited format (see: /data/database_of_genomic_variants/hg19_DGv_Annotation.tsv),
  • cosmic census file : cosmic census file from sanger (see: /data/cosmic/cancer_gene_census.tsv),
  • cosmic fusion counts file : cosmic fusion counts file from from cosmic fusion export (see: /data/cosmic/cosmic_fusion_counts.tsv),
  • structural variants file : File containing the breakpoint information,
  • output prefix : Output Prefix for the output files (.xlsx,.json,.txt),

  * output directory : Directory where the output needs to be written :Example:

  makeCommandLineForAEA = "-r " + repeatregionFilePath + " -d " + dgvFilePath + " -c " + ccFilePath + " -cct " + cctFilePath + " -s " + svFilePath + " -ofp AnnotatedSV" + " -o " + outputDir

  AddExternalAnnotations.main(makeCommandLineForAEA)

AnnotateForRepeatRegion module

iAnnotateSV.AnnotateForRepeatRegion

• This module has two submodules will read and annotate each breakpoint for Repeat Region

  1. ReadRepeatFile

     • This will read a tab-delimited file into a panadas dataframe

  2. AnnotateRepeatRegion

     • This is will annotate the breakpoints for repeat region.

     Example

     AnnotateRepeatRegion(verbose, count, svObject, repeatregionDict)

AnnotateForDGv module

iAnnotateSV.AnnotateForDGv

• This module has two submodules will read and annotate each breakpoint for Database of Genomic Variants

  1. ReadDGv

     • This will read a tab-delimited file into a panadas dataframe

  2. AnnotateDGv

     * This is will annotate the breakpoints for Database of Genomic Variants. :Example:

     AnnotateDGv(verbose, count, svObject, dgvDict)

AnnotateForCosmic module

iAnnotateSV.AnnotateForCosmic

• This module will annotate each breakpoint for Cosmic Census

  Example

  AnnotateFromCosmicCensusFile(comic_census_filename, verbose, count, svObject)

  Example

  AnnotateFromComicFusionCountsFile(comic_fusion_counts_filename, verbose, count, svObject)

helper module

iAnnotateSV.helper

• This module has multiple funtions

  1. ReadFile()
     • This will read a tab-delimited file into a panadas dataframe
  2. ExtendPromoterRegion()
     • This will extend the promoter region to a given length
  3. bp2str()
     • This will convert base pair information to string information

VisualizeSV module

iAnnotateSV.VisualizeSV

• This module will annotate each breakpoint taking in:

  • svDataFrame : Annotated structurla varaints dataframe obtained from PredictFuntion Module,
  • referenceDataFrame : a pandas data-frame that will store reference information,
  • uniprotDataFrame : making a dataframe from the uniprot data.

  * args : This has all the arguments that are generated from iAnnotateSV module :Example:

  VisualizeSV(svDataFrame,referenceDataFrame,uniprotDataFrame,args)

  Example Plot

  

iAnnotateSV main funtion

iAnnotateSV.iAnnotateSV

• This module is the driver module, it takes user information and runs all other module to produce proper structural variant annotation

Here is the Usage again:

usage: iAnnotateSV.py [options]

Annotate SV based on a specific human reference

optional arguments:

    -h, --help            show this help message and exit
    -v, --verbose         make lots of noise [default]
    -r hg19, --refFileVersion hg19
                        Which human reference file to be used, hg18,hg19 or
                        hg38
    -ofp test, --outputFilePrefix test
                        Prefix for the output file
    -o /somedir, --outputDir /somedir
                        Full Path to the output dir
    -i svfile.txt, --svFile svfile.txt
                        Location of the structural variants file to annotate
    -d 3000, --distance 3000
                        Distance used to extend the promoter region
    -a, --autoSelect      Auto Select which transcript to be used[default]
    -c canonicalExons.txt, --canonicalTranscripts canonicalExons.txt
                        Location of canonical transcript list for each gene.
                        Use only if you want the output for specific
                        transcripts for each gene.
    -p, --plotSV          Plot the structural variant in question[default]
    -u uniprot.txt, --uniprotFile uniprot.txt
                        Location of UniProt list contain information for
                        protein domains. Use only if you want to plot the
                        structural variant
    -rr RepeatRegionFile.tsv, --repeatFile RepeatRegionFile.tsv
                        Location of the Repeat Region Bed File
    -dgv DGvFile.tsv, --dgvFile DGvFile.tsv
                        Location of the Database of Genomic Variants Bed File
    -cc CosmicConsensus.tsv, --cosmicConsensusFile CosmicConsensus.tsv
                        Location of the Cosmic Consensus TSV file
    -cct CosmicFusionCounts.tsv, --cosmicCountsFile CosmicConsensus.tsv
                        Location of the Cosmic Fusion Counts TSV file