TobiasJu / SAPA

SNP annotation programm for AML

SAPA - SNP annotation programm for AML

75% of a Acute myeloid leukemia (AML) patients SNPs are unique to them and their impact is still unresolved.

SAPA is designed to add additional information to an Illumina truseq amplicon variants csv file. Such as Scores for nonsynonymous scoring matrices, divided into 3 different combined Scores, function prediction scores, conservation scores and one ensemble score. So that the viewer gets more Information whether a mutation is deleterious or tolerated. Due to performance and data reasons, SAPA uses annovar¹ and their precomputed SNP database, to quickly gather the deleterious prediction methods scores.

Update:

Now with HTML heatmap output!

First run

If you don't have PiP and Dominate you have to install them, before you can run SAPA, so type:

sudo apt-get install python-pip sudo pip install dominate

To get started simply type in:

./main.py -i example_data/truseq_example_data.csv

this will run the program with the example dataset. Your output will be saved in the output.txt and output.html files.

Please Note: During the first run of the program, the required database will be downloaded. This may take some time, depending on your internet connection. You may want to use the -fast argument to minimize the download size.

Parameter

-h, --help : show the help message and exit

-q, --quiet : prevent output in command line, run the program and don't bother

-m, --manual : display the manual for this program

-i, --input_file : truseq amplicon table containing SNPs > e.g. -i data/truseq_example_data.csv

-d, --detail : write detailed output file, with all the single scores of the deleteriousness prediction methods for nonsynonymous SNVs and amino acid substitution (if any)

-s, --separator : set the input file separator (default: ",") > e.g. -s ";" this will set the column separator to a semicolon

-t, --text_delimiter : set the input text delimiter (default: ") > e.g. -t "'" this will set the text delimiter to an apostrophe, if a column contains multiple entries

-b, --buildver : buildversion e.g hg19 (GRCh37) or hg38 (GRCh38) database (default hg19) > e.g. -b hg38 set the reference genome to hg38

-o, --output_file : output file name (default output.txt) > e.g. -o annotated_snps_detailed.txt -d running the detailed operation mode, and saving it in the file annotated_snps_detailed.txt

-f, --fast : run annotation just with a region based approach, for faster computing and less download file demand

-fi, --filter : filter all entries and only use nonsynonymus and clinically significant (>95%) SNPs in the output file

Features

Added data to a SNP csv file

No worries your input file will not be overwritten, a new file with the data from the input file will be created with the following scores added.:

• function prediction scores
• conservation scores
• ensemble score
• final prediction

Scoring explained (dbnsfp30a)

• SIFT

  • Score ranges from 1 to 0
  • Lower scores are more deleterious
  • The amino acid substitution is predicted damaging if the score is <= 0.05, and tolerated if the score is > 0.05.
  • http://sift.jcvi.org/www/SIFT_help.html

• Polyphen2

  • Scores ranges from 0 to 1
  • D: Probably damaging (>=0.957), P: possibly damaging (0.453<=pp2<=0.956); B: benign (pp2<=0.452)
  • http://genetics.bwh.harvard.edu/pph2/dokuwiki/overview

• LRT

  • Scores ranges from 1 to 0
  • Deleterious threshold < 0.002 (observed)
  • Lower scores are more deleterious
  • D: Deleterious; N: Neutral; U: Unknown
  • https://www.researchgate.net/profile/Justin_Fay/publication/26671245_Identification_of_deleterious_mutations_within_three_human_genomes/links/00b49529eabbc6aa2b000000.pdf

• MutationTaster

  • Scores ranges from 0 to 1
  • Deleterious threshold > 0.5 (https://wikis.utexas.edu/display/bioiteam/Annovar+Annotations)
  • A = "disease_causing_automatic"; D =“disease_causing”; N = “polymorphism”; P = “polymorphism_automatic”
  • http://www.mutationtaster.org/

• MutationAssessor

  • Scores ranges from -5.545 to 5.975
  • Deleterious threshold > 0.65
  • H: high; M: medium; L: low; N: neutral. H/M means functional and L/N means non-functional
  • http://mutationassessor.org/r3/

• FATHMM

  • Scores ranges from -16.13 to 10.64
  • Deleterious threshold < -1 (observed)
  • D: Deleterious; T: Tolerated
  • http://fathmm.biocompute.org.uk/

• PROVEAN

  • Scores ranges from -13 to 4
  • Deleterious PROVEAN < -2,5 ; Tolerated PROVEAN > -2,5
  • http://provean.jcvi.org/about.php

• VEST3

  • Scores ranges from 0 to 1
  • The larger the score the more likely the mutation may cause functional change.
  • http://karchinlab.org/apps/appVest.html

• CADD_raw

  • Score ranges from -2,375 to 7,6 (observed)
  • The larger the score the more likely the SNP has damaging effect.
  • http://cadd.gs.washington.edu/

• CADD_phred

  • Scores range from 0 to 60
  • Deleterious threshold > 15
  • This is phred-like rank score based on whole genome CADD raw scores.
  • The larger the score the more likely the SNP has damaging effect.

• DANN

  • Scores ranges from 0.01 to 0.999
  • Deleterious threshold > 0.993 (observed)
  • The higher the score the more likely the mutation may cause functional change.
  • DANN uses the same feature set and training data as CADD to train a deep neural network (DNN)
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341060/

• FATHMM_MKL_coding_score

  • Scores ranges from 0 - 1
  • Deleterious threshold > 0.63 (observed)
  • The higher the score, the greater the confidence of a functional mutation.
  • http://fathmm.biocompute.org.uk/fathmmMKL.htm

• MetaSVM

  • Scores ranges from -1 to +1
  • Deleterious threshold > 0 (observed)
  • D: Deleterious; T: Tolerated
  • Uses a radial SVM model to train prediction model, using all available scoring algorithm normalized scores
  • http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#-metasvm-annotation

• MetaLR

  • Scores ranges from 0 to 1
  • Deleterious threshold > 0.5
  • D: Deleterious; T: Tolerated
  • very similar to MetaSVM, with similar performance
  • http://annovar.openbioinformatics.org/en/latest/user-guide/filter/#-metalr-annotation

• integrated fitCons

  • Scores ranges from 0.0 to 1.0
  • higher scores indicating more potential
    • the fraction of genomic positions evincing a particular pattern (or "fingerprint") of functional assay results, that are under selective pressure
  • http://compgen.cshl.edu/fitCons/

• GERP RS

  • RS = (rejected substitutions)
  • Scores ranges from -12.3 to 6.17
  • The larger the score, the more conserved the site.
  • http://mendel.stanford.edu/SidowLab/downloads/gerp/

• phyloP7way_vertebrate

  • Scores ranges from -1.844 to 1.062 (observed)
  • The larger the score, the more conserved.
    • higher scores are more deleterious
  • Phylogenetic p-values for 7 vertebrate species
  • https://brb.nci.nih.gov/seqtools/colexpanno.html#dbnsfp

• phyloP20way_mammalian

  • Scores ranges from -3.095 to 1.186 (observed)
  • The larger the score, the more conserved.
    • higher scores are more deleterious
  • Phylogenetic p-values for 20 mammalian species
  • https://brb.nci.nih.gov/seqtools/colexpanno.html#dbnsfp

• phastCons7way

  • Scores ranges from 0 to 1
  • The larger the score, the more conserved the site
    • higher scores are more deleterious
  • http://compgen.cshl.edu/phast/phastCons-HOWTO.html

• phastCons20way

  • Scores ranges from 0 to 1
  • The larger the score, the more conserved the site
    • higher scores are more deleterious
  • http://compgen.cshl.edu/phast/phastCons-HOWTO.html

• SiPhy

  • Scores ranges from 0.0003 to 37.9718
  • The larger the score, the more conserved the site
    • higher scores are more deleterious
  • http://garberlab.umassmed.edu/software.html

Databases

All required databases will be downloaded in the hg19/ directory. If some error during downloading occurs, you can read furhter details in the annovar_downdb.log in the hg19/ folder. Several commonly used databases are integrated:

• cytoBand

  • for the chromosome coordinate of each cytogenetic band

• exac03

  • for the variants reported in the Exome Aggregation Consortium (version 0.3)

• dbnsfp30a

  • for various functional deleteriousness prediction scores from the dbNSFP database

• clinvar_20140929

  • for the variants reported in the ClinVar database (version 20140929).

For functional prediction of variants in whole-genome data:

• GERP++

  • functional prediction scores for 9 billion mutations based on selective constraints across human genome. You can optionally use gerp++gt2 instead since it includes only RS score greater than 2, which provides high sensitivity while still strongly enriching for truly constrained sites

• CADD

  • Combined Annotation Dependent Depletion score for 9 billion mutations. It is basically constructed by a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants, using ~70 different features.

• DANN

  • functional prediction score generated by deep learning, using the identical set of training data as cadd but with much improved performance than CADD.

• FATHMM

  • a hidden markov model to predict the functional importance of both coding and non-coding variants (that is, two separate scores are provided) on 9 billion mutations.

• EIGEN

  • a spectral approach integrating functional genomic annotations for coding and noncoding variants on 9 billion mutations, without labelled training data (that is, unsupervised approach)

• GWAVA

  • genome-wide annotation of variants that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations on 9 billion mutations.

For functional prediction of variants in whole-exome data:

• dbnsfp30a
  • this dataset already includes SIFT, PolyPhen2 HDIV, PolyPhen2 HVAR, LRT, MutationTaster, MutationAssessor, FATHMM, MetaSVM, MetaLR, VEST, CADD, GERP++, DANN, fitCons, PhyloP and SiPhy scores, but ONLY on coding variants
  • for more information check: http://varianttools.sourceforge.net/Annotation/DbNSFP

Input File

As input file is a Illumina truseq amplicon variants file needed, which contains the patients SNPs in a character separated format. A example file is included in the /data directory and listed below.

Pos	Ref	Alt	Type	Context	Consequence	dbSNP	COSMIC	ClinVar	Qual	Alt Freq	Total Depth	Ref Depth	Alt Depth	Strand Bias
25463566	C	A	SNV	Coding	missense_variant				100	0.334	6568	4358	2194	-100.0
128204951	C	T	SNV	Coding,Intergenic	missense_variant,upstream_gene_variant	rs2335052	COSM445531		100	0.549	1312	587	720	-100.0
128205860	G	C	SNV	Coding,Intergenic	synonymous_variant,upstream_gene_variant	rs1573858			100	0.997	1925	0	1920	-100.0
55141055	A	G	SNV	Coding	synonymous_variant	rs1873778	COSM1430082		100	0.997	12713	35	12670	-100.0
106196829	T	G	SNV	Coding	missense_variant	rs34402524	COSM87176		100	0.482	38535	19801	18585	-100.0
106196937	AT	A	Deletion	Coding	frameshift_variant,feature_truncation				100	0.385	31380	19297	12083	-100.0
101917521	G	A	SNV			rs803064			100	0.558	9023	3973	5033	-100.0
101921289	A	G	SNV			rs2230103,rs79334660			100	0.37	2903	1830	1073	-100.0
21971127	A	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				100	0.172	157	120	27	-100.0
21971127	A	T	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				44	0.057	157	120	9	-100.0
21971129	T	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant				44	0.057	158	143	9	-100.0
21971130	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				68	0.074	163	144	12	-100.0
21971131	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant		COSM13766		44	0.057	157	144	9	-100.0
7579472	G	C	SNV	Coding	missense_variant	rs1042522	COSM45985	non-pathogenic	100	0.591	3567	1452	2108	-100.0
74733099	G	A	SNV	Intergenic,Coding,Intron,5P_UTR	downstream_gene_variant,upstream_gene_variant,synonymous_variant,intron_variant,5_prime_UTR_variant	rs237057			100	0.995	7558	34	7518	-100.0
31022959	T	C	SNV	Coding	missense_variant	rs6058694			100	0.997	17877	39	17831	-100.0
36252877	C	T	SNV	Coding	missense_variant		COSM96546		100	0.465	8562	4573	3978	-100.0
39933339	A	G	SNV	Coding	synonymous_variant	rs5917933			100	0.998	14616	18	14592	-100.0
123195650	A	T	SNV	Coding	missense_variant				47	0.073	109	100	8	-100.0

Output File (default)

The generated outputfile output.html, will be in the current working directory, pelase note that if you move the output.html to a different folder, you need to copy the resources folder too. Also the output.txt, which contains the patients annotated SNPs in a character separated format, will be in the current working directory. Using the included example dataset in the /data directory, the following output will be generated.

ID	Chr	Pos	Ref	Alt	Type	Context	Consequence	dbSNP	COSMIC	ClinVar	Qual	Alt Freq [%]	Total Depth	Ref Depth	Alt Depth	Strand Bias	Gene	function prediction scores [0-1]	conservation scores[-12.3-6.17]	ensemble scores[0-60]	final prediction
0	chr2	25463566	C	A	SNV	Coding	missense_variant				100	0,334	6568	4358	2194	-100	DNMT3A	0,863	5,76	34	Deleterious
1	chr3	128204951	C	T	SNV	Coding,Intergenic	missense_variant,upstream_gene_variant	rs2335052	COSM445531		100	0,549	1312	587	720	-100	GATA2	0	1,83	13,22	Tolerated
2	chr3	128205860	G	C	SNV	Coding,Intergenic	synonymous_variant,upstream_gene_variant	rs1573858			100	0,997	1925	0	1920	-100	GATA2	0	0	0	Tolerated (synonymous)
3	chr4	55141055	A	G	SNV	Coding	synonymous_variant	rs1873778	COSM1430082		100	0,997	12713	35	12670	-100	PDGFRA	0	0	0	Tolerated (synonymous)
4	chr4	106196829	T	G	SNV	Coding	missense_variant	rs34402524	COSM87176		100	0,482	38535	19801	18585	-100	TET2	0	5,16	21,5	Tolerated
5	chr4	106196937	T	-0	Deletion	Coding	frameshift_variant,feature_truncation				100	0,385	31380	19297	12083	-100	TET2	0	0	0	0
6	chr7	101917521	G	A	SNV			rs803064			100	0,558	9023	3973	5033	-100	CUX1	0	-0,106	5,82	Tolerated
7	chr7	101921289	A	G	SNV			rs2230103,rs79334660			100	0,37	2903	1830	1073	-100	CUX1	0,004	3,4	7,655	Tolerated
8	chr9	21971127	A	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				100	0,172	157	120	27	-100	CDKN2A	0,192	0,609	1,37	Tolerated
9	chr9	21971127	A	T	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				44	0,057	157	120	9	-100	CDKN2A	0,178	0,609	0,119	Tolerated
10	chr9	21971129	T	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant				44	0,057	158	143	9	-100	CDKN2A	0,379	5,79	23,7	Tolerated
11	chr9	21971130	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				68	0,074	163	144	12	-100	CDKN2A	0,224	3,74	10,53	Tolerated
13	chr17	7579472	G	C	SNV	Coding	missense_variant	rs1042522	COSM45985	non-pathogenic	100	0,591	3567	1452	2108	-100	TP53	0	1,87	0,355	Tolerated
12	chr9	21971131	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant		COSM13766		44	0,057	157	144	9	-100	CDKN2A	0,109	-0,271	10,15	Tolerated
14	chr17	74733099	G	A	SNV	Intergenic,Intergenic,Coding,Intron,5P_UTR	downstream_gene_variant,upstream_gene_variant,synonymous_variant,intron_variant,5_prime_UTR_variant	rs237057			100	0,995	7558	34	7518	-100	SRSF2	0	0	0	Tolerated (synonymous)
15	chr20	31022959	T	C	SNV	Coding	missense_variant	rs6058694			100	0,997	17877	39	17831	-100	ASXL1	0	0	0	0
16	chr21	36252877	C	T	SNV	Coding	missense_variant		COSM96546		100	0,465	8562	4573	3978	-100	RUNX1	0,991	5,31	33	Deleterious
17	chrX	39933339	A	G	SNV	Coding	synonymous_variant	rs5917933			100	0,998	14616	18	14592	-100	BCOR	0	0	0	Tolerated (synonymous)
18	chrX	123195650	A	T	SNV	Coding	missense_variant				47	0,073	109	100	8	-100	STAG2	0,303	4,77	28,6	Tolerated

Output File (detailed)

ID	Chr	Pos	Ref	Alt	Type	Context	Consequence	dbSNP	COSMIC	ClinVar	Qual	Alt Freq [%]	Total Depth	Ref Depth	Alt Depth	Strand Bias	Chr	Start	End	Ref	Alt	Func_refGene	Gene_refGene	GeneDetail_refGene	ExonicFunc_refGene	AAChange_refGene	cytoBand	esp6500siv2_all	avsnp147	SIFT_score	SIFT_pred	Polyphen2_HDIV_score	Polyphen2_HDIV_pred	Polyphen2_HVAR_score	Polyphen2_HVAR_pred	LRT_score	LRT_pred	MutationTaster_score	MutationTaster_pred	MutationAssessor_score	MutationAssessor_pred	FATHMM_score	FATHMM_pred	PROVEAN_score	PROVEAN_pred	VEST3_score	CADD_raw	CADD_phred	DANN_score	fathmm_MKL_coding_score	fathmm_MKL_coding_pred	MetaSVM_score	MetaSVM_pred	MetaLR_score	MetaLR_pred	integrated_fitCons_score	integrated_confidence_value	GERP_RS	phyloP7way_vertebrate	phyloP20way_mammalian	phastCons7way_vertebrate	phastCons20way_mammalian	SiPhy_29way_logOdds	final prediction
0	chr2	25463566	C	A	SNV	Coding	missense_variant				100	33,4	6568	4358	2194	-100	chr2	25463566	25463566	C	A	exonic	DNMT3A	0	nonsynonymous SNV	DNMT3A:NM_153759:exon14:c,G1549T:p,G517W,DNMT3A:NM_022552:exon18:c,G2116T:p,G706W,DNMT3A:NM_175629:exon18:c,G2116T:p,G706W	2p23,3	0	rs749365376	0	D	1	D	1	D	0	D	1	D	3,675	H	-2,3	D	-7,86	D	0,979	7,514	34	0,997	0,985	D	0,946	D	0,863	D	0,707	0	5,76	0,791	0,935	0,998	1	18,523	Deleterious
1	chr3	128204951	C	T	SNV	Coding,Intergenic	missense_variant,upstream_gene_variant	rs2335052	COSM445531		100	54,9	1312	587	720	-100	chr3	128204951	128204951	C	T	exonic	GATA2	0	nonsynonymous SNV	GATA2:NM_001145662:exon3:c,G490A:p,A164T,GATA2:NM_032638:exon3:c,G490A:p,A164T,GATA2:NM_001145661:exon4:c,G490A:p,A164T	3q21,3	0,1638	rs2335052	0,429	T	0,013	B	0,025	B	0,004	N	0,729	P	-0,175	N	-4,32	D	-0,43	N	0,117	1,482	13,22	0,99	0,91	D	-1,065	T	0	T	0,543	0	1,83	0,598	0,739	0,999	0,999	9,555	Tolerated
2	chr3	128205860	G	C	SNV	Coding,Intergenic	synonymous_variant,upstream_gene_variant	rs1573858			100	99,7	1925	0	1920	-100	chr3	128205860	128205860	G	C	exonic	GATA2	0	synonymous SNV	GATA2:NM_001145662:exon2:c,C15G:p,P5P,GATA2:NM_032638:exon2:c,C15G:p,P5P,GATA2:NM_001145661:exon3:c,C15G:p,P5P	3q21,3	0,6934	rs1573858	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Tolerated (synonymous)
3	chr4	55141055	A	G	SNV	Coding	synonymous_variant	rs1873778	COSM1430082		100	99,7	12713	35	12670	-100	chr4	55141055	55141055	A	G	exonic	PDGFRA	0	synonymous SNV	PDGFRA:NM_006206:exon12:c,A1701G:p,P567P	4q12	0,9589	rs1873778	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Tolerated (synonymous)
4	chr4	106196829	T	G	SNV	Coding	missense_variant	rs34402524	COSM87176		100	48,2	38535	19801	18585	-100	chr4	106196829	106196829	T	G	exonic	TET2	0	nonsynonymous SNV	TET2:NM_001127208:exon11:c,T5162G:p,L1721W	4q24	0,1233	rs34402524	0,003	D	0,794	P	0,754	P	0	0	1	P	0	N	4,35	T	0,03	N	0,232	2,834	21,5	0,932	0,78	D	-0,626	T	0	T	0,707	0	5,16	0,991	1,011	0,775	0,124	13,59	Tolerated
5	chr4	106196937	T	-0	Deletion	Coding	frameshift_variant,feature_truncation				100	38,5	31380	19297	12083	-100	chr4	106196937	106196937	T	-0	exonic	TET2	0	frameshift deletion	TET2:NM_001127208:exon11:c,5270delA:p,H1757fs	4q24	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
6	chr7	101917521	G	A	SNV			rs803064			100	55,8	9023	3973	5033	-100	chr7	101917521	101917521	G	A	exonic	CUX1	0	nonsynonymous SNV	CUX1:NM_001202544:exon15:c,G1342A:p,A448T,CUX1:NM_001202545:exon15:c,G1252A:p,A418T,CUX1:NM_001202546:exon15:c,G1273A:p,A425T,CUX1:NM_001913:exon16:c,G1390A:p,A464T,CUX1:NM_181500:exon16:c,G1384A:p,A462T	7q22,1	0,575	rs803064	0,902	T	0,229	B	0,052	B	0	0	1	P	1,24	L	1,51	T	-1,17	N	0,273	0,313	5,82	0,736	0,84	D	-0,953	T	0	T	0,706	0	-0,106	-0,683	-0,839	0,437	0	7,499	Tolerated
7	chr7	101921289	A	G	SNV			rs2230103,rs79334660			100	37	2903	1830	1073	-100	chr7	101921289	101921289	A	G	exonic	CUX1	0	nonsynonymous SNV	CUX1:NM_001202544:exon17:c,A1585G:p,I529V,CUX1:NM_001202545:exon17:c,A1495G:p,I499V,CUX1:NM_001202546:exon17:c,A1516G:p,I506V,CUX1:NM_001913:exon18:c,A1633G:p,I545V,CUX1:NM_181500:exon18:c,A1627G:p,I543V	7q22,1	0,0382	rs2230103	1	T	0,139	B	0,07	B	0	0	0,995	N	0,5	N	1,63	T	0,03	N	0,326	0,53	7,655	0,604	0,967	D	-1,033	T	0,004	T	0,706	0	3,4	0,697	1,186	0,993	1	10,046	Tolerated
8	chr9	21971127	A	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				100	17,2	157	120	27	-100	chr9	21971127	21971127	A	G	exonic	CDKN2A	0	nonsynonymous SNV	CDKN2A:NM_058195:exon2:c,T274C:p,S92P	9p21,3	0	0	1	T	0	B	0	B	0,471	N	1	D	0	0	-0,88	T	3,69	N	0	-0,153	1,37	0,714	0,169	N	-0,976	T	0,192	T	0,677	0	0,609	-0,056	0,149	0,536	0,933	4,699	Tolerated
9	chr9	21971127	A	T	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				44	5,7	157	120	9	-100	chr9	21971127	21971127	A	T	exonic	CDKN2A	0	nonsynonymous SNV	CDKN2A:NM_058195:exon2:c,T274A:p,S92T	9p21,3	0	0	0,236	T	0,167	B	0,045	B	0,471	N	1	D	0	0	-0,95	T	-0,45	N	0	-0,609	0,119	0,96	0,799	D	-0,99	T	0,178	T	0,677	0	0,609	-0,056	0,149	0,536	0,933	4,699	Tolerated
10	chr9	21971129	T	G	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant				44	5,7	158	143	9	-100	chr9	21971129	21971129	T	G	exonic	CDKN2A	0	nonsynonymous SNV	CDKN2A:NM_000077:exon2:c,A229C:p,T77P,CDKN2A:NM_001195132:exon2:c,A229C:p,T77P,CDKN2A:NM_058195:exon2:c,A272C:p,H91P	9p21,3	0	0	0,008	D	0,998	D	0,915	D	0,029	N	0,989	N	0,975	L	-0,99	T	-6,87	D	0	4,105	23,7	0,982	0,938	D	-0,261	T	0,379	T	0,677	0	5,79	0,991	1,061	0,464	0,913	10,099	Tolerated
11	chr9	21971130	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,synonymous_variant				68	7,4	163	144	12	-100	chr9	21971130	21971130	G	A	exonic	CDKN2A	0	nonsynonymous SNV	CDKN2A:NM_058195:exon2:c,C271T:p,H91Y	9p21,3	0	0	0,197	T	0,417	B	0,158	B	0,029	N	1	N	0	0	-0,89	T	-3,72	D	0	0,976	10,53	0,987	0,797	D	-0,845	T	0,224	T	0,677	0	3,74	-0,711	-0,197	0,328	0,866	5,67	Tolerated
13	chr17	7579472	G	C	SNV	Coding	missense_variant	rs1042522	COSM45985	non-pathogenic	100	59,1	3567	1452	2108	-100	chr17	7579472	7579472	G	C	exonic	TP53	0	nonsynonymous SNV	TP53:NM_001126118:exon3:c,C98G:p,P33R,TP53:NM_000546:exon4:c,C215G:p,P72R,TP53:NM_001126112:exon4:c,C215G:p,P72R,TP53:NM_001126113:exon4:c,C215G:p,P72R,TP53:NM_001126114:exon4:c,C215G:p,P72R,TP53:NM_001276695:exon4:c,C98G:p,P33R,TP53:NM_001276696:exon4:c,C98G:p,P33R,TP53:NM_001276760:exon4:c,C98G:p,P33R,TP53:NM_001276761:exon4:c,C98G:p,P33R	17p13,1	0,63	rs1042522	0,642	T	0,745	P	0,372	B	0,371	U	1	P	0	N	-2,05	D	-0,19	N	0,267	-0,415	0,355	0,57	0,361	N	-0,929	T	0	T	0,722	0	1,87	0,518	1,045	0,001	0,001	9,773	Tolerated
12	chr9	21971131	G	A	SNV	Intron,Intergenic,Coding	intron_variant,NMD_transcript_variant,downstream_gene_variant,missense_variant		COSM13766		44	5,7	157	144	9	-100	chr9	21971131	21971131	G	A	exonic	CDKN2A	0	nonsynonymous SNV	CDKN2A:NM_000077:exon2:c,C227T:p,A76V,CDKN2A:NM_001195132:exon2:c,C227T:p,A76V	9p21,3	0	0	0,276	T	0,004	B	0,001	B	0	0	1	N	0	0	-0,16	T	-1,74	N	0,46	0,913	10,15	0,974	0,098	N	-1,03	T	0,109	T	0,677	0	-0,271	-0,011	-0,239	0,309	0,856	6,3	Tolerated
14	chr17	74733099	G	A	SNV	Intergenic,Intergenic,Coding,Intron,5P_UTR	downstream_gene_variant,upstream_gene_variant,synonymous_variant,intron_variant,5_prime_UTR_variant	rs237057			100	99,5	7558	34	7518	-100	chr17	74733099	74733099	G	A	exonic	SRSF2	0	synonymous SNV	SRSF2:NM_001195427:exon1:c,C144T:p,D48D,SRSF2:NM_003016:exon1:c,C144T:p,D48D	17q25,1	0,797	rs237057	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Tolerated (synonymous)
15	chr20	31022959	T	C	SNV	Coding	missense_variant	rs6058694			100	99,7	17877	39	17831	-100	chr20	31022959	31022959	T	C	exonic	ASXL1	0	nonsynonymous SNV	ASXL1:NM_015338:exon12:c,T2444C:p,L815P	20q11,21	0,9999	rs6058694	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
16	chr21	36252877	C	T	SNV	Coding	missense_variant		COSM96546		100	46,5	8562	4573	3978	-100	chr21	36252877	36252877	C	T	exonic	RUNX1	0	nonsynonymous SNV	RUNX1:NM_001001890:exon2:c,G404A:p,R135K,RUNX1:NM_001122607:exon2:c,G404A:p,R135K,RUNX1:NM_001754:exon5:c,G485A:p,R162K	21q22,12	0	0	0,001	D	0,999	D	0,997	D	0	D	1	D	3,195	M	-6,37	D	-2,5	D	0,954	7,021	33	0,998	0,989	D	0,994	D	0,991	D	0,722	0	5,31	0,871	0,932	1	1	19,335	Deleterious
17	chrX	39933339	A	G	SNV	Coding	synonymous_variant	rs5917933			100	99,8	14616	18	14592	-100	chrX	39933339	39933339	A	G	exonic	BCOR	0	synonymous SNV	BCOR:NM_001123383:exon4:c,T1260C:p,D420D,BCOR:NM_001123384:exon4:c,T1260C:p,D420D,BCOR:NM_001123385:exon4:c,T1260C:p,D420D,BCOR:NM_017745:exon4:c,T1260C:p,D420D	Xp11,4	0,8957	rs5917933	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Tolerated (synonymous)
18	chrX	123195650	A	T	SNV	Coding	missense_variant				47	7,3	109	100	8	-100	chrX	123195650	123195650	A	T	exonic	STAG2	0	nonsynonymous SNV	STAG2:NM_006603:exon16:c,A1564T:p,I522F,STAG2:NM_001042749:exon17:c,A1564T:p,I522F,STAG2:NM_001042750:exon17:c,A1564T:p,I522F,STAG2:NM_001042751:exon17:c,A1564T:p,I522F,STAG2:NM_001282418:exon17:c,A1564T:p,I522F	Xq25	0	0	0,001	D	0,955	P	0,774	P	0	D	1	D	2,66	M	1,51	T	-3,81	D	0,906	6,183	28,6	0,989	0,973	D	-0,343	T	0,303	T	0	0	4,77	1,062	1,088	0,999	1	13,668	Tolerated

Validation

For validation i used 2023 Pathogenic SNPs from Multiple submitters out of Clinvar and 2000 benign SNPs also from Multiple submitters. Pathogenic file: 1419 deleterious (true positive), 564 tolerated (false negative), (40 miss) Benign file: 153 deleterious (false positives), 1736 tolerated (true negative), (111 miss) This yielded in a MCC of 0.64 (0.78) If you want to validate it yourself, you can use the clinvar datasets in the data/ folder or download a set yourself from clinvar and convert it to the SAPA format with the ClinVar_to_SAPA_parser.py

Known bugs and missing features

longer deletions are not handled correctly and often there are no scores for longer indels available. Also there is no scoring system for non coding variants, this may change later.

References

¹ http://annovar.openbioinformatics.org/en/latest/ https://www.gesundheit.gv.at/labor/laborwerte/blutbild/blasten http://annovar.openbioinformatics.org/ http://cadd.gs.washington.edu/download http://www.ensembl.org/info/genome/variation/predicted_data.html http://varianttools.sourceforge.net/Annotation/DbNSFP https://brb.nci.nih.gov/seqtools/colexpanno.html#dbnsfp http://www.illumina.com/products/by-type/clinical-research-products/trusight-myeloid.html http://www.enlis.com/blog/2015/03/17/the-best-variant-prediction-method-that-no-one-is-using/

Literatur

• Papaemmanuil, Elli; Gerstung, Moritz; Bullinger, Lars; Gaidzik, Verena I.; Paschka, Peter; Roberts, Nicola D. et al. (2016): Genomic Classification and Prognosis in Acute Myeloid Leukemia. In: The New England journal of medicine 374 (23), S. 2209–2221. DOI: 10.1056/NEJMoa1516192
• Yang, Hui; Wang, Kai (2015): Genomic variant annotation and prioritization with ANNOVAR and wANNOVAR. In: Nature protocols 10 (10), S. 1556–1566. DOI: 10.1038/nprot.2015.105
• Liu, Xiaoming; Jian, Xueqiu; Boerwinkle, Eric (2011): dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. In: Human mutation 32 (8), S. 894–899. DOI: 10.1002/humu.21517
• Dong, Chengliang; Wei, Peng; Jian, Xueqiu; Gibbs, Richard; Boerwinkle, Eric; Wang, Kai; Liu, Xiaoming (2015): Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. In: Human molecular genetics 24 (8), S. 2125–2137. DOI: 10.1093/hmg/ddu733
• Wakita, S.; Yamaguchi, H.; Ueki, T.; Usuki, K.; Kurosawa, S.; Kobayashi, Y. et al. (2016): Complex molecular genetic abnormalities involving three or more genetic mutations are important prognostic factors for acute myeloid leukemia. In: Leukemia 30 (3), S. 545–554. DOI: 10.1038/leu.2015.288.

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