SNP Extraction Tool for Human Variations

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SETH is a software that performs named entity recognition (NER) of genetic variants (with an emphasis on single nucleotide polymorphisms (SNPs) and other short sequence variations) from natural language texts. SETH allows to recognize the following mutation subtypes: substitution, deletion, insertion, duplication, insertion-deletion (insdel), inversion, conversion, translocation, frameshift, short-sequence repeat, and literal dbSNP mention. Recognized mutation mentions can be grounded to the Human Mutation Nomenclature (HGVS) and normalized to dbSNP identifiers or UniProt sequences. For NER SETH builds on four individual components:

1.) Mutations following the HGVS nomenclature (den Dunnen and Antonarakis, 2000) are recognized by implementing an Extended Backus–Naur (EBNF) grammar proposed by Laros et al. (2011) using Scala combinators. We modified this grammar to allow to detect frequently observed deviations from the nomenclature

2.)To get hold of substitutions not following the nomenclature, SETH integrates MutationFinder (Caporaso et al., 2007). SETH modifies MutationFinder’s original capabilities in order to match a wider scope of substitutions (DNA substitutions, nonsense mutations, and ambiguous mutations) not following the HGVS nomenclature. This is done by modifying the original MutationFinder implementation together with additional and modified regular expressions.

3.) Mutations (substitutions, deletions, insersions, frameshifts, …) not following the HGVS nomenclature, but earlier proposals for a nomenclature, are recognized using a separate set of regular expressions.

4.) Mutations described as literal dbSNP-identifiers are recongized using a regular expression.

Results from the four different components are collected, merged, and represented as the following object MutationMention. The general NER-workflow is also depicted in the following figure.


If possible, extracted SNP mentions are linked to dbSNP or UniProt-KB seqeuence. This process is referred to as named entity normalization (NEN). For normalization SETH requires a list of potential entrez gene candidates/identifiers as well as a local dbSNP or UniProt database. Gene names may either come from dedicated gene name recognition and normaluzation tools, such as GNAT. Alternatively, we recomend the use of NCBI’s gene2pubmed database. SETH currently uses these two data-sources but can easily extended with other gene-NER tools.


Download ready-to-use version 1.3.1 (released 12.12.2016) from

Or build SETH on your own:

git clone
mvn clean compile assembly:single
mv ./target/seth-1.2-Snapshot-jar-with-dependencies.jar seth.jar

Or import in Maven from jitpack

For maven, add a new repository pointing to jitpack.


And add the following dependency, which uses the release 1.3.1 version



SETH detects and normalizes genetic variants in text.


  Title= {SETH detects and normalizes genetic variants in text.},
  Author= {Thomas, Philippe and Rockt{\"{a}}schel, Tim and Hakenberg, J{\"{o}}rg and Lichtblau, Yvonne and Leser, Ulf},
  Journal= {Bioinformatics},
  Year= {2016},
  Month= {Jun},
  Doi= {10.1093/bioinformatics/btw234},  
  Language = {eng},
  Medline-pst = {aheadofprint},
  Pmid = {27256315},
  Url = {}


Thomas, P., Rocktäschel, T., Hakenberg, J., Mayer, L., and Leser, U. (2016). SETH detects and normalizes genetic variants in text. Bioinformatics (2016)

Download precomputed PubMed results

Precomputed results are available in GeneView here or in RVS here

Examples for NER

Command-line Usage

java -cp seth.jar seth.ner.wrapper.SETHNERAppMut "Causative GJB2 mutations were identified in 31 (15.2%) patients, and two common mutations, c.35delG and L90P (c.269T>C), accounted for 72.1% and 9.8% of GJB2 disease alleles."
MutationMention [span=91-99, location=35, wtResidue=G, text=c.35delG, type=DELETION, tool=SETH]
MutationMention [span=104-108, mutResidue=P, location=90, wtResidue=L, text=L90P, type=SUBSTITUTION, tool=MUTATIONFINDER]
MutationMention [span=110-118, mutResidue=C, location=269, wtResidue=T, text=c.269T>C, type=SUBSTITUTION, tool=SETH]
java -cp seth.jar seth.ner.wrapper.SETHNERAppMut "G-banding and spectral karyotyping showed 46,XX,t(9;11)(p22;p15)."
MutationMention [span=42-64, text=46,XX,t(9;11)(p22;p15), type=COPY_NUMBER_VARIATION, tool=SETH]	


Given mentions of SNPs and a list of genes (i.e. Entrez gene identifiers), SETH normalizes SNPs to dbSNP identifiers. To extract gene mentions, we use the output of the tool GNAT (Hakenberg et al., 2011) together with the gene2pubmed information from NCBI. Parts of the dbSNP database have to be locally installed for speeding up the normalization process. For user convenience, we provide a dump as embedded Derby database (~2GB). Please note, that this derby database dump contains only human data (UniProt, dbSNP, GNAT, gene2Pubmed). Otherwise the resulting derby database becomes too large for distribution. At the end of this readme we describe the process to generate the derby database. This process can be adapted for other species. Feel free to contact us if you observe any problems, or if you would like to host database dumps for species other than human.

Command-line Usage

To use SETH’s NEN component from the command line, you need to provide a XML property file that handles the connection to the Derby database. Subsequently, you can provide a tab-seperated file (with PubMed ID, mutation mention, start- and end-position) SETH should normalize to dbSNP (i.e. rs numbers).

java -cp seth.jar resources/property.xml resources/snpExample.txt

Normalising mutations from 'resources/snpExample.txt' and properties from 'resources/property.xml'
16 mutations for normalisation loaded
    PMID    Mutation	Start	End	dbSNP
15345705      G1651A      419   425
15290009       V158M      149   158    rs4680
15645182        A72S       15    23    rs6267
14973783       V103I      208   213 rs2229616
15457404       V158M      937   946    rs4680
15098000       V158M      349   358    rs4680
12600718       R219K      599   604 rs2230806
12600718      R1587K      945   951 rs2230808
15245581       T321C     1356  1361 rs3747174
15464268        S12T      621   625    rs1937
11933203       C707T      685   691
14984467     C17948T      577   585
15268889      G2014A     1316  1322
15670788      C2757G      726   733
15670788      C5748T      755   762
15564288        R72P     1204  1212 rs1042522
15564288       K751Q      747   756   rs13181
15564288       D312N      722   731 rs1799793
14755442      E2578G      699   709 rs1009382
15615772       P141L     1040  1045 rs2227564

Normalization possible for 14/20 mentions

Code Example

SETH allows simple integration into your Java-Projects. A complete pipeline performing all steps (NER+NEN) can be found here: Java-Code

Reproducing our results

Evaluate NER

SETH corpus (630 abstracts)

java -cp seth.jar seth.seth.eval.ApplyNER resources/SETH-corpus/corpus.txt resources/mutations.txt false resources/SETH-corpus.seth
java -cp seth.jar seth.seth.eval.EvaluateNER resources/SETH-corpus.seth resources/SETH-corpus/yearMapping.txt  resources/SETH-corpus/annotations/

Precision 0.98 Recall 0.86 F₁ 0.91

MutationFinder-development corpus using original MutationFinder evaluation scripts (Caporaso et al., 2007)

java -cp seth.jar seth.seth.eval.ApplyNER resources/mutationfinder/corpus/devo_text.txt resources/mutations.txt true resources/devo_text.seth
python resources/mutationfinder/origDist/ resources/devo_text.seth  resources/mutationfinder/corpus/devo_gold_std.txt

Precision 0.98 Recall 0.83 F₁ 0.90

MutationFinder-test corpus using original MutationFinder evaluation scripts (Caporaso et al., 2007)

java -cp seth.jar seth.seth.eval.ApplyNER resources/mutationfinder/corpus/test_text.txt resources/mutations.txt true resources/test_text.seth
python resources/mutationfinder/origDist/ resources/test_text.seth  resources/mutationfinder/corpus/test_gold_std.txt

Precision 0.98 Recall 0.82 F₁ 0.89

Corpus of Wei et al. (2013); train

java -cp seth.jar seth.seth.eval.ApplyNERToWei resources/Wei2013/train.txt  resources/mutations.txt  resources/Wei2013.seth
java -cp seth.jar seth.seth.eval.EvaluateWei resources/Wei2013/train.txt resources/Wei2013.seth

Precision 0.93 Recall 0.80 F₁ 0.86

Corpus of Wei et al. (2013); test

java -cp seth.jar seth.seth.eval.ApplyNERToWei resources/Wei2013/test.txt  resources/mutations.txt  resources/Wei2013.seth
java -cp seth.jar seth.seth.eval.EvaluateWei resources/Wei2013/test.txt resources/Wei2013.seth

Precision 0.95 Recall 0.77 F₁ 0.85

Corpus of Verspoor et al. (2013)

java -cp seth.jar seth.seth.eval.ApplyNerToVerspoor resources/Verspoor2013/corpus/ resources/mutations.txt resources/Verspoor2013.seth
java -cp seth.jar seth.seth.eval.EvaluateVerspoor resources/Verspoor2013/annotations/ resources/Verspoor2013.seth

Precision 0.87 Recall 0.14 F₁ 0.24

Evaluate NEN

Corpus of Thomas et al. (2011)

java -cp seth.jar myProperty.xml resources/thomas2011/corpus.txt

Precision 0.96 Recall 0.57 F₁ 0.72 Details: TP 303; FP 14; FN 224

Corpus of OSIRIS (Furlong et al., 2008)

java -cp seth.jar myProperty.xml resources/OSIRIS/corpus.xml

Precision 0.94 Recall 0.69 F₁ 0.79 Details: TP 179; FP 11; FN 79


Rebuilding the database used for SNP normalization

WARNING: We provide a stand-alone (embedded) Derby database. The following steps are only needed if you want to build the database from scratch. This database is only required for normalization to either dbSNP or UniProt.

The import script is tailored towards a mySQL database, but theoretically any other database can be used. However, in this case you have to adopt the following description to your database type. We would be happy to get feedback about using SETH with other databases.

Set up the database with all necessary tables

mysql <dbName> -h <hostname> -u <username> -p<password> resources/table.sql

Download the necessary files

Download a XML dump from dbSNP

gunzip gene2pubmed.gz

Download UniProt-KB


Download UniProt to Entrez gene mapping


Import the data files needed for normalization

Parse dbSNP-XML dump

This takes some compute resources and disk-space.

time java -cp seth.jar  /path/with/dbSNP-XML/files/... #Parse dbSNP dump
cat hgvs.tsv | cut -f 1-3 > hgvs2.tsv #Remove refseq information for derby-DB (only necessary to reduce database size)
#Remove duplicated entries 
split -l100000000 hgvs2.tsv '_tmp'; 
ls -1 _tmp* | while read FILE; do echo $FILE; sort $FILE -o $FILE ; done; #Individual sort
sort -u -m _tmp* -o hgvs.tsv.sorted #Merge sort

mysqlimport  --fields-terminated-by='\t' --delete --local --verbose --host <hostname> --user=<username> --password=<password> <dbName> PSM.tsv
mysqlimport  --fields-terminated-by='\t' --delete --local --verbose --host <hostname> --user=<username> --password=<password> <dbName> hgvs.tsv.sorted

Parse UniProt-XML for protein-sequence mutations (PSM) and post-translational modifications (e.g. signaling peptides)

Requires as input the UniProt-KB dump (uniprot_sprot.xml.gz) and the mapping from Entrez to UniProt (idmapping.dat.gz). Produces uniprot.dat and PSM.dat files

java -cp seth.jar seth.Uniprot2Tab uniprot_sprot.xml.gz idmapping.dat.gz uniprot.dat PSM.dat

Import gene2pubmed, UniProt and PSM into the mySQL Database

mysqlimport  --fields-terminated-by='\t' --delete --local --verbose --host <hostname> --user=<username> --password=<password> <dbName> gene2pubmed
mysqlimport  --fields-terminated-by='\t' --delete --local --verbose --host <hostname> --user=<username> --password=<password> <dbName> uniprot.dat
mysqlimport  --fields-terminated-by='\t' --local --verbose --host <hostname> --user=<username> --password=<password> <dbName> PSM.dat

Additionally, we included results from the gene name recognition tool GNAT applied on all of PubMed and PubMed Central. This data is only meant as a starting point, we recommend integrating other gene-NER tools. Updated gene-ner results are available on the GeneView web site (

Database migration

Finally, to allow for a better portability of SETH, we converted the original mySQL database into an embedded Derby database. For this we used Apache ddlUtils. For large databases we observed a high memory requirement using ddlutils. Therefore, we implemented a rather simple migration “script”, which exports the MySQL database to CSV and bulk imports the CSV files to a local Derby database. This script is only added for documentation purposes and should not be executed on the command shell. The script can be found here.

Latest derby database (18th May 2016)

Due to public request, we now also provide a derby database for the (currently) latest human dbSNP dump dbSNP147. Please be warned that the download is 7.5GB compressed and requires 51 GB uncompressed space. Runtime requirements for normalization also substantially increases with this version of dbSNP in comparison to the smaller dump. For example, normalization of the 296 documents from Thomas et al. (2011) increases from approximately 30 seconds to 140 seconds on a commodity laptop. We highly encourage the use of an dedicated database, such as MySQL or PostgreSQL to increase runtime.
Performance of this model on the previously introduced normalization corpora:

Corpus Precision Recall F₁
Thomas 0.89 0.59 0.71
Osiris 0.94 0.69 0.79

On both corpora we observe an increase in recall, accompanied with a decrease in precision. This behaviour is expected, as the larger database contains many more SNP candidates than the smaller database. For a detailed analysis, a larger normalization corpus with articles from different time periods would be required.

Bug reports

Issues and feature requests can be filed online


For questions and remarks please contact Philippe Thomas:

thomas [at] informatik [dot] hu-berlin [dot] de