comparison snpSift_dbnsfp.xml @ 1:13191d4914f7 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tool_collections/snpsift/snpsift_dbnsfp commit 21b46ae2c90ba7e569b2b3a9eaf938f8dedb2c31

0:dc480609d9c1

<tool id="snpSift_dbnsfp" name="SnpSift dbNSFP" version="4.0.0">
    <description>Add Annotations from dbNSFP</description>
    <expand macro="requirements" />
    <macros>
        <import>snpSift_macros.xml</import>
    </macros>
    <command>
        java -Xmx6G -jar \$SNPEFF_JAR_PATH/SnpSift.jar dbnsfp -v
        #if $db.dbsrc == 'cached' :
          -db $db.dbnsfp 
          #if $db.annotations and $db.annotations.__str__ != '':
            -f "$db.annotations"
          #end if
        #else :
          -db "${db.dbnsfpdb.extra_files_path}/${db.dbnsfpdb.metadata.bgzip}"
          #if $db.annotations and $db.annotations.__str__ != '':
            -f "$db.annotations"
          #end if
        #end if          
        $input > $output  
        2> tmp.err &amp;&amp; grep -v file tmp.err
    </command>
    <inputs>
        <param name="input" type="data" format="vcf" label="Variant input file in VCF format"/>
        <conditional name="db">
            <param name="dbsrc" type="select" label="dbNSFP ">
                <option value="cached">Locally installed dbNSFP database </option>
                <option value="history">dbNSFP database from your history</option>
            </param>
            <when value="cached">
                <param name="dbnsfp" type="select" label="Genome">
                    <options from_data_table="snpsift_dbnsfp">
                        <column name="name" index="1"/>
                        <column name="value" index="2"/>
                    </options>
                </param>
                <param name="annotations" type="select" multiple="true" display="checkboxes" label="Annotate with">
                    <options from_data_table="snpsift_dbnsfp">
                        <column name="name" index="3"/>
                        <column name="value" index="3"/>
                        <filter type="param_value" ref="dbnsfp" column="2" />
                        <filter type="multiple_splitter" column="3" separator=","/>
                    </options>
                </param>
            </when>
            <when value="history">
                <param name="dbnsfpdb" type="data" format="snpsiftdbnsfp" label="DbNSFP"/>

                  </options>
                </param>
            </when>
        </conditional>
    </inputs>
    <expand macro="stdio" />
    <outputs>
        <data format="vcf" name="output" />
    </outputs>
    <tests>
        <test>

                    <has_text text="dbNSFP_SIFT_score=0.15" />
                </assert_contents>
            </output>
        </test>
    </tests>
    <help>

The dbNSFP is an integrated database of functional predictions from multiple algorithms (SIFT, Polyphen2, LRT and MutationTaster, PhyloP and GERP++, etc.).


  1000Gp1_AC
    Alternative allele counts in the whole 1000 genomes phase 1 (1000Gp1) data
  1000Gp1_AF

  ESP6500_AA_AF
    Alternative allele frequency in the African American samples of the NHLBI GO Exome Sequencing Project (ESP6500 data set)
  ESP6500_EA_AF
    Alternative allele frequency in the European American samples of the NHLBI GO Exome Sequencing Project (ESP6500 data set)
  FATHMM_pred
    If a FATHMM_score is <=-1.5 (or rankscore <=0.81415) the corresponding non-synonymous SNP is predicted as "D(AMAGING)"; otherwise it is predicted as "T(OLERATED)". Multiple predictions separated by ";"
  FATHMM_rankscore
    FATHMMori scores were ranked among all FATHMMori scores in dbNSFP. The rankscore is the ratio of the rank of the score over the total number of FATHMMori scores in dbNSFP. If there are multiple scores, only the most damaging (largest) rankscore is presented. The scores range from 0 to 1
  FATHMM_score
    FATHMM default score (FATHMMori)
  fold-degenerate

  LR_score
    Our logistic regression (LR) based ensemble prediction score, which incorporated 10 scores (SIFT, PolyPhen-2 HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor, FATHMM, LRT, SiPhy, PhyloP) and the maximum frequency observed in the 1000 genomes populations. Larger value means the SNV is more likely to be damaging. Scores range from 0 to 1
  LRT_Omega
    Estimated nonsynonymous-to-synonymous-rate ratio (Omega, reported by LRT)
  LRT_converted_rankscore
    LRTori scores were first converted as LRTnew=1-LRTori*0.5 if Omega<1, or LRTnew=LRTori*0.5 if Omega>=1. Then LRTnew scores were ranked among all LRTnew scores in dbNSFP. The rankscore is the ratio of the rank over the total number of the scores in dbNSFP. The scores range from 0.00166 to 0.85682
  LRT_pred
    LRT prediction, D(eleterious), N(eutral) or U(nknown), which is not solely determined by the score
  LRT_score
    The original LRT two-sided p-value (LRTori), ranges from 0 to 1
  MutationAssessor_pred

  Reliability_index
    Number of observed component scores (except the maximum frequency in the 1000 genomes populations) for RadialSVM and LR. Ranges from 1 to 10. As RadialSVM and LR scores are calculated based on imputed data, the less missing component scores, the higher the reliability of the scores and predictions
  SIFT_converted_rankscore
    SIFTori scores were first converted to SIFTnew=1-SIFTori, then ranked among all SIFTnew scores in dbNSFP. The rankscore is the ratio of the rank the SIFTnew score over the total number of SIFTnew scores in dbNSFP. If there are multiple scores, only the most damaging (largest) rankscore is presented. The rankscores range from 0.02654 to 0.87932
  SIFT_pred
    If SIFTori is smaller than 0.05 (rankscore>0.55) the corresponding non-synonymous SNP is predicted as "D(amaging)"; otherwise it is predicted as "T(olerated)". Multiple predictions separated by ";"
  SIFT_score
    SIFT score (SIFTori). Scores range from 0 to 1. The smaller the score the more likely the SNP has damaging effect. Multiple scores separated by ";"
  SiPhy_29way_logOdds
    SiPhy score based on 29 mammals genomes. The larger the score, the more conserved the site
  SiPhy_29way_pi

    Uniprot ID number. Multiple entries separated by ";"
  UniSNP_ids
    rs numbers from UniSNP, which is a cleaned version of dbSNP build 129, in format: rs number1;rs number2;...


The website for  dbNSFP database is https://sites.google.com/site/jpopgen/dbNSFP  and there is only annotation for human hg18 and hg19 genome builds. 

However, any dbNSFP-like tabular file that be can used with SnpSift dbnsfp if it has::

	- The first line of the file must be column headers that name the annotations.  
	- The first 4 columns are required and must be::

	4	100239319	T	A	H	L	ADH1B	0
	4	100239319	T	C	H	R	ADH1B	0.15
	4	100239319	T	G	H	P	ADH1B	0


The uploaded tabular file should be set to datatype: "dbnsfp.tabular"
Using "Convert Format" the "dbnsfp.tabular" can be converted to the correct format for SnpSift dbnsfp.

The procedure for preparing the dbNSFP data for use in SnpSift dbnsfp is in the SnpSift documentation.


@EXTERNAL_DOCUMENTATION@
	http://snpeff.sourceforge.net/SnpSift.html#dbNSFP

@CITATION_SECTION@


    </help>
</tool>

1:13191d4914f7

<tool id="snpSift_dbnsfp" name="SnpSift dbNSFP" version="@WRAPPER_VERSION@.0">
    <description>Add Annotations from dbNSFP or similar annotation DBs</description>
    <macros>
        <import>snpSift_macros.xml</import>
    </macros>
    <expand macro="requirements" />
    <expand macro="stdio" />
    <expand macro="version_command" />
    <command><![CDATA[
        java -Xmx6G -jar "\$SNPEFF_JAR_PATH/SnpSift.jar" dbnsfp -v
        #if $db.dbsrc == 'cached':
          -db "$db.dbnsfp"
          #if $db.annotations and str($db.annotations) != '':
            -f "$db.annotations"
          #end if
        #else:
          -db "${db.dbnsfpdb.extra_files_path}/${db.dbnsfpdb.metadata.bgzip}"
          #if $db.annotations and str($db.annotations) != '':
            -f "$db.annotations"
          #end if
        #end if
        "$input" > "$output"
        2> tmp.err && grep -v file tmp.err
]]>
    </command>
    <inputs>
        <param name="input" type="data" format="vcf" label="Variant input file in VCF format"/>
        <conditional name="db">
            <param name="dbsrc" type="select" label="dbNSFP ">
                <option value="cached">Locally installed dbNSFP database </option>
                <option value="history">dbNSFP database from your history</option>
            </param>
            <when value="cached">
                <param name="dbnsfp" type="select" label="Genome">
                    <options from_data_table="snpsift_dbnsfps">
                        <column name="name" index="2"/>
                        <column name="value" index="3"/>
                    </options>
                </param>
                <param name="annotations" type="select" multiple="true" display="checkboxes" label="Annotate with">
                    <options from_data_table="snpsift_dbnsfps">
                        <column name="name" index="4"/>
                        <column name="value" index="4"/>
                        <filter type="param_value" ref="dbnsfp" column="3" />
                        <filter type="multiple_splitter" column="4" separator=","/>
                    </options>
                </param>
            </when>
            <when value="history">
                <param name="dbnsfpdb" type="data" format="snpsiftdbnsfp" label="DbNSFP"/>

                  </options>
                </param>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="vcf" name="output" />
    </outputs>
    <tests>
        <test>

                    <has_text text="dbNSFP_SIFT_score=0.15" />
                </assert_contents>
            </output>
        </test>
    </tests>
    <help><![CDATA[

The dbNSFP is an integrated database of functional predictions from multiple algorithms (SIFT, Polyphen2, LRT and MutationTaster, PhyloP and GERP++, etc.).
It contains variant annotations such as:


  1000Gp1_AC
    Alternative allele counts in the whole 1000 genomes phase 1 (1000Gp1) data
  1000Gp1_AF

  ESP6500_AA_AF
    Alternative allele frequency in the African American samples of the NHLBI GO Exome Sequencing Project (ESP6500 data set)
  ESP6500_EA_AF
    Alternative allele frequency in the European American samples of the NHLBI GO Exome Sequencing Project (ESP6500 data set)
  FATHMM_pred
    If a FATHMM_score is <=-1.5 (or rankscore <=0.81415) the corresponding non-synonymous SNP is predicted as "D(AMAGING)"; otherwise it is predicted as "T(OLERATED)". Multiple predictions separated by ";"
  FATHMM_rankscore
    FATHMMori scores were ranked among all FATHMMori scores in dbNSFP. The rankscore is the ratio of the rank of the score over the total number of FATHMMori scores in dbNSFP. If there are multiple scores, only the most damaging (largest) rankscore is presented. The scores range from 0 to 1
  FATHMM_score
    FATHMM default score (FATHMMori)
  fold-degenerate

  LR_score
    Our logistic regression (LR) based ensemble prediction score, which incorporated 10 scores (SIFT, PolyPhen-2 HDIV, PolyPhen-2 HVAR, GERP++, MutationTaster, Mutation Assessor, FATHMM, LRT, SiPhy, PhyloP) and the maximum frequency observed in the 1000 genomes populations. Larger value means the SNV is more likely to be damaging. Scores range from 0 to 1
  LRT_Omega
    Estimated nonsynonymous-to-synonymous-rate ratio (Omega, reported by LRT)
  LRT_converted_rankscore
    LRTori scores were first converted as LRTnew=1-LRTori*0.5 if Omega<1, or LRTnew=LRTori*0.5 if Omega>=1. Then LRTnew scores were ranked among all LRTnew scores in dbNSFP. The rankscore is the ratio of the rank over the total number of the scores in dbNSFP. The scores range from 0.00166 to 0.85682
  LRT_pred
    LRT prediction, D(eleterious), N(eutral) or U(nknown), which is not solely determined by the score
  LRT_score
    The original LRT two-sided p-value (LRTori), ranges from 0 to 1
  MutationAssessor_pred

  Reliability_index
    Number of observed component scores (except the maximum frequency in the 1000 genomes populations) for RadialSVM and LR. Ranges from 1 to 10. As RadialSVM and LR scores are calculated based on imputed data, the less missing component scores, the higher the reliability of the scores and predictions
  SIFT_converted_rankscore
    SIFTori scores were first converted to SIFTnew=1-SIFTori, then ranked among all SIFTnew scores in dbNSFP. The rankscore is the ratio of the rank the SIFTnew score over the total number of SIFTnew scores in dbNSFP. If there are multiple scores, only the most damaging (largest) rankscore is presented. The rankscores range from 0.02654 to 0.87932
  SIFT_pred
    If SIFTori is smaller than 0.05 (rankscore>0.55) the corresponding non-synonymous SNP is predicted as "D(amaging)"; otherwise it is predicted as "T(olerated)". Multiple predictions separated by ";"
  SIFT_score
    SIFT score (SIFTori). Scores range from 0 to 1. The smaller the score the more likely the SNP has damaging effect. Multiple scores separated by ";"
  SiPhy_29way_logOdds
    SiPhy score based on 29 mammals genomes. The larger the score, the more conserved the site
  SiPhy_29way_pi

    Uniprot ID number. Multiple entries separated by ";"
  UniSNP_ids
    rs numbers from UniSNP, which is a cleaned version of dbSNP build 129, in format: rs number1;rs number2;...


The website for  dbNSFP database is https://sites.google.com/site/jpopgen/dbNSFP and there is only annotation for human genome builds. 

The procedure for preparing the dbNSFP data for use in SnpSift dbnsfp is in the SnpSift documentation:
*( It also provides links for dbNSFP databases prebuilt for SnpSift )*
http://snpeff.sourceforge.net/SnpSift.html#dbNSFP

However, any dbNSFP-like tabular file that be can used with SnpSift dbnsfp if it has::

	- The first line of the file must be column headers that name the annotations.  
	- The first 4 columns are required and must be::

	4	100239319	T	A	H	L	ADH1B	0
	4	100239319	T	C	H	R	ADH1B	0.15
	4	100239319	T	G	H	P	ADH1B	0


The galaxy datatypes for dbNSFP can automatically convert the specially formatted tabular file for use by SnpSift dbNSFP:
  1. Upload the tabular file, set the datatype as: **"dbnsfp.tabular"**
  2. Edit the history dataset attributes (pencil icon): Use "Convert Format" to convert the **"dbnsfp.tabular"** to the correct format for SnpSift dbnsfp: **"snpsiftdbnsfp"**.


@EXTERNAL_DOCUMENTATION@
	http://snpeff.sourceforge.net/SnpSift.html#dbNSFP

@CITATION_SECTION@

]]>
    </help>
    <expand macro="citations">
        <citation type="doi">DOI: 10.1002/humu.21517</citation>
        <citation type="doi">DOI: 10.1002/humu.22376</citation>
        <citation type="doi">DOI: 10.1002/humu.22932</citation>
        <citation type="doi">doi: 10.1093/hmg/ddu733</citation>
        <citation type="doi">doi: 10.1093/nar/gku1206</citation>
        <citation type="doi">doi: 10.3389/fgene.2012.00035</citation>
    </expand>
</tool>