dataesr/scientific-paragraphs-categorization huggingface dataset:
ph1. uni-koeln. de/</HomepageUrl> <Name>CDMS database</Name> <Query>select * where (RadTransWavelength &gt;= 2. 6006E7 AND RadTransWavelength &lt;= 2. 6008E7) AND ((MoleculeStoichiometricFormula = 'CO'))</Query> <OriginIdentifier>ivo://vamdc/cdms/vamdc-tap_12. 07</OriginIdentifier> </Origin> <Species> <Molecules> <Molecule speciesID="XCDMS-83"> <MolecularChemicalSpecies> <OrdinaryStructuralFormula> <Value>CO</Value> </OrdinaryStructuralFormula> <StoichiometricFormula>CO</StoichiometricFormula> <ChemicalName> <Value>Carbon Monoxide</Value> </ChemicalName> <InChI>1S/CO/c1-2</InChI> <InChIKey>UGFAIRIUMAVXCW-UHFFFAOYSA-N</InChIKey> xmlns:hundb="http://vamdc. org/xml/xsams/1. 0/cases/hundb" xmlns:lpcs="http://vamdc. org/xml/xsams/1. 0/cases/lpcs" xmlns:lpos="http://vamdc. org/xml/xsams/1. 0/cases/lpos" xmlns:ltcs="http://vamdc.
vamdc. org/activities/research/software/spectcol/</HomepageUrl <Name>Spectcol</Name> <Comments>Merging between collisional data from Basecol and spectroscopic data from CDMS by cross matching state with same J values in CO</Comments> <Origin xsi:type="VamdcNodeOriginType"> <Timestamp>2015-12-03T15:50:21+01:00</Timestamp> <Version versionID="VERCDMS1" global="false" timestamp="2000-10-01T12:00:00+01:00"> <SpeciesRef>XCDMS-83</SpeciesRef> <StateRef>SCDMS-83-1</StateRef> <StateRef>SCDMS-83-2</StateRef> <StateRef>SCDMS-origin-83</StateRef> <ProcessRef>PCDMS-R15140649</ProcessRef> </Version> <HomepageUrl>http://cdms. ph1. uni-koeln. de/</HomepageUrl> <Name>CDMS database</Name> <Comments>Spectroscopic data extracted from CDMS VAMDC node. </Comments> <Query>select * where (RadTransWavelength &gt;= 2.
The Seventh Framework Program. Call topic: INFRA-2008-1. 2. 2 and INFRA-2012 Scientific Data Infrastructure. Grant Agreement numbers: 239108 and 313284. We acknowledge support from Paris Astronomical Data Center. We thank Thomas Marquart, Guy Rixon and Mickail Doronin from the VAMDC Consortium for fruitful discussions. BCDMS0"> <Comments>This Source is a self-reference. It represents the database and the query that produced the xml document. The sourceID contains a timestamp. The full URL is given in the tag UniformResourceIdentifier but you need to unescape ampersands and angle brackets to re-use it. <Mass> <Value units="amu">4. 0026</Value> </Mass> </IsotopeParameters> <Ion speciesID="XBAS2"> <IonCharge>0</IonCharge> <AtomicState stateID="SBASET54-1"> <Comments>Energy level of He (no structure)</Comments> <SourceRef>BBAS0</SourceRef> <AtomicNumericalData> <StateEnergy> <Value units="1/cm">0.
5E-11 2. 6E-11</DataList> </Y> </TabulatedData> </DataSet> </DataSets> </CollisionalTransition> </Collisions> </Processes> <Sources> <Source sourceID="BBAS0"> <Category>database</Category> <SourceName>BASECOL database</SourceName> <Year>2015</Year> <Authors> <Author> <Name>M. -L. Dubernet</Name> </Author> </Authors> <UniformResourceIdentifier>http://basecol. obspm. fr</UniformResourceIdentifier> <ProductionDate>2015-12-03</ProductionDate> <Comments>select * where ((target. MoleculeStoichiometricFormula = 'CO')) AND ((collider. AtomSymbol = 'he'))</Comments> </Source> <Source sourceID="BBAS849"> <Category>journal</Category> <SourceName>apj</SourceName> <Year>2002</Year> <UniformResourceIdentifier> http://adsabs. harvard. edu/cgi-bin/nph-bib_query? bibcode=2002JChPh. 116. 4517K&db_key=PHY </UniformResourceIdentifier> </Source> </Sources> </XSAMSData> 60.
org/xml/xsams/1. 0/cases/dcs" xsi:type="dcs:Case" caseID="dcs" xsi:schemaLocation="http://vamdc. org/xml/xsams/1. 0/cases/dcs. /. /cases/dcs. xsd"> <dcs:QNs> <dcs:ElecStateLabel>X</dcs:ElecStateLabel> <dcs:v>0</dcs:v> <dcs:J>1</dcs:J> </dcs:QNs> <Probability> <TransitionProbabilityA> <Value units="1/cm">7. 20360334988e-08</Value> </TransitionProbabilityA> <IdealisedIntensity> <Value units="unitless">-5. 0105</Value> </IdealisedIntensity> <Multipole>E2</Multipole> </Probability> <ProcessClass> <Code>rota</Code> </ProcessClass> </RadiativeTransition> </Radiative> </Processes> <Sources> <Source sourceID="BCDMS0"> <Comments>This Source is a self-reference. It represents the database and the query that produced the xml document. The sourceID contains a timestamp. The full URL is given in the tag UniformResourceIdentifier but you need to unescape ampersands and angle brackets to re-use it.
Query was: select * where ((target. MoleculeStoichiometricFormula = 'CO')) AND ((collider. AtomSymbol = 'he'))</Comments> <Year>2015</Year> <Category>database</Category> <UniformResourceIdentifier>http://cdms. ph1. uni-koeln. de/cdms/tap/sync? </Author> <Author> <Name>Schlemmer, S. </Name> </Author> <Author> <Name>Stutzki, J. </Name> </Author> </Authors> <Title /> <Category>database</Category> <Year>2012</Year> <SourceName>CDMS database</SourceName> </Source> <Source sourceID="BCDMS-1681"> <Authors> <Author> <Name>Winnewisser, G. </Name> </Author> <Author> <Name>Belov, S. P. </Name> </Author> <Author> <Name>Klaus, T. </Name> </Author> <Author> <Name>Schieder, R. </Name> </Author> </Authors> <Title /> <Category>journal</Category> <Timestamp>2015-12-03T14:40:21+01:00</Timestamp> <Version versionID="VER001" global="false" timestamp="2015-09-01T08:10:12+01:00"> <SpeciesRef>XBAS2</SpeciesRef> <StateRef>SBASET54-1</StateRef> </Version> <HomepageUrl>http://basecol.
20360334988E-8</Value> <SpeciesRef>XCDMS-83</SpeciesRef> <StateRef>SCDMS-83-1</StateRef> </Product> <Product> <SpeciesRef>XBAS2</SpeciesRef> <StateRef>SBASET54-1</StateRef> </Product> <DataSets> <DataSet dataDescription="rateCoefficient"> <TabulatedData> <Comments>Rate coefficients</Comments> <X units="K"> <DataList>5. 0 10. 0 20. 0 40. 0 60. 0 80. 0 100. 0 200. 0 300. 0 500. 0</DataList> </X> <Y units="cm3/s"> <DataList>3. 4E-11 3. 2E-11 3. 0E-11 2. 8E-11 2. 7E-11 2. 6E-11 2. 6E-11 2. 5E-11 2. 5E-11 2. 6E-11</DataList> </Y> </TabulatedData> </Author> <Author> <Name>A. Dalgarno</Name> </Author> <Author> <Name>C. Cecchi-Pestellini</Name> </Author> <Author> <Name>E. Bodo</Name> </Author> </Authors> <Title>Rotational and Vibrational Excitation of CO Molecules by Collisions with $^{4}$He Atoms</Title> <Volume>571</Volume> <PageBegin>1015</PageBegin> <PageEnd>1020</PageEnd> <UniformResourceIdentifier> http://adsabs.
harvard. edu/cgi-bin/nph-bib_query? bibcode=2002JChPh. 116. 4517K&db_key=PHY </UniformResourceIdentifier> </Source> <Author> <Name>Mller, H. S. P. </Name> </Author> <Author> <Name>Endres, C. P. </Name> </Author> <Author> <Name>Schlemmer, S. </Name> </Author> <Author> <Name>Stutzki, J. </Name> </Author> </Authors> <Title /> <Category>database</Category> <Year>2012</Year> <SourceName>CDMS database</SourceName> </Source> <Source sourceID="BCDMS-1681"> <Authors> <Author> <DataList>1. 072 1. 148 1. 23 1. 318 1. 413 1. 514 1. 622 <Value units="1/cm">0. 0</Value> <Case xmlns:xsi="http://www. w3. org/2001/XMLSchema-instance" xsi:type="dcs:Case" caseID="dcs"> <dcs:QNs> <dcs:J>1</dcs:J> </dcs:QNs> </Case> </MolecularState> <MolecularState auxillary="true" </TransitionProbabilityA> <IdealisedIntensity> <Value units="unitless">-5.
0105</Value> </IdealisedIntensity> <Multipole>E2</Multipole> </Probability> <ProcessClass> <Code>rota</Code> </ProcessClass> </DataSet> </DataSets> </CollisionalTransition> </Collisions> </Processes> <Sources> <Source sourceID="BBAS0"> <Category>database</Category> <SourceName>BASECOL database</SourceName> <Name>Winnewisser, G. </Name> </Author> <Author> <Name>Belov, S. P. </Name> </Author> <Author> <Name>Klaus, T. </Name> </Author> <Source sourceID="Query was: select * where <Author> </AtomicComposition> </StateEnergy> </Reactant> <Authors> xsi:type="dcs:Case" caseID="dcs" </Case> LANG=VSS2&amp; <Year>1997</Year> <Ion speciesID="XBAS2"> stateID="SCDMS-origin-83"> </RadiativeTransition> <Year>2015</Year> ((target. MoleculeStoichiometricFormula = 'CO')) AND <Name>Schieder, R. </Name> </AtomicState> </MolecularStateCharacterisation> <Product> <Author> xsi:schemaLocation="http://vamdc.
0</Value> <CollisionalTransition id="PBASC50t2T1c1C1"> <Name>M. -L. Dubernet</Name> <Category>database</Category> <Title /> </Isotope> xsi:type="dcs:Case" caseID="dcs"> </Product> <Author> <dcs:ElecStateLabel>X</dcs:ElecStateLabel> </Species> UniformResourceIdentifier> <DigitalObjectIdentifier>10. 1006/jmsp. 1997. 7341</DigitalObjectIdentifier> structure)</Comments> </StateEnergy> <Comments> Rotational de-excitation of CO (v=0) by He </Author> <UniformResourceIdentifier>http://cdms. ph1. uni-koeln. de/cdms/tap/sync? <Category>journal</Category> </Atom> <dcs:QNs> <Product> <Name>A. Dalgarno</Name> <dcs:v>0</dcs:v> <Processes> <ProductionDate>2015-12-03</ProductionDate> </Source> <SourceRef>BBAS0</SourceRef> <TotalStatisticalWeight>1</TotalStatisticalWeight> (Cecchi-Pestellini &amp; al, 2002)</Comments> </Authors> LANG=VSS2&amp; <Year>1997</Year> </Atoms> <SpeciesRef>XBAS2</SpeciesRef> <dcs:ElecStateLabel>X</dcs:ElecStateLabel> </Author> <dcs:J>0</dcs:J> <Radiative> <Authors> </Sources> <AtomicNumericalData> <NuclearStatisticalWeight>1</NuclearStatisticalWeight> <SourceRef>BBAS0</SourceRef> <UniformResourceIdentifier>http://basecol.
obspm. fr</UniformResourceIdentifier> REQUEST=doQuery&amp;FORMAT=XSAMS&amp;QUERY= select * <SourceName>J. Mol. Spectrosc. </SourceName> <Molecules> <dcs:J>0</dcs:J> <StateRef>SBASET54-1</StateRef> <Author> </dcs:QNs> <RadiativeTransition id="PCDMS-R15140649" <Author> </XSAMSData> <StateEnergy> </MolecularStateCharacterisation> <ProcessClass> <ProductionDate>2015-12-03</ProductionDate> where ((target. MoleculeStoichiometricFormula = 'CO')) <Volume>184</Volume> <Molecule speciesID="XBAS52"> <MolecularChemicalSpecies> </dcs:QNs> </Case> </Product> <DataSets> <Name>C. Cecchi-Pestellini</Name> </Author> </Case> </MolecularState> process="excitation"> <SourceRef>BCDMS0</SourceRef> <Name>N. N. </Name> </Author> <Value units="1/cm">0. 0</Value> <Case <Code>inel</Code> <Comments>select * where AND ((collider.
AtomSymbol = 'he'))</ <PageBegin>468</PageBegin> C Merged output from SpectCol </StateEnergy> xmlns:dcs="http://vamdc. org/xml/xsams/1. 0/cases/dcs" </ProcessClass> ((target. MoleculeStoichiometricFormula = 'CO')) AND UniformResourceIdentifier> <DigitalObjectIdentifier>10. 1006/jmsp. 1997. 7341</DigitalObjectIdentifier> <OrdinaryStructuralFormula> </MolecularState> <DataSet dataDescription="rateCoefficient"> <Author> <MolecularState stateID="SCDMS-83-1"> <EnergyWavelength> </Authors> </AtomicNumericalData> xsi:type="dcs:Case" caseID="dcs" <Reactant> ((collider. AtomSymbol = 'he'))</Comments> <ProductionDate>2015-12-03</ProductionDate> </Source> A Sample output from BASECOL 1 <?xml version="1. 0" encoding="UTF-8" standalone="yes"?> 2 <XSAMSData xmlns="http://vamdc. org/xml/xsams/1. 0" 3 xmlns:cml="http://www.
The evolved XSAMS standard described in section 3 allows a data provider to univocally tag version of their data. Even if we strongly suggest that the VAMDC nodes give access (through the VAMC infrastructure) to all the subsequent versions of their data, we cannot enforce the data policy on the federated nodes. A given node may delete an old version when a new one is available. In that case the metadata policy will always allow the unambiguous identification of the extracted data, but data extraction re-execution will not be possible.
A highdensity, RAD-seq-based genetic map was used to anchor sequence scaffolds onto the chromosomes, considerably improving the large-scale assembly of the genome. In addition, a complete reannotation of the genome was carried out based on extensive RNA-seq data. This updated version of the genome annotation includes information about transcript isoforms and integrates non-coding loci such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Finally, we report additional resources including a genome-wide set of single nucleotide polymorphisms for genetic mapping and improvements to the genome database such as the addition of a JBrowse-based genome browser that allows multiple types of genomewide data to be visualised simultaneously.
RNA-seq The analyses carried out in this study used RNA-seq data generated for biological replicate (duplicate) samples of partheno-sporophytes and of both young and mature samples for both male and female gametophytes (ten libraries in all). The production of the young  (Lipinska et al. , 2015)  and mature  (Ahmed et al. , 2014)  gametophyte RNA-seq data has been described previously. For each of the replicate partheno-sporophyte samples, total RNA was extracted and used as a template by Fasteris (CH-1228 Plan-les-Ouates, Switzerland) to synthesise cDNA using an oligo-dT primer. The cDNA libraries were sequenced with Illumina HiSeq 2000 technology to generate 100 bp single-end reads. Data quality was assessed using the FASTX toolkit (http://hannonlab. cshl. edu/fastx_toolkit/index. html) and the reads were trimmed and filtered using a quality threshold of 25 (base calling) and a minimal size of 60 bp.
Only reads in which more than 75% of nucleotides had a minimal quality threshold of 20 were retained. Table  S1  shows the number of raw reads generated per sample and the number of reads remaining after trimming and filtering (cleaned reads). The cleaned reads were mapped to the Ectocarpus sp. genome  (Cock et al. , 2010 ) (available at Orcae;  Sterck et al. , 2012)  using Tophat2 and the Bowtie2 aligner  (Kim et al. , 2013). More than 90% of the sequencing reads for each library mapped to the genome. De novo assembly of the pooled RNA-seq data from the ten libraries was carried out using Trinity  (Grabherr et al. , 2011)  in normalized mode with default parameters. Weakly expressed transcripts (isoform percentage <1 and RPKM <1) were removed from the dataset. The remaining transcripts were aligned against the Ectocarpus reference genome (Ec32) using GenomeThreader  (Gremme et al.
Gene prediction Gene prediction was carried out using the EuGene program  (Foissac et al. , 2008) , as described previously  (Cock et al. , 2010). Alignments of the Trinity RNA-seq-derived transcripts against the Ectocarpus sp. reference genome were added to the EuGene pipeline in addition to the data used for the v1 annotation, which included splice site predictions generated by SpliceMachine  (Degroeve et al. , 2005)  and Ectocarpus Sanger EST data. The new set of EuGene gene structure predictions were compared with the gene structures of the v1 annotation using AEGeAn  (Standage & Brendel, 2012)  and a combination of automated and manual approaches was used to select the optimal gene structures. Briefly, automatic validation of new predictions was applied for genes where there were modifications to the UTRs, where additional exons were added or where there were modifications to the detailed structure of existing exons.
Manual annotation The v2 annotation took into account the functional and structural annotation of 325 and 410 genes, respectively, carried out through the Orcae database  (Sterck et al. , 2012)  since the publication of the v1 annotation. Many of the structural annotations were based on the same set of RNA-seq data that was used for the genome-wide gene structure prediction but exploited transcripts that had been generated using a reference-guided approach with Tophat2 and Cufflinks2  (Trapnell et al. , 2010; Kim et al. , 2013). Tophat2 was able to map 92% of the cleaned reads to the genome sequence and 36,565 transcripts were assembled by Cufflinks2 (including multiple transcripts for some loci) using the mapping information and the initial gene models as guides.
Annotation of gene functions Putative functions were assigned to the v2 genes based on the identification of protein domains using InterProScan, which carried out searches against all its component databases  (Jones et al. , 2014). Gene ontology categories were assigned using Blast2GO  (Conesa et al. , 2005). For genes where a manually assigned function was already available (3,442 genes), the InterProScan-based prediction was compared manually with the existing annotation and the most relevant annotation retained.
Detection of alternative transcripts To detect alternative transcripts of the set of 17,418 protein-coding loci, 507,634,855 million reads of RNA-seq data corresponding to diverse tissues and life cycle stages (Table  S1 ) were mapped to the Ectocarpus genome using Bowtie2  (Langmead et al. , 2009)  and transcripts were predicted genome-wide using Stringtie  (Pertea et al. , 2015)  with default parameters, guided by the annotation file from the v2 annotation. A Stringtie prediction was made for each library based on TopHat2 mapping files. The results were merged using Cuffmerge  (Trapnell et al. , 2010). Cuffcompare was used to assign the predicted transcripts to the reference genes. Transcripts with 3' UTRs > 9300 bp and/or 5' UTRs > 2500 bp were discarded. Only potential isoforms (class code = J, O and C) were retained.
Prediction of the coding regions of the alternative transcripts was carried out using Transdecoder  (Haas et al. , 2013). ORF predictions were filtered to retain complete coding sequences with both initiation and stop codons. The longest ORF was retained for each transcript. A global classification and quantification of the different types of alternative splicing that generated the transcript isoforms was obtained using SplAdder  (Kahles et al. , 2016)  based on the mapping of the pooled RNA-seq data.
Ectocarpus lncRNA loci were detected using FEELnc (https://github. com/tderrien/FEELnc) with default parameters and the output transcripts of the Stringtie analysis described in the previous section. The same specificity threshold (0. 97) was used for both protein-coding and non-coding transcripts to predict lncRNA loci. Transcripts overlapping annotated protein-coding genes (v2 annotation) were eliminated and a random forest approach based on ORF coverage (i. e. length of the longest ORF / length of the lncRNA transcript), transcript size and k-mer frequency was implemented to classify the remaining transcripts as mRNAs or lncRNAs. Loci with mono-exonic transcripts were eliminated to limit the inclusion of false positive loci due to read mapping ambiguity. An arbitrary minimum size of 200 nt was applied to eliminate loci encoding small RNA transcripts.
FEELnc also classifies the predicted lncRNA loci by determining 1) if they overlap (genic) or not (intergenic) with the nearest gene on the genome, designated the adjacent gene (and which can be a proteincoding gene or small-RNA-encoding locus), 2) if genic lncRNAs overlap with intron or exon regions of the adjacent gene and in which orientation, sense or antisense, and 3) how intergenic lncRNAs are orientated with respect to the adjacent gene (within 10 kbp) on the chromosome (same strand, convergent or divergent). A similar approach was used to detect S. japonica lncRNA loci. For this genome, the Stringtie transcript prediction used as input for FEELnc was based on mapping of  220, 551, 196  million RNA-seq reads to the S. japonica genome  (Ye et al. , 2015). ). Alignments of lncRNA sequences were carried out with SIM (http://web.
Genome-wide identification of sequence variants Genome sequence data was generated for the female outcrossing line Ec568 using Illumina HiSeq2500 technology (Fasteris, Switzerland), which produced 25,976,388,600 bp of 2x100 bp paired-end sequence. Sequence variants were detected as described previously  (Godfroy et al. , 2015). To determine whether sequence variants behaved as Mendelian loci, a cross between a UVmutagenised derivative of the reference genome strain Ec32 (strain Ec722) and the female outcrossing line Ec568  (Heesch et al. , 2010)  was used to generate a population of 180 progeny each corresponding to an independent meiotic event, segregating the two parental alleles of each variant locus. Two libraries were constructed with pools of 84 and 96 haploid, parthenosporophyte individuals and sequenced using Illumina HiSeq2500 technology (Fasteris, Switzerland) to generate 20,785,058,400 bp and 23,429,143,400 bp of 2x100 bp paired-end sequence, respectively.
Sequence variants were detected in each dataset as described previously  (Godfroy et al. , 2015)  and VarScan was used to identify SNPs shared by the two pools of haploid individuals. For each of these SNPs the sum of the variant frequencies observed in the two pools was calculated, and only those for which this sum was between 0. 8 and 1. 2 were retained. VarScan compare was then used to extract the Ec568 variants from the list of Mendelian segregating SNPs.
Reannotation of gene structure based on RNA-seq data The initial set of Ectocarpus gene models (referred to hereafter as the v1 annotation) was generated using EuGene  (Foissac et al. , 2008)  based on a limited amount of transcriptomic information (91,041 Sanger expressed sequence tags, ESTs;  Cock et al. , 2010)  and therefore involved a significant amount of de novo prediction. The v1 annotation has been gradually improved since 2010 by the addition of 325 functional and 410 structural annotations for individual genes through the Orcae database  (Sterck et al. , 2012). This gene-by-gene approach improved the quality of the annotation of a number of selected genes but it was necessary to extend the approach to improve annotation quality across the whole genome. A genome-wide reannotation, hereafter referred to as the v2 annotation, was therefore carried out based on the analysis of 642 million reads of RNA-seq data from ten different libraries  (Ahmed et al.
, 2014; Lipinska et al. , 2015 and  this study; Table  S1 ). This data was assembled into 34,551 de novo transcripts using Trinity  (Grabherr et al. , 2011). GenomeThreader  (Gremme et al. , 2005)  was able to align 91% of these transcripts to the genome. Gene prediction for the v2 annotation was then carried out using EuGene and the 34,551 de novo transcripts, along with 83,502 Sanger ESTs and SpliceMachine  (Degroeve et al. , 2005)  splice site predictions. The 21,958 preliminary gene models generated by this prediction were then compared with the 16,256 genes of the v1 annotation  (Cock et al. , 2010)  using AEGeAn  (Standage & Brendel, 2012 ) and a combination of automatic and manual criteria were used to evaluate the predictions and select the optimal gene model for each locus. This genome-wide reannotation integrated the results of the manual gene-by-gene annotation carried out since publication of the v1 annotation by preferentially retaining high quality, expert functional and structural annotations.
Of the former, 784 involved gene fusions (to produce 404 genes in the v2 annotation), 19 involved splitting v1 annotation gene predictions (to create 38 genes in the v2 annotation) and 123 genes were removed. The v2 annotation now includes coordinates for at least one of the UTR regions for 78. 7% of the 17,418 genes (compared to 52. 6% for the v1 annotation; Fig. 3 , Table  1 ). The v2 annotation is publically available through the ORCAE database (http://bioinformatics. psb. ugent. be/orcae/overview/Ectsi; Sterck et al. , 2012). The Ectocarpus genome database was modified to take into account the large-scale assembly of the sequence scaffolds. In particular, the sequentially numbered locusIDs were modified to indicate sequencial position on the pseudochromosome. The correspondence between the LocusIDs of the v1 and v2 annotations is given in Table  S2  and is also available as a download from the genome database (https://bioinformatics.
Prediction of gene function The final 17,418 genes of the v2 annotation were further analysed to improve the prediction of gene function by comparing protein sequences with the InterPro database using InterProScan  (Jones et al. , 2014)  and by using Blast2GO  (Conesa et al. , 2005)  to assign gene ontology (GO) categories. This process allowed functional annotations and GO categories to be assigned to 10,688 and 7,383 of the 17,418 v2 annotation genes, respectively (compared with 5,583 and 5,989, respectively, for the v1 annotation; Table  1 ). Of the 2,030 genes that were present in the.