InterPro in 2011: new developments in the family and domain prediction database

Hunter, Sarah; Jones, Philip; Mitchell, Alex L.; Apweiler, Rolf; Attwood, Teresa K.; Bateman, Alex; Bernard, Thomas; Binns, David; Bork, Peer; Burge, Sarah; Castro, Edouard de; Coggill, Penny; Corbett, Matthew J.; Das, Ujjwal; Daugherty, Louise C.; Duquenne, Lauranne; Finn, Robert D.; Fraser, Matthew; Gough, Julian; Haft, Daniel H.; Hulo, Nicolas; Kahn, Daniel; Kelly, Elizabeth A.; Letunić, Ivica; Lonsdale, David; López, Rodrigo; Madera, Martin; Maslen, John; McAnulla, Craig; McDowall, Jennifer; McMenamin, Conor; Mi, Huaiyu; Mutowo, Prudence; Mulder, Nicola; Natale, Darren A.; Orengo, Christine A.; Pesseat, Sebastien; Punta, Marco; Quinn, Antony F.; Rivoire, Catherine; Sangrador-Vegas, Amaia; Selengut, Jeremy D.; Sigrist, Christian J. A.; Scheremetjew, Maxim; Tate, John; Thimmajanarthanan, Manjulapramila; Thomas, Paul D.; Wu, Cathy H.; Yeats, Corin; Yong, Siew-Yit

doi:10.1093/nar/gkr948

Cited by 940 publications

(749 citation statements)

References 28 publications

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“…In addition to homology, there exist many AFP methods that exploit additional information extracted from the genome sequence, e.g., conserved gene neighborhoods (Ling et al, 2009), phylogenetic distribution (Pellegrini et al, 1999), protein motifs and biophysical properties (Ofer and Linial, 2015), codon usage biases (Kriško et al, 2014), remote homology (Hawkins et al, 2009;Sokolov and Ben-Hur, 2010), and composition of protein domains (Hunter et al, 2011;Punta et al, 2011). Moreover, inference using genomic information can be further supplemented by experimental data: gene expression (Tian et al, 2008), protein-protein interactions (Cao and Cheng, 2015) or protein structure (Wass et al, 2012), and also by text-mining the scientific literature .…”

Section: Introductionmentioning

confidence: 99%

Extensive complementarity between gene function prediction methods

2016

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Motivation: The number of sequenced genomes rises steadily, but we still lack the knowledge about the biological roles of many genes. Automatic function prediction (AFP) is thus a necessity. We hypothesize that AFP approaches which draw on distinct genome features may be useful for predicting different types of gene functions, motivating a systematic analysis of the benefits gained by obtaining and integrating such predictions. Results: Our pipeline amalgamates 5,133,543 genes from 2,071 genomes in a single massive analysis that evaluates five established genomic AFP methodologies. While 1,227 Gene Ontology terms yielded reliable predictions, the majority of these functions were accessible to only one or two of the methods. Moreover, different methods tend to assign a GO term to non-overlapping sets of genes. Thus, inferences made by diverse AFP methods display a striking complementary, both gene-wise and function-wise. Because of this, a viable integration strategy is to simply rely on a single mostconfident prediction per gene/function, instead of enforcing agreement across multiple AFP methods. Using an information-theoretic approach, we estimate that current databases contain 29.2 bits/gene of known E. coli gene functions. This can be increased by up to 5.5 bits/gene using individual AFP methods, or by 11 additional bits/gene upon integration, thereby providing a highly-ranking predictor on the CAFA2 community benchmark. Availability of more sequenced genomes boosts the predictive accuracy of AFP approaches and also the benefit from integrating them.

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Section: Introductionmentioning

confidence: 99%

Extensive complementarity between gene function prediction methods

2016

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“…Only transcripts with a C-score ≥ 0.5, and peptide coverage ≥ 0.5, were retained. Finally, gene models with more than 30% of their coding peptides annotated as Pfam 65 or Interprot 66 TE domains were filtered out. Functional annotation of protein-coding genes was achieved using BLASTP 67 (E-value 1e-05) against two integrated protein sequence databases; SwissProt and TrEMBL 68 .…”

mentioning

confidence: 99%

“…Functional annotation of protein-coding genes was achieved using BLASTP 67 (E-value 1e-05) against two integrated protein sequence databases; SwissProt and TrEMBL 68 . Protein domains were annotated by searching against the InterPro (V32.0) 66 and Pfam (V27.0) databases 65 , using InterProScan (V4.8) and HMMER 69 (V3.1), respectively. The Gene Ontology 70 (GO) terms for each gene were obtained from the corresponding InterPro or Pfam entry.…”

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confidence: 99%

Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement

et al. 2015

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“…To identify protein domains, the nucleotide sequences were conceptually translated via ESTScan (Lottaz et al 2003) and subsequently analysed via InterProScan (Hunter et al 2011). Parasitiformes mRNA and UniGene sequences were downloaded from NCBI to generate the customized scoring matrix used in ESTScan translation.…”

Section: Functional Sequence Annotationmentioning

confidence: 99%

Whole transcriptome analysis of the poultry red mite Dermanyssus gallinae (De Geer, 1778)

Schicht

Qi²,

Poveda³

et al. 2013

Parasitology

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SUMMARY Although the poultry red mite Dermanyssus gallinae (De Geer, 1778) is the major parasitic pest in poultry farming causing substantial economic losses every year, nucleotide data are rare in the public databases. Therefore, de novo sequencing covering the transcriptome of D. gallinae was carried out resulting in a dataset of 232 097 singletons and 42 130 contiguous sequences (contigs) which were subsequently clustered into 24 140 isogroups consisting of 35 788 isotigs. After removal of sequences possibly originating from bacteria or the chicken host, 267 464 sequences (231 657 singletons, 56 contigs and 35 751 isotigs) remained, of which 10·3% showed homology to proteins derived from other organisms. The most significant Blast top-hit species was the mite Metaseiulus occidentalis followed by the tick Ixodes scapularis. To gain functional knowledge of D. gallinae transcripts, sequences were mapped to Gene Ontology terms, Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways and parsed to InterProScan. The transcriptome dataset provides new insights in general mite genetics and lays a foundation for future studies on stage-specific transcriptomics as well as genomic, proteomic, and metabolomic explorations and might provide new perspectives to control this parasitic mite by identifying possible drug targets or vaccine candidates. It is also worth noting that in different tested species of the class Arachnida no 28S rRNA was detectable in the rRNA profile, indicating that 28S rRNA might consists of two separate, hydrogen-bonded fragments, whose (heat-induced) disruption may led to co-migration with 18S rRNA.

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InterPro in 2011: new developments in the family and domain prediction database

Cited by 940 publications

References 28 publications

Extensive complementarity between gene function prediction methods

Extensive complementarity between gene function prediction methods

Sequencing of allotetraploid cotton (Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement

Whole transcriptome analysis of the poultry red mite Dermanyssus gallinae (De Geer, 1778)

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