InterProScan 5: genome-scale protein function classification

Jones, Philip; Binns, David; Chang, Hsin-Yu; Fraser, Matthew; Li, Weizhong; McAnulla, Craig; McWilliam, Hamish; Maslen, John; Mitchell, Alex; Nuka, Gift; Pesseat, Sebastien; Quinn, Antony F.; Sangrador-Vegas, Amaia; Scheremetjew, Maxim; Yong, Siew-Yit; López, Rodrigo; Hunter, Sarah

doi:10.1093/bioinformatics/btu031

Cited by 6,811 publications

(5,764 citation statements)

References 28 publications

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“…First, we performed BLAST searches of taimen transcripts against several databases and found that 157,172 (82.5%) transcripts had significant sequence homology to the databases, and most (96.3%) were annotated with reference transcripts and proteins of Atlantic salmon (Lien et al., 2016) (Table 1). A homology search of the Pfam databases showed that 52,762 transcripts had at least one significant hit, and 79,800 transcripts were annotated using Interproscan (Jones et al., 2014). …”

Section: Resultsmentioning

confidence: 99%

“…The sequences with significant hits in the Uniprot database or Pfam database were assigned GO terms using the Trinotate package, and the GO terms were assigned using Interproscan (Jones et al., 2014). Among the total transcripts, 72,728 transcripts were assigned to 15,107 GO terms, including 10,185 biological process terms, 1,429 cellular component terms, and 3,493 molecular function terms (Figure 3).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, these analysis results were integrated with Trinotate (https://trinotate.github.io/) to perform biological function annotations and Gene Ontology (GO) classifications. We also used Interproscan to search the protein conserved domains and perform GO classifications (Jones et al., 2014). KEGG annotation was performed using GhostKOALA (Kanehisa, Sato, & Morishima, 2016).…”

Section: Methodsmentioning

confidence: 99%

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De novo assembly and characterization of the Hucho taimen transcriptome

Tong

Xü

Zhang

et al. 2017

Ecology and Evolution

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Taimen (Hucho taimen) is an important ecological and economic species that is classified as vulnerable by the IUCN Red List of Threatened Species; however, limited genomic information is available on this species. RNA‐Seq is a useful tool for obtaining genetic information and developing genetic markers for nonmodel species in addition to its application in gene expression profiling. In this study, we performed a comprehensive RNA‐Seq analysis of taimen. We obtained 157 M clean reads (14.7 Gb) and used them to de novo assemble a high‐quality transcriptome with a N50 size of 1,060 bp. In the assembly, 82% of the transcripts were annotated using several databases, and 14,666 of the transcripts contained a full open reading frame. The assembly covered 75% of the transcripts of Atlantic salmon and 57.3% of the protein‐coding genes of rainbow trout. To learn about the genome evolution, we performed a systematic comparative analysis across 11 teleosts including eight salmonids and found 313 unique gene families in taimen. Using Atlantic salmon and rainbow trout transcriptomes as the background, we identified 250 positive selection transcripts. The pathway enrichment analysis revealed a unique characteristic of taimen: It possesses more immune‐related genes than Atlantic salmon and rainbow trout; moreover, some genes have undergone strong positive selection. We also developed a pipeline for identifying microsatellite marker genotypes in samples and successfully identified 24 polymorphic microsatellite markers for taimen. These data and tools are useful for studying conservation genetics, phylogenetics, evolution among salmonids, and selective breeding for threatened taimen.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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De novo assembly and characterization of the Hucho taimen transcriptome

Tong

Xü

Zhang

et al. 2017

Ecology and Evolution

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“…Removed TE consisted of hits above the e‐value threshold of 1e‐5, with more than 40% of query coverage and longer than 100 nt. The gene models predicted by MAKER‐P were functionally analysed using InterProScan version 5.16.55 (Jones et al ., 2014) including annotation with Gene Ontology (GO) and biological pathway information. The InterProScan results were further parsed for additional functional evidence (GO terms and KEGG pathway) using interproscanParser script available at iPlant.…”

Section: Methodsmentioning

confidence: 99%

Sequencing of Australian wild rice genomes reveals ancestral relationships with domesticated rice

Brożyńska

Copetti

Furtado

et al. 2017

Plant Biotechnology Journal

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SummaryThe related A genome species of the Oryza genus are the effective gene pool for rice. Here, we report draft genomes for two Australian wild A genome taxa: O. rufipogon‐like population, referred to as Taxon A, and O. meridionalis‐like population, referred to as Taxon B. These two taxa were sequenced and assembled by integration of short‐ and long‐read next‐generation sequencing (NGS) data to create a genomic platform for a wider rice gene pool. Here, we report that, despite the distinct chloroplast genome, the nuclear genome of the Australian Taxon A has a sequence that is much closer to that of domesticated rice (O. sativa) than to the other Australian wild populations. Analysis of 4643 genes in the A genome clade showed that the Australian annual, O. meridionalis, and related perennial taxa have the most divergent (around 3 million years) genome sequences relative to domesticated rice. A test for admixture showed possible introgression into the Australian Taxon A (diverged around 1.6 million years ago) especially from the wild indica/O. nivara clade in Asia. These results demonstrate that northern Australia may be the centre of diversity of the A genome Oryza and suggest the possibility that this might also be the centre of origin of this group and represent an important resource for rice improvement.

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“…In particular, the FASTA2RDF, GeneCaller (a semantic wrapper for Prodigal (2.6.2) 17 ) and InterPro (interproscan-5.17-56.0) (a semantic wrapper for InterProScan 18 ) modules were used to handle and re-annotate the genome sequences. This resulted in 446 annotated genomes (7 × 60 genomes and 1 × 26 genomes) with provenance.…”

Section: Methodsmentioning

confidence: 99%

Protein domain architectures provide a fast, efficient and scalable alternative to sequence-based methods for comparative functional genomics

et al. 2016

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A functional comparative genome analysis is essential to understand the mechanisms underlying bacterial evolution and adaptation. Detection of functional orthologs using standard global sequence similarity methods faces several problems; the need for defining arbitrary acceptance thresholds for similarity and alignment length, lateral gene acquisition and the high computational cost for finding bi-directional best matches at a large scale. We investigated the use of protein domain architectures for large scale functional comparative analysis as an alternative method. The performance of both approaches was assessed through functional comparison of 446 bacterial genomes sampled at different taxonomic levels. We show that protein domain architectures provide a fast and efficient alternative to methods based on sequence similarity to identify groups of functionally equivalent proteins within and across taxonomic bounderies. As the computational cost scales linearly, and not quadratically with the number of genomes, it is suitable for large scale comparative analysis. Running both methods in parallel pinpoints potential functional adaptations that may add to bacterial fitness.

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InterProScan 5: genome-scale protein function classification

Cited by 6,811 publications

References 28 publications

De novo assembly and characterization of the Hucho taimen transcriptome

De novo assembly and characterization of the Hucho taimen transcriptome

Sequencing of Australian wild rice genomes reveals ancestral relationships with domesticated rice

Protein domain architectures provide a fast, efficient and scalable alternative to sequence-based methods for comparative functional genomics

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