CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

Cui, Xuefeng; Lu, Zhiwu; Wang, Sheng; Wang, Jim Jing-Yan; Gao, Xin

doi:10.1093/bioinformatics/btw271

Cited by 57 publications

(42 citation statements)

References 43 publications

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“…Extensive experiments demonstrate the power of our method on recovering missing associations, and on discovering associations for novel genes and/or diseases that are not seen in the training. Our framework is generic and can be readily applied to tackle other important problems in computational biology, such as drug disease association (Pushpakom et al, 2019) and homolog detection for protein structure prediction (Cui et al, 2016).…”

Section: Resultsmentioning

confidence: 99%

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

Kuwahara

Yang

et al. 2019

Preprint

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Motivation: Proper prioritization of candidate genes is essential to the genome-based diagnostics of a range of genetic diseases. However, it is a highly challenging task involving limited and noisy knowledge of genes, diseases and their associations. While a number of computational methods have been developed for the disease gene prioritization task, their performance is largely limited by manually crafted features, network topology, or pre-defined rules of data fusion. Results: Here, we propose a novel graph convolutional networkbased disease gene prioritization method, PGCN, through the systematic embedding of the heterogeneous network made by genes and diseases, as well as their individual features. The embedding learning model and the association prediction model are trained together in an end-to-end manner. We compared PGCN with five state-of-the-art methods on the Online Mendelian Inheritance in Man (OMIM) dataset for tasks to recover missing associations and discover associations between novel genes and diseases. Results show significant improvements of PGCN over the existing methods. We further demonstrate that our embedding has biological meaning and can capture functional groups of genes. Availability: The main program and the data are available at https: //github.com/lykaust15/Disease_gene_prioritization_GCN.

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Section: Resultsmentioning

confidence: 99%

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

Kuwahara

Yang

et al. 2019

Preprint

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“…We performed genome scans for small RNAs across 12 bee genomes (Table S2) using covariance models implemented with Infernal cmsearch using the gathering threshold for inclusion (--cut_ga) [45] to find all Rfam accessions in each bee genome. We used Spearman rank regressions to test for significant associations between miRNA copy-number and social biology.…”

Section: Methodsmentioning

confidence: 99%

Brain microRNAs among social and solitary bees

Kapheim

Jones

Søvik

et al. 2019

Preprint

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37Evolutionary transitions to a social lifestyle in insects are associated with lineage-specific 38 changes in gene expression, but the key nodes that drive these regulatory changes are largely 39 unknown. We tested the hypothesis that changes in gene regulation associated with social 40 evolution are facilitated by lineage-specific function of microRNAs (miRNAs). Genome scans 41 across 12 bee species showed that miRNA copy-number is mostly conserved and not associated 42 with sociality. However, deep sequencing of small RNAs in six bee species revealed a 43 substantial proportion (20-35%) of detected miRNAs had lineage-specific expression in the 44 brain, 24-72% of which did not have homologs in other species. Lineage-specific miRNAs 45 disproportionately target lineage-specific genes, and have lower expression levels than shared 46 miRNAs. The predicted targets of lineage-specific miRNAs are enriched for genes related to 47 social behavior in social species, but they are not enriched for genes under positive selection. 48Together, these results suggest that novel miRNAs may contribute to lineage-specific patterns of 49 social evolution. Our analyses also support the hypothesis that many new miRNAs are purged by 50 selection due to deleterious effects on mRNA targets, and suggest genome structure is not as 51 influential in regulating bee miRNA evolution as has been shown for mammalian miRNAs. 52 53 specific 55 56 57Eusociality has evolved several times in the hymenopteran insects. In its most basic form, 58 this lifestyle involves reproductive queens living with their worker daughters who forego direct 59 reproduction to cooperatively defend the nest, care for their siblings, and forage for the colony. 60 Due to the complex nature of this lifestyle, the evolution of eusociality likely requires 61 modification of molecular pathways related to development, behavior, neurobiology, physiology, 62 and morphology [1]. The evolution of eusociality is thus expected to involve both genetic 63 changes as well as changes in the way the genome responds to the environment [2]. It is 64therefore unsurprising that recent studies aimed at identifying the genomic signatures of eusocial 65 evolution in insects have found that social species share an increased capacity for gene regulation 66 [3,4]. Evidence for this comes from signatures of rapid evolution of genes involved in 67 transcription and translation, gene family expansions of transcription factors, and increasing 68 potential for DNA methylation and transcription factor binding activity in conserved genes. 69Interestingly, while these types of regulatory changes are common to independent origins and 70 elaborations of eusociality, the specific genes and regulatory elements involved are unique to 71 each lineage [3][4][5]. This suggests that lineage-specific processes are influential in generating new 72 patterns of gene regulation that contribute to social behavior. 73 74 Small, non-coding RNAs such as microRNAs (miRNAs) may be an important source of 75 regulatory novel...

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“…We include a stringent filter against these putative structured RNAs being coding by requiring no significant hits to the nr database via BLASTx 12 and no significant p-values from RNAcode 13 , which narrowed our list of putative structured RNAs to 1,862. To avoid proposing the same structure multiple times, we filtered this list to 1,525 unique putative structured RNAs by ensuring the same structures do not match the same regions using CMsearch 15 . To ensure that the predicted structure motifs were transcribed, we calculated RPKM values specifically for the structure motifs (instead of the entire intergenic region), and found that 1,213 of these structures contained strong RNA-Seq signal (RPKM > 20).…”

Section: Rna-seq Along With Comparative Genomics Enables Targeted Himentioning

confidence: 99%

“…In separate searches after our Prokka 17 predictions were generated, we then searched for the Rfam 14 database against these predictions to determine overlap. CMsearch version 1.1.2 15,41 was used for all searches with an e-value cutoff of 1 x 10 5 considered as a significant match.…”

Section: Motif Searchesmentioning

confidence: 99%

A combined RNA-Seq and comparative genomics approach identifies 1,085 candidate structured RNAs expressed in human microbiomes

Fremin

Bhatt

2020

Preprint

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Structured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches by searching for motif structures in genomic sequence data. However, only a subset of these candidate structured RNAs, those from culturable, well-studied microbes, have been shown to be transcribed. As the human microbiome contains thousands of species and strains of microbes, we sought to apply both informatic and experimental approaches to these organisms to identify novel transcribed structured RNAs. We combine an experimental approach, RNA-Seq, with an informatic approach, comparative genomics across the human microbiome project, to discover 1,085 candidate, conserved structured RNAs that are actively transcribed in human fecal microbiomes.These predictions include novel tracrRNAs that associate with Cas9 and RNA structures encoded in overlapping regions of the genome that are in opposing orientations. In summary, this combined experimental and computational approach enables the discovery of thousands of novel candidate structured RNAs.

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CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

Cited by 57 publications

References 43 publications

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networks

Brain microRNAs among social and solitary bees

A combined RNA-Seq and comparative genomics approach identifies 1,085 candidate structured RNAs expressed in human microbiomes

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