Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms

AbstractSelective adhesion of fungal cells to one another and to foreign surfaces is fundamental for the development of multicellular growth forms and the successful colonization of substrates and host organisms. Accordingly, fungi possess diverse cell wall-associated adhesins, mostly large glycoproteins, which present N-terminal adhesion domains at the cell surface for ligand recognition and binding. In order to function as robust adhesins, these glycoproteins must be covalently linkedto the cell wall via C-terminal glycosylphosphatidylinositol (GPI) anchors by transglycosylation. In this review, we summarize the current knowledge on the structural and functional diversity of so far characterized protein families of adhesion domains and set it into a broad context by an in-depth bioinformatics analysis using sequence similarity networks. In addition, we discuss possible mechanisms for the membrane-to-cell wall transfer of fungal adhesins by membrane-anchored Dfg5 transglycosidases.

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Community detection in sequence similarity networks based on attribute clustering

PLoS ONE ◽

10.1371/journal.pone.0178650 ◽

2017 ◽

Vol 12 (7) ◽

pp. e0178650

Author(s):

Janamejaya Chowdhary ◽

Frank E. Löffler ◽

Jeremy C. Smith

Keyword(s):

Community Detection ◽

Sequence Similarity ◽

Similarity Networks ◽

Sequence Similarity Networks ◽

Attribute Clustering

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Exploring the sequence, function, and evolutionary space of protein superfamilies using sequence similarity networks and phylogenetic reconstructions

Methods in Enzymology - New Approaches for Flavin Catalysis ◽

10.1016/bs.mie.2019.03.015 ◽

2019 ◽

pp. 315-347 ◽

Cited By ~ 4

Author(s):

Janine N. Copp ◽

Dave W. Anderson ◽

Eyal Akiva ◽

Patricia C. Babbitt ◽

Nobuhiko Tokuriki

Keyword(s):

Sequence Similarity ◽

Protein Superfamilies ◽

Phylogenetic Reconstructions ◽

Similarity Networks ◽

Sequence Similarity Networks

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Utilizing Whole Fusobacterium Genomes To Identify, Correct, and Characterize Potential Virulence Protein Families

Journal of Bacteriology ◽

10.1128/jb.00273-19 ◽

2019 ◽

Vol 201 (23) ◽

Cited By ~ 5

Author(s):

Ariana Umaña ◽

Blake E. Sanders ◽

Christopher C. Yoo ◽

Michael A. Casasanta ◽

Barath Udayasuryan ◽

...

Keyword(s):

Colorectal Cancer ◽

Virulence Factors ◽

Serine Proteases ◽

Sequence Similarity ◽

Protein Families ◽

Emerging Pathogens ◽

Content Type ◽

Virulence Protein ◽

Similarity Networks ◽

Sequence Similarity Networks

ABSTRACT Fusobacterium spp. are Gram-negative, anaerobic, opportunistic pathogens involved in multiple diseases, including a link between the oral pathogen Fusobacterium nucleatum and the progression and severity of colorectal cancer. The identification and characterization of virulence factors in the genus Fusobacterium has been greatly hindered by a lack of properly assembled and annotated genomes. Using newly completed genomes from nine strains and seven species of Fusobacterium, we report the identification and corrected annotation of verified and potential virulence factors from the type 5 secreted autotransporter, FadA, and MORN2 protein families, with a focus on the genetically tractable strain F. nucleatum subsp. nucleatum ATCC 23726 and type strain F. nucleatum subsp. nucleatum ATCC 25586. Within the autotransporters, we used sequence similarity networks to identify protein subsets and show a clear differentiation between the prediction of outer membrane adhesins, serine proteases, and proteins with unknown function. These data have identified unique subsets of type 5a autotransporters, which are key proteins associated with virulence in F. nucleatum. However, we coupled our bioinformatic data with bacterial binding assays to show that a predicted weakly invasive strain of F. necrophorum that lacks a Fap2 autotransporter adhesin strongly binds human colonocytes. These analyses confirm a gap in our understanding of how autotransporters, MORN2 domain proteins, and FadA adhesins contribute to host interactions and invasion. In summary, we identify candidate virulence genes in Fusobacterium, and caution that experimental validation of host-microbe interactions should complement bioinformatic predictions to increase our understanding of virulence protein contributions in Fusobacterium infections and disease. IMPORTANCE Fusobacterium spp. are emerging pathogens that contribute to mammalian and human diseases, including colorectal cancer. Despite a validated connection with disease, few proteins have been characterized that define a direct molecular mechanism for Fusobacterium pathogenesis. We report a comprehensive examination of virulence-associated protein families in multiple Fusobacterium species and show that complete genomes facilitate the correction and identification of multiple, large type 5a secreted autotransporter genes in previously misannotated or fragmented genomes. In addition, we use protein sequence similarity networks and human cell interaction experiments to show that previously predicted noninvasive strains can indeed bind to and potentially invade human cells and that this could be due to the expansion of specific virulence proteins that drive Fusobacterium infections and disease.

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A Clustering Method for Analysis of Sequence Similarity Networks of Proteins Using Maximal Components of Graphs

IPSJ Digital Courier ◽

10.2197/ipsjdc.4.207 ◽

2008 ◽

Vol 4 ◽

pp. 207-216

Author(s):

Morihiro Hayashida ◽

Tatsuya Akutsu ◽

Hiroshi Nagamochi

Keyword(s):

Sequence Similarity ◽

Clustering Method ◽

Similarity Networks ◽

Sequence Similarity Networks

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Sequence Similarity Networks for the Protein Universe

The FASEB Journal ◽

10.1096/fasebj.29.1_supplement.573.17 ◽

2015 ◽

Vol 29 (S1) ◽

Author(s):

Katie Whalen ◽

Boris Sadkhin ◽

Daniel Davidson ◽

John Gerlt

Keyword(s):

Sequence Similarity ◽

Protein Universe ◽

Similarity Networks ◽

Sequence Similarity Networks

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Transfer index, NetUniFrac and some useful shortest path-based distances for community analysis in sequence similarity networks

Bioinformatics ◽

10.1093/bioinformatics/btaa043 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2740-2749

Author(s):

Henry Xing ◽

Steven W Kembel ◽

Vladimir Makarenkov

Keyword(s):

Gene Transfer ◽

Horizontal Gene Transfer ◽

Phylogenetic Tree ◽

Shortest Path ◽

Sequence Similarity ◽

Community Analysis ◽

Supplementary Information ◽

Similarity Networks ◽

Transfer Index ◽

Sequence Similarity Networks

Abstract Motivation Phylogenetic trees and the methods for their analysis have played a key role in many evolutionary, ecological and bioinformatics studies. Alternatively, phylogenetic networks have been widely used to analyze and represent complex reticulate evolutionary processes which cannot be adequately studied using traditional phylogenetic methods. These processes include, among others, hybridization, horizontal gene transfer, and genetic recombination. Nowadays, sequence similarity and genome similarity networks have become an efficient tool for community analysis of large molecular datasets in comparative studies. These networks can be used for tackling a variety of complex evolutionary problems such as the identification of horizontal gene transfer events, the recovery of mosaic genes and genomes, and the study of holobionts. Results The shortest path in a phylogenetic tree is used to estimate evolutionary distances between species. We show how the shortest path concept can be extended to sequence similarity networks by defining five new distances, NetUniFrac, Spp, Spep, Spelp and Spinp, and the Transfer index, between species communities present in the network. These new distances can be seen as network analogs of the traditional UniFrac distance used to assess dissimilarity between species communities in a phylogenetic tree, whereas the Transfer index is intended for estimating the rate and direction of gene transfers, or species dispersal, between different phylogenetic, or ecological, species communities. Moreover, NetUniFrac and the Transfer index can be computed in linear time with respect to the number of edges in the network. We show how these new measures can be used to analyze microbiota and antibiotic resistance gene similarity networks. Availability and implementation Our NetFrac program, implemented in R and C, along with its source code, is freely available on Github at the following URL address: https://github.com/XPHenry/Netfrac. Supplementary information Supplementary data are available at Bioinformatics online.

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Motif Analysis in k-mer Networks: An Approach towards Understanding SARS-CoV-2 Geographical Shifts

10.1101/2020.10.04.325662 ◽

2020 ◽

Author(s):

Sourav Biswas ◽

Suparna Saha ◽

Sanghamitra Bandyopadhyay ◽

Malay Bhattacharyya

Keyword(s):

Geographical Origin ◽

Sequence Similarity ◽

Motif Analysis ◽

Significant Difference ◽

The Usa ◽

Under Sampling ◽

Similarity Networks ◽

Sequence Similarity Networks ◽

Frequency Counts ◽

Day By Day

AbstractWith an increasing number of SARS-CoV-2 sequences available day by day, new genomic information is getting revealed to us. As SARS-CoV-2 sequences highlight wide changes across the samples, we aim to explore whether these changes reveal the geographical origin of the corresponding samples. The k-mer distributions, denoting normalized frequency counts of all possible combinations of nucleotide of size upto k, are often helpful to explore sequence level patterns. Given the SARS-CoV-2 sequences are highly imbalanced by its geographical origin (relatively with a higher number samples collected from the USA), we observe that with proper under-sampling k-mer distributions in the SARS-CoV-2 sequences predict its geographical origin with more than 90% accuracy. The experiments are performed on the samples collected from six countries with maximum number of sequences available till July 07, 2020. This comprises SARS-CoV-2 sequences from Australia, USA, China, India, Greece and France. Moreover, we demonstrate that the changes of genomic sequences characterize the continents as a whole. We also highlight that the network motifs present in the sequence similarity networks have a significant difference across the said countries. This, as a whole, is capable of predicting the geographical shift of SARS-CoV-2.

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Eukaryote Genes Are More Likely than Prokaryote Genes to Be Composites

Genes ◽

10.3390/genes10090648 ◽

2019 ◽

Vol 10 (9) ◽

pp. 648

Author(s):

Yaqing Ou ◽

James O. McInerney

Keyword(s):

Sequence Similarity ◽

Evolutionary Process ◽

Mobile Genetic Elements ◽

Functional Categories ◽

Genetic Elements ◽

New Genes ◽

Domains Of Life ◽

Similarity Networks ◽

Partial Homology ◽

Sequence Similarity Networks

The formation of new genes by combining parts of existing genes is an important evolutionary process. Remodelled genes, which we call composites, have been investigated in many species, however, their distribution across all of life is still unknown. We set out to examine the extent to which genomes from cells and mobile genetic elements contain composite genes. We identify composite genes as those that show partial homology to at least two unrelated component genes. In order to identify composite and component genes, we constructed sequence similarity networks (SSNs) of more than one million genes from all three domains of life, as well as viruses and plasmids. We identified non-transitive triplets of nodes in this network and explored the homology relationships in these triplets to see if the middle nodes were indeed composite genes. In total, we identified 221,043 (18.57%) composites genes, which were distributed across all genomic and functional categories. In particular, the presence of composite genes is statistically more likely in eukaryotes than prokaryotes.

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