BRIDES: A New Fast Algorithm and Software for Characterizing Evolving Similarity Networks Using Breakthroughs, Roadblocks, Impasses, Detours, Equals and Shortcuts

Lord, Étienne; Cam, Margaux Le; Bapteste, Éric; Méheust, Raphaël; Lapointe, François‐Joseph

doi:10.1371/journal.pone.0161474

Cited by 4 publications

(6 citation statements)

References 33 publications

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“…Since the network approach is based on concepts from graph theory, there exists a full mathematical toolkit to evaluate central ecological and evolutionary theories (e.g. Forster et al, 2015;Corel et al, 2016;Lord et al, 2016). But it was not until recently that the strength of SSNs could be exploited for large HTS datasets.…”

Section: Introductionmentioning

confidence: 99%

Improving eDNA‐based protist diversity assessments using networks of amplicon sequence variants

Forster

Lentendu

Filker

et al. 2019

Environmental Microbiology

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Summary Effective and precise grouping of highly similar sequences remains a major bottleneck in the evaluation of high‐throughput sequencing datasets. Amplicon sequence variants (ASVs) offer a promising alternative that may supersede the widely used operational taxonomic units (OTUs) in environmental sequencing studies. We compared the performance of a recently developed pipeline based on the algorithm DADA2 for obtaining ASVs against a pipeline based on the algorithm SWARM for obtaining OTUs. Illumina‐sequencing of 29 individual ciliate species resulted in up to 11 ASVs per species, while SWARM produced up to 19 OTUs per species. To improve the congruency between species diversity and molecular diversity, we applied sequence similarity networks (SSNs) for second‐level sequence grouping into network sequence clusters (NSCs). At 100% sequence similarity in SWARM‐SSNs, NSC numbers decreased from 7.9‐fold overestimation without abundance filter, to 4.5‐fold overestimation when an abundance filter was applied. For the DADA2‐SSN approach, NSC numbers decreased from 3.5‐fold to 3‐fold overestimation. Rand index cluster analyses predicted best binning results between 97% and 94% sequence similarity for both DADA2‐SSNs and SWARM‐SSNs. Depending on the ecological questions addressed in an environmental sequencing study with protists we recommend ASVs as replacement for OTUs, best in combination with SSNs.

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Section: Introductionmentioning

confidence: 99%

Improving eDNA‐based protist diversity assessments using networks of amplicon sequence variants

Forster

Lentendu

Filker

et al. 2019

Environmental Microbiology

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“…2 and supplementary fig. 2 , Supplementary Material online) (see Materials and Methods and Lord 2016 ). This allowed us to contrast the topologies of networks built exclusively from REF nodes (original networks) with that in which ENV nodes had been included (augmented networks).…”

Section: Resultsmentioning

confidence: 99%

“…BRIDES software characterizes new paths that are created when extra nodes are added to an original network ( Lord et al. 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Gene Similarity Networks Unveil a Potential Novel Unicellular Group Closely Related to Animals from the Tara Oceans Expedition

Arroyo

Iannes

Bapteste

et al. 2020

Genome Biology and Evolution

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The Holozoa clade comprises animals and several unicellular lineages (choanoflagellates, filastereans and teretosporeans). Understanding their full diversity is essential to address the origins of animals and other evolutionary questions. However, they are poorly known. To provide more insights into the real diversity of holozoans and check for undiscovered diversity, we here analysed 18S rDNA metabarcoding data from the global Tara Oceans expedition. To overcome the low phylogenetic information contained in the metabarcoding dataset (composed of sequences from the short V9 region of the gene), we used similarity networks by combining two datasets: unknown environmental sequences from Tara Oceans and known reference sequences from GenBank. We then calculated network metrics to compare environmental to reference sequences. These metrics reflected the divergence between both types of sequences and provided an effective way to search for evolutionary relevant diversity, further validated by phylogenetic placements. Our results showed that the percentage of unicellular holozoan diversity remains hidden. We found novelties in several lineages, especially in Acanthoecida choanoflagellates. We also identified a potential new holozoan group that could not be assigned to any of the described extant clades. Data on geographical distribution showed that, although ubiquitous, each unicellular holozoan lineage exhibits a different distribution pattern. We also identified a positive association between new animal hosts and the ichthyosporean symbiont Creolimax fragrantissima, as well as for other holozoans previously reported as free-living. Overall, our analyses provide a fresh perspective into the diversity and ecology of unicellular holozoans, highlighting the amount of undescribed diversity.

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“…Gene-sharing networks are useful tools for exploring overall patterns of gene sharing between genomes. Recently, Lord et al developed BRIDES, a software package that specifically identifies different kinds of patterns in evolving gene-sharing networks after the addition of new genome nodes [144]. However, in genesharing networks the kind of gene families that are being shared is often overlooked.…”

Section: Exploiting Ssns To Analyze Classificationsmentioning

confidence: 99%

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution

Watson

Lannes

Pathmanathan

et al. 2019

Methods in Molecular Biology

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In the post genomic era, large and complex molecular datasets from genome and metagenome sequencing projects expand the limits of what is possible for bioinformatic analyses. Network-based methods are increasingly used to complement phylogenetic analysis in studies in molecular evolution, including comparative genomics, classification, and ecological studies. Using network methods, the vertical and horizontal relationships between all genes or genomes, whether they are from cellular chromosomes or mobile genetic elements, can be explored in a single expandable graph. In recent years, development of new methods for the construction and analysis of networks has helped to broaden the availability of these approaches from programmers to a diversity of users. This chapter introduces the different kinds of networks based on sequence similarity that are already available to tackle a wide range of biological questions, including sequence similarity networks, gene-sharing networks and bipartite graphs, and a guide for their construction and analyses.

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BRIDES: A New Fast Algorithm and Software for Characterizing Evolving Similarity Networks Using Breakthroughs, Roadblocks, Impasses, Detours, Equals and Shortcuts

Cited by 4 publications

References 33 publications

Improving eDNA‐based protist diversity assessments using networks of amplicon sequence variants

Improving eDNA‐based protist diversity assessments using networks of amplicon sequence variants

Gene Similarity Networks Unveil a Potential Novel Unicellular Group Closely Related to Animals from the Tara Oceans Expedition

The Methodology Behind Network Thinking: Graphs to Analyze Microbial Complexity and Evolution

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