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Fungal Small RNAs Suppress Plant Immunity by Hijacking Host RNA Interference Pathways

RT @aemonten: [Report] Fungal Small RNAs Suppress Plant Immunity by Hijacking Host RNA Interference Pathways http://t.co/wTysUyLgtP
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Plant Elite Squad: First Defense Line and Resistance Genes – Identification, Diversity and Functional Roles

Plant Elite Squad: First Defense Line and Resistance Genes – Identification, Diversity and Functional Roles | Computational biology | Scoop.it
Plants exhibit sensitive mechanisms to respond to environmental stresses, presenting some specific and non-specific reactions when attacked by pathogens, including organisms from different classes and complexity, as viroids, viruses, bacteria, fungi and nematodes. A crucial step to define the fate of the plant facing an invading pathogen is the activation of a compatible Resistance (R) gene, the focus of the present review. Different aspects regarding R-genes and their products are discussed, including pathogen recognition mechanisms, signaling and effects on induced and constitutive defense processes, splicing and post transcriptional mechanisms involved. There are still countless challenges to the complete understanding of the mechanisms involving R-genes in plants, in particular those related to the interactions with other genes of the pathogen and of the host itself, their regulation, acting mechanisms at transcriptional and post-transcriptional levels, as well as the influence of other types of stress over their regulation. A magnification of knowledge is expected when considering the novel information from the omics and systems biology.

Via Christophe Jacquet
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Rescooped by Asela Wijeratne from Pathogens, speciation, domestication, genomics, fungi, biotic interactions
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Synima: a Synteny imaging tool for annotated genome assemblies - BMC Bioinformatics

Synima: a Synteny imaging tool for annotated genome assemblies - BMC Bioinformatics | Computational biology | Scoop.it

Background
Ortholog prediction and synteny visualization across whole genomes are valuable methods for detecting and representing a range of evolutionary processes such as genome expansion, chromosomal rearrangement, and chromosomal translocation. Few standalone methods are currently available to visualize synteny across any number of annotated genomes.

Results
Here, I present a Synteny Imaging tool (Synima) written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima – and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from https://github.com/rhysf/Synima under the MIT License.

Conclusions
Synima should be a valuable tool for visualizing synteny between two or more annotated genome assemblies.


Via Ronny Kellner, Pierre Gladieux
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Rescooped by Asela Wijeratne from The science toolbox
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Tutorial: How to upload your data to the evil Sequence Read Archive (SRA)?

Tutorial: How to upload your data to the evil Sequence Read Archive (SRA)? | Computational biology | Scoop.it

I am writing this tutorial because THE WHOLE UPLOADING PROCESS IS KILLING ME!!! I hope my sacrifice could save everybody's time and efforts in the future. In this tutorial, I am going to show you how to upload large RNA-Seq data (>10GB) to SRA and create Bioproject and BioSample for the first time user. You may ask "Why also Bioproject and BioSample?" That's because they are the prerequisite data to SRA. I will get the that point later.


Via Pierre Gladieux, Niklaus Grunwald
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Rescooped by Asela Wijeratne from Plants & Evolution
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New reference genome sequences of hot pepper reveal the massive evolution of plant disease-resistance genes by retroduplication

Background Transposable elements are major evolutionary forces which can cause new genome structure and species diversification. The role of transposable elements in the expansion of nucleotide-binding and leucine-rich-repeat proteins (NLRs), the major disease-resistance gene families, has been unexplored in plants. Results We report two high-quality de novo genomes (Capsicum baccatum and C. chinense) and an improved reference genome (C. annuum) for peppers. Dynamic genome rearrangements involving translocations among chromosomes 3, 5, and 9 were detected in comparison between C. baccatum and the two other peppers. The amplification of athila LTR-retrotransposons, members of the gypsy superfamily, led to genome expansion in C. baccatum. In-depth genome-wide comparison of genes and repeats unveiled that the copy numbers of NLRs were greatly increased by LTR-retrotransposon-mediated retroduplication. Moreover, retroduplicated NLRs are abundant across the angiosperms and, in most cases, are lineage-specific. Conclusions Our study reveals that retroduplication has played key roles for the massive emergence of NLR genes including functional disease-resistance genes in pepper plants.


Via Pierre-Marc Delaux
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Rescooped by Asela Wijeratne from Bioinformatics, Comparative Genomics and Molecular Evolution
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Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
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Arjen ten Have's curator insight, October 20, 2017 8:08 AM
imagine this is a survey done in the US.
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De novo yeast genome assemblies from MinION, PacBio and MiSeq platforms

De novo yeast genome assemblies from MinION, PacBio and MiSeq platforms | Computational biology | Scoop.it

Long-read sequencing technologies such as Pacific Biosciences and Oxford Nanopore MinION are capable of producing long sequencing reads with average fragment lengths of over 10,000 base-pairs and maximum lengths reaching 100,000 base- pairs. Compared with short reads, the assemblies obtained from long-read sequencing platforms have much higher contig continuity and genome completeness as long fragments are able to extend paths into problematic or repetitive regions. Many successful assembly applications of the Pacific Biosciences technology have been reported ranging from small bacterial genomes to large plant and animal genomes. Recently, genome assemblies using Oxford Nanopore MinION data have attracted much attention due to the portability and low cost of this novel sequencing instrument. In this paper, we re-sequenced a well characterized genome, the Saccharomyces cerevisiae S288C strain using three different platforms: MinION, PacBio and MiSeq. We present a comprehensive metric comparison of assemblies generated by various pipelines and discuss how the platform associated data characteristics affect the assembly quality. With a given read depth of 31X, the assemblies from both Pacific Biosciences and Oxford Nanopore MinION show excellent continuity and completeness for the 16 nuclear chromosomes, but not for the mitochondrial genome, whose reconstruction still represents a significant challenge.


Via Pierre Gladieux
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Data-driven modeling of collaboration networks: a cross-domain analysis

Data-driven modeling of collaboration networks: a cross-domain analysis | Computational biology | Scoop.it

We analyze large-scale data sets about collaborations from two different domains: economics, specifically 22,000 R&D alliances between 14,500 firms, and science, specifically 300,000 co-authorship relations between 95,000 scientists. Considering the different domains of the data sets, we address two questions: (a) to what extent do the collaboration networks reconstructed from the data share common structural features, and (b) can their structure be reproduced by the same agent-based model. In our data-driven modeling approach we use aggregated network data to calibrate the probabilities at which agents establish collaborations with either newcomers or established agents. The model is then validated by its ability to reproduce network features not used for calibration, including distributions of degrees, path lengths, local clustering coefficients and sizes of disconnected components. Emphasis is put on comparing domains, but also sub-domains (economic sectors, scientific specializations). Interpreting the link probabilities as strategies for link formation, we find that in R&D collaborations newcomers prefer links with established agents, while in co-authorship relations newcomers prefer links with other newcomers. Our results shed new light on the long-standing question about the role of endogenous and exogenous factors (i.e., different information available to the initiator of a collaboration) in network formation.

 

Data-driven modeling of collaboration networks: a cross-domain analysis
Mario V Tomasello, Giacomo VaccarioEmail authorView ORCID ID profile and Frank Schweitzer
EPJ Data Science20176:22
https://doi.org/10.1140/epjds/s13688-017-0117-5


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assignPOP: An R package for population assignment using genetic, non‐genetic, or integrated data in a machine‐learning framework

assignPOP: An R package for population assignment using genetic, non‐genetic, or integrated data in a machine‐learning framework | Computational biology | Scoop.it

1.The use of biomarkers (e.g., genetic, microchemical, and morphometric characteristics) to discriminate among and assign individuals to a population can benefit species conservation and management by facilitating our ability to understand population structure and demography. 2.Tools that can evaluate the reliability of large genomic datasets for population discrimination and assignment, as well as allow their integration with non-genetic markers for the same purpose, are lacking. Our R package, assignPOP, provides both functions in a supervised machine-learning framework. 3.assignPOP uses Monte-Carlo and K-fold cross-validation procedures, as well as principal component analysis (PCA), to estimate assignment accuracy and membership probabilities, using training (i.e., baseline source population) and test (i.e., validation) datasets that are independent. A user then can build a specified predictive model based on the relative sizes of these datasets and classification functions, including linear discriminant analysis, support vector machine, naïve Bayes, decision tree, and random forest. 4.assignPOP can benefit any researcher who seeks to use genetic or non-genetic data to infer population structure and membership of individuals. assignPOP is a freely available R package under the GPL license, and can be downloaded from CRAN or at https://github.com/alexkychen/assignPOP. A comprehensive tutorial can also be found at https://alexkychen.github.io/assignPOP/.


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An Overview of Python Deep Learning Frameworks

An Overview of Python Deep Learning Frameworks | Computational biology | Scoop.it
By Madison May, indico. I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
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Rescooped by Asela Wijeratne from Viruses, Immunology & Bioinformatics from Virology.uvic.ca
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A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome

A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome | Computational biology | Scoop.it
Advancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines. Chicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150–199 for GS FLX+ and bases 90–99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME. The three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies.

Via Chris Upton + helpers
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All biology is computational biology

All biology is computational biology | Computational biology | Scoop.it
Here, I argue that computational thinking and techniques are so central to the quest of understanding life that today all biology is computational biology. Computational biology brings order into our understanding of life, it makes biological concepts rigorous and testable, and it provides a reference map that holds together individual insights. The next modern synthesis in biology will be driven by mathematical, statistical, and computational methods being absorbed into mainstream biological training, turning biology into a quantitative science.

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Science’s 1%: How income inequality is getting worse in research

Science’s 1%: How income inequality is getting worse in research | Computational biology | Scoop.it
Wages for top scientists are shooting skywards while others are being left behind.

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Bayesian molecular dating: opening up the black box

Bayesian molecular dating: opening up the black box | Computational biology | Scoop.it

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a ‘molecular clock’. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the ‘black box’ of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales.


Via Pierre Gladieux
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New Phytologist: Multiple strategies for pathogen perception by plant immune receptors (2017)

New Phytologist: Multiple strategies for pathogen perception by plant immune receptors (2017) | Computational biology | Scoop.it

Plants have evolved a complex immune system to protect themselves against phytopathogens. A major class of plant immune receptors called nucleotide-binding domain and leucine-rich repeat-containing proteins (NLRs) is ubiquitous in plants and is widely used for crop disease protection, making these proteins critical contributors to global food security. Until recently, NLRs were thought to be conserved in their modular architecture and functional features. Investigation of their biochemical, functional and structural properties has revealed fascinating mechanisms that enable these proteins to perceive a wide range of pathogens. Here, I review recent insights demonstrating that NLRs are more mechanistically and structurally diverse than previously thought. I also discuss how these findings provide exciting future prospects to improve plant disease resistance.


Via Kamoun Lab @ TSL, Pierre Gladieux
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Bridget Barker's curator insight, November 21, 2017 9:22 AM
Always thinking about links between animal and plant pathogens
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The fundamental advantages of temporal networks

Historically, network science focused on static networks, in which nodes are connected by permanent links. However, in networked systems ranging from protein-protein interactions to social networks, links change. Although it might seem that permanent links would make it easier to control a system, Li et al. demonstrate that temporality has advantages in real and simulated networks. Temporal networks can be controlled more efficiently and require less energy than their static counterparts.

 

The fundamental advantages of temporal networks
A. Li, S. P. Cornelius, Y.-Y. Liu, L. Wang, A.-L. Barabási
Science  24 Nov 2017:
Vol. 358, Issue 6366, pp. 1042-1046
DOI: 10.1126/science.aai7488


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Marcelo Errera's curator insight, November 27, 2017 6:57 PM
One more interesting article on the evolution of networks. Networks, or anything, can only evolve in time only if there is degree of freedom. Live networks will never stay static in time. If it's static, it must be dead in the design evolution sense.
It's a law of physics.
Rescooped by Asela Wijeratne from The science toolbox
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A Bayesian method for detecting pairwise associations in compositional data

A Bayesian method for detecting pairwise associations in compositional data | Computational biology | Scoop.it

Data from many fields are available primarily in the form of proportions, also referred to as compositions, which impose mathematical constraints on identifying interactions among components in the underlying systems. In particular, correlations cannot be calculated directly from proportions or from count data that give rise to them. Methods that work around this difficulty generally do so by imposing strong assumptions about the distribution of underlying data or associated correlations, and these in turn often prevent quantifying uncertainty in the resulting estimates of correlation. We developed a statistical model (BAnOCC: Bayesian Analysis of Compositional Covariance) that both estimates correlations between counts or proportions and provides a posterior distribution for each correlation that quantifies how uncertain the estimate is. BAnOCC does well at controlling the number of false positives in simulated data and can be practically applied to a wide range of proportional data types.


Via Niklaus Grunwald
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DNA sequencing at 40: past, present and future : Nature : Nature Research

DNA sequencing at 40: past, present and future : Nature : Nature Research | Computational biology | Scoop.it

Via Chris Upton + helpers
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Sources of Fungal Genetic Variation and Associating It with Phenotypic Diversity

The first eukaryotic genome to be sequenced was fungal, and there continue to be more sequenced genomes in the kingdom Fungi than in any other eukaryotic kingdom. Comparison of these genomes reveals many sources of genetic variation, from single nucleotide polymorphisms to horizontal gene transfer and on to changes in the arrangement and number of chromosomes, not to mention endofungal bacteria and viruses. Population genomics shows that all sources generate variation all the time and implicate natural selection as the force maintaining genome stability. Variation in wild populations is a rich resource for associating genetic variation with phenotypic variation, whether through quantitative trait locus mapping, genome-wide association studies, or reverse ecology. Subjects of studies associating genetic and phenotypic variation include model fungi, e.g., Saccharomyces and Neurospora, but pioneering studies have also been made with fungi pathogenic to plants, e.g., Pyricularia (= Magnaporthe), Zymoseptoria, and Fusarium, and to humans, e.g., Coccidioides, Cryptococcus, and Candida.

Via Pierre Gladieux
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An ace in the hole for DNA sequencing : Nature

An ace in the hole for DNA sequencing : Nature | Computational biology | Scoop.it
Christopher Mason has a trick that he likes to break out at conferences. By harvesting DNA from swabs collected from a volunteer's phone, he and his colleagues can perform on-site ancestry analyses within an hour, and even recount details of a donor's day. “We were able to predict who had just eaten an orange, and who had eaten pork, from what was left on their phones,” says Mason, a computational biologist at Weill Cornell Medicine in New York City.

Mason achieves this speedy analysis using a handheld sequencing device called MinION, developed by the UK firm Oxford Nanopore Technologies (ONT). MinION reads sequence information by threading long DNA strands through a tiny aperture known as a nanopore and detecting minute changes in electrical current caused by DNA's four component nucleotides. Mason's demonstrations provide a light-hearted illustration of the device's capabilities, but early users have also racked up some high-profile scientific achievements. MinION played a prominent part in monitoring the 2015 Ebola virus outbreak, has voyaged to Antarctica and even gone into orbit.

But MinION — which is roughly the size of a deck of cards — accounts for a relatively small fraction of the world's sequencing output, which is still dominated by Illumina of San Diego, California. Illumina has a nearly 10-year head start, but ONT and its users are also grappling with technical challenges, most notably higher error rates. Meanwhile, competing firms are hoping to surpass ONT with innovative spins on this conceptually simple but technically complex sequencing strategy.

Via Francis Martin
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More on the Best Evolutionary Rate for Phylogenetic Analysis

More on the Best Evolutionary Rate for Phylogenetic Analysis | Computational biology | Scoop.it

The accumulation of genome-scale molecular data sets for nonmodel taxa brings us ever closer to resolving the tree of life of all living organisms. However, despite the depth of data available, a number of studies that each used thousands of genes have reported conflicting results. The focus of phylogenomic projects must thus shift to more careful experimental design. Even though we still have a limited understanding of what are the best predictors of the phylogenetic informativeness of a gene, there is wide agreement that one key factor is its evolutionary rate; but there is no consensus as to whether the rates derived as optimal in various analytical, empirical, and simulation approaches have any general applicability. We here use simulations to infer optimal rates in a set of realistic phylogenetic scenarios with varying tree sizes, numbers of terminals, and tree shapes. Furthermore, we study the relationship between the optimal rate and rate variation among sites and among lineages. Finally, we examine how well the predictions made by a range of experimental design methods correlate with the observed performance in our simulations. We find that the optimal level of divergence is surprisingly robust to differences in taxon sampling and even to among-site and among-lineage rate variation as often encountered in empirical data sets. This finding encourages the use of methods that rely on a single optimal rate to predict a gene’s utility. Focusing on correct recovery either of the most basal node in the phylogeny or of the entire topology, the optimal rate is about 0.45 substitutions from root to tip in average Yule trees and about 0.2 in difficult trees with short basal and long-apical branches, but all rates leading to divergence levels between about 0.1 and 0.5 perform reasonably well. Testing the performance of six methods that can be used to predict a gene’s utility against our simulation results, we find that the probability of resolution, signal-noise analysis, and Fisher information are good predictors of phylogenetic informativeness, but they require specification of at least part of a model tree. Likelihood quartet mapping also shows very good performance but only requires sequence alignments and is thus applicable without making assumptions about the phylogeny. Despite them being the most commonly used methods for experimental design, geometric quartet mapping and the integration of phylogenetic informativeness curves perform rather poorly in our comparison. Instead of derived predictors of phylogenetic informativeness, we suggest that the number of sites in a gene that evolve at near-optimal rates (as inferred here) could be used directly to prioritize genes for phylogenetic inference. In combination with measures of model fit, especially with respect to compositional biases and among-site and among-lineage rate variation, such an approach has the potential to greatly improve marker choice and should be tested on empirical data.


Via Pierre Gladieux, Niklaus Grunwald
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Deep Learning – Past, Present, and Future

Deep Learning – Past, Present, and Future | Computational biology | Scoop.it
By Henry H. Eckerson, Eckerson Group. From Neural Networks and Deep Learning, by Michael Nielsen. Deep learning is exploding. According to Gartner, the number of open positions for deep learning experts grew from almost zero in 2014 to 41,000 today.
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Single master regulatory gene coordinates the evolution and development of butterfly color and iridescence

The optix gene has been implicated in butterfly wing pattern adaptation by genetic association, mapping, and expression studies. The actual developmental function of this gene has remained unclear, however. Here we used CRISPR/Cas9 genome editing to show that optix plays a fundamental role in nymphalid butterfly wing pattern development, where it is required for determination of all chromatic coloration. optix knockouts in four species show complete replacement of color pigments with melanins, with corresponding changes in pigment-related gene expression, resulting in black and gray butterflies. We also show that optix simultaneously acts as a switch gene for blue structural iridescence in some butterflies, demonstrating simple regulatory coordination of structural and pigmentary coloration. Remarkably, these optix knockouts phenocopy the recurring “black and blue” wing pattern archetype that has arisen on many independent occasions in butterflies. Here we demonstrate a simple genetic basis for structural coloration, and show that optix plays a deeply conserved role in butterfly wing pattern development.


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Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing | Autodesk Research

Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing | Autodesk Research | Computational biology | Scoop.it
Datasets which are identical over a number of statistical properties, yet produce dissimilar graphs, are frequently used to illustrate the importance of graphical representations when exploring data. This paper presents a novel method for generating such datasets, along with several examples. Our technique varies from previous approaches in that new datasets are iteratively generated from a seed dataset through random perturbations of individual data points, and can be directed towards a desired outcome through a simulated annealing optimization strategy.

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Phylogenomic analysis of gene co‐expression networks reveals the evolution of functional modules

Phylogenomic analysis of gene co‐expression networks reveals the evolution of functional modules | Computational biology | Scoop.it

Molecular evolutionary studies correlate genomic and phylogenetic information with the emergence of new traits of organisms. These traits are, however, the consequence of dynamic gene networks composed of functional modules, which might not be captured by genomic analyses. Here, we established a method, which combines large-scale genomic and phylogenetic data with gene co-expression networks, to extensively study the evolutionary make-up of modules in the moss Physcomitrella patens and in the angiosperms Arabidopsis thaliana and rice. We first show that younger genes are less annotated than older genes. By mapping genomic data onto the co-expression networks, we found that genes from the same evolutionary period tend to be connected, while old and young genes tend to be disconnected. Consequently, the analysis revealed modules that emerged at a specific time in plant evolution. To uncover the evolutionary relationships of the modules that are conserved across the plant kingdom, we added phylogenetic information which revealed duplication and speciation events on the module level. This combined analysis revealed an independent duplication of cell wall modules in bryophytes and angiosperms, suggesting a parallel evolution of cell wall pathways in land plants. We provide an online tool allowing plant researchers to perform these analyses at www.gene2function.de.


Via Pierre-Marc Delaux, Jean-Michel Ané
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Genomic Analyses of Dominant U.S. Clonal Lineages of Phytophthora infestans Reveals a Shared Common Ancestry for Clonal Lineages US11 and US18 and a Lack of Recently Shared Ancestry Among All Other...

Genomic Analyses of Dominant U.S. Clonal Lineages of Phytophthora infestans Reveals a Shared Common Ancestry for Clonal Lineages US11 and US18 and a Lack of Recently Shared Ancestry Among All Other... | Computational biology | Scoop.it
Populations of the potato and tomato late-blight pathogen Phytophthora infestans are well known for emerging as novel clonal lineages. These successions of dominant clones have historically been named US1 through US24, in order of appearance, since their first characterization using molecular markers. Hypothetically, these lineages can emerge through divergence from other U.S. lineages, recombination among lineages, or as novel, independent lineages originating outside the United States. We tested for the presence of phylogenetic relationships among U.S. lineages using a population of 31 whole-genome sequences, including dominant U.S. clonal lineages as well as available samples from global populations. We analyzed ancestry of the whole mitochondrial genome and samples of nuclear loci, including supercontigs 1.1 and 1.5 as well as several previously characterized coding regions. We found support for a shared ancestry among lineages US11 and US18 from the mitochondrial genome as well as from one nuclear haplotype on each supercontig analyzed. The other nuclear haplotype from each sample assorted independently, indicating an independent ancestry. We found no support for emergence of any other of the U.S. lineages from a common ancestor shared with the other U.S. lineages. Each of the U.S. clonal lineages fit a model where populations of new clonal lineages emerge via migration from a source population that is sexual in nature and potentially located in central Mexico or elsewhere. This work provides novel insights into patterns of emergence of clonal lineages in plant pathogen genomes.

Via Niklaus Grunwald
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