Computational biology
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Rescooped by Asela Wijeratne from Plant Biology Teaching Resources (Higher Education)
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Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies

Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies | Computational biology | Scoop.it

Really useful article from PLOS Comp Bio - what should students learn in a comp bio / bioinformatics degree program? Also differentiates between the skills needed for a bioinformatics user / scientist / engineer.


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Great article!

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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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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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Rescooped by Asela Wijeratne from The science toolbox
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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.

Via Niklaus Grunwald
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Rescooped by Asela Wijeratne from Python Resources for Bioinformatics
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VoigtLab/dnaplotlib

VoigtLab/dnaplotlib | Computational biology | Scoop.it
dnaplotlib - DNA plotting library for Python

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Rescooped by Asela Wijeratne from Plant immunity and legume symbiosis
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Advances and Challenges in Genomic Selection for Disease Resistance - Annual Review of Phytopathology,

Advances and Challenges in Genomic Selection for Disease Resistance - Annual Review of Phytopathology, | Computational biology | Scoop.it
Breeding for disease resistance is a central focus of plant breeding programs, as any successful variety must have the complete package of high yield, disease resistance, agronomic performance, and end-use quality. With the need to accelerate the development of improved varieties, genomics-assisted breeding is becoming an important tool in breeding programs. With marker-assisted selection, there has been success in breeding for disease resistance; however, much of this work and research has focused on identifying, mapping, and selecting for major resistance genes that tend to be highly effective but vulnerable to breakdown with rapid changes in pathogen races. In contrast, breeding for minor-gene quantitative resistance tends to produce more durable varieties but is a more challenging breeding objective. As the genetic architecture of resistance shifts from single major R genes to a diffused architecture of many minor genes, the best approach for molecular breeding will shift from marker-assisted selection to genomic selection. Genomics-assisted breeding for quantitative resistance will therefore necessitate whole-genome prediction models and selection methodology as implemented for classical complex traits such as yield. Here, we examine multiple case studies testing whole-genome prediction models and genomic selection for disease resistance. In general, whole-genome models for disease resistance can produce prediction accuracy suitable for application in breeding. These models also largely outperform multiple linear regression as would be applied in marker-assisted selection. With the implementation of genomic selection for yield and other agronomic traits, whole-genome marker profiles will be available for the entire set of breeding lines, enabling genomic selection for disease at no additional direct cost. In this context, the scope of implementing genomics selection for disease resistance, and specifically for quantitative resistance and quarantined pathogens, becomes a tractable and powerful approach in breeding programs.

Via Christophe Jacquet
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Rescooped by Asela Wijeratne from Bioinformática
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ITIS, a bioinformatics tool for accurate identification of transposon insertion sites using next-generation sequencing data

ITIS, a bioinformatics tool for accurate identification of transposon insertion sites using next-generation sequencing data | Computational biology | Scoop.it
Background
Transposable elements constitute an important part of the genome and are essential in adaptive mechanisms. Transposition events associated with phenotypic changes occur naturally or are induced in insertional mutant populations. Transposon mutagenesis results in multiple random insertions and recovery of most/all the insertions is critical for forward genetics study. Using genome next-generation sequencing data and appropriate bioinformatics tool, it is plausible to accurately identify transposon insertion sites, which could provide candidate causal mutations for desired phenotypes for further functional validation.

Results
We developed a novel bioinformatics tool, ITIS (Identification of Transposon Insertion Sites), for localizing transposon insertion sites within a genome. It takes next-generation genome re-sequencing data (NGS data), transposon sequence, and reference genome sequence as input, and generates a list of highly reliable candidate insertion sites as well as zygosity information of each insertion. Using a simulated dataset and a case study based on an insertional mutant line from Medicago truncatula, we showed that ITIS performed better in terms of sensitivity and specificity than other similar algorithms such as RelocaTE, RetroSeq, TEMP and TIF. With the case study data, we demonstrated the efficiency of ITIS by validating the presence and zygosity of predicted insertion sites of the Tnt1 transposon within a complex plant system, M. truncatula.

Conclusion
This study showed that ITIS is a robust and powerful tool for forward genetic studies in identifying transposable element insertions causing phenotypes. ITIS is suitable in various systems such as cell culture, bacteria, yeast, insect, mammal and plant.

Via Jean-Michel Ané, Susana Agra Rama
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It´ll be very usefull for my Mycrobiology students.

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Susana Agra Rama's curator insight, March 6, 2015 1:42 PM

It´ll be very usefull for my Mycrobiology students.

Rescooped by Asela Wijeratne from The science toolbox
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VcfR: an R package to manipulate and visualize VCF format data

VcfR: an R package to manipulate and visualize VCF format data | Computational biology | Scoop.it

Software to call single nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with VCF files. While an increasing number of software exists to read VCF data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the R package vcfR to address this issue. We developed a VCF file exploration tool implemented in the R language because R provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write VCF files into R as well as functions to extract portions of the data and to plot summary statistics of the data are implemented. VcfR further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (FASTA) and annotation data (GFF) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the vcfR data structure to formats used by other R genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate VCF data exploration, including intuitive methods for data quality control and easy export to other R packages for further analysis. VcfR thus provides essential, novel tools currently not available in R.


Via Niklaus Grunwald
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Rescooped by Asela Wijeratne from The science toolbox
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SimPhy: Phylogenomic Simulation of Gene, Locus and Species Trees

SimPhy: Phylogenomic Simulation of Gene, Locus and Species Trees | Computational biology | Scoop.it

We present a fast and flexible software package –SimPhy– for the simulation of multiple gene families evolving under incomplete lineage sorting, gene duplication and loss, horizontal gene transfer –all three potentially leading to species-tree/gene-tree discordance– and gene conversion. SimPhy implements a hierarchical phylogenetic model in which the evolution of species, locus and gene trees is governed by global and local parameters (e.g., genome-wide, species-specific, locus-specific), that can be fixed or be sampled from a priori statistical distributions.SimPhy also incorporates comprehensive models of substitution rate variation among lineages (uncorrelated relaxed clocks) and the capability of simulating partitioned nucleotide, codon and protein multilocus sequence alignments under a plethora of substitution models using the program INDELible. We validate SimPhy’s output using theoretical expectations and other programs, and show that it scales extremely well with complex models and/or large trees, being an order of magnitude faster than the most similar program (DLCoal-Sim). In addition, we demonstrate how SimPhy can be useful to understand interactions among different evolutionary processes, conducting a simulation study to characterize the systematic overestimation of the duplication time when using standard reconciliation methods. SimPhy is available at https://github.com/adamallo/SimPhy, where users can find the source code, pre-compiled executables, a detailed manual and example cases. 


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Rescooped by Asela Wijeratne from Viruses and Bioinformatics from Virology.uvic.ca
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PLOS Computational Biology: A Quick Introduction to Version Control with Git and GitHub

PLOS Computational Biology: A Quick Introduction to Version Control with Git and GitHub | Computational biology | Scoop.it

Share Your Code Once you have your files saved in a Git repository, you can share it with your collaborators and the wider scientific community by putting your code online (Fig 3).


Via Chris Upton + helpers
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Nice overview of version control!

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7 ways to visualize data analysis

Here are the 7 different ways to visualize data analysis slides. You can download these slides from SlideCEO.com.
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Rescooped by Asela Wijeratne from Python Resources for Bioinformatics
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google/skflow - Simplified interface for TensorFlow (mimicking Scikit Learn)

google/skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) | Computational biology | Scoop.it

skflow - Simplified interface for TensorFlow (mimicking Scikit Learn)


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Rescooped by Asela Wijeratne from Python Resources for Bioinformatics
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Python Teaching Resources for a Modern Classroom - DataJoy

Easy to use, online data processing with Python and R. No installation, a full scripting environment, lots of examples, version control, and much more.

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Platform independent python GUI (in the browser) remi

Platform independent python GUI (in the browser) remi | Computational biology | Scoop.it
remi - Python REMote Interface library. Platform independent. In less than 100 Kbytes, perfect for your diet.

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Rescooped by Asela Wijeratne from Viruses and Bioinformatics from Virology.uvic.ca
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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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Rescooped by Asela Wijeratne from Plant-Microbe Symbiosis
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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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Rescooped by Asela Wijeratne from Phytophthora biology
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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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Rescooped by Asela Wijeratne from The science toolbox
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The 7 biggest problems facing science, according to 270 scientists

The 7 biggest problems facing science, according to 270 scientists | Computational biology | Scoop.it
These are dark times for science so we asked hundreds of researchers how to fix it.

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Rescooped by Asela Wijeratne from Viruses and Bioinformatics from Virology.uvic.ca
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BMC Bioinformatics



Reduction, alignment and visualisation of large diverse sequence families.



Background Current volumes of sequence data can lead to large numbers of hits identified on a search, typically in the range of 10s to 100s of thousands. It is often quite difficult to tell from these raw results whether the search has been a success or has picked-up sequences with little or no relationship to the query. The best approach to this problem is to cluster and align the resulting families, however, existing methods concentrate on fast clustering and either do not align the sequences or only perform a limited alignment. Results A method (MULSEL) is presented that combines fast peptide-based pre-sorting with a following cascade of mini-alignments, each of which are generated with a robust profile/profile method. From these mini-alignments, a representative sequence is selected, based on a variety of intrinsic and user-specified criteria that are combined to produce the sequence collection for the next cycle of alignment. For moderate sized sequence collections (10s of thousands) the method executes on a laptop computer within seconds or minutes. Conclusions MULSEL bridges a gap between fast clustering methods and slower multiple sequence alignment methods and provides a seamless transition from one to the other. Furthermore, it presents the resulting reduced family in a graphical manner that makes it clear if family members have been misaligned or if there are sequences present that appear inconsistent.

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Rescooped by Asela Wijeratne from Viruses and Bioinformatics from Virology.uvic.ca
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Microbial bioinformatics for food safety and production

In the production of fermented foods, microbes play an important role. Optimization of fermentation processes or starter culture production traditionally was a trial-and-error approach inspired by expert knowledge of the fermentation process. Current developments in high-throughput ‘omics’ technologies allow developing more rational approaches to improve fermentation processes both from the food functionality as well as from the food safety perspective. Here, the authors thematically review typical bioinformatics techniques and approaches to improve various aspects of the microbial production of fermented food products and food safety.

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Rescooped by Asela Wijeratne from Plants & Evolution
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Genome and transcriptome analysis of the Mesoamerican common bean and the role of gene duplications in establishing tissue and temporal specialization of genes

Background

Legumes are the third largest family of angiosperms and the second most important crop class. Legume genomes have been shaped by extensive large-scale gene duplications, including an approximately 58 million year old whole genome duplication shared by most crop legumes.

Results

We report the genome and the transcription atlas of coding and non-coding genes of a Mesoamerican genotype of common bean (Phaseolus vulgaris L., BAT93). Using a comprehensive phylogenomics analysis, we assessed the past and recent evolution of common bean, and traced the diversification of patterns of gene expression following duplication. We find that successive rounds of gene duplications in legumes have shaped tissue and developmental expression, leading to increased levels of specialization in larger gene families. We also find that many long non-coding RNAs are preferentially expressed in germ-line-related tissues (pods and seeds), suggesting that they play a significant role in fruit development. Our results also suggest that most bean-specific gene family expansions, including resistance gene clusters, predate the split of the Mesoamerican and Andean gene pools.

Conclusions

The genome and transcriptome data herein generated for a Mesoamerican genotype represent a counterpart to the genomic resources already available for the Andean gene pool. Altogether, this information will allow the genetic dissection of the characters involved in the domestication and adaptation of the crop, and their further implementation in breeding strategies for this important crop.


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Rescooped by Asela Wijeratne from Social Media, SEO, Mobile, Digital Marketing
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How to Network Like a Pro: 10 Ways to Make a Long List of Meaningful Connections

How to Network Like a Pro: 10 Ways to Make a Long List of Meaningful Connections | Computational biology | Scoop.it

Hone your networking abilities and make a ton of connections by following this list of helpful tips.


Via Kamal Bennani
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Marco Favero's curator insight, February 5, 2016 4:25 AM

aggiungi la tua intuizione ...

Mike Allen's curator insight, February 5, 2016 1:59 PM

Useful summary but random networking can soak up a lot of time so set yourself a limit.

Rescooped by Asela Wijeratne from Plants & Evolution
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Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters

Genome-wide association and high-resolution phenotyping link Oryza sativa panicle traits to numerous trait-specific QTL clusters | Computational biology | Scoop.it

Rice panicle architecture is a key target of selection when breeding for yield and grain quality. However, panicle phenotypes are difficult to measure and susceptible to confounding during genetic mapping due to correlation with flowering and subpopulation structure. Here we quantify 49 panicle phenotypes in 242 tropical rice accessions with the imaging platform PANorama. Using flowering as a covariate, we conduct a genome-wide association study (GWAS), detect numerous subpopulation-specific associations, and dissect multi-trait peaks using panicle phenotype covariates. Ten candidate genes in pathways known to regulate plant architecture fall under GWAS peaks, half of which overlap with quantitative trait loci identified in an experimental population. This is the first study to assess inflorescence phenotypes of field-grown material using a high-resolution phenotyping platform. Herein, we establish a panicle morphocline for domesticated rice, propose a genetic model underlying complex panicle traits, and demonstrate subtle links between panicle size and yield performance.


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Big Data and Data Analysis: Data Quality Can Be the Most Difficult Challenge ... - Formtek Blog (blog)

Big Data and Data Analysis: Data Quality Can Be the Most Difficult Challenge ...
Formtek Blog (blog)
Customer-facing apps are among the most common big data and advanced analytics apps.
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Rescooped by Asela Wijeratne from Python Resources for Bioinformatics
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The Best Machine Learning Libraries in Python

The Best Machine Learning Libraries in Python | Computational biology | Scoop.it
Introduction There is no doubt that neural networks, and machine learning in general, has been one of the hottest topics in tech the past few years or so. It's easy to see why with all of the really interesting use-cases...

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Rescooped by Asela Wijeratne from Viruses and Bioinformatics from Virology.uvic.ca
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Genomic Pathogen Typing Using Solid-State Nanopores

Genomic Pathogen Typing Using Solid-State Nanopores | Computational biology | Scoop.it
In clinical settings, rapid and accurate characterization of pathogens is essential for effective treatment of patients; however, subtle genetic changes in pathogens which elude traditional phenotypic typing may confer dangerous pathogenic properties such as toxicity, antibiotic resistance, or virulence. Existing options for molecular typing techniques characterize the critical genomic changes that distinguish harmful and benign strains, yet the well-established approaches, in particular those

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The Little Book of Python Anti-Patterns — Python Anti-Patterns documentation

The Little Book of Python Anti-Patterns — Python Anti-Patterns documentation | Computational biology | Scoop.it

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