Example of PLOS Computational Biology format
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Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format
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Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format Example of PLOS Computational Biology format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access ISSN: 1553734X e-ISSN: 15537358
recommended Recommended

PLOS Computational Biology — Template for authors

Publisher: PLOS
Categories Rank Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics #40 of 647 down down by 6 ranks
Modeling and Simulation #20 of 290 down down by 16 ranks
Ecology #31 of 400 down down by 17 ranks
Computational Theory and Mathematics #14 of 133 down down by 6 ranks
Genetics #59 of 325 down down by 9 ranks
Molecular Biology #103 of 382 down down by 21 ranks
Cellular and Molecular Neuroscience #25 of 88 down down by 9 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2539 Published Papers | 18577 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 02/07/2020
Insights & related journals
General info
Top papers
Popular templates
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

4.7

6% from 2018

Impact factor for PLOS Computational Biology from 2016 - 2019
Year Value
2019 4.7
2018 4.428
2017 3.955
2016 4.542
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 6% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

7.3

CiteRatio for PLOS Computational Biology from 2016 - 2020
Year Value
2020 7.3
2019 7.3
2018 7.2
2017 7.8
2016 7.9
graph view Graph view
table view Table view

insights Insights

  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

2.628

10% from 2019

SJR for PLOS Computational Biology from 2016 - 2020
Year Value
2020 2.628
2019 2.91
2018 2.949
2017 3.097
2016 3.243
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 10% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.713

11% from 2019

SNIP for PLOS Computational Biology from 2016 - 2020
Year Value
2020 1.713
2019 1.537
2018 1.408
2017 1.372
2016 1.366
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 11% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Related Journals

open access Open Access ISSN: 15749541

Elsevier

CiteRatio: 4.9 | SJR: 0.774 | SNIP: 1.158
open access Open Access ISSN: 15537390 e-ISSN: 15537404
recommended Recommended

PLOS

CiteRatio: 9.0 | SJR: 3.587 | SNIP: 1.457
open access Open Access ISSN: 9406360 e-ISSN: 14321890
recommended Recommended

Springer

CiteRatio: 6.1 | SJR: 1.095 | SNIP: 1.178
open access Open Access ISSN: 15671348 e-ISSN: 15677257

Elsevier

CiteRatio: 5.2 | SJR: 1.085 | SNIP: 1.175
PLOS Computational Biology

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PLOS

PLOS Computational Biology

PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods.... Read More

Biological data

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Last updated on
02 Jul 2020
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ISSN
1553-7358
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Impact Factor
High - 1.402
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Acceptance Rate
30%
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Green faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
plos2015
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 1982;25(7):4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PCBI.1003537
BEAST 2: A Software Platform for Bayesian Evolutionary Analysis

Abstract:

We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of p... We present a new open source, extensible and flexible software platform for Bayesian evolutionary analysis called BEAST 2. This software platform is a re-design of the popular BEAST 1 platform to correct structural deficiencies that became evident as the BEAST 1 software evolved. Key among those deficiencies was the lack of post-deployment extensibility. BEAST 2 now has a fully developed package management system that allows third party developers to write additional functionality that can be directly installed to the BEAST 2 analysis platform via a package manager without requiring a new software release of the platform. This package architecture is showcased with a number of recently published new models encompassing birth-death-sampling tree priors, phylodynamics and model averaging for substitution models and site partitioning. A second major improvement is the ability to read/write the entire state of the MCMC chain to/from disk allowing it to be easily shared between multiple instances of the BEAST software. This facilitates checkpointing and better support for multi-processor and high-end computing extensions. Finally, the functionality in new packages can be easily added to the user interface (BEAUti 2) by a simple XML template-based mechanism because BEAST 2 has been re-designed to provide greater integration between the analysis engine and the user interface so that, for example BEAST and BEAUti use exactly the same XML file format. read more read less

Topics:

Software development (55%)55% related to the paper, Software release life cycle (52%)52% related to the paper, Software (52%)52% related to the paper, XML (51%)51% related to the paper, User interface (50%)50% related to the paper
View PDF
4,267 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PCBI.0010042
The Human Connectome: A Structural Description of the Human Brain
Olaf Sporns1, Giulio Tononi, Rolf Kötter

Abstract:

The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale an... The connection matrix of the human brain (the human “connectome”) represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact. read more read less

Topics:

Human Connectome (66%)66% related to the paper, Connectome (62%)62% related to the paper, Connectomics (54%)54% related to the paper
View PDF
2,537 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PCBI.1005595
Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads.
Ryan R. Wick1, Louise M. Judd1, Claire L. Gorrie1, Kathryn E. Holt1

Abstract:

The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive an... The Illumina DNA sequencing platform generates accurate but short reads, which can be used to produce accurate but fragmented genome assemblies. Pacific Biosciences and Oxford Nanopore Technologies DNA sequencing platforms generate long reads that can produce complete genome assemblies, but the sequencing is more expensive and error-prone. There is significant interest in combining data from these complementary sequencing technologies to generate more accurate "hybrid" assemblies. However, few tools exist that truly leverage the benefits of both types of data, namely the accuracy of short reads and the structural resolving power of long reads. Here we present Unicycler, a new tool for assembling bacterial genomes from a combination of short and long reads, which produces assemblies that are accurate, complete and cost-effective. Unicycler builds an initial assembly graph from short reads using the de novo assembler SPAdes and then simplifies the graph using information from short and long reads. Unicycler uses a novel semi-global aligner to align long reads to the assembly graph. Tests on both synthetic and real reads show Unicycler can assemble larger contigs with fewer misassemblies than other hybrid assemblers, even when long-read depth and accuracy are low. Unicycler is open source (GPLv3) and available at github.com/rrwick/Unicycler. read more read less

Topics:

Nanopore sequencing (53%)53% related to the paper
View PDF
2,245 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PCBI.1003118
Software for computing and annotating genomic ranges.

Abstract:

We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable dat... We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization. read more read less

Topics:

Bioconductor (64%)64% related to the paper
View PDF
2,200 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PCBI.0030017
Efficiency and cost of economical brain functional networks.
Sophie Achard1, Edward T. Bullmore1

Abstract:

Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N... Brain anatomical networks are sparse, complex, and have economical small-world properties. We investigated the efficiency and cost of human brain functional networks measured using functional magnetic resonance imaging (fMRI) in a factorial design: two groups of healthy old (N = 11; mean age = 66.5 years) and healthy young (N = 15; mean age = 24.7 years) volunteers were each scanned twice in a no-task or “resting” state following placebo or a single dose of a dopamine receptor antagonist (sulpiride 400 mg). Functional connectivity between 90 cortical and subcortical regions was estimated by wavelet correlation analysis, in the frequency interval 0.06–0.11 Hz, and thresholded to construct undirected graphs. These brain functional networks were small-world and economical in the sense of providing high global and local efficiency of parallel information processing for low connection cost. Efficiency was reduced disproportionately to cost in older people, and the detrimental effects of age on efficiency were localised to frontal and temporal cortical and subcortical regions. Dopamine antagonism also impaired global and local efficiency of the network, but this effect was differentially localised and did not interact with the effect of age. Brain functional networks have economical small-world properties—supporting efficient parallel information transfer at relatively low cost—which are differently impaired by normal aging and pharmacological blockade of dopamine transmission. read more read less

Topics:

Brain mapping (55%)55% related to the paper, Functional magnetic resonance imaging (51%)51% related to the paper
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1,923 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

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What to expect from SciSpace?

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With SciSpace, you do not need a word template for PLOS Computational Biology.

It automatically formats your research paper to PLOS formatting guidelines and citation style.

You can download a submission ready research paper in pdf, LaTeX and docx formats.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

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Easy support from all your favorite tools

PLOS Computational Biology format uses plos2015 citation style.

Automatically format and order your citations and bibliography in a click.

SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the PLOS Computational Biology guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the PLOS Computational Biology citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per PLOS Computational Biology's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

Sure. You can request any template and we'll have it up and running within a matter of 3 working days. You can find the request box in the Journal Gallery on the right sidebar under the heading, "Couldn't find the format you were looking for?".

After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

Our journal submission experts are skilled in submitting papers to various international journals.

After uploading your paper on SciSpace, you would see a button to request a journal submission service for PLOS Computational Biology.

Each submission service is completed within 4 - 5 working days.

Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in PLOS Computational Biology Endnote style, according to plos guidelines.

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Typset automatically formats your research paper to PLOS Computational Biology formatting guidelines and citation style.

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I spent hours with MS word for reformatting. It was frustrating - plain and simple. With SciSpace, I can draft my manuscripts and once it is finished I can just submit. In case, I have to submit to another journal it is really just a button click instead of an afternoon of reformatting.

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