Example of PLOS Computational Biology format
Recent searches

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
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
Look Inside
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
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access
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
Related journals
Insights
General info
Top papers
Popular templates
Get started guide
Why choose from SciSpace
FAQ

Related Journals

open access Open Access

Elsevier

Quality:  
High
CiteRatio: 4.9
SJR: 0.774
SNIP: 1.158
open access Open Access
recommended Recommended

PLOS

Quality:  
High
CiteRatio: 9.0
SJR: 3.587
SNIP: 1.457
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 6.1
SJR: 1.095
SNIP: 1.178
open access Open Access

Elsevier

Quality:  
High
CiteRatio: 5.2
SJR: 1.085
SNIP: 1.175

Journal Performance & Insights

Impact Factor

CiteRatio

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.

A measure of average citations received per peer-reviewed paper published in the journal.

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

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

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

insights Insights

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

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

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

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

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

insights Insights

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

Guideline source: View

All company, product and service names used in this website are for identification purposes only. All product names, trademarks and registered trademarks are property of their respective owners.

Use of these names, trademarks and brands does not imply endorsement or affiliation. Disclaimer Notice

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

i
Last updated on
02 Jul 2020
i
ISSN
1553-7358
i
Impact Factor
High - 1.402
i
Acceptance Rate
30%
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
plos2015
i
Citation Type
Numbered
[25]
i
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
5,183 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
3,005 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
2,908 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.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
View PDF
2,208 Citations
Author Pic

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.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

Get MS-Word and LaTeX output to any Journal within seconds
1
Choose a template
Select a template from a library of 40,000+ templates
2
Import a MS-Word file or start fresh
It takes only few seconds to import
3
View and edit your final output
SciSpace will automatically format your output to meet journal guidelines
4
Submit directly or Download
Submit to journal directly or Download in PDF, MS Word or LaTeX

(Before submission check for plagiarism via Turnitin)

clock Less than 3 minutes

What to expect from SciSpace?

Speed and accuracy over MS Word

''

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?

Turnitin Stats
Publisher Logos

Freedom from formatting guidelines

One editor, 100K journal formats – world's largest collection of journal templates

With such a huge verified library, what you need is already there.

publisher-logos

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

1. Can I write PLOS Computational Biology in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the PLOS Computational Biology guidelines and auto format it.

2. Do you follow the PLOS Computational Biology guidelines?

Yes, the template is compliant with the PLOS Computational Biology guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in PLOS Computational Biology?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the PLOS Computational Biology citation style.

4. Can I use the PLOS Computational Biology templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for PLOS Computational Biology.

5. Can I use a manuscript in PLOS Computational Biology that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper PLOS Computational Biology that you can download at the end.

6. How long does it usually take you to format my papers in PLOS Computational Biology?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in PLOS Computational Biology.

7. Where can I find the template for the PLOS Computational Biology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per PLOS Computational Biology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the PLOS Computational Biology's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. PLOS Computational Biology an online tool or is there a desktop version?

SciSpace's PLOS Computational Biology is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like PLOS Computational Biology?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like PLOS Computational Biology?”

11. What is the output that I would get after using PLOS Computational Biology?

After writing your paper autoformatting in PLOS Computational Biology, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is PLOS Computational Biology's impact factor high enough that I should try publishing my article there?

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 these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for PLOS Computational Biology?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for PLOS Computational Biology. The table below indicates the level of access a journal has as per Sherpa Romeo's 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.

14. What are the most common citation types In PLOS Computational Biology?

The 5 most common citation types in order of usage for PLOS Computational Biology are:.

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

15. How do I submit my article to the PLOS Computational Biology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per PLOS Computational Biology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download PLOS Computational Biology in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in PLOS Computational Biology Endnote style according to Elsevier guidelines.

Fast and reliable,
built for complaince.

Instant formatting to 100% publisher guidelines on - SciSpace.

Available only on desktops 🖥

No word template required

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

Verifed journal formats

One editor, 100K journal formats.
With the largest collection of verified journal formats, what you need is already there.

Trusted by academicians

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.

Andreas Frutiger
Researcher & Ex MS Word user
Use this template