Example of PLOS Genetics format
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Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format
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Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics format Example of PLOS Genetics 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
recommended Recommended

PLOS Genetics — Template for authors

Publisher: PLOS
Categories Rank Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics #27 of 647 down down by 10 ranks
Genetics #39 of 325 down down by 18 ranks
Genetics (clinical) #11 of 87 down down by 5 ranks
Molecular Biology #65 of 382 down down by 30 ranks
Cancer Research #44 of 207 down down by 21 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 2204 Published Papers | 19754 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 16/07/2020
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Related Journals

open access Open Access
recommended Recommended

PLOS

Quality:  
High
CiteRatio: 7.3
SJR: 2.628
SNIP: 1.713
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 15.2
SJR: 5.564
SNIP: 2.245
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.

5.174

1% from 2018

Impact factor for PLOS Genetics from 2016 - 2019
Year Value
2019 5.174
2018 5.224
2017 5.54
2016 6.1
graph view Graph view
table view Table view

9.0

CiteRatio for PLOS Genetics from 2016 - 2020
Year Value
2020 9.0
2019 9.0
2018 9.7
2017 11.1
2016 12.1
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 1% 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.

3.587

4% from 2019

SJR for PLOS Genetics from 2016 - 2020
Year Value
2020 3.587
2019 3.744
2018 4.001
2017 4.829
2016 5.457
graph view Graph view
table view Table view

1.457

7% from 2019

SNIP for PLOS Genetics from 2016 - 2020
Year Value
2020 1.457
2019 1.359
2018 1.317
2017 1.403
2016 1.55
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 4% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
PLOS Genetics

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PLOS

PLOS Genetics

PLOS Genetics publishes human studies, as well as research on model organisms—from mice and flies, to plants and bacteria. Our emphasis is on studies of broad interest that provide significant mechanistic insight into a biological process or processes. Topics include (but are ...... Read More

Ecology, Evolution, Behavior and Systematics

Genetics(clinical)

Cancer Research

Molecular Biology

Agricultural and Biological Sciences

i
Last updated on
16 Jul 2020
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ISSN
1553-7390
i
Impact Factor
High - 1.658
i
Acceptance Rate
27%
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
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
Beenakker CWJ. Specular Andreev Reflection in Graphene. Phys Rev Lett. 2006;97(6):067007.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.0020190
Population structure and eigenanalysis
Nick Patterson1, Alkes L. Price2, Alkes L. Price1, David Reich1, David Reich2
22 Dec 2006 - PLOS Genetics

Abstract:

Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a... Current methods for inferring population structure from genetic data do not provide formal significance tests for population differentiation. We discuss an approach to studying population structure (principal components analysis) that was first applied to genetic data by Cavalli-Sforza and colleagues. We place the method on a solid statistical footing, using results from modern statistics to develop formal significance tests. We also uncover a general “phase change” phenomenon about the ability to detect structure in genetic data, which emerges from the statistical theory we use, and has an important implication for the ability to discover structure in genetic data: for a fixed but large dataset size, divergence between two populations (as measured, for example, by a statistic like FST) below a threshold is essentially undetectable, but a little above threshold, detection will be easy. This means that we can predict the dataset size needed to detect structure. read more read less

Topics:

Population (57%)57% related to the paper, Statistical theory (53%)53% related to the paper, Statistic (51%)51% related to the paper
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4,456 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.1000529
A flexible and accurate genotype imputation method for the next generation of genome-wide association studies.
Bryan Howie1, Peter Donnelly1, Peter Donnelly2, Jonathan Marchini1
19 Jun 2009 - PLOS Genetics

Abstract:

Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon ... Genotype imputation methods are now being widely used in the analysis of genome-wide association studies. Most imputation analyses to date have used the HapMap as a reference dataset, but new reference panels (such as controls genotyped on multiple SNP chips and densely typed samples from the 1,000 Genomes Project) will soon allow a broader range of SNPs to be imputed with higher accuracy, thereby increasing power. We describe a genotype imputation method (IMPUTE version 2) that is designed to address the challenges presented by these new datasets. The main innovation of our approach is a flexible modelling framework that increases accuracy and combines information across multiple reference panels while remaining computationally feasible. We find that IMPUTE v2 attains higher accuracy than other methods when the HapMap provides the sole reference panel, but that the size of the panel constrains the improvements that can be made. We also find that imputation accuracy can be greatly enhanced by expanding the reference panel to contain thousands of chromosomes and that IMPUTE v2 outperforms other methods in this setting at both rare and common SNPs, with overall error rates that are 15%–20% lower than those of the closest competing method. One particularly challenging aspect of next-generation association studies is to integrate information across multiple reference panels genotyped on different sets of SNPs; we show that our approach to this problem has practical advantages over other suggested solutions. read more read less

Topics:

Imputation (genetics) (63%)63% related to the paper, SNP genotyping (53%)53% related to the paper, International HapMap Project (52%)52% related to the paper
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3,902 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.1002967
Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data
Joseph K. Pickrell1, Jonathan K. Pritchard1, Jonathan K. Pritchard2
15 Nov 2012 - PLOS Genetics

Abstract:

Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. ... Many aspects of the historical relationships between populations in a species are reflected in genetic data. Inferring these relationships from genetic data, however, remains a challenging task. In this paper, we present a statistical model for inferring the patterns of population splits and mixtures in multiple populations. In our model, the sampled populations in a species are related to their common ancestor through a graph of ancestral populations. Using genome-wide allele frequency data and a Gaussian approximation to genetic drift, we infer the structure of this graph. We applied this method to a set of 55 human populations and a set of 82 dog breeds and wild canids. In both species, we show that a simple bifurcating tree does not fully describe the data; in contrast, we infer many migration events. While some of the migration events that we find have been detected previously, many have not. For example, in the human data, we infer that Cambodians trace approximately 16% of their ancestry to a population ancestral to other extant East Asian populations. In the dog data, we infer that both the boxer and basenji trace a considerable fraction of their ancestry (9% and 25%, respectively) to wolves subsequent to domestication and that East Asian toy breeds (the Shih Tzu and the Pekingese) result from admixture between modern toy breeds and “ancient” Asian breeds. Software implementing the model described here, called TreeMix, is available at http://treemix.googlecode.com. read more read less

Topics:

Population (54%)54% related to the paper, Population genetics (53%)53% related to the paper, Genetic drift (52%)52% related to the paper
View PDF
1,881 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.0030161
Capturing heterogeneity in gene expression studies by surrogate variable analysis.
Jeffrey T. Leek1, John D. Storey1
01 Jan 2005 - PLOS Genetics

Abstract:

It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through... It has unambiguously been shown that genetic, environmental, demographic, and technical factors may have substantial effects on gene expression levels. In addition to the measured variable(s) of interest, there will tend to be sources of signal due to factors that are unknown, unmeasured, or too complicated to capture through simple models. We show that failing to incorporate these sources of heterogeneity into an analysis can have widespread and detrimental effects on the study. Not only can this reduce power or induce unwanted dependence across genes, but it can also introduce sources of spurious signal to many genes. This phenomenon is true even for well-designed, randomized studies. We introduce “surrogate variable analysis” (SVA) to overcome the problems caused by heterogeneity in expression studies. SVA can be applied in conjunction with standard analysis techniques to accurately capture the relationship between expression and any modeled variables of interest. We apply SVA to disease class, time course, and genetics of gene expression studies. We show that SVA increases the biological accuracy and reproducibility of analyses in genome-wide expression studies. read more read less
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1,779 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.1004383
Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.
15 May 2014 - PLOS Genetics

Abstract:

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of t... Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways. read more read less

Topics:

Genome-wide association study (54%)54% related to the paper, Expression quantitative trait loci (53%)53% related to the paper
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1,711 Citations
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With SciSpace, you do not need a word template for PLOS Genetics.

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.

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Time taken to format a paper and Compliance with guidelines

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PLOS Genetics format uses plos2015 citation style.

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

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Frequently asked questions

1. Can I write PLOS Genetics 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 Genetics guidelines and auto format it.

2. Do you follow the PLOS Genetics guidelines?

Yes, the template is compliant with the PLOS Genetics 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 Genetics?

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 Genetics citation style.

4. Can I use the PLOS Genetics 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 Genetics.

5. Can I use a manuscript in PLOS Genetics 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 Genetics that you can download at the end.

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

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 Genetics.

7. Where can I find the template for the PLOS Genetics?

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 Genetics'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 Genetics'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 Genetics an online tool or is there a desktop version?

SciSpace's PLOS Genetics 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 Genetics?

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 Genetics?”

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

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

12. Is PLOS Genetics'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 Genetics?

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 Genetics. 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 Genetics?

The 5 most common citation types in order of usage for PLOS Genetics 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 Genetics?

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 Genetics's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download PLOS Genetics 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 Genetics Endnote style according to Elsevier guidelines.

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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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