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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open access Open Access ISSN: 15537390 e-ISSN: 15537404
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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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.

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

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.

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

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

  • 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.

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

insights Insights

  • SJR of this journal has decreased by 4% 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.

1.457

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

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

Related Journals

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CiteRatio: 15.2 | SJR: 5.564 | SNIP: 2.245
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CiteRatio: 6.1 | SJR: 1.095 | SNIP: 1.178
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CiteRatio: 5.2 | SJR: 1.085 | SNIP: 1.175
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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3,847 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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3,559 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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1,567 Citations
open accessOpen access Journal Article DOI: 10.1371/JOURNAL.PGEN.0030115
Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits.
01 Jan 2005 - PLOS Genetics

Abstract:

The obesity epidemic is responsible for a substantial economic burden in developed countries and is a major risk factor for type 2 diabetes and cardiovascular disease. The disease is the result not only of several environmental risk factors, but also of genetic predisposition. To take advantage of recent advances in gene-mapp... The obesity epidemic is responsible for a substantial economic burden in developed countries and is a major risk factor for type 2 diabetes and cardiovascular disease. The disease is the result not only of several environmental risk factors, but also of genetic predisposition. To take advantage of recent advances in gene-mapping technology, we executed a genome-wide association scan to identify genetic variants associated with obesity-related quantitative traits in the genetically isolated population of Sardinia. Initial analysis suggested that several SNPs in the FTO and PFKP genes were associated with increased BMI, hip circumference, and weight. Within the FTO gene, rs9930506 showed the strongest association with BMI (p ¼ 8.6 310 � 7 ), hip circumference (p ¼ 3.4 3 10 � 8 ), and weight (p ¼ 9.1 3 10 � 7 ). In Sardinia, homozygotes for the rare ‘‘G’’ allele of this SNP (minor allele frequency ¼ 0.46) were 1.3 BMI units heavier than homozygotes for the common ‘‘A’’ allele. Within the PFKP gene, rs6602024 showed very strong association with BMI (p ¼4.9 310 � 6 ). Homozygotes for the rare ‘‘A’’ allele of this SNP (minor allele frequency ¼0.12) were 1.8 BMI units heavier than homozygotes for the common ‘‘G’’ allele. To replicate our findings, we genotyped these two SNPs in the GenNet study. In European Americans (N ¼ 1,496) and in Hispanic Americans (N ¼ 839), we replicated significant association between rs9930506 in the FTO gene and BMI (p-value for meta-analysis of European American and Hispanic American follow-up samples, p ¼0.001), weight (p ¼0.001), and hip circumference (p ¼0.0005). We did not replicate association between rs6602024 and obesity-related traits in the GenNet sample, although we found that in European Americans, Hispanic Americans, and African Americans, homozygotes for the rare ‘‘A’’ allele were, on average, 1.0–3.0 BMI units heavier than homozygotes for the more common ‘‘G’’ allele. In summary, we have completed a whole genome– association scan for three obesity-related quantitative traits and report that common genetic variants in the FTO gene are associated with substantial changes in BMI, hip circumference, and body weight. These changes could have a significant impact on the risk of obesity-related morbidity in the general population. read more read less

Topics:

Minor allele frequency (59%)59% related to the paper, FTO gene (57%)57% related to the paper, Single-nucleotide polymorphism (53%)53% related to the paper, Genetics of obesity (53%)53% related to the paper, Allele (52%)52% related to the paper
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1,527 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. Pritchard2, Jonathan K. Pritchard1
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
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1,423 Citations
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PLOS Genetics 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 Genetics 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 Genetics 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 Genetics'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

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

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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 Genetics Endnote style, according to plos guidelines.

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