Example of Microbiome format
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Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome format Example of Microbiome 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
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Microbiome — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Microbiology (medical) #4 of 116 -
Microbiology #6 of 150 up up by 2 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 689 Published Papers | 15024 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 15/07/2020
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Related Journals

open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 28.2
SJR: 7.305
SNIP: 3.41
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 6.5
SJR: 1.812
SNIP: 1.485
open access Open Access

Frontiers Media

Quality:  
High
CiteRatio: 7.3
SJR: 1.701
SNIP: 1.558
open access Open Access

Elsevier

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

Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

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

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.

21.8

43% from 2019

CiteRatio for Microbiome from 2016 - 2020
Year Value
2020 21.8
2019 15.2
2018 9.8
2017 11.3
2016 13.0
graph view Graph view
table view Table view

5.297

0% from 2019

SJR for Microbiome from 2016 - 2020
Year Value
2020 5.297
2019 5.282
2018 4.466
2017 5.171
2016 5.142
graph view Graph view
table view Table view

3.028

22% from 2019

SNIP for Microbiome from 2016 - 2020
Year Value
2020 3.028
2019 2.482
2018 2.633
2017 2.325
2016 2.378
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 43% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 0% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 22% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Microbiome

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Springer

Microbiome

Approved by publishing and review experts on SciSpace, this template is built as per for Microbiome formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 494 authors to write and format their manuscripts to this journal.

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Last updated on
15 Jul 2020
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ISSN
1606-8610
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
White faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25(7), 4515–4532 (1982)

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/S40168-018-0470-Z
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin
17 May 2018 - Microbiome

Abstract:

Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized s... Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data. The naive-Bayes, BLAST+-based, and VSEARCH-based classifiers implemented in QIIME 2 meet or exceed the species-level accuracy of other commonly used methods designed for classification of marker gene sequences that were evaluated in this work. These evaluations, based on 19 mock communities and error-free sequence simulations, including classification of simulated “novel” marker-gene sequences, are available in our extensible benchmarking framework, tax-credit ( https://github.com/caporaso-lab/tax-credit-data ). Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for these classifiers under a range of standard operating conditions. q2-feature-classifier and tax-credit are both free, open-source, BSD-licensed packages available on GitHub. read more read less

Topics:

Naive Bayes classifier (50%)50% related to the paper
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2,475 Citations
open accessOpen access Journal Article DOI: 10.1186/S40168-017-0237-Y
Normalization and microbial differential abundance strategies depend upon data characteristics
03 Mar 2017 - Microbiome

Abstract:

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of tw... Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets ( 20 samples per group) but also critically the only method tested that has a good control of false discovery rate. These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study. read more read less

Topics:

Normalization (statistics) (58%)58% related to the paper
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1,292 Citations
open accessOpen access Journal Article DOI: 10.1186/S40168-018-0605-2
Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data.
17 Dec 2018 - Microbiome

Abstract:

The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate ... The accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants—DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam ( https://github.com/benjjneb/decontam ), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples and are often found in negative controls. Decontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome and that some low-frequency taxa seemingly associated with preterm birth were contaminants. Decontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. Decontam integrates easily with existing MGS workflows and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost. read more read less

Topics:

Metagenomics (58%)58% related to the paper
View PDF
1,287 Citations
open accessOpen access Journal Article DOI: 10.1186/2049-2618-2-6
An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform
Douglas Fadrosh1, Bing Ma1, Pawel Gajer1, Naomi Sengamalay1, Sandra Ott1, Rebecca M. Brotman1, Jacques Ravel1
24 Feb 2014 - Microbiome

Abstract:

To take advantage of affordable high-throughput next-generation sequencing technologies to characterize microbial community composition often requires the development of improved methods to overcome technical limitations inherent to the sequencing platforms. Sequencing low sequence diversity libraries such as 16S rRNA amplico... To take advantage of affordable high-throughput next-generation sequencing technologies to characterize microbial community composition often requires the development of improved methods to overcome technical limitations inherent to the sequencing platforms. Sequencing low sequence diversity libraries such as 16S rRNA amplicons has been problematic on the Illumina MiSeq platform and often generates sequences of suboptimal quality. Here we present an improved dual-indexing amplification and sequencing approach to assess the composition of microbial communities from clinical samples using the V3-V4 region of the 16S rRNA gene on the Illumina MiSeq platform. We introduced a 0 to 7 bp “heterogeneity spacer” to the index sequence that allows an equal proportion of samples to be sequenced out of phase. Our approach yields high quality sequence data from 16S rRNA gene amplicons using both 250 bp and 300 bp paired-end MiSeq protocols and provides a flexible and cost-effective sequencing option. read more read less

Topics:

Illumina dye sequencing (59%)59% related to the paper, Massive parallel sequencing (58%)58% related to the paper
View PDF
1,261 Citations
open accessOpen access Journal Article DOI: 10.1186/S40168-016-0222-X
Gut microbiota dysbiosis contributes to the development of hypertension.
01 Feb 2017 - Microbiome

Abstract:

Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigat... Recently, the potential role of gut microbiome in metabolic diseases has been revealed, especially in cardiovascular diseases. Hypertension is one of the most prevalent cardiovascular diseases worldwide, yet whether gut microbiota dysbiosis participates in the development of hypertension remains largely unknown. To investigate this issue, we carried out comprehensive metagenomic and metabolomic analyses in a cohort of 41 healthy controls, 56 subjects with pre-hypertension, 99 individuals with primary hypertension, and performed fecal microbiota transplantation from patients to germ-free mice. Compared to the healthy controls, we found dramatically decreased microbial richness and diversity, Prevotella-dominated gut enterotype, distinct metagenomic composition with reduced bacteria associated with healthy status and overgrowth of bacteria such as Prevotella and Klebsiella, and disease-linked microbial function in both pre-hypertensive and hypertensive populations. Unexpectedly, the microbiome characteristic in pre-hypertension group was quite similar to that in hypertension. The metabolism changes of host with pre-hypertension or hypertension were identified to be closely linked to gut microbiome dysbiosis. And a disease classifier based on microbiota and metabolites was constructed to discriminate pre-hypertensive and hypertensive individuals from controls accurately. Furthermore, by fecal transplantation from hypertensive human donors to germ-free mice, elevated blood pressure was observed to be transferrable through microbiota, and the direct influence of gut microbiota on blood pressure of the host was demonstrated. Overall, our results describe a novel causal role of aberrant gut microbiota in contributing to the pathogenesis of hypertension. And the significance of early intervention for pre-hypertension was emphasized. read more read less

Topics:

Dysbiosis (66%)66% related to the paper, Microbiome (60%)60% related to the paper, Gut flora (59%)59% related to the paper, Enterotype (58%)58% related to the paper, Gastrointestinal Microbiome (52%)52% related to the paper
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965 Citations
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With SciSpace, you do not need a word template for Microbiome.

It automatically formats your research paper to Springer 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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Frequently asked questions

1. Can I write Microbiome in LaTeX?

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

2. Do you follow the Microbiome guidelines?

Yes, the template is compliant with the Microbiome 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 Microbiome?

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 Microbiome citation style.

4. Can I use the Microbiome 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 Microbiome.

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

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

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

7. Where can I find the template for the Microbiome?

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 Microbiome'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 Microbiome'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. Microbiome an online tool or is there a desktop version?

SciSpace's Microbiome 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 Microbiome?

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 Microbiome?”

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

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

12. Is Microbiome'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 Microbiome?

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 Microbiome. 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 Microbiome?

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

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

16. Can I download Microbiome 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 Microbiome 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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