Example of BMC Evolutionary Biology format
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Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format
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Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format Example of BMC Evolutionary Biology format
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open access Open Access

BMC Evolutionary Biology — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics #71 of 647 down down by 3 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 859 Published Papers | 5020 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 24/06/2020
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Related Journals

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SJR: 0.785
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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.

3.058

0% from 2018

Impact factor for BMC Evolutionary Biology from 2016 - 2019
Year Value
2019 3.058
2018 3.045
2017 3.027
2016 3.221
graph view Graph view
table view Table view

5.8

12% from 2019

CiteRatio for BMC Evolutionary Biology from 2016 - 2020
Year Value
2020 5.8
2019 5.2
2018 5.4
2017 5.5
2016 5.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 0% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 12% in last years.
  • 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.

1.533

0% from 2019

SJR for BMC Evolutionary Biology from 2016 - 2020
Year Value
2020 1.533
2019 1.531
2018 1.687
2017 1.656
2016 2.006
graph view Graph view
table view Table view

1.282

7% from 2019

SNIP for BMC Evolutionary Biology from 2016 - 2020
Year Value
2020 1.282
2019 1.198
2018 1.196
2017 1.25
2016 1.369
graph view Graph view
table view Table view

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 7% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

BMC Evolutionary Biology

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Springer

BMC Evolutionary Biology

Approved by publishing and review experts on SciSpace, this template is built as per for BMC Evolutionary Biology formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 786 authors to write and format their manuscripts to this journal.

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Last updated on
24 Jun 2020
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ISSN
1606-8610
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Open Access
Yes
i
Sherpa RoMEO Archiving Policy
White faq
i
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: 10.1186/1471-2148-7-214
BEAST: Bayesian evolutionary analysis by sampling trees
Alexei J. Drummond1, Andrew Rambaut2
08 Nov 2007 - BMC Evolutionary Biology

Abstract:

The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis o... The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis. read more read less

Topics:

Alignment-free sequence analysis (59%)59% related to the paper, Multiple sequence alignment (56%)56% related to the paper, Molecular Sequence Variation (53%)53% related to the paper
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11,916 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2148-13-93
A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes
Robert Alexander Pyron1, Frank T. Burbrink2, John J. Wiens3
29 Apr 2013 - BMC Evolutionary Biology

Abstract:

The extant squamates (>9400 known species of lizards and snakes) are one of the most diverse and conspicuous radiations of terrestrial vertebrates, but no studies have attempted to reconstruct a phylogeny for the group with large-scale taxon sampling. Such an estimate is invaluable for comparative evolutionary studies, and to... The extant squamates (>9400 known species of lizards and snakes) are one of the most diverse and conspicuous radiations of terrestrial vertebrates, but no studies have attempted to reconstruct a phylogeny for the group with large-scale taxon sampling. Such an estimate is invaluable for comparative evolutionary studies, and to address their classification. Here, we present the first large-scale phylogenetic estimate for Squamata. The estimated phylogeny contains 4161 species, representing all currently recognized families and subfamilies. The analysis is based on up to 12896 base pairs of sequence data per species (average = 2497 bp) from 12 genes, including seven nuclear loci (BDNF, c-mos, NT3, PDC, R35, RAG-1, and RAG-2), and five mitochondrial genes (12S, 16S, cytochrome b, ND2, and ND4). The tree provides important confirmation for recent estimates of higher-level squamate phylogeny based on molecular data (but with more limited taxon sampling), estimates that are very different from previous morphology-based hypotheses. The tree also includes many relationships that differ from previous molecular estimates and many that differ from traditional taxonomy. We present a new large-scale phylogeny of squamate reptiles that should be a valuable resource for future comparative studies. We also present a revised classification of squamates at the family and subfamily level to bring the taxonomy more in line with the new phylogenetic hypothesis. This classification includes new, resurrected, and modified subfamilies within gymnophthalmid and scincid lizards, and boid, colubrid, and lamprophiid snakes. read more read less

Topics:

Squamata (59%)59% related to the paper, Lamprophiidae (55%)55% related to the paper, Amphisbaenia (54%)54% related to the paper, Gekkota (54%)54% related to the paper, Phylogenetic tree (53%)53% related to the paper
View PDF
1,381 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2148-4-18
TREEFINDER: a powerful graphical analysis environment for molecular phylogenetics
Jobb G1, von Haeseler A2, von Haeseler A3, Korbinian Strimmer1
28 Jun 2004 - BMC Evolutionary Biology

Abstract:

Most analysis programs for inferring molecular phylogenies are difficult to use, in particular for researchers with little programming experience
View PDF
1,141 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2148-10-210
BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments
Alexis Criscuolo1, Simonetta Gribaldo1
13 Jul 2010 - BMC Evolutionary Biology

Abstract:

The quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction meth... The quality of multiple sequence alignments plays an important role in the accuracy of phylogenetic inference. It has been shown that removing ambiguously aligned regions, but also other sources of bias such as highly variable (saturated) characters, can improve the overall performance of many phylogenetic reconstruction methods. A current scientific trend is to build phylogenetic trees from a large number of sequence datasets (semi-)automatically extracted from numerous complete genomes. Because these approaches do not allow a precise manual curation of each dataset, there exists a real need for efficient bioinformatic tools dedicated to this alignment character trimming step. Here is presented a new software, named BMGE (Block Mapping and Gathering with Entropy), that is designed to select regions in a multiple sequence alignment that are suited for phylogenetic inference. For each character, BMGE computes a score closely related to an entropy value. Calculation of these entropy-like scores is weighted with BLOSUM or PAM similarity matrices in order to distinguish among biologically expected and unexpected variability for each aligned character. Sets of contiguous characters with a score above a given threshold are considered as not suited for phylogenetic inference and then removed. Simulation analyses show that the character trimming performed by BMGE produces datasets leading to accurate trees, especially with alignments including distantly-related sequences. BMGE also implements trimming and recoding methods aimed at minimizing phylogeny reconstruction artefacts due to compositional heterogeneity. BMGE is able to perform biologically relevant trimming on a multiple alignment of DNA, codon or amino acid sequences. Java source code and executable are freely available at ftp://ftp.pasteur.fr/pub/GenSoft/projects/BMGE/ . read more read less

Topics:

BLOSUM (58%)58% related to the paper, Multiple sequence alignment (58%)58% related to the paper
View PDF
1,080 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2148-6-29
Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified
24 Mar 2006 - BMC Evolutionary Biology

Abstract:

In recent years, model based approaches such as maximum likelihood have become the methods of choice for constructing phylogenies. A number of authors have shown the importance of using adequate substitution models in order to produce accurate phylogenies. In the past, many empirical models of amino acid substitution have bee... In recent years, model based approaches such as maximum likelihood have become the methods of choice for constructing phylogenies. A number of authors have shown the importance of using adequate substitution models in order to produce accurate phylogenies. In the past, many empirical models of amino acid substitution have been derived using a variety of different methods and protein datasets. These matrices are normally used as surrogates, rather than deriving the maximum likelihood model from the dataset being examined. With few exceptions, selection between alternative matrices has been carried out in an ad hoc manner. We start by highlighting the potential dangers of arbitrarily choosing protein models by demonstrating an empirical example where a single alignment can produce two topologically different and strongly supported phylogenies using two different arbitrarily-chosen amino acid substitution models. We demonstrate that in simple simulations, statistical methods of model selection are indeed robust and likely to be useful for protein model selection. We have investigated patterns of amino acid substitution among homologous sequences from the three Domains of life and our results show that no single amino acid matrix is optimal for any of the datasets. Perhaps most interestingly, we demonstrate that for two large datasets derived from the proteobacteria and archaea, one of the most favored models in both datasets is a model that was originally derived from retroviral Pol proteins. This demonstrates that choosing protein models based on their source or method of construction may not be appropriate. read more read less

Topics:

Model selection (55%)55% related to the paper
View PDF
1,067 Citations
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Frequently asked questions

1. Can I write BMC Evolutionary 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 BMC Evolutionary Biology guidelines and auto format it.

2. Do you follow the BMC Evolutionary Biology guidelines?

Yes, the template is compliant with the BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary Biology citation style.

4. Can I use the BMC Evolutionary 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 BMC Evolutionary Biology.

5. Can I use a manuscript in BMC Evolutionary 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 BMC Evolutionary Biology that you can download at the end.

6. How long does it usually take you to format my papers in BMC Evolutionary 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 BMC Evolutionary Biology.

7. Where can I find the template for the BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary 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. BMC Evolutionary Biology an online tool or is there a desktop version?

SciSpace's BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary Biology?”

11. What is the output that I would get after using BMC Evolutionary Biology?

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

12. Is BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary Biology?

The 5 most common citation types in order of usage for BMC Evolutionary 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 BMC Evolutionary 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 BMC Evolutionary Biology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download BMC Evolutionary 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 BMC Evolutionary Biology Endnote style according to Elsevier guidelines.

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