Example of BMC Genomics format
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Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics format Example of BMC Genomics 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

BMC Genomics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Biotechnology #66 of 282 down down by 31 ranks
Genetics #79 of 325 down down by 12 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 3829 Published Papers | 24614 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 20/06/2020
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Insights
General info
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FAQ

Related Journals

open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 6.4
SJR: 0.745
SNIP: 1.278
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.1
SJR: 0.833
SNIP: 0.855
open access Open Access
recommended Recommended

Springer

Quality:  
High
CiteRatio: 6.8
SJR: 1.35
SNIP: 1.956
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.0
SJR: 0.724
SNIP: 0.788

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.

3.594

3% from 2018

Impact factor for BMC Genomics from 2016 - 2019
Year Value
2019 3.594
2018 3.501
2017 3.73
2016 3.729
graph view Graph view
table view Table view

6.4

7% from 2019

CiteRatio for BMC Genomics from 2016 - 2020
Year Value
2020 6.4
2019 6.0
2018 6.6
2017 7.2
2016 6.9
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 3% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 7% 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.

1.547

5% from 2019

SJR for BMC Genomics from 2016 - 2020
Year Value
2020 1.547
2019 1.629
2018 1.829
2017 2.11
2016 2.163
graph view Graph view
table view Table view

1.162

2% from 2019

SNIP for BMC Genomics from 2016 - 2020
Year Value
2020 1.162
2019 1.14
2018 1.11
2017 1.184
2016 1.12
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 5% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 2% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

BMC Genomics

Guideline source: View

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Springer

BMC Genomics

Approved by publishing and review experts on SciSpace, this template is built as per for BMC Genomics formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 393 authors to write and format their manuscripts to this journal.

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Last updated on
20 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: 10.1186/1471-2164-9-75
The RAST Server: Rapid Annotations using Subsystems Technology
08 Feb 2008 - BMC Genomics

Abstract:

The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes... The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes. read more read less

Topics:

Genome project (55%)55% related to the paper
View PDF
9,397 Citations
open accessOpen access Journal Article DOI: 10.1186/S12864-019-6413-7
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
Davide Chicco, Giuseppe Jurman1
02 Jan 2020 - BMC Genomics

Abstract:

To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. A... To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset. In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F1 score in evaluating binary classification tasks by all scientific communities. read more read less

Topics:

Matthews correlation coefficient (66%)66% related to the paper, F1 score (64%)64% related to the paper, Binary classification (63%)63% related to the paper, Confusion matrix (54%)54% related to the paper, False positive paradox (53%)53% related to the paper
View PDF
2,358 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2164-12-402
BLAST Ring Image Generator (BRIG) : simple prokaryote genome comparisons
Nabil-Fareed Alikhan1, Nicola K. Petty1, Nouri L. Ben Zakour1, Scott A. Beatson1
08 Aug 2011 - BMC Genomics

Abstract:

Visualisation of genome comparisons is invaluable for helping to determine genotypic differences between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve the validation, interpretation and communication of genome sequence information; especially with the increasing amount... Visualisation of genome comparisons is invaluable for helping to determine genotypic differences between closely related prokaryotes. New visualisation and abstraction methods are required in order to improve the validation, interpretation and communication of genome sequence information; especially with the increasing amount of data arising from next-generation sequencing projects. Visualising a prokaryote genome as a circular image has become a powerful means of displaying informative comparisons of one genome to a number of others. Several programs, imaging libraries and internet resources already exist for this purpose, however, most are either limited in the number of comparisons they can show, are unable to adequately utilise draft genome sequence data, or require a knowledge of command-line scripting for implementation. Currently, there is no freely available desktop application that enables users to rapidly visualise comparisons between hundreds of draft or complete genomes in a single image. read more read less

Topics:

Genome (53%)53% related to the paper, Genomics (52%)52% related to the paper
View PDF
2,254 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2164-7-142
Centering, scaling, and transformations: improving the biological information content of metabolomics data
08 Jun 2006 - BMC Genomics

Abstract:

Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differen... Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important. read more read less

Topics:

Data analysis (54%)54% related to the paper, Autoscaling (52%)52% related to the paper, Data set (51%)51% related to the paper
View PDF
1,987 Citations
open accessOpen access Journal Article DOI: 10.1186/1471-2164-13-341
A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers.
24 Jul 2012 - BMC Genomics

Abstract:

Next generation sequencing (NGS) technology has revolutionized genomic and genetic research. The pace of change in this area is rapid with three major new sequencing platforms having been released in 2011: Ion Torrent’s PGM, Pacific Biosciences’ RS and the Illumina MiSeq. Here we compare the results obtained with those platfo... Next generation sequencing (NGS) technology has revolutionized genomic and genetic research. The pace of change in this area is rapid with three major new sequencing platforms having been released in 2011: Ion Torrent’s PGM, Pacific Biosciences’ RS and the Illumina MiSeq. Here we compare the results obtained with those platforms to the performance of the Illumina HiSeq, the current market leader. In order to compare these platforms, and get sufficient coverage depth to allow meaningful analysis, we have sequenced a set of 4 microbial genomes with mean GC content ranging from 19.3 to 67.7%. Together, these represent a comprehensive range of genome content. Here we report our analysis of that sequence data in terms of coverage distribution, bias, GC distribution, variant detection and accuracy. Sequence generated by Ion Torrent, MiSeq and Pacific Biosciences technologies displays near perfect coverage behaviour on GC-rich, neutral and moderately AT-rich genomes, but a profound bias was observed upon sequencing the extremely AT-rich genome of Plasmodium falciparum on the PGM, resulting in no coverage for approximately 30% of the genome. We analysed the ability to call variants from each platform and found that we could call slightly more variants from Ion Torrent data compared to MiSeq data, but at the expense of a higher false positive rate. Variant calling from Pacific Biosciences data was possible but higher coverage depth was required. Context specific errors were observed in both PGM and MiSeq data, but not in that from the Pacific Biosciences platform. All three fast turnaround sequencers evaluated here were able to generate usable sequence. However there are key differences between the quality of that data and the applications it will support. read more read less

Topics:

Ion semiconductor sequencing (54%)54% related to the paper, DNA sequencing (51%)51% related to the paper
View PDF
1,967 Citations
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Frequently asked questions

1. Can I write BMC Genomics 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 Genomics guidelines and auto format it.

2. Do you follow the BMC Genomics guidelines?

Yes, the template is compliant with the BMC Genomics 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 Genomics?

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 Genomics citation style.

4. Can I use the BMC Genomics 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 Genomics.

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

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

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 Genomics.

7. Where can I find the template for the BMC Genomics?

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

SciSpace's BMC Genomics 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 Genomics?

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 Genomics?”

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

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

12. Is BMC Genomics'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 Genomics?

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 Genomics. 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 Genomics?

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

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

16. Can I download BMC Genomics 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 Genomics 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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