Example of Genome Biology format
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Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format
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Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format Example of Genome Biology format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
open access Open Access
recommended Recommended

Genome Biology — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Ecology, Evolution, Behavior and Systematics #11 of 647 down down by 6 ranks
Genetics #13 of 325 down down by 8 ranks
Cell Biology #19 of 279 down down by 6 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 983 Published Papers | 16235 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 03/07/2020
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Related Journals

open access Open Access

Springer

Quality:  
Medium
CiteRatio: 1.0
SJR: 0.271
SNIP: 0.686
open access Open Access
recommended Recommended

PLOS

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

PLOS

Quality:  
High
CiteRatio: 9.0
SJR: 3.587
SNIP: 1.457
open access Open Access
recommended Recommended

Nature

Quality:  
High
CiteRatio: 28.2
SJR: 7.305
SNIP: 3.41

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.

16.5

13% from 2019

CiteRatio for Genome Biology from 2016 - 2020
Year Value
2020 16.5
2019 18.9
2018 19.9
2017 21.9
2016 18.6
graph view Graph view
table view Table view

9.027

5% from 2019

SJR for Genome Biology from 2016 - 2020
Year Value
2020 9.027
2019 9.479
2018 9.867
2017 12.74
2016 11.203
graph view Graph view
table view Table view

2.682

4% from 2019

SNIP for Genome Biology from 2016 - 2020
Year Value
2020 2.682
2019 2.794
2018 2.618
2017 3.167
2016 2.864
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has decreased by 13% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

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 decreased by 4% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Genome Biology

Guideline source: View

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Springer

Genome Biology

Approved by publishing and review experts on SciSpace, this template is built as per for Genome Biology formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 837 authors to write and format their manuscripts to this journal.

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Last updated on
03 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
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/S13059-014-0550-8
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Michael I. Love1, Michael I. Love2, Wolfgang Huber, Simon Anders
05 Dec 2014 - Genome Biology

Abstract:

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical ... In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html . read more read less

Topics:

MRNA Sequencing (54%)54% related to the paper, Integrator complex (51%)51% related to the paper, Count data (50%)50% related to the paper, Fold change (50%)50% related to the paper
View PDF
47,038 Citations
open accessOpen access Journal Article DOI: 10.1186/GB-2009-10-3-R25
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
Ben Langmead1, Cole Trapnell1, Mihai Pop1, Steven L. Salzberg1
04 Mar 2009 - Genome Biology

Abstract:

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techni... Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu. read more read less

Topics:

Hybrid genome assembly (51%)51% related to the paper
View PDF
20,335 Citations
open accessOpen access Journal Article DOI: 10.1186/GB-2002-3-7-RESEARCH0034
Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
18 Jun 2002 - Genome Biology

Abstract:

Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the r... Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences. read more read less

Topics:

Reference genes (62%)62% related to the paper, Normalization (statistics) (60%)60% related to the paper, Housekeeping gene (58%)58% related to the paper, Gene expression profiling (50%)50% related to the paper
View PDF
18,261 Citations
open accessOpen access Journal Article DOI: 10.1186/GB-2010-11-10-R106
Differential expression analysis for sequence count data.
27 Oct 2010 - Genome Biology

Abstract:

High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We p... High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package. read more read less

Topics:

Count data (63%)63% related to the paper, Bioconductor (54%)54% related to the paper, Binomial distribution (52%)52% related to the paper, Negative binomial distribution (51%)51% related to the paper, Statistical power (50%)50% related to the paper
View PDF
13,356 Citations
open accessOpen access Journal Article DOI: 10.1186/GB-2008-9-9-R137
Model-based Analysis of ChIP-Seq (MACS)
17 Sep 2008 - Genome Biology

Abstract:

We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to ef... We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available. read more read less

Topics:

Peak calling (54%)54% related to the paper, Chromatin binding (51%)51% related to the paper
View PDF
13,008 Citations
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SciSpace is a very innovative solution to the formatting problem and existing providers, such as Mendeley or Word did not really evolve in recent years.

- Andreas Frutiger, Researcher, ETH Zurich, Institute for Biomedical Engineering

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What to expect from SciSpace?

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With SciSpace, you do not need a word template for Genome Biology.

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.

Time comparison

Time taken to format a paper and Compliance with guidelines

Plagiarism Reports via Turnitin

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Using this service, researchers can compare submissions against more than 170 million scholarly articles, a database of 70+ billion current and archived web pages. How Turnitin Integration works?

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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

1. Can I write Genome 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 Genome Biology guidelines and auto format it.

2. Do you follow the Genome Biology guidelines?

Yes, the template is compliant with the Genome 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 Genome 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 Genome Biology citation style.

4. Can I use the Genome 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 Genome Biology.

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

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

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

SciSpace's Genome 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 Genome 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 Genome Biology?”

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

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

12. Is Genome 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 Genome 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 Genome 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 Genome Biology?

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

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

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Typset automatically formats your research paper to Genome Biology formatting guidelines and citation style.

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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.

Andreas Frutiger
Researcher & Ex MS Word user
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