Example of Bioinformatics format
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Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format
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Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format Example of Bioinformatics format
Sample paper formatted on SciSpace - SciSpace
This content is only for preview purposes. The original open access content can be found here.
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Bioinformatics — Template for authors

Categories Rank Trend in last 3 yrs
Statistics and Probability #3 of 239 down down by 1 rank
Computational Mathematics #5 of 152 down down by 3 ranks
Computational Theory and Mathematics #7 of 133 down down by 3 ranks
Computer Science Applications #49 of 693 down down by 42 ranks
Biochemistry #37 of 415 down down by 21 ranks
Molecular Biology #51 of 382 down down by 25 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 3642 Published Papers | 36047 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 22/07/2020
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Related Journals

open access Open Access

SAGE

Quality:  
Good
CiteRatio: 2.9
SJR: 0.556
SNIP: 0.503
open access Open Access

Springer

Quality:  
High
CiteRatio: 4.3
SJR: 0.696
SNIP: 1.325
open access Open Access

Bentham Science

Quality:  
Good
CiteRatio: 2.7
SJR: 0.306
SNIP: 0.383
open access Open Access

Springer

Quality:  
Good
CiteRatio: 1.8
SJR: 0.426
SNIP: 0.832

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.

5.61

24% from 2018

Impact factor for Bioinformatics from 2016 - 2019
Year Value
2019 5.61
2018 4.531
2017 5.481
2016 7.307
graph view Graph view
table view Table view

9.9

15% from 2019

CiteRatio for Bioinformatics from 2016 - 2020
Year Value
2020 9.9
2019 8.6
2018 9.7
2017 12.8
2016 10.8
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 24% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 15% 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.

3.599

1% from 2019

SJR for Bioinformatics from 2016 - 2020
Year Value
2020 3.599
2019 3.567
2018 4.549
2017 6.14
2016 5.21
graph view Graph view
table view Table view

2.056

13% from 2019

SNIP for Bioinformatics from 2016 - 2020
Year Value
2020 2.056
2019 1.813
2018 1.908
2017 2.533
2016 2.336
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 1% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

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

Bioinformatics

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Oxford University Press

Bioinformatics

The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sect...... Read More

Mathematics

i
Last updated on
22 Jul 2020
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ISSN
1367-4803
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Acceptance Rate
Not provided
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Frequency
Not provided
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Open Access
No
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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
unsrt
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Citation Type
Author Year
(Blonder et al., 1982)
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Bibliography Example
Blonder, G. E., Tinkham, M., and Klapwijk, T. M. (1982). Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B, 25(7), 4515–4532.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1093/BIOINFORMATICS/BTP352
The Sequence Alignment/Map format and SAMtools
01 Aug 2009 - Bioinformatics

Abstract:

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which ali... Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: [email protected] read more read less

Topics:

Variant Call Format (62%)62% related to the paper, Stockholm format (61%)61% related to the paper, FASTQ format (56%)56% related to the paper, Multiple sequence alignment (54%)54% related to the paper, Alignment-free sequence analysis (52%)52% related to the paper
View PDF
45,957 Citations
open accessOpen access Journal Article DOI: 10.1093/BIOINFORMATICS/BTP324
Fast and accurate short read alignment with Burrows–Wheeler transform
Heng Li1, Richard Durbin1
01 Jul 2009 - Bioinformatics

Abstract:

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a si... Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: [email protected] read more read less

Topics:

Hybrid genome assembly (54%)54% related to the paper, Sequence assembly (53%)53% related to the paper, 2 base encoding (52%)52% related to the paper, Peak calling (52%)52% related to the paper, DNA sequencing theory (51%)51% related to the paper
View PDF
43,862 Citations
open accessOpen access Journal Article DOI: 10.1093/BIOINFORMATICS/BTU170
Trimmomatic: a flexible trimmer for Illumina sequence data
Anthony Bolger1, Marc Lohse1, Bjoern Usadel1
01 Aug 2014 - Bioinformatics

Abstract:

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient pre... Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: ed.nehcaa-htwr.1oib@ledasu Supplementary information: Supplementary data are available at Bioinformatics online. read more read less
39,291 Citations
open accessOpen access Journal Article DOI: 10.1093/BIOINFORMATICS/BTS635
STAR: ultrafast universal RNA-seq aligner
01 Jan 2013 - Bioinformatics

Abstract:

Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rate... Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/. read more read less

Topics:

MRNA Sequencing (57%)57% related to the paper
View PDF
30,684 Citations
open accessOpen access Journal Article DOI: 10.1093/BIOINFORMATICS/BTP616
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
Mark D. Robinson1, Davis J. McCarthy1, Gordon K. Smyth1
01 Jan 2010 - Bioinformatics

Abstract:

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for... Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). read more read less

Topics:

Bioconductor (64%)64% related to the paper
View PDF
29,413 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 Bioinformatics.

It automatically formats your research paper to Oxford University Press 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

SciSpace has partnered with Turnitin, the leading provider of Plagiarism Check software.

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?

Turnitin Stats
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Bioinformatics format uses unsrt 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

1. Can I write Bioinformatics in LaTeX?

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

2. Do you follow the Bioinformatics guidelines?

Yes, the template is compliant with the Bioinformatics 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 Bioinformatics?

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 Bioinformatics citation style.

4. Can I use the Bioinformatics 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 Bioinformatics.

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

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

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

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

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

SciSpace's Bioinformatics 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 Bioinformatics?

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 Bioinformatics?”

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

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

12. Is Bioinformatics'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 Bioinformatics?

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 Bioinformatics. 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 Bioinformatics?

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

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

16. Can I download Bioinformatics 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 Bioinformatics 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.

Andreas Frutiger
Researcher & Ex MS Word user
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