World's Best Scientists 2026 revealed!
Nature Biotechnology
H-index 164

Nature Biotechnology

1087-0156

Published by: Springer

https://www.nature.com/nbt/

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Biology and Biochemistry 2 479 460 130

Additional Metrics

Number of Best Scientists*: 1435
Documents by Best Scientists*: 877
Top 100 Ranked Scientists*: 97
SCIMAGO H-index: 531
SCIMAGO SJR: 19.006
Impact Factor: 41.7

Overview

Top Research Topics at Nature Biotechnology?

The journal primarily focuses on research topics in Computational biology, Genetics, Biochemistry, Biotechnology and Molecular biology. Genetics, which encompasses Gene and Genome, is the main subject of Nature Biotechnology.

  • Computational biology (10.09%)
  • Genetics (8.17%)
  • Biochemistry (7.30%)

What are the most cited papers published in the journal?

  • Full-length transcriptome assembly from RNA-Seq data without a reference genome. (11720 citations)
  • Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation (10515 citations)
  • MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. (9173 citations)

Research areas of the most cited articles at Nature Biotechnology:

The published papers primarily focus on research topics in Genetics, Biochemistry, Computational biology, Gene and Molecular biology. Issues in Computational biology were discussed in the most cited papers, taking into consideration concepts from other disciplines like RNA, Sequence analysis and Bioinformatics. The studies on Molecular biology discussed at the published articles can also contribute to research in the domains of Antibody and Cell biology.

What topics the last edition of the journal is best known for?

  • Gene
  • Enzyme
  • DNA

The previous edition focused in particular on these issues:

The journal mostly deals with topics like Computational biology, MEDLINE, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Coronavirus disease 2019 (COVID-19) and 2019-20 coronavirus outbreak. It explores topics in Computational biology which can be helpful for research in disciplines like CRISPR, RNA, Genome, Chromatin and Gene. The journal focused on RNA research but expanded to cover DNA.

The majority of Genome studies are focused on the issues of Human genome. The journal explores the study of 2019-20 coronavirus outbreak to improve our understanding of the broader topic of Virology. The study on Virology presented in Nature Biotechnology intersects with the topics under Antibody.

The most cited articles from the last journal are:

  • A unified catalog of 204,938 reference genomes from the human gut microbiome. (126 citations)
  • A genomic catalog of Earth’s microbiomes (89 citations)
  • CRISPR C-to-G base editors for inducing targeted DNA transversions in human cells (81 citations)

Papers citation over time

A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing in Nature Biotechnology (based on the number of publications) are:

  • Jeffrey L. Fox (507 papers) absent at the last edition,
  • John Hodgson (269 papers) published 1 paper at the last edition, 1 less than at the previous edition,
  • Bernard Dixon (206 papers) absent at the last edition,
  • Arthur Klausner (205 papers) absent at the last edition,
  • Cormac Sheridan (198 papers) published 10 papers at the last edition, 2 less than at the previous edition.

The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing in Nature Biotechnology (based on the number of publications) are:

  • Harvard University (484 papers) published 35 papers at the last edition, 1 less than at the previous edition,
  • Massachusetts Institute of Technology (308 papers) published 20 papers at the last edition, 5 more than at the previous edition,
  • Stanford University (281 papers) published 21 papers at the last edition, 5 more than at the previous edition,
  • National Institutes of Health (166 papers) published 8 papers at the last edition, 2 more than at the previous edition,
  • Howard Hughes Medical Institute (149 papers) published 9 papers at the last edition, 6 less than at the previous edition.

The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2021 edition, 24.56% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 43.72% were posted by at least one author from the top 10 institutions publishing in the journal. Another 7.44% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 18.60% of all publications and 30.23% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Career Opportunities in Nature Biotechnology

While this article has extensively covered research topics, cited papers, and authorship trends in Nature Biotechnology, a crucial section that would give additional depth and essential information to readers is an overview of career opportunities in this field. A focus on career paths within nature biotechnology not only provides insights into potential jobs in this field but gives our readers a comprehensive understanding of how research in computational biology, genetics, biochemistry, biotechnology, and molecular biology can be applied in real-world scenarios, contributing to their professional growth.

This could be especially helpful to students or professionals who are inspired by the research topics and breakthroughs discussed in the Journal and looking for a guide to venture into this field professionally. For instance, a career in nutritional science is one such opportunity where an individual can apply the knowledge gained from the research in genetics, biochemistry, and molecular biology to devise personalized diet plans, assess nutritional needs, and educate people on healthy eating habits.

If you’re interested in launching a career in nutritional science, this guide on how to become a certified nutritionist in Massachusetts can be a good starting point. It provides detailed information about the education, certification, and skills required to be a nutritionist in Massachusetts.

Of course, there are numerous other career opportunities in nature biotechnology. Bioinformaticians, genetic counselors, research scientists, biotechnological engineers, medical scientists, and many more professions exist at the intersection of biology and technology. It’s a constantly evolving field that promises many exciting prospects for future researchers and professionals.

Top Publications

  • PICRUSt2 for prediction of metagenome functions.

    Gavin M. Douglas;Vincent J. Maffei;Jesse R. Zaneveld;Svetlana N. Yurgel

    (2020)
    3869 Citations
  • CRISPR-Cas12-based detection of SARS-CoV-2.

    James P. Broughton;Xianding Deng;Guixia Yu;Clare L. Fasching

    (2020)
    2981 Citations
  • SignalP 6.0 predicts all five types of signal peptides using protein language models

    (2022)
    2214 Citations
  • Generalizing RNA velocity to transient cell states through dynamical modeling.

    Volker Bergen;Marius Lange;Stefan Peidli;F Alexander Wolf

    (2020)
    2088 Citations
  • Genome editing with CRISPR–Cas nucleases, base editors, transposases and prime editors

    Andrew V. Anzalone;Luke W. Koblan;Luke W. Koblan;Luke W. Koblan;David R. Liu;David R. Liu;David R. Liu

    (2020)
    1653 Citations
  • A SARS-CoV-2 surrogate virus neutralization test based on antibody-mediated blockage of ACE2-spike protein-protein interaction.

    Chee Wah Tan;Wan Ni Chia;Xijian Qin;Pei Liu

    (2020)
    1261 Citations
  • COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis.

    Robert Lorenz Chua;Soeren Lukassen;Saskia Trump;Bianca P. Hennig

    (2020)
    1252 Citations
  • Dictionary learning for integrative, multimodal and scalable single-cell analysis

    (2023)
    1110 Citations

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Best Scientists Contributing to This Journal