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Genetics
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2026

D-Index & Metrics

Best Scientists

D-Index
207
Citations
191135
World Ranking
223
National Ranking
146

Genetics

D-Index
212
Citations
201748
World Ranking
18
National Ranking
12

Michael Snyder publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Michael Snyder sits on this spectrum.

45–54 publications: 6 scientists 55–64 publications: 10 scientists 65–74 publications: 35 scientists 75–84 publications: 84 scientists 85–94 publications: 102 scientists 95–104 publications: 151 scientists 105–114 publications: 175 scientists 115–124 publications: 203 scientists 125–134 publications: 217 scientists 135–144 publications: 205 scientists 145–154 publications: 193 scientists 155–164 publications: 188 scientists 165–174 publications: 170 scientists 175–184 publications: 178 scientists 185–194 publications: 164 scientists 195–204 publications: 173 scientists 205–214 publications: 159 scientists 215–224 publications: 134 scientists 225–234 publications: 143 scientists 235–244 publications: 105 scientists 245–254 publications: 114 scientists 255–264 publications: 92 scientists 265–274 publications: 88 scientists 275–284 publications: 87 scientists 285–294 publications: 80 scientists 295–304 publications: 62 scientists 305–314 publications: 75 scientists 315–324 publications: 67 scientists 325–334 publications: 60 scientists 335–344 publications: 52 scientists 345–354 publications: 40 scientists 355–364 publications: 48 scientists 365–374 publications: 47 scientists 375–384 publications: 46 scientists 385–394 publications: 31 scientists 395–404 publications: 27 scientists 405–414 publications: 40 scientists 415–424 publications: 30 scientists 425–434 publications: 43 scientists 435–444 publications: 29 scientists 445–454 publications: 14 scientists 455–464 publications: 28 scientists 465–474 publications: 21 scientists 475–484 publications: 21 scientists 485–494 publications: 22 scientists 495–504 publications: 17 scientists 505–514 publications: 12 scientists 515–524 publications: 11 scientists 525–534 publications: 8 scientists 535–544 publications: 8 scientists 545–554 publications: 14 scientists 555–564 publications: 4 scientists 565–574 publications: 11 scientists 575–584 publications: 5 scientists 585–594 publications: 11 scientists 595–604 publications: 12 scientists 605–614 publications: 7 scientists 615–624 publications: 6 scientists 625–634 publications: 10 scientists 635–644 publications: 9 scientists 645–654 publications: 10 scientists 655–664 publications: 6 scientists 665–674 publications: 6 scientists 675–684 publications: 6 scientists 685–694 publications: 4 scientists 695–702 publications: 6 scientists 703+ publications: 100 scientists
45 publications 703+

This scientist: 830 publications — 99th percentile

99% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Michael Snyder D-index placement in Genetics in 2026

The chart shows the D-index (discipline H-index) distribution of Genetics scientists ranked by Research.com in 2026. The highlighted bar marks where Michael Snyder sits on this spectrum.

40–41 D-Index: 24 scientists 42–43 D-Index: 52 scientists 44–45 D-Index: 84 scientists 46–47 D-Index: 112 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 141 scientists 52–53 D-Index: 143 scientists 54–55 D-Index: 145 scientists 56–57 D-Index: 179 scientists 58–59 D-Index: 162 scientists 60–61 D-Index: 175 scientists 62–63 D-Index: 191 scientists 64–65 D-Index: 172 scientists 66–67 D-Index: 184 scientists 68–69 D-Index: 164 scientists 70–71 D-Index: 158 scientists 72–73 D-Index: 150 scientists 74–75 D-Index: 136 scientists 76–77 D-Index: 127 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 111 scientists 82–83 D-Index: 110 scientists 84–85 D-Index: 110 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 102 scientists 90–91 D-Index: 66 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 70 scientists 96–97 D-Index: 54 scientists 98–99 D-Index: 60 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 55 scientists 104–105 D-Index: 45 scientists 106–107 D-Index: 42 scientists 108–109 D-Index: 28 scientists 110–111 D-Index: 39 scientists 112–113 D-Index: 25 scientists 114–115 D-Index: 31 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 34 scientists 120–121 D-Index: 29 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 18 scientists 126–127 D-Index: 27 scientists 128–129 D-Index: 22 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 11 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 12 scientists 138–139 D-Index: 21 scientists 140–141 D-Index: 4 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 14 scientists 146–147 D-Index: 6 scientists 148–149 D-Index: 10 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 9 scientists 154–155 D-Index: 8 scientists 156–157 D-Index: 8 scientists 158–159 D-Index: 9 scientists 160+ D-Index: 96 scientists
40 D-Index 160+

This scientist: 212 D-Index — 100th percentile

100% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Genetics in United States Leader Award
  • 2025 - Research.com Best Scientists Award
  • 2025 - Research.com Genetics in United States Leader Award
  • 2024 - Research.com Genetics in United States Leader Award
  • 2024 - Research.com Genetics and Molecular Biology in United States Leader Award
  • 2023 - Research.com Genetics in United States Leader Award
  • 2015 - Fellow of the American Academy of Arts and Sciences
  • 2014 - Fellow of the American Association for the Advancement of Science (AAAS)

Overview

Michael Snyder is affiliated with Stanford University in the United States. Their research spans multiple domains within biochemistry, genetics, molecular biology, and medicine. The scientist has contributed extensively to both foundational and applied aspects of these disciplines, focusing on various subfields such as molecular biology, genetics, physiology, immunology, and neurology.

The main topics of Michael Snyder's research encompass:

  • Single-cell and spatial transcriptomics
  • Metabolomics and mass spectrometry studies
  • Gut microbiota and health
  • Epigenetics and DNA methylation
  • RNA modifications and cancer
  • RNA research and splicing
  • Adipose tissue and metabolism

They have published frequently in venues including ENCODE Datasets, bioRxiv (Cold Spring Harbor Laboratory), Nature Communications, Cell, and Cell Reports.

Selected recent papers highlight a range of research interests:

  • Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices, 2021, published in Nature Methods
  • Exerkines in health, resilience and disease, 2022, published in Nature Reviews Endocrinology
  • The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution, 2020, published in Cell
  • Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy, 2020, published in Cell
  • Biomarkers of aging for the identification and evaluation of longevity interventions, 2023, published in Cell

Michael Snyder's frequent collaborators include Kévin Contrepois, Sai Zhang, Xiaotao Shen, Johnathan Cooper-Knock, and Ahmed A. Metwally.

In recognition of their contributions to science, they have been awarded fellowships by the American Academy of Arts and Sciences in 2015 and the American Association for the Advancement of Science (AAAS) in 2014.

Best Publications

  • RNA-Seq: a revolutionary tool for transcriptomics

    Zhong Wang;Mark Gerstein;Michael Snyder

  • Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

    Ewan Birney;John A. Stamatoyannopoulos;Anindya Dutta;Roderic Guigó

  • The GTEx Consortium atlas of genetic regulatory effects across human tissues

    F Aguet;AN Barbeira;R Bonazzola;A Brown

  • Functional profiling of the Saccharomyces cerevisiae genome.

    Guri Giaever;Angela M. Chu;Li Ni;Carla Connelly

  • Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

    Elizabeth A. Winzeler;Daniel D. Shoemaker;Anna Astromoff;Hong Liang

  • The ENCODE (ENCyclopedia of DNA elements) Project

    E. A. Feingold;P. J. Good;M. S. Guyer;S. Kamholz

  • The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing

    Ugrappa Nagalakshmi;Zhong Wang;Karl Waern;Chong Shou

  • Global analysis of protein activities using proteome chips

    Michael Snyder;Heng Zhu;Paul Bertone;Scott M. Bidlingmaier

  • Annotation of functional variation in personal genomes using RegulomeDB

    Alan P. Boyle;Eurie L. Hong;Manoj Hariharan;Yong Cheng

  • An integrated encyclopedia of DNA elements in the human genome

    Ian Dunham;Anshul Kundaje;Shelley F. Aldred;Patrick J. Collins

  • Expanded encyclopaedias of DNA elements in the human and mouse genomes

    Jill E. Moore;Michael J. Purcaro;Henry E. Pratt;Charles B. Epstein

  • ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

    Stephen G. Landt;Georgi K. Marinov;Anshul Kundaje;Pouya Kheradpour

  • A comparative encyclopedia of DNA elements in the mouse genome

    Feng Yue;Feng Yue;Yong Cheng;Alessandra Breschi;Jeff Vierstra

  • Single-cell chromatin accessibility reveals principles of regulatory variation

    Jason D. Buenrostro;Beijing Wu;Ulrike M. Litzenburger;Dave Ruff

  • Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing

    Gordon Robertson;Martin Hirst;Matthew Bainbridge;Misha Bilenky

  • Architecture of the human regulatory network derived from ENCODE data

    Mark B Gerstein;Anshul Kundaje;Manoj Hariharan;Stephen G Landt

  • High-Quality Binary Protein Interaction Map of the Yeast Interactome Network

    Haiyuan Yu;Pascal Braun;Muhammed A Yildirim;Irma Lemmens

  • CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing

    Alexej Abyzov;Alexander Eckehart Urban;Michael Snyder;Mark Gerstein

  • A Bayesian networks approach for predicting protein-protein interactions from genomic data.

    Ronald Jansen;Haiyuan Yu;Dov Greenbaum;Yuval Kluger

  • Paired-end mapping reveals extensive structural variation in the human genome.

    Jan O. Korbel;Alexander Eckehart Urban;Jason P. Affourtit;Brian Godwin

Frequent Co-Authors

Mark Gerstein
Mark Gerstein Yale University
Joel Rozowsky
Joel Rozowsky Yale University
Sherman M. Weissman
Sherman M. Weissman Yale University
Anshul Kundaje
Anshul Kundaje Stanford University
Rui Chen
Rui Chen Capital Medical University
Joseph C. Wu
Joseph C. Wu Stanford University
Thomas R. Gingeras
Thomas R. Gingeras Cold Spring Harbor Laboratory
Paul Bertone
Paul Bertone University of Cambridge
Chao Cheng
Chao Cheng Baylor College of Medicine

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