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D-Index & Metrics

Genetics

D-Index
97
Citations
44309
World Ranking
830
National Ranking
417

Molecular Biology

D-Index
97
Citations
44309
World Ranking
585
National Ranking
322

Michael J. MacCoss publication distribution in Molecular Biology in 2026

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

47–56 publications: 7 scientists 57–66 publications: 17 scientists 67–76 publications: 65 scientists 77–86 publications: 90 scientists 87–96 publications: 125 scientists 97–106 publications: 131 scientists 107–116 publications: 162 scientists 117–126 publications: 177 scientists 127–136 publications: 158 scientists 137–146 publications: 158 scientists 147–156 publications: 146 scientists 157–166 publications: 159 scientists 167–176 publications: 131 scientists 177–186 publications: 110 scientists 187–196 publications: 112 scientists 197–206 publications: 100 scientists 207–216 publications: 89 scientists 217–226 publications: 98 scientists 227–236 publications: 74 scientists 237–246 publications: 72 scientists 247–256 publications: 63 scientists 257–266 publications: 53 scientists 267–276 publications: 54 scientists 277–286 publications: 49 scientists 287–296 publications: 52 scientists 297–306 publications: 43 scientists 307–316 publications: 46 scientists 317–326 publications: 41 scientists 327–336 publications: 42 scientists 337–346 publications: 31 scientists 347–356 publications: 28 scientists 357–366 publications: 29 scientists 367–376 publications: 26 scientists 377–386 publications: 24 scientists 387–396 publications: 24 scientists 397–406 publications: 14 scientists 407–416 publications: 13 scientists 417–426 publications: 20 scientists 427–436 publications: 12 scientists 437–446 publications: 20 scientists 447–456 publications: 11 scientists 457–466 publications: 10 scientists 467–476 publications: 14 scientists 477–486 publications: 14 scientists 487–496 publications: 10 scientists 497–506 publications: 13 scientists 507–516 publications: 13 scientists 517–526 publications: 2 scientists 527–536 publications: 4 scientists 537–546 publications: 6 scientists 547–556 publications: 8 scientists 557–563 publications: 6 scientists 564+ publications: 100 scientists
47 publications 564+

This scientist: 281 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 564 publications or more.

Michael J. MacCoss D-index placement in Molecular Biology in 2026

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

40–41 D-Index: 36 scientists 42–43 D-Index: 101 scientists 44–45 D-Index: 115 scientists 46–47 D-Index: 121 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 130 scientists 52–53 D-Index: 106 scientists 54–55 D-Index: 116 scientists 56–57 D-Index: 113 scientists 58–59 D-Index: 129 scientists 60–61 D-Index: 120 scientists 62–63 D-Index: 105 scientists 64–65 D-Index: 131 scientists 66–67 D-Index: 95 scientists 68–69 D-Index: 97 scientists 70–71 D-Index: 106 scientists 72–73 D-Index: 83 scientists 74–75 D-Index: 89 scientists 76–77 D-Index: 77 scientists 78–79 D-Index: 70 scientists 80–81 D-Index: 73 scientists 82–83 D-Index: 60 scientists 84–85 D-Index: 48 scientists 86–87 D-Index: 45 scientists 88–89 D-Index: 50 scientists 90–91 D-Index: 31 scientists 92–93 D-Index: 51 scientists 94–95 D-Index: 43 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 39 scientists 100–101 D-Index: 41 scientists 102–103 D-Index: 29 scientists 104–105 D-Index: 33 scientists 106–107 D-Index: 35 scientists 108–109 D-Index: 20 scientists 110–111 D-Index: 38 scientists 112–113 D-Index: 19 scientists 114–115 D-Index: 28 scientists 116–117 D-Index: 13 scientists 118–119 D-Index: 23 scientists 120–121 D-Index: 16 scientists 122–123 D-Index: 15 scientists 124–125 D-Index: 11 scientists 126–127 D-Index: 21 scientists 128–129 D-Index: 7 scientists 130–131 D-Index: 13 scientists 132–133 D-Index: 14 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 9 scientists 138–139 D-Index: 8 scientists 140–141 D-Index: 16 scientists 142–143 D-Index: 7 scientists 144 D-Index: 7 scientists 145+ D-Index: 100 scientists
40 D-Index 145+

This scientist: 97 D-Index — 81st percentile

81% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 145 D-Index or more.

Overview

Michael J. MacCoss is affiliated with the University of Washington in the United States. Their research primarily spans Biochemistry, Genetics and Molecular Biology, and Chemistry, with a significant focus on Molecular Biology and Spectroscopy as key subfields.

The main topics covered by their work include:

  • Advanced Proteomics Techniques and Applications
  • Mass Spectrometry Techniques and Applications
  • Metabolomics and Mass Spectrometry Studies
  • Adipose Tissue and Metabolism
  • Alzheimer's disease research and treatments
  • RNA and protein synthesis mechanisms
  • Genetics, Aging, and Longevity in Model Organisms

Michael J. MacCoss has contributed to numerous publications, with frequent appearances in the following venues:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of Proteome Research
  • Nature Methods
  • GeroScience
  • Clinical Chemistry

Notable recent publications include:

  • The ProteomeXchange consortium at 10 years: 2023 update, 2022, Nucleic Acids Research
  • Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics, 2020, Journal of Proteome Research
  • Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries, 2020, Molecular & Cellular Proteomics
  • Evaluating the Performance of the Astral Mass Analyzer for Quantitative Proteomics Using Data-Independent Acquisition, 2023, Journal of Proteome Research
  • Proteogenomic data and resources for pan-cancer analysis, 2023, Cancer Cell

Their collaborative network includes frequent co-authors such as:

  • Gennifer E. Merrihew
  • Brendan MacLean
  • Richard S. Johnson
  • Michael Riffle
  • William Stafford Noble

Best Publications

  • Skyline: an open source document editor for creating and analyzing targeted proteomics experiments

    Brendan MacLean;Daniela M. Tomazela;Nicholas Shulman;Matthew Chambers

  • A cross-platform toolkit for mass spectrometry and proteomics

    Matthew C Chambers;Brendan Maclean;Robert Burke;Dario Amodei

  • An integrated encyclopedia of DNA elements in the human genome

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

  • Aminoglycoside antibiotics induce bacterial biofilm formation

    Lucas R. Hoffman;David A. D'Argenio;Michael J. MacCoss;Zhaoying Zhang

  • Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project

    Mark B. Gerstein;Zhi John Lu;Eric L. Van Nostrand;Chao Cheng

  • How many human proteoforms are there

    Ruedi Aebersold;Jeffrey N. Agar;I. Jonathan Amster;Mark S. Baker

  • An expansive human regulatory lexicon encoded in transcription factor footprints

    Shane Neph;Jeff Vierstra;Andrew B. Stergachis;Alex P. Reynolds

  • Using iRT, a normalized retention time for more targeted measurement of peptides

    Claudia Escher;Lukas Reiter;Brendan MacLean;Reto Ossola

  • The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.

    Lindsay K. Pino;Brian C. Searle;James G. Bollinger;Brook Nunn

  • Assigning significance to peptides identified by tandem mass spectrometry using decoy databases.

    Lukas Käll;John D. Storey;Michael J. MacCoss;William Stafford Noble

  • The ProteomeXchange consortium in 2020: enabling 'big data' approaches in proteomics.

    Eric W. Deutsch;Nuno Bandeira;Nuno Bandeira;Vagisha Sharma;Yasset Pérez-Riverol

  • Molecular architecture and assembly of the DDB1-CUL4A ubiquitin ligase machinery.

    Stephane Angers;Ti Li;Xianhua Yi;Michael J. MacCoss

  • Shotgun identification of protein modifications from protein complexes and lens tissue

    Michael J. MacCoss;W. Hayes McDonald;Anita Saraf;Rovshan Sadygov

  • Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry

    Brian C. Searle;Lindsay K. Pino;Jarrett D. Egertson;Ying S. Ting

  • Overexpression of Catalase Targeted to Mitochondria Attenuates Murine Cardiac Aging

    Dao Fu Dai;Luis Fernando Santana;Marc Vermulst;Daniela M. Tomazela

  • Platform-independent and Label-free Quantitation of Proteomic Data Using MS1 Extracted Ion Chromatograms in Skyline APPLICATION TO PROTEIN ACETYLATION AND PHOSPHORYLATION

    Birgit Schilling;Matthew J. Rardin;Brendan X. MacLean;Anna M. Zawadzka

  • Wilms Tumor Suppressor WTX Negatively Regulates WNT/β-Catenin Signaling

    Michael B. Major;Nathan D. Camp;Jason D. Berndt;Xianhua Yi

  • The PINK1-Parkin pathway promotes both mitophagy and selective respiratory chain turnover in vivo.

    Evelyn S. Vincow;Gennifer Merrihew;Ruth E. Thomas;Nicholas J. Shulman

  • The KLHL12-Cullin-3 ubiquitin ligase negatively regulates the Wnt-β-catenin pathway by targeting Dishevelled for degradation

    Stephane Angers;Chris J. Thorpe;Travis L. Biechele;Seth J. Goldenberg

  • MS1, MS2, and SQT—three unified, compact, and easily parsed file formats for the storage of shotgun proteomic spectra and identifications

    W. Hayes McDonald;David L. Tabb;David L. Tabb;Rovshan G. Sadygov;Michael J. MacCoss

Frequent Co-Authors

Brendan MacLean
Brendan MacLean University of Washington
William Stafford Noble
William Stafford Noble University of Washington
Richard J. Johnson
Richard J. Johnson University of Colorado Denver
Peter S. Rabinovitch
Peter S. Rabinovitch University of Washington
Stewart M. Gray
Stewart M. Gray Cornell University
Trisha N. Davis
Trisha N. Davis University of Washington
John R. Yates
John R. Yates Scripps Research Institute
Willie J. Swanson
Willie J. Swanson University of Washington
Michael Snyder
Michael Snyder Stanford University
Chao Cheng
Chao Cheng Baylor College of Medicine

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