Eric J. Alm spends much of his time researching Genome, Microbiome, Genetics, Ecology and Human microbiome. The study incorporates disciplines such as Archaea, Phylogenetic tree and Metagenomics in addition to Genome. His Human Microbiome Project study in the realm of Microbiome connects with subjects such as Correlation.
His Human Microbiome Project research incorporates themes from Evolutionary biology and Earth Microbiome Project. His research in the fields of Ecology and Species diversity overlaps with other disciplines such as Sampling. The concepts of his Human microbiome study are interwoven with issues in Host, Computational biology, Gastrointestinal Microbiome and Bacteroidetes.
The scientist’s investigation covers issues in Microbiome, Genetics, Computational biology, Ecology and Gene. His research in Microbiome is mostly concerned with Human microbiome. His biological study focuses on Genome.
His work investigates the relationship between Computational biology and topics such as Metagenomics that intersect with problems in Mobile genetic elements. His Ecology research is mostly focused on the topic Ecology. His studies deal with areas such as Biological dispersal, Human Microbiome Project and Genomics as well as Evolutionary biology.
Eric J. Alm mainly investigates Microbiome, Computational biology, Metagenomics, Fecal bacteriotherapy and Evolutionary biology. His Microbiome research focuses on Human microbiome in particular. His Computational biology research integrates issues from Covariate, Horizontal gene transfer, False discovery rate and Benchmark.
Eric J. Alm combines subjects such as Ecology, Gene expression, Antibiotic resistance and Prophage with his study of Metagenomics. His Fecal bacteriotherapy research incorporates elements of Anaerobic exercise, Clinical trial and Intensive care medicine. His Evolutionary biology study incorporates themes from Adaptation, Host and Phylogenetics.
Eric J. Alm mostly deals with Microbiome, Metagenomics, Human microbiome, Immunology and Gut flora. His Microbiome research includes themes of DNA microarray, Pooling data and Physiology. The various areas that he examines in his Metagenomics study include Biodiversity, Gene expression, Regulatory sequence and Synthetic gene.
His studies in Human microbiome integrate themes in fields like Evolutionary biology, Endospore formation and Transmission. His Evolutionary biology research includes elements of Adaptation and Genomics. His biological study spans a wide range of topics, including Case-control study and Inflammatory bowel disease.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
.
Nature Biotechnology (2019)
Structure, function and diversity of the healthy human microbiome
Curtis Huttenhower;Curtis Huttenhower;Dirk Gevers;Rob Knight;Rob Knight;Sahar Abubucker.
Nature (2012)
Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2.
.
Nature Biotechnology (2019)
A framework for human microbiome research
Barbara A. Methé;Karen E. Nelson;Mihai Pop;Heather H. Creasy.
Nature (2012)
Inferring Correlation Networks from Genomic Survey Data
Jonathan Friedman;Eric J. Alm;Eric J. Alm.
PLOS Computational Biology (2012)
Ecology drives a global network of gene exchange connecting the human microbiome
Chris S. Smillie;Mark B. Smith;Jonathan Friedman;Otto X. Cordero.
Nature (2011)
Host lifestyle affects human microbiota on daily timescales.
Lawrence A David;Lawrence A David;Arne C Materna;Jonathan Friedman;Maria I Campos-Baptista.
Genome Biology (2014)
QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science
Evan Bolyen;Jai Ram Rideout;Matthew R Dillon;Nicholas A Bokulich.
(2018)
MicrobesOnline: an integrated portal for comparative and functional genomics
Paramvir S. Dehal;Marcin P. Joachimiak;Morgan N. Price;John T. Bates.
Nucleic Acids Research (2010)
Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
.
The ISME Journal (2016)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
Lawrence Berkeley National Laboratory
University of Washington
University of Tennessee at Knoxville
University of Oklahoma
Sun Yat-sen University
Broad Institute
MIT
Montana State University
Swinburne University of Technology
Johnson & Johnson
Tsinghua University
University of Tulsa
University of Illinois at Urbana-Champaign
Technical University of Denmark
National Sun Yat-sen University
Delft University of Technology
University of Lapland
Utrecht University
University of Natural Resources and Life Sciences
University of Illinois at Urbana-Champaign
Virginia Commonwealth University
South Dakota State University
Cornell University
University of Tübingen
Duke University
University of Central Lancashire