Skip to content

Academic references

This page lists academic and software references for tools used or supported by MTD Explorer.

The list is intended to help users cite the original methods, databases, and software components used in an analysis.

For manuscripts, always check the citation policy of each tool and database, and cite the exact software versions used when required.

MTD and MTD Explorer

The original MTD pipeline should be cited whenever MTD Explorer is used, because MTD Explorer is built upon and extends the original MTD workflow.

  • Wu, F., Liu, Y.-Z., & Ling, B. (2022). MTD: a unique pipeline for host and meta-transcriptome joint and integrative analyses of RNA-seq data. Briefings in Bioinformatics, 23(3), bbac111. https://doi.org/10.1093/bib/bbac111

MTD Explorer itself should be cited according to the citation information provided in the MTD Explorer repository, release, manuscript, or Zenodo archive when available.

Workflow execution and command-line utilities

GNU Parallel

GNU Parallel is used to run jobs in parallel.

GNU Parallel explicitly asks users to cite it in publications. The most accurate citation for the installed version can be obtained with:

parallel --citation

Recommended reference:

  • Tange, O. (2011). GNU Parallel: The Command-Line Power Tool. ;login: The USENIX Magazine, 36(1), 42–47.

Some GNU Parallel releases may recommend citing a version-specific Zenodo record. Use parallel --citation to obtain the preferred citation for the version used in a specific analysis.

R

  • R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Python

  • Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.

Read preprocessing and quality control

fastp

  • Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34(17), i884–i890. https://doi.org/10.1093/bioinformatics/bty560

Taxonomic classification and abundance estimation

Kraken 2

  • Wood, D. E., Lu, J., & Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biology, 20, 257. https://doi.org/10.1186/s13059-019-1891-0

Bracken

  • Lu, J., Breitwieser, F. P., Thielen, P., & Salzberg, S. L. (2017). Bracken: estimating species abundance in metagenomics data. PeerJ Computer Science, 3, e104. https://doi.org/10.7717/peerj-cs.104

KrakenTools

  • Lu, J. (2022). KrakenTools. GitHub repository. https://github.com/jenniferlu717/KrakenTools

When KrakenTools is used for report manipulation or contaminant removal, also cite Kraken 2 and Bracken when those outputs are used downstream.

Taxonomic visualization

Krona

  • Ondov, B. D., Bergman, N. H., & Phillippy, A. M. (2011). Interactive metagenomic visualization in a Web browser. BMC Bioinformatics, 12, 385. https://doi.org/10.1186/1471-2105-12-385

GraPhlAn

  • Asnicar, F., Weingart, G., Tickle, T. L., Huttenhower, C., & Segata, N. (2015). Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ, 3, e1029. https://doi.org/10.7717/peerj.1029

Host read alignment and gene counting

HISAT2

  • Kim, D., Paggi, J. M., Park, C., Bennett, C., & Salzberg, S. L. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology, 37, 907–915. https://doi.org/10.1038/s41587-019-0201-4

Magic-BLAST

  • Boratyn, G. M., Thierry-Mieg, J., Thierry-Mieg, D., Busby, B., & Madden, T. L. (2019). Magic-BLAST, an accurate RNA-seq aligner for long and short reads. BMC Bioinformatics, 20, 405. https://doi.org/10.1186/s12859-019-2996-x

featureCounts / Subread

  • Liao, Y., Smyth, G. K., & Shi, W. (2014). featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7), 923–930. https://doi.org/10.1093/bioinformatics/btt656

SAMtools

  • Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., & Durbin, R. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352

Differential expression and visualization

DESeq2

  • Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 550. https://doi.org/10.1186/s13059-014-0550-8

EnhancedVolcano

  • Blighe, K., Rana, S., & Lewis, M. (2024). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. Bioconductor package. https://bioconductor.org/packages/EnhancedVolcano/

Functional enrichment and gene-set activity

Gene Ontology

  • Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M., & Sherlock, G. (2000). Gene Ontology: tool for the unification of biology. Nature Genetics, 25, 25–29. https://doi.org/10.1038/75556

  • The Gene Ontology Consortium. (2023). The Gene Ontology knowledgebase in 2023. Genetics, 224(1), iyad031. https://doi.org/10.1093/genetics/iyad031

KEGG

  • Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28(1), 27–30. https://doi.org/10.1093/nar/28.1.27

  • Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M. (2023). KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Research, 51(D1), D587–D592. https://doi.org/10.1093/nar/gkac963

clusterProfiler

MTD Explorer uses clusterProfiler for functional enrichment analysis of omics data.

Version used in the documented MTD Explorer environment:

clusterProfiler v4.2.2

Help and documentation:

Recommended citation:

  • Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., Zhan, L., Fu, X., Liu, S., Bo, X., & Yu, G. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation, 2(3), 100141.

GSEA and ssGSEA

  • Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. https://doi.org/10.1073/pnas.0506580102

  • Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., Schinzel, A. C., Sandy, P., Meylan, E., Scholl, C., Fröhling, S., Chan, E. M., Sos, M. L., Michel, K., Mermel, C., Silver, S. J., Weir, B. A., Reiling, J. H., Sheng, Q., ... Gilliland, D. G. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462, 108–112. https://doi.org/10.1038/nature08460

Microbial functional profiling

HUMAnN / bioBakery

  • Franzosa, E. A., McIver, L. J., Rahnavard, G., Thompson, L. R., Schirmer, M., Weingart, G., Lipson, K. S., Knight, R., Caporaso, J. G., Segata, N., & Huttenhower, C. (2018). Species-level functional profiling of metagenomes and metatranscriptomes. Nature Methods, 15, 962–968. https://doi.org/10.1038/s41592-018-0176-y

  • Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., Mailyan, A., Manghi, P., Scholz, M., Thomas, A. M., Valles-Colomer, M., Weingart, G., Zhang, Y., Zolfo, M., Huttenhower, C., Franzosa, E. A., & Segata, N. (2021). Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife, 10, e65088. https://doi.org/10.7554/eLife.65088

Microbiome statistics and association models

ANCOM-BC

  • Lin, H., & Peddada, S. D. (2020). Analysis of compositions of microbiomes with bias correction. Nature Communications, 11, 3514. https://doi.org/10.1038/s41467-020-17041-7

MaAsLin2

  • Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E. H., Chatterjee, S., Thompson, K. N., Wilkinson, J. E., Subramanian, A., Lu, Y., Waldron, L., Paulson, J. N., Franzosa, E. A., Bravo, H. C., & Huttenhower, C. (2021). Multivariable association discovery in population-scale meta-omics studies. PLOS Computational Biology, 17(11), e1009442. https://doi.org/10.1371/journal.pcbi.1009442

vegan / ANOSIM

  • Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R. B., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., ... Weedon, J. (2024). vegan: Community Ecology Package. R package. https://CRAN.R-project.org/package=vegan

Integration analysis

HAllA

  • Ghazi, A. R., Franzosa, E. A., Buhimschi, C. S., Annavajhala, M. K., Hyman, R. W., Huh, J. W., Ravel, J., Buhimschi, I. A., & Huttenhower, C. (2022). High-sensitivity pattern discovery in large, paired multi-omic datasets. Bioinformatics, 38(Supplement_1), i378–i385. https://doi.org/10.1093/bioinformatics/btac234

Plotting and R ecosystem

ggplot2

  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

pheatmap

  • Kolde, R. (2019). pheatmap: Pretty Heatmaps. R package. https://CRAN.R-project.org/package=pheatmap

RColorBrewer

  • Neuwirth, E. (2022). RColorBrewer: ColorBrewer Palettes. R package. https://CRAN.R-project.org/package=RColorBrewer

Notes for manuscripts

When preparing a manuscript based on MTD Explorer results, cite at minimum:

  1. the original MTD paper;
  2. MTD Explorer, if a DOI or manuscript is available;
  3. the main tools used in the specific analysis;
  4. the databases used for taxonomic, functional, host, GO, and KEGG annotation;
  5. GNU Parallel when used to process data for publication.

For exact software-specific citations in R, users can also run:

citation()
citation("DESeq2")
citation("ANCOMBC")
citation("Maaslin2")
citation("vegan")
citation("ggplot2")