Page Comparison

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• Templates:

  • RNAseq

  • single-cellRNAseq

  • nanostring

  • immunosassay

  • metabolomics

  • proteomics

Multiomics refers to the study of multiple biological systems or processes simultaneously using various techniques that generate different types of data. Here is a summary of some common omics data types, their assays, analyses, and file formats:

1. Genomics: This involves studying DNA sequencing and analysis, as well as gene expression
   profiling through RNA sequencing.

• Common assays: Sanger sequencing, whole-genome sequencing (WGS), RNA sequencing (RNA-seq)

• Analyses: Variant calling, assembly, alignment to reference genome, differential expression
  analysis

• File formats: FASTQ, BAM, VCF, TSV

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1287035/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4694403/ >

2. Proteomics: This refers to the study of proteins and their interactions in biological systems.

• Common assays: Mass spectrometry (MS), microarray analysis, immunoprecipitation (IP)

• Analyses: Peptide identification, protein quantification, network analysis, pathway enrichment

• File formats: MGF, MSG, TAB, PDB

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2564307/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1893633/ >

3. Metabolomics: This involves studying metabolic pathways and biochemical processes in living
   organisms.

• Common assays: GC-MS, LC-MS, NMR spectroscopy, mass spectrometry (MS) ionization

• Analyses: Metabolite identification, quantification, pathway analysis, network analysis

• File formats: CSV, MATLAB, SIMCA, JUPYTER notebooks

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1592706/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604083/ >

4. Transcriptomics: This refers to the study of RNA expression patterns in biological systems.

• Common assays: RNA sequencing (RNA-seq)

• Analyses: Differential expression analysis, gene set enrichment analysis, pathway analysis,
  network analysis

• File formats: FASTQ, BAM, VCF, TSV

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2571094/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2600283/ >

5. Epigenomics: This involves studying changes in DNA methylation patterns or other epigenetic
   marks that regulate gene expression without changing the underlying DNA sequence.

• Common assays: Whole-genome bisulfite sequencing (WGBS), ChIP-seq, ATAC-seq

• Analyses: Methylation profiling, ChIP-binding site analysis, histone modification analysis,
  network analysis

• File formats: BAM, VCF, TSV, MATLAB

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2918536/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4078701/ >

6. Immunomics: This refers to the study of the immune system and its interactions with other
   biological systems.

• Common assays: Flow cytometry, mass cytometry, single-cell RNA sequencing (scRNA-seq)

• Analyses: Cellular composition analysis, functional annotation, network analysis, clustering

• File formats: FCS, MATLAB, R, Seurat

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6072154/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5803139/ >

7. Metagenomics: This involves studying genetic material from microbial communities in various
   environments.

• Common assays: Shotgun sequencing

• Analyses: Taxonomic classification, functional annotation, network analysis, phylogenetic
  reconstruction

• File formats: FASTQ, QIIME, MG-RAST, Metagenome Assembly Tool (MAT)

• Resources: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189520/ >,
  <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4790965/ >

Each of these omics data types has its own specific requirements for analysis and interpretation,
which may involve specialized software tools or expertise in specific analytical techniques.
However, integrating multiomics data can provide a more comprehensive understanding of biological
systems and mechanisms, leading to new insights into disease and potential therapeutic targets.

Manifest

A tab-delimited manifest file allows you to upload and download many data files, and set annotations, at once a client (Python, R, command line). Each row in the manifest species the file to be uploaded and the annotations to be applied.

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