Omics Analysis Tools: What to Use for Your Own Genome, Transcriptome, and Proteome

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If you have your own molecular data and want to analyze it, the working toolchain is smaller than the directories suggest: bwa-mem2 or DRAGEN plus DeepVariant and Ensembl VEP for the genome, salmon plus tximport and DESeq2 for RNA, DIA-NN or FragPipe for mass-spectrometry proteomics (or a vendor NPX/RFU matrix if you used an affinity panel), iglu for continuous glucose data, and MOFA2 or mixOmics when you want the layers in one model. Everything else in a 4,000-tool catalog is either a wrapper around these, a web front end, or specific to an assay you do not have. The hard part is not choosing software. It is knowing which reference build, annotation version, and normalization your files already assume, and what a single person’s data can and cannot support statistically.

What “omics” covers and which layers you will have

“Omics” (pronounced OH-miks) is a suffix, not an acronym, generalized from “genome” to mean the complete set of some class of molecule: genomics for DNA, transcriptomics for RNA, proteomics for proteins, metabolomics for small molecules, epigenomics for DNA methylation and chromatin state. Those four or five layers are what people mean by multi-omics. A survey of more than 10,000 public omics datasets shows how uneven the field is: transcriptomics dominates by volume, and typical study designs are far smaller and more heterogeneous than the methods literature assumes 1.

For a personal profile the practical set is a whole genome (fixed, sequence once), a transcriptome from whole blood or PBMCs (a snapshot of expression at draw time), a plasma proteome (a few thousand analytes), a clinical chemistry panel, and CGM. Each arrives in a different format with different assumptions baked in, and mixing them without checking those assumptions is the most common way an analysis goes quietly wrong.

Genome: FASTQ to an annotated, queryable variant set

Your raw deliverable should be paired-end FASTQ (compressed reads plus base qualities) or an aligned CRAM. If you got only a VCF, ask for the CRAM as well, because you cannot re-call, re-align, or check coverage from a variant file.

Align to GRCh38 with alt contigs, or to T2T-CHM13 if you care about centromeric and acrocentric regions and are willing to lose easy annotation joins. A standard run:

bwa-mem2 mem -t 32 -K 100000000 \
  -R '@RG\tID:L1\tSM:me\tPL:ILLUMINA\tLB:lib1' \
  GRCh38_full_analysis_set_plus_decoy_hla.fa r1.fq.gz r2.fq.gz \
| samtools sort -@ 8 -m 2G -o me.cram --reference GRCh38.fa

Then call with DeepVariant (--model_type=WGS), which on 30x short-read data gives cleaner SNV and small-indel precision than a hand-tuned GATK pipeline without the filtering work. Normalize before you do anything else, because indel representation differs between callers and will break every join you attempt later:

bcftools norm -m -any -f GRCh38.fa -Oz -o me.norm.vcf.gz me.vcf.gz

Annotate with VEP offline (--cache --offline --assembly GRCh38 --everything --pick_allele_gene --vcf), then attach population frequencies from gnomAD v4 and clinical assertions from ClinVar with bcftools annotate. Load the result into Hail or DuckDB and you have a table you can query in seconds. Two numbers to check before trusting anything: mean coverage (30x nominal, but look at the fraction of the callable genome above 20x) and the transition/transversion ratio, which should land near 2.0 genome-wide. Structural variants need separate callers (Manta, or Sniffles if you have long reads) and short-read SV calls carry a high false-positive rate; treat them as leads, not findings. Any variant you would act on medically needs orthogonal confirmation in a clinical laboratory and interpretation by a genetics clinician.

Transcriptome: counts, not just TPMs

For bulk RNA-seq, selective alignment with salmon is fast and accurate enough that STAR plus featureCounts is rarely worth the compute unless you need to look at splice junctions or novel transcripts directly.

salmon quant -i gencode_v45_index -l A \
  -1 r1.fq.gz -2 r2.fq.gz --validateMappings \
  --gcBias --seqBias --numBootstraps 30 -p 16 -o quant

Import with tximport(type="salmon", countsFromAbundance="lengthScaledTPM") and analyze counts in DESeq2 rather than comparing TPM columns by eye. Record the GENCODE version in your metadata: annotation changes between releases move gene boundaries and retire identifiers, and a v39-to-v45 mismatch will silently drop or duplicate genes in a join. In whole blood, globin and immunoglobulin transcripts can consume a large share of reads, so check the fraction of the library taken by HBB, HBA1, and HBA2 before concluding a sample is low-complexity.

Functional interpretation is where most people stall. Rather than eyeballing a ranked list, use gene set enrichment (fgsea against MSigDB Hallmark and Reactome) and pathway-level visualization. Purpose-built annotation pipelines exist for exactly this step: UPIMAPI, reCOGnizer, and KEGGCharter form a chain from sequence to UniProt and KEGG identifiers to a rendered pathway map, which is more reproducible than clicking through a web tool 2.

Proteomics and the normalization you inherit

Affinity panels (Olink, SomaScan) hand you a matrix, not spectra. Olink NPX values are log2, relative, and comparable across samples within a panel but not across panels or absolute in concentration. SomaScan RFUs need their own normalization chain. Check the fraction of measurements below the limit of detection before modeling: a protein at 60% LOD is a censored variable, and imputing it with a constant will manufacture structure.

For mass spectrometry, DIA-NN in library-free mode (--fasta-search --predictor --matrices) on data-independent acquisition runs is the default we would choose, with FragPipe (MSFragger plus Philosopher) for data-dependent acquisition. Both write a protein-group matrix with missing values that are missing-not-at-random, which is a different problem from RNA dropout. Quality control and normalization for these matrices deserve a dedicated tool rather than ad hoc scripts; pmartR implements QC, filtering, normalization selection, and statistics across proteomics, metabolomics, and lipidomics with a consistent interface 3.

Integration, and the statistics of one person

Integration means one of two things. Vertical integration combines layers on the same samples: MOFA2 (a factor model that finds latent variation shared across or private to each omics block) and mixOmics DIABLO (supervised, sparse, for finding correlated multi-block signatures) are the two we reach for first. Both need a design with real sample-to-sample variation, which for a single person means repeated timepoints rather than repeated tissues.

Machine-learning integration works when the cohort is large and the outcome is defined. A recent study combining metabolomics, glycomics, and proteomics with feature selection identified candidate hepatocellular carcinoma markers in an Egyptian cohort, and the design is instructive: the modeling step is easy, the validation is what makes it credible 4. Epigenomic layers add another axis, and methylation integration in particular requires methods that respect the correlation structure of CpG sites rather than treating them as independent features 5. The general failure mode is well described in the biomarker literature: high-dimensional omics data will yield apparently significant features from noise unless statistical rigor is paired with domain judgment about plausibility 6.

For n=1 longitudinal data, drop the cohort mindset. Your comparison group is your own past. Build a personal reference interval from at least five to seven measurements of the same analyte under matched conditions (same fasting state, same time of day, same lab), compute your intra-individual coefficient of variation, and only then ask whether a new value has moved. Population reference ranges tell you where the middle 95% of a reference population sits, not where you usually sit. Model time explicitly with autocorrelation in mind, and expect analytic variability of roughly 5-10% on many chemistry assays to swamp small true changes.

Questions people also ask

What does omics stand for? It does not stand for anything. It is a suffix extracted from “genome” and “genomics,” now attached to any comprehensive molecular layer: proteomics, metabolomics, epigenomics, lipidomics, exposomics.

What are the four omics? The usual four are genomics, transcriptomics, proteomics, and metabolomics: DNA, RNA, protein, and small molecules. Epigenomics is frequently added as a fifth, and exposomics, which measures cumulative environmental chemical exposure, is an active area with its own mass-spectrometry methods 7.

What is omics analysis? It is the pipeline from instrument output to interpretable features: quality control, alignment or feature extraction, normalization, statistical testing, and functional annotation. Every one of those steps encodes assumptions, which is why keeping the raw files matters more than keeping the report.

What are omics used for? In research, for discovering biological mechanism and candidate biomarkers across everything from stem cell aging to tumor microenvironments 8. For an individual, for establishing a personal baseline and tracking deviation from it over time. Interpretation that touches health decisions belongs with a clinician.

Oak builds longitudinal molecular profiles of individuals: whole-genome sequencing, RNA sequencing, proteomics, blood biomarkers, and continuous glucose data, integrated into one model of you. Build your profile.

Footnotes

  1. Eva Brombacher, Oliver Schilling, Clemens Kreutz. Characterizing the omics landscape based on 10,000+ datasets. Scientific Reports, 2025. https://doi.org/10.1038/s41598-025-87256-5 ↩

  2. João C. Sequeira, Miguel Rocha, M. Madalena Alves, et al. UPIMAPI, reCOGnizer and KEGGCharter: Bioinformatics tools for functional annotation and visualization of (meta)-omics datasets. Computational and Structural Biotechnology Journal, 2022. https://doi.org/10.1016/j.csbj.2022.03.042 ↩

  3. David J. Degnan, Kelly G. Stratton, Rachel Richardson, et al. pmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes. Journal of Proteome Research, 2023. https://doi.org/10.1021/acs.jproteome.2c00610 ↩

  4. Rency S. Varghese, Xinran Zhang, Muhammad S. Sajid, et al. Machine Learning-Based Multi-Omics Integration for Identification of Hepatocellular Carcinoma Biomarkers in an Egyptian Cohort. Journal of Proteome Research, 2025. https://doi.org/10.1021/acs.jproteome.5c00741 ↩

  5. Shikhi Baruri, Lalit Batra, Sohome Adhikari, et al. Integrative Epigenomics: Bioinformatics Strategies for Multi-Omics Data Analysis in Health and Disease. Epigenomes, 2026. https://doi.org/10.3390/epigenomes10030053 ↩

  6. Jason E McDermott, Jing Wang, Hugh Mitchell, et al. Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opinion on Medical Diagnostics, 2012. https://doi.org/10.1517/17530059.2012.718329 ↩

  7. Jarod Grossman, Anthony Macherone. Review of the 32nd Sanibel Conference on Mass Spectrometry: Unraveling the Exposome. Journal of the American Society for Mass Spectrometry, 2021. https://doi.org/10.1021/jasms.1c00065 ↩

  8. Bongsoo Park, Hagai Yanai, Jun Ding, et al. Hematopoietic stem cell aging: a review of transcriptional and multi-omics insights and potential paths for AI integration. Experimental & Molecular Medicine, 2026. https://doi.org/10.1038/s12276-026-01805-0 ↩