What Software to Use for Analyzing Your Own Omics Data

Oak
omics bioinformatics multi-omics genomics tooling
Five glowing colored fluid lines from a bioluminescent valley converge through a glass-and-brass frame into one bright braided ribbon.

If you have your own molecular data and you can write code, use the Python single-cell/omics ecosystem (AnnData, MuData, scanpy, muon) as your in-memory data model, bcftools/plink2/samtools for genomics on disk, and R (limma, DESeq2, edgeR) for anything involving differential statistics on counts or intensities. Do the exploratory work in a notebook against local files. Treat the web platforms (Omics Playground, the Analyst suite, DNAnexus) as second opinions and for pathway enrichment, not as the place your data lives. That recommendation holds because the hard part of personal omics is not running a method, it is keeping five heterogeneous assays aligned to one subject across time, and no click-through tool models that well.

What omics data analysis consists of

Four distinct problems get bundled under the phrase, and they need different tools.

Primary processing turns instrument output into standard files: FASTQ to BAM/CRAM to VCF for DNA, FASTQ to a gene-by-sample count matrix for RNA, raw spectra or Olink NPX for proteins. This is pipeline work, mostly done for you by whoever generated the data, and you should not redo it unless you have a specific reason.

Quality control is where you decide whether the files are usable. This is the step most people skip and most regret.

Per-assay analysis is single-omic: variant annotation, differential expression, biomarker trajectories.

Integration is combining assays on shared identifiers and shared samples. This is the step with the most software and the least consensus 1.

The stack we’d use

Genome (WGS). You will receive a CRAM or BAM plus a VCF or gVCF. Start by checking the VCF is what you think it is:

bcftools stats sample.vcf.gz | grep -E '^SN|^TSTV'

For a 30x human WGS you expect roughly 4–5 million variants and a transition/transversion ratio near 2.0 genome-wide (higher, ~3.0, restricted to exonic sites). Ti/Tv well below 2.0 on a whole-genome call set means something is wrong upstream.

Annotate with Ensembl VEP, not a web uploader:

vep -i sample.vcf.gz --cache --offline --assembly GRCh38 \
  --everything --vcf --fork 8 --plugin CADD,whole_genome_SNVs.tsv.gz \
  -o sample.vep.vcf.gz --compress_output bgzip

--everything pulls in SIFT, PolyPhen, gnomAD frequencies, and consequence terms. Then filter by allele frequency and ClinVar status before you look at anything. Without an AF filter you will surface thousands of rare-but-meaningless variants and spend a weekend on them. For pharmacogenes, PharmCAT takes a VCF and emits star-allele calls for CYP2C19, CYP2D6, and others, which is far more reliable than reading raw variants yourself. Anything from ClinVar or PharmCAT that looks clinically consequential needs confirmation in a clinical (CLIA/CAP) lab and a conversation with a genetic counselor or physician. Research-grade calls are not diagnoses.

For polygenic scores, plink2 --score with weights from the PGS Catalog, and be careful about ancestry: scores derived in European-ancestry cohorts transfer poorly and the absolute value is close to meaningless outside its training population. Percentiles within a matched reference set are the only interpretable output.

Transcriptome. Quantify with salmon or kallisto against a decoy-aware GENCODE index if you have FASTQs, otherwise start from the count matrix. Import with tximport, then:

dds <- DESeqDataSetFromTximport(txi, colData, ~ timepoint)
dds <- dds[rowSums(counts(dds) >= 10) >= 3, ]
vsd <- vst(dds, blind = FALSE)

For a single person sampled longitudinally, the biological question is different from a case-control study. You have n=1 with repeated measures, so “differential expression” usually means deviation from your own baseline. Fit per-gene models with time or an exposure as the covariate and treat the residual variance as your noise floor. Expect a large fraction of apparent signal to be white blood cell composition drift. Deconvolve with CIBERSORTx or a simpler reference-based method and include the cell fractions as covariates, or you will keep rediscovering that you had a cold.

Proteomics. Olink gives you NPX values on a log2 scale, already normalized. Mass-spec gives you intensities with structured missingness. Perseus remains a reasonable environment for the MS case, particularly its handling of imputation and its statistical modules 2, but for reproducibility we prefer doing the same steps in R so the whole thing is a script. Do not mean-impute missing proteomic values across an intensity range that spans six orders of magnitude. Missing-not-at-random values sit at the detection limit, so left-censored imputation (draw from a downshifted Gaussian, typically shifted 1.8 SD with width 0.3 SD) is the convention and at least it is explicit about its assumption.

Biomarkers and CGM. This is small tabular data and the temptation is to underthink it. Use a tidy long format (subject, date, analyte, value, unit, loinc, assay_vendor) and keep the LOINC code. Lab-to-lab variation in a value like ferritin or hs-CRP can exceed the biological change you are chasing, so a vendor switch mid-series will look like a physiological event. For CGM, compute the standard metrics yourself from the raw 5-minute stream: time in range, coefficient of variation, MODD, and postprandial area under curve anchored to logged meals. CV above about 36% is the usual threshold for calling glucose variability high.

How to integrate

The answer about integration is that most published methods assume many subjects and few timepoints, and personal omics is the transpose. Three approaches, in the order we’d try them.

Start with identifier mapping and joins. Get everything onto stable IDs: Ensembl gene IDs for RNA, UniProt accessions for proteins, HGNC symbols only as a display layer. Symbol drift is a real source of silent data loss. A MuData object with one modality per assay, sharing a timepoint index, gets you 80% of the value and costs an afternoon 3.

Then do pathway-level integration rather than feature-level. Mapping RNA and protein changes onto the same pathway or network gives an interpretable common coordinate system, and there are mature tools for the visualization side 4. The Analyst suite (MetaboAnalyst, ExpressAnalyst, and relatives) implements documented multi-omics workflows end to end, and running your data through it as a cross-check is cheap 5.

Only then reach for latent-factor methods (MOFA+, DIABLO, similarity network fusion). These find shared variance across assays, which is genuinely useful when you have enough samples, and misleading when you do not. A comparison of integration strategies on real multi-omic data shows the results depend heavily on preprocessing and on which method you pick, which is a reason to run more than one and look for agreement 6. The metabolomics community has made the same point about method-dependence in integration results 7.

Where the web platforms fit

Omics Playground, iSODA, Wekemo Bioincloud, and similar tools are well built and lower the barrier to a first pass 8. Their limits are the same in every case: your data model is whatever the upload form accepts, longitudinal n=1 designs are not what the statistics were written for, and the analysis is not a file you can diff. Use them to sanity-check an enrichment result. Do not build your baseline in them.

Deep learning tools for omics exist and work for specific well-posed tasks with large training sets 9. For a single person’s five assays, a linear model you can read beats a network you cannot.

Questions people also ask

What is the best software for data analysis? For personal omics, Python plus R, not one package. Python (AnnData/MuData, scanpy, muon, pandas) for data management and exploration, R for the differential statistics where the reference implementations live. If you want one hosted environment as a check, the Analyst suite is the most thoroughly documented 5.

Can I use ChatGPT to analyze data? Yes, for code generation, format wrangling, and explaining what a VEP consequence term means. Give an agent the processed matrices and let it write the analysis, then read the code. Do not paste a variant list and accept the interpretation at face value, and do not take anything resembling a clinical conclusion from a model without a clinician reviewing it.

How to integrate multi-omics data? Shared sample IDs, stable feature IDs, one object per assay in a container that keeps them aligned, then pathway-level overlay, then latent factor models if you have the samples to support them 1.

How is omics used in drug discovery? Mainly target identification and stratification: finding genes or proteins whose variation tracks a phenotype, and defining subgroups that respond differently 10. The methods overlap with personal analysis, the sample sizes do not.

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. Efi Athieniti, George M. Spyrou. A guide to multi-omics data collection and integration for translational medicine. Computational and Structural Biotechnology Journal, 2022. https://doi.org/10.1016/j.csbj.2022.11.050 ↩ ↩2

  2. Stefka Tyanova, Tikira Temu, Pavel Sinitcyn, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods, 2016. https://doi.org/10.1038/nmeth.3901 ↩

  3. Isaac Virshup, Danila Bredikhin, Lukas Heumos, et al. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nature Biotechnology, 2023. https://doi.org/10.1038/s41587-023-01733-8 ↩

  4. Bianca Habermann, Jose Villaveces, Prasanna Koti. Tools for visualization and analysis of molecular networks, pathways, and -omics data. Advances and Applications in Bioinformatics and Chemistry, 2015. https://doi.org/10.2147/aabc.s63534 ↩

  5. Jessica D. Ewald, Guangyan Zhou, Yao Lu, et al. Web-based multi-omics integration using the Analyst software suite. Nature Protocols, 2024. https://doi.org/10.1038/s41596-023-00950-4 ↩ ↩2

  6. Samuel M. Lancaster, Akshay Sanghi, Si Wu, et al. A Customizable Analysis Flow in Integrative Multi-Omics. Biomolecules, 2020. https://doi.org/10.3390/biom10121606 ↩

  7. Farhana R. Pinu, David J. Beale, Amy M. Paten, et al. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites, 2019. https://doi.org/10.3390/metabo9040076 ↩

  8. Yunyun Gao, Guoxing Zhang, Shunyao Jiang, et al. Wekemo Bioincloud: A user‐friendly platform for meta‐omics data analyses. iMeta, 2024. https://doi.org/10.1002/imt2.175 ↩

  9. Cecile M. Cres, Andrew Tritt, Kristofer E. Bouchard, et al. DL-TODA: A Deep Learning Tool for Omics Data Analysis. Biomolecules, 2023. https://doi.org/10.3390/biom13040585 ↩

  10. Mohamad Hesam Shahrajabian, Wenli Sun. Survey on Multi-omics, and Multi-omics Data Analysis, Integration and Application. Current Pharmaceutical Analysis, 2023. https://doi.org/10.2174/1573412919666230406100948 ↩