In display-based discovery campaigns, finding clones with a high fold change across selection rounds is necessary but not sufficient. A clone might expand simply because it binds a carrier protein, the solid support, or grows efficiently in the host organism—not because it binds the target antigen. Historically, separating true antigen-specific binders from non-specific expanders has required costly and time-consuming downstream wet-lab validation.

The Limits of Fold Change

Relying solely on the traditional criterion—ranking clusters by the highest fold change—conflates three biologically distinct phenomena:

• genuine antigen-specific selection

• non-specific enrichment of fast-growing clones

• background amplification of library-dominant sequences

While aggressive washing attempts to minimize this noise, real-world campaigns rarely achieve a perfect ideal. Without a computational means of discrimination, non-specific binders routinely occupy the top of the enrichment list.

Solution: Multidimensional Enrichment Analysis

The Clonotype Enrichment analysis in Platforma addresses this gap computationally. By easily integrating negative control pannings (such as rounds against BSA or a surface without antigen) , the platform evaluates candidates beyond simple log₂ fold change. It classifies each antibody family along two independent axes:

• Enrichment Quality: How consistently and progressively a clone was selected across rounds.

• Binding Specificity: Whether enrichment was driven by the target antigen or by non-specific binding to controls.

Additionally, its modular architecture enables cross-antigen specificity analysis: by running parallel enrichment blocks for different targets, teams can identify monospecific binders or cross-reactive candidates from a single campaign without additional wet-lab experiments. Operating on cluster-level data, these classifications feed directly into automated candidate delivery.

Advanced Capabilities of the Enrichment Analysis

This workflow details a computational workflow for distinguishing true binders from background noise by evaluating antibody families based on cluster-level abundance, enrichment quality, and binding specificity.

1. Configuring enrichment on antibody clusters

Analysis begins by linking directly to the cluster-level abundance generated upstream. By aggregating the signal across sequence variants within a single lineage, the platform recovers rare binders that might otherwise fall below the detection threshold if analyzed individually. The workflow automatically accounts for the chronological progression of panning rounds, normalizes for sequencing depth differences via downsampling, and filters for persistent binders to eliminate single-round noise (Fig. 1a)

To provide immediate interpretive context beyond a raw Log2 Fold Change score, the output classifies each cluster’s trajectory (Fig. 1b). This allows teams to distinguish candidates with a reliable monotonic response from those with ambiguous trajectories before committing to synthesis. The Enrichment Quality labels include:

• Stable Binder: Consistently enriched in every round.

• Rescuer: Enriched in later rounds after an initial dip.

• Parasite: High early but declining.

• Weak Binder: Marginally enriched.

2. Binding specificity classification

When an experiment includes negative control pannings, the workflow enables a second critical layer of analysis. By mapping sample metadata to distinct target and negative control conditions (e.g., BSA or bare plastic), the platform independently computes fold change for each environment (Fig. 2a). This is flexible enough to support campaigns where target and control pannings follow completely different round structures.

Based on where the enrichment signal originates, the workflow assigns a Binding Specificity label to every cluster:

• Antigen-Specific: Enriched in target rounds, not in controls.

• Non-Specific: Enriched in both target and control rounds.

• Negative-Control: Enriched exclusively in controls—acting as a clear liability flag.

• Not-Enriched: No significant enrichment in either condition.

These labels are exported as structured columns, immediately available for downstream lead selection. Furthermore, this data generates a Control Scatter Plot (Fig. 2b) that makes the specificity landscape of the full repertoire instantly readable. Antigen-specific candidates isolate cleanly into the upper-left quadrant (high target enrichment, low control enrichment), while non-specific expanders track predictably along the diagonal

Fig. 2a - Antigen and negative control configuration. The “Target selection” section activates binding specificity analysis. The Antigen column dropdown selects the metadata column mapping each sample to its antigen. The Target antigen dropdown identifies which value represents the selection target; the Negative controls multi-select captures the irrelevant antigens used as specificity references (e.g., BSA, Plastic). The condition order for control rounds is specified independently of the main target condition order, supporting campaigns with different round structures for target and control pannings.

3. Cross-antigen specificity analysis

Multi-antigen campaigns—where a single library is panned against two or more distinct targets—introduce a critical question: which clusters are selective for a single antigen, and which are broadly cross-reactive? Platforma’s modular architecture resolves this computationally, requiring no additional sequencing or wet-lab experiments.

By running parallel Clonotype Enrichment analyses for each target antigen within the same project, the platform independently calculates enrichment scores and specificity classifications for each target (Fig. 3a). Because these parallel analyses feed into a unified data environment, the Lead Selection step can query multiple enrichment profiles simultaneously (Fig. 3b).

By applying specific filters within the Lead Selection, you can directly identify your exact desired binding profiles:

• Monospecific Binders: Isolated by requiring a cluster to be “Antigen-Specific” for one target while remaining “Not-Enriched” or “Non-Specific” for all others.

• Cross-Reactive Candidates: Identified by requiring strong, target-specific enrichment across multiple parallel analyses.

4. Spatial validation of the candidate panel

The final selected leads are overlaid on the UMAP projection of the full repertoire, colored by Binding Specificity from each enrichment block. Monospecific candidates occupy distinct topological regions from cross-reactive clusters, providing spatial confirmation that the selection criteria isolated the intended specificity profile. The combination of Enrichment Quality, Binding Specificity, and cross-antigen filter settings converts what was previously a manual, multi-step analytical process into a single, reproducible configuration within one project.

Conclusion

The advanced capabilities of the Clonotype Enrichment block transform enrichment analysis from a single-metric ranking into a multi-dimensional characterization of each antibody family’s selection behavior. By evaluating cluster-level data—which aggregates signal across sequence variants to rescue rare, low-abundance binders—discovery teams can confidently characterize:

• Enrichment Quality: Distinguishing stable, progressive binders from transient or declining clones.

• Binding Specificity: Separating genuine antigen-driven enrichment from non-specific background amplification using negative controls.

• Cross-Antigen Profiles: Automatically identifying monospecific and cross-reactive candidates directly from NGS data without additional experiments.

These classifications flow seamlessly into automated candidate selection, serving simultaneously as stringent hard filters and ranking criteria. This workflow ensures that every lead candidate delivered for synthesis carries a quantitative, data-driven justification that is fully traceable to the original sequencing data.

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The ultimate goal of any terminal single-cell in vivo campaign is to efficiently identify a diversified panel of developable lead candidates. However, when sequencing data is generated from pooled, sacrificed donors without longitudinal timepoints or distinct tissue compartmentalization, isolating genuine, antigen-driven clonal expansion from background noise becomes a significant computational hurdle. Platforma provides a unified, end-to-end workflow that natively supports major single-cell sequencing technologies to systematically isolate high-confidence binders.

The challenge of clonal dominance

In terminal in vivo campaigns, relying purely on clone frequency is a common pitfall.

Frequency-based ranking often biases selection toward a single highly abundant lineage or non-specific expanding cells, effectively allowing one clonal family to hijack the downstream synthesis and testing budget. Furthermore, failing to enforce structural diversity across the selected cohort drastically increases the risk of developmental failure due to shared physicochemical liabilities.

Methodology

Platforma shifts the paradigm from raw frequency ranking to a multi-dimensional evaluation. By pairing precise MiXCR clonotyping with advanced paratope grouping, liability screening, and somatic hypermutation (SHM) metrics, it algorithmically balances clonal abundance with structural maturation. This enables teams to mine complex repertoires and prioritize variants with optimal immunological fitness.

The workflow integrates four key pillars:

• Clonotyping: Leverages MiXCR for exact V(D)J assembly, error correction, and extraction of critical SHM maturation profiles.

• Dual-Tier Clustering: Enforces structural diversity by grouping variants via sequence similarity and predicted binding interfaces.

• Liability Screening: Scans the repertoire for physicochemical weaknesses to filter out severe aggregation/cleavage motifs prior to synthesis.

• Algorithmic Scoring: Employs In Vivo Score to rank candidates based on a composite of clonal expansion and affinity maturation.
  

Building a Diversified Lead Panel

Platforma’s reproducible decision matrix allows teams to execute either an unbiased global panel diversification or targeted seed-based NGS mining within the same dataset.

1. High-Precision Annotation & Repertoire Characterization

The workflow utilizes the MiXCR engine to perform exact V(D)J assembly and extract full-length sequences. It automatically calculates comprehensive SHM metrics—such as CDR mutation fractions—which are critical for identifying affinity-matured clones. Researchers can also assess macro-level biological responses by calculating theoretical clonal richness (Chao1 Estimate) and visualizing dominant germline scaffolds.

2. Lineage And Paratope Clustering

To enforce structural diversity, unique sequence variants must be grouped into biological families. The pipeline performs sequence-based clustering to group functional somatic variants (Lineage Grouping), followed by predictive paratope clustering. This ensures that selected sequences represent distinct functional binding interfaces rather than redundant somatic cousins.

3. Sequence Liability Screening

To protect the downstream synthesis budget, the algorithm scans the repertoire for physicochemical weaknesses that compromise developability. Candidates are evaluated for risks such as fragmentation, isomerization, N-linked glycosylation, and deamidation, allowing teams to filter out “High Risk” severe aggregation or cleavage motifs before lead selection.

4. Assay Data Integration & UMAP Visualization

Platforma bridges computational discovery with wet-lab validation. Users can import functional hit datasets (e.g., K.D., ELISA scores) and use fuzzy matching to capture naturally occurring somatic variants of baseline seeds. The entire multidimensional repertoire is then projected onto a UMAP landscape.

5. Lead Selection Fitting

The core of Platforma’s lead selection is the In Vivo Score—a multi-parametric algorithm that substitutes raw frequency ranking with balanced sequence selection. The score mathematically evaluates to: 40% (clonal frequency) + 35% (CDR mutation fraction) + 25% (nucleotide mutations).

Using this funnel, teams can isolate class-switched cells, reject unmutated background clones, require candidates to belong to an expanded clonal family, and force the sampling of distinct paratope clusters to generate a globally diversified, developable panel.

Conclusion

Platforma’s computational workflow transforms raw, endpoint single-cell in vivo data into a highly specific, developable panel of affinity-matured variants. By systematically evaluating baseline abundance alongside targeted somatic hypermutation rates and physicochemical liabilities, discovery teams can avoid the pitfalls of frequency bias, maximize topological coverage, and significantly reduce downstream developmental risk.

Accelerate your antibody discovery

Selecting a digital backbone for your discovery pipeline is a strategic decision that directly impacts research speed, data quality, and team productivity. The following criteria help ensure that your chosen platform doesn’t just store data, but actively drives your candidates toward the clinic.

• End-to-end workflow coverage: The platform should support a complete, end-to-end workflow, taking you from raw NGS and functional assay data through clonotyping, clustering, enrichment analysis, and lead selection, without the need to stitch together multiple tools or spreadsheets.

• Scientist-first usability: Wet-lab scientists should be able to explore data, apply filters, and identify leads without writing scripts, managing Excel files, or relying on bioinformatics support for every iteration.

• Integrated downstream decision-making: Sequence analysis alone is not enough. Look for platforms that integrate enrichment, developability metrics, liabilities, and functional assay data (e.g., ELISA, SPR) directly into lead prioritization.

• Computational scalability & throughput: Modern discovery generates millions of NGS reads. Your platform must handle high-throughput sequencing data without lag, supporting massive-scale clonotyping, clustering, and filtering as your dataset grows.

• Flexible deployment & data control: In IP-sensitive therapeutic discovery, maintaining full ownership and control of your data is non-negotiable. Many teams are increasingly opting for on-premise deployments to ensure full data ownership and compliance.

Built for discovery teams: Platforma

Platforma is the go-to solution for labs that need to scale without hiring a fleet of bioinformaticians. Built on MiXCR and other transparent algorithms, Platforma combines its gold-standard accuracy with rich visualizations for an end-to-end antibody discovery workflow. The software supports custom algorithms and on-premise deployment to ensure the data stays secure. Users have reported that while using Platforma, they can cut lead selection time from weeks to hours.

Geneious Biologics:

Geneious has been a staple in molecular biology for decades. Now part of the Dotmatics and Siemens ecosystem, its Biologics suite is strong for sequence annotation and visualization, but it’s primarily suited for analyzing individual sequences rather than the massive, multi-dimensional datasets generated by modern high-throughput antibody discovery. Scientists working with millions of reads may need a platform that offers scalable workflows, high-performance clustering, and interactive visualizations to accelerate lead selection and make large-scale discovery more practical.

PipeBio:

PipeBio is a cloud-based platform for antibody, TCR, and peptide sequence analysis, offering automated workflows for clonotyping, clustering, and basic lead identification. While the software promises to process tens of millions of NGS reads in hours via the cloud, users at large pharmaceutical companies have reported that the lack of an on-premise solution is a deal-breaker for their data security requirements. Researchers also frequently have to perform manual analysis via Excel sheets to bridge the gaps between disconnected outputs. This fragmentation breaks the chain of custody for data, introduces human error, and slows the transition from sequence to lead.

Enpicom:

Enpicom is a user-friendly and lightweight platform for antibody discovery, enabling scientists to analyze their Rep-Seq data through a suite of apps on its platform. However, like Pipe Bio, Enpicom is entirely cloud-based, which can pose regulatory or data sovereignty challenges for organizations that cannot transfer sensitive data outside their own borders.

Other Tools

• CROs: Outsourced antibody discovery services provide end-to-end project support but often limit data ownership, control, and repeatability. When you rely on a CRO, you typically receive only the “final leads,” losing access to the raw NGS data and reproducibility insights.

• Open-source tools (e.g., MiXCR): Powerful for immune repertoire analysis and supports any chemistry and species, but require command-line expertise and additional tools for downstream analysis.

Final Verdict: Why Choose Platforma

When evaluating antibody discovery platforms, teams must balance usability, performance, flexibility, and data control. While competitors like Geneious, PipeBio, and Enpicom excel in specific areas, Platforma is the only platform that delivers a truly end-to-end solution:

• Complete workflow: From raw NGS and functional assay data to clonotyping, clustering, enrichment analysis, developability assessment and lead selection within a single platform

• Flexible deployment & data control: Supports secure on-premise or private cloud deployment, meeting strict data sovereignty requirements.

• Built for scientists: A user-friendly interface that eliminates the need for any manual intervention or Excel files.

• High-performance handling of large datasets: Efficiently manages millions of clonotypes, and rounds of enrichment without workflow slowdowns with an open, transparent algorithm

• Supports any species, chemistry, and sequencer: Handles diverse experimental designs, including UMI-enabled and single-cell datasets

In short, Platforma is the only solution designed to accelerate discovery without compromise, enabling teams to move from raw data to actionable leads faster, securely, and with complete confidence.

The primary objective of high-throughput antibody discovery is the delivery of a diversified panel of developable lead candidates. Traditional workflows often fail to meet this objective due to a critical analysis gap—the disconnect between massive NGS datasets and the limited capacity of downstream characterization. Here, we outline a scientifically validated workflow utilizing the Platforma ecosystem. By bridging upstream antibody annotation with downstream functional prediction, this protocol mitigates the risk of selecting redundant or liable scaffolds, ensuring the final candidate pool is maximized for both epitope coverage and developability.

Why NGS for antibody discovery?

Traditional screening methods typically sample only 100–300 clones—capturing just the tip of the iceberg. This limited sampling introduces significant bias toward fast growers—clones that replicate efficiently in the host system but may lack superior binding affinity. Deep mining (~10M+ reads) with NGS provides the sequencing depth and coverage needed to access the submerged diversity, revealing rare high-affinity binders and distinct backup/alternative lineages that are invisible to shallow screening methods.

Methodology

This workflow enables the end-to-end discovery of VHH, scFv, and Fab candidates within a managed computational environment. Prioritizing algorithmic transparency over black-box solutions, the system is built upon the Platforma SDK and utilizes the proprietary P-frame architecture (columnar and bioinformatics-native storage layer) to ensure data integrity and traceability.

The underlying logic is transparent and open source [1]. At its core, the pipeline performs the sequence of steps for large-scale antibody discovery: annotation, clustering, enrichment analysis and developability assessment. On top of this established workflow, Platforma introduces two innovations that significantly enhance discovery outcomes:

• Clonotype Space: Projects the full repertoire view into an interactive landscape. Unlike static lists, it reveals distinct specificity clusters, allowing leads to be visually mapped against the global repertoire topology.

• Algorithmic Lead Selection: Replaces traditional Top-N picking with a diversity-first algorithm. It prioritizes coverage of "Submerged" families, ensuring the final panel represents distinct, developable scaffolds rather than redundant dominant clones.

These components combine to form a standardized, high-throughput pipeline that converts raw FastQ files into a diversified, developable lead candidate table.

Building a Diversified Lead Panel

Validation of Sequencing Depth & Selection Pressure: Before clone/cluster-level analysis, the quality of the libraries and the efficacy of the selection pressure is validated statistically.

Antibody Clustering & Enrichment Analysis: Once data quality is established, the analysis focuses on identifying the "active" components of the repertoire. Functional clustering is performed by grouping clonotypes into families based on amino acid identity [2], reducing redundancy and allowing for the analysis of functional lineages rather than individual reads.

Developability Prediction & Lead Selection: The final stage refines the enriched pool into a diversified, low-risk panel for synthesis. To bridge the wet-lab/dry-lab divide, available screening data (e.g., ELISA or SPR hits from random colony picking) can be mapped directly to the NGS clusters. This validates the biological relevance of the computational families and allows for the discovery of superior somatic variants within the lineages of known binders.

Conclusion

Modern antibody discovery campaigns generate massive datasets that often outpace the analytical capabilities of fragmented toolchains. This Application Note introduces a unified, end-to-end computational workflow within Platforma designed to close the analysis gap between high-throughput Next-Generation Sequencing (NGS) and the selection of developable therapeutic leads.

By replacing isolated scripts and spreadsheets with a single, reproducible environment, this pipeline integrates rigorous upstream annotation, ultra-fast clustering and downstream functional validation. The workflow emphasizes algorithmic transparency and data provenance, allowing discovery teams to trace every candidate from its initial raw read to its final selection.

Accelerate your antibody discovery

References:

[1] accessible at https://github.com/platforma-open/

[2] Clustering performed using MMSeqs2, Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988

[3] Journal of Open Source Software, 3(29), 861, https://doi.org/10.21105/joss.00861

[4] Proc. Natl. Acad. Sci. U.S.A. 114 (5) 944-949, https://doi.org/10.1073/pnas.1616408114

Between 2024 and 2025, our ecosystem processed metadata from over 3.5 million distinct samples, representing more than 3.2 petabytes of data.

To clarify this scale, we must stratify the data: 'Primary Targeted Sequencing' (dedicated TCR/BCR runs) versus 'Secondary Data Mining' (repertoire extraction from bulk RNA-seq and historical data). While the former drives high-fidelity clinical insights, our unique ability to unlock immune signals from standard RNA-seq accounts for the massive scale of the latter.

This article summarizes the key trends we have observed across this massive dataset. A critical note on scope: This report analyzes metadata from the MiXCR ecosystem. While MiXCR is a widely cited standard in academic immune repertoire sequencing, this dataset does not capture data processed exclusively through closed proprietary platforms (e.g., Adaptive Biotechnologies) or alternative open-source pipelines. Therefore, ‘Global Trends’ refers specifically to the trends observed within our extensive, cross-continental user base, which serves as a robust proxy for the broader open-science market.

Here is what the data tells us about the future of immunology.

The Data Explosion: A Trajectory of Scale

The total volume of TCR and BCR sequencing data has shown a consistent upward trajectory. In 2023, the total data volume processed was approximately 0.65 PB. This figure grew to roughly 1.0 PB in 2024 and reached over 1.5 PB in 2025.

Note on Methodology: We distinguish ‘Unique Samples’ from ‘Total Processing Volume.’ A portion of this volume stems from distinct re-analyses of public datasets (e.g., TCGA) to validate internal pipelines.

What does 1.5 Petabytes actually mean? Beyond the sheer storage, this metric validates the compute intensity of the modern immune pipeline. This surge from 1.0 to 1.5 PB isn’t just about data storage; it represents a fundamental shift in utility. We are seeing the transition from “discovery-mode” (smaller, targeted datasets) to “screening-mode” (massive, high-throughput comparisons). The industry is moving from asking “What does this receptor look like?” to asking “How does this repertoire interact with a population?”

While North America remains our largest user base, the “science of sequencing” is decentralizing. Our data shows that Oceania and Asia experienced the highest year-over-year growth, with usage in Oceania increasing by +85.9% and Asia by +71%. NGS analysis is no longer centralized in a few western powerhouses; it is becoming a truly global endeavor.

Top TCR/BCR Sequencing Centers

Our analysis shows that the world’s leading academic and government research centers are among the heaviest users of the ecosystem. To respect the privacy of internal research pipelines, we have anonymized the specific institutions below.

Interestingly, the top 5 institutions for historical data volume are not in the top 5 for sample count. This suggests they focus on deep, complex, or large-output sequencing protocols that generate immense data per experiment, such as full-length RNA-seq or exome sequencing.

Modalities: The Dominance of Amplicon & The Rise of Single-Cell

When we look at what is being sequenced, we see a market defined by distinct tiers:

• Amplicon Sequencing (The Standard): This remains the primary operational standard for 2025. For routine, high-throughput applications, bulk amplicon workflows are the undisputed workhorse, driven by cost-efficiency and scalability.

• RNA/Exome Sequencing (Data Mining): This occupies a distinct second tier with approx. 10% of amplicon samples volume.

• Single-Cell: in absolute terms, single-cell comprises a marginal share of the total volume. Clarification on Single-Cell metrics: to ensure clarity, our single-cell figures count ‘patient samples’ (libraries), not individual cells. If we were to count individual cells—where one sample contains ~5,000+ cells—the figures would be orders of magnitude higher. We have stuck to ‘samples’ to maintain a consistent unit of measurement across modalities.

However, volume does not equal velocity. While bulk sequencing demonstrates reliable volume stability (showing 33% year-over-year growth in our dataset), single-cell adoption is accelerating, with sample volumes growing by over 50% annually.

In the industrial sector specifically, we see a strategic divergence. While academia remains reliant on bulk sequencing, Industry is increasingly valuing the resolution of single-cell analysis for specific pipelines. Throughout 2025, single-cell adoption in industry shifted from ~5% in Q1 to reaching an observed 10-11% share as we close Q4.

The Strategic Divide: Make vs. Buy

One of the most striking findings in our 2025 data is the “Make vs. Buy” polarization between Academia and Industry.

Academia: The “In-House” Retrenchment Academic institutions overwhelmingly favor in-house protocols, which account for 85-90% of their samples. Commercial kits represent a negligible fraction, likely due to cost constraints and the prevalence of custom, “home-brewed” methods. In 2025, academia appeared to double down on this strategy, with the share of commercial kits shrinking further compared to 2024.

Industry: The “Commercial” Pivot The industrial sector tells a completely different story. In 2024, industry usage within our ecosystem was roughly split between In-house and Commercial methods. However, 2025 data shows an aggressive pivot where Commercial protocols expanded to become the dominant standard among our industrial partners.

Note on Data Interpretation: This sharp trend may reflect the onboarding of specific high-volume industrial players in 2025 who favor standardized kits. However, it strongly signals that as companies scale, they prioritize the reproducibility and regulatory compatibility of commercial solutions over the cost-savings of custom assays.

This signals a maturing industrial market that prioritizes scalability, reproducibility, and regulatory compatibility over the cost-savings of custom assays.

The Single-Cell Landscape: 10x Genomics vs. Others

When we zoom in on the single-cell segment, the vendor landscape further highlights the difference in priorities between sectors.

Industry: Efficiency and Standardization

10x Genomics commands a near-monopoly in the industrial sector, capturing 74.21% of all samples. The remaining share is largely “In-house,” with virtually no adoption of alternative commercial vendors.

Academia - Depth and Fidelity: While Academia also favors 10x Genomics (~60%), it retains a distinct sub-market for SmartSeq (~25%), a high-fidelity method virtually absent in industry.

A Matter of Fit, Not Quality: This discrepancy likely isn’t about the quality of the chemistry, but rather the operational requirements of the user.

• 10x Genomics samples typically have a lower, consistent data footprint (median ~2-3 GB), aligning perfectly with industrial requirements for operational efficiency and predictable cloud costs.

• SmartSeq samples demand significantly higher data resources (median ~20GB), reflecting the academic need for full-length transcript coverage and higher fidelity, even at the cost of storage and speed.

Academia’s usage of SmartSeq reflects a prioritization of sequencing depth and full-length transcript coverage over pure scalability. While ‘home-brew’ reagents are inexpensive, the requirement for deeper sequencing leads to a significantly higher total cost per cell compared to droplet-based methods.

Commercial Kits: The Era of Specialization

When analyzing the commercial kit landscape (aggregated TCR and BCR), we see a market that has reached maturity. The volatile “land grab” phase appears to be over, replaced by a stable hierarchy of established players.

• The Market Leader: Takara Bio retains a commanding lead, holding approximately 50% of the market share across both 2024 and 2025. Their SMARTer technology effectively remains the industry standard.

• The Major Players: Cellecta and NEB maintain strong, consistent positions as the primary alternatives, showing stable adoption rates year-over-year.

• The Ecosystem: Other vendors like Invivoscribe, Illumina, and iRepertoire continue to serve specific niches but show little fluctuation in total market share.

Conclusively, the 2024–2025 data reveals a “winner-takes-half” dynamic. Users have largely settled into their preferred workflows, resulting in high vendor stickiness and low churn between suppliers.

Conclusion: From Discovery to Application

The story the data tells is clear: immune repertoire analysis has matured from a niche exploratory tool into a cornerstone of modern biology.

We are witnessing a bimodal leadership structure emerge. Academia continues to drive discovery through deep, high-fidelity, and custom-designed protocols. Meanwhile, Industry is setting the direction for translation, aggressively consolidating around standardized, scalable commercial solutions (like 10x Genomics and amplicon kits) to drive real-world application.

Despite this growth, the field faces headwinds. Budgetary constraints in academia have slowed the adoption of more expensive high-fidelity protocols, and the complexity of single-cell data integration remains a bottleneck for clinical translation. However, the industry’s shift toward standardization suggests these hurdles are being actively addressed.

As we move into the next era of immune profiling, the focus will shift from simple data accumulation to integration—combining bulk structure with single-cell resolution to decode the immune system with unprecedented clarity.

Traditional seroepidemiology, the study of antibodies in populations, has been central to understanding infectious disease dynamics. However, antibody levels often wane quickly, vary by disease severity, and provide a limited view of long-term immunity. During the COVID-19 pandemic, researchers sought to go beyond serology to measure T cell–mediated immunity, which is often more durable and nuanced.

Objective: What is Celluloepidemiology and Why It Matters

To address this gap, the team at the University of Antwerp introduced celluloepidemiology, a paradigm leveraging population-level T cell profiling and TCR sequencing to more accurately quantify infection dynamics, identify hidden infections, and stratify disease severity in large populations.

Challenge:

Celluloepidemiology offered transformative potential, but faced key barriers:

• Reproducibility in Library Prep: To study hundreds of samples, researchers needed a standardized, high-quality workflow for TCR sequencing libraries; variability could have undermined results.

• Complexity of T-cell analysis: T-cell assays are inherently multi-parameter, with T-cells expressing a diversity of proteins on their surface, requiring advanced computational tools to cluster, classify, and interpret patterns.

• Cross-reactivity risks: T cells often target epitopes shared across coronaviruses, demanding precise sequence-level analysis to avoid false signals.

Without strong wet-lab and computational solutions, scaling celluloepidemiology to population studies would not be practical.

Solution: QIAGEN + MiXCR-Powered TCR Profiling

The research team integrated QIAGEN’s standardized library prep with MiXCR’s computational engine, enabling scalable and validated TCR repertoire analysis.

• QIAGEN QIAseq Immune Repertoire RNA Library Kit delivered scalable, reproducible, and high-quality TCR libraries, ensuring comprehensive capture of immune diversity across hundreds of samples.

• MiXCR transformed raw sequencing data into actionable immune insights by assembling clonotypes, clustering TCRs, and annotating antigen specificity at population scale.

• Beyond QIAGEN and MiXCR, the study also leveraged flow cytometry and other technologies to provide complementary phenotypic and antibody-based insights alongside TCR repertoire analysis.

“MiXCR + Qiagen offers a solution to sequence all four TCR chains in a single kit. This is a large benefit for us, as it is often unknown in advance if we are searching for an αβ or γδ lineage T-cell. “

- Pieter Meysman, Associate Professor at the University of Antwerp

Results and Impact:

The celluloepidemiology approach demonstrated significant capabilities beyond conventional serology:

• Uncovered hidden infections: The study identified asymptomatic cases missed by antibody testing.

• Stratified disease severity: T cell markers and TCR repertoires correlated with mild, moderate, and severe COVID-19.

• Differentiated healthcare workers: Groups indistinguishable by serology showed distinct TCR repertoire signatures.

• Characterized long COVID: Patients with persistent symptoms exhibited unique T cell activation and exhaustion profiles.

• Validated immunity: SARS-CoV-2–specific TCR clusters were enriched in recovered patients.

The Qiagen chemistry cut prep time by 25%, accelerating sample to insight and enabling faster scientific discoveries.

“MiXCR works out of the box with the Qiagen chemistry to handle VDJ processing, so you can go from raw FASTQ files to clonotypes in a single command. We know that we get the maximum quality output from our sequencing experiments.”

The study concludes that the celluloepidemiology paradigm is complementary to seroepidemiology, providing high-dimensionality, sensitivity, and deep insights into the heterogeneity of human immune responses. This framework unlocks powerful new possibilities, with applications that extend far beyond COVID-19 to transform how we study infectious diseases, monitor vaccine effectiveness, and advance public health surveillance worldwide.

Read the full article in Science: https://www.science.org/doi/10.1126/sciadv.adt2926

Learn more about QIAseq TCR-seq kits and get access to MiXCR, through Platforma’s intuitive user interface.

What are diversity and clonality?

Two key concepts—diversity and clonality—are central to repertoire analysis.

• Diversity reflects the number and distribution of unique clonotypes in a sample. A broad, evenly distributed repertoire generally indicates immune health and flexibility.

• Clonality describes dominance by a small subset of clonotypes. This is often seen during infection, after vaccination, or in cancer, where specific immune responses drive the expansion of a few clones.

Analyzing both metrics together provides a detailed view of immune status, allowing researchers to answer questions about richness, balance, and immune reactivity.

What metrics are used to measure diversity and clonality?

Different biological questions call for different diversity and clonality metrics:

Selecting the right mix of metrics ensures your analysis aligns with your research goals, whether you are comparing treatment groups, tracking vaccine responses, or screening large patient cohorts.

Why is downsampling important in repertoire analysis?

Comparisons across samples with unequal sequencing depths can be misleading. More reads almost always reveal more clonotypes. Downsampling corrects this bias by normalizing datasets to the same effective size.

Common approaches include:

• Random Sampling – The most rigorous method; selects equal numbers of reads/UMIs across samples.

• Auto/Min – Automatically set to the largest possible depth that includes all samples, or the size of the smallest sample.

• Fixed Depth – Manually set to a defined threshold for reproducibility.

• Top N / Cumulative Top – Focus on dominant clones rather than overall diversity. Useful for clonality analyses but should not replace true normalization.

For most studies, random sampling (auto or min) provides the fairest basis for comparing repertoires across conditions.

Practical Scenarios and Recommendations

How can I visualize repertoire diversity?

The most powerful way to interpret diversity is through visualization.

• Line plots capture temporal changes in clonality or diversity.

• Boxplots/violins reveal group-level differences.

• Significance overlays (e.g., Wilcoxon or Kruskal-Wallis tests) validate observed trends.

These graphical outputs make it easier to identify biological patterns, compare experimental groups, and generate publication-ready figures.

Putting It All Together

Repertoire diversity analysis bridges raw sequencing data with actionable insights into immune function. By carefully selecting metrics, applying proper normalization, and visualizing results effectively, researchers can:

• Detect signs of immunodeficiency or dysregulation.

• Track immune responses to vaccination, infection, or therapy.

• Benchmark diversity across patient groups or experimental conditions.

To streamline this process, Platforma, a no-code bioinformatics platform, integrates clonotyping, diversity calculation, normalization strategies, and visualization into drag-and-drop blocks, enabling researchers to transform sequencing data to immune repertoire insights without the technical bottleneck.

Leverage the Repertoire Diversity block and the Graph Maker in Platforma to generate publication-quality visualizations of your immune repertoire dynamics in just a few clicks.

Background:

Founded by Flagship Pioneering, Quotient Therapeutics develops breakthrough medicines informed by natural somatic genetic diversity present in patients. Through the Company’s integrated somatic genomics and computational technologies, they gain unbiased, unprecedented resolution into disease-causal drug targets that they leverage to design and develop first-in-class therapies.

Objective:

To study the somatic mutations for a rare autoimmune condition, the team set out to gain a better understanding of the immune cell landscape - a critical step in identifying the right cell types to target with therapies. Their goal was to leverage somatic genomic data to characterize the presence and diversity of T-cell and B-cell receptors (TCRs and BCRs), enabling more precise and confident target identification for drug development.

Challenge:

In pursuit of their goal, the Quotient Tx team performed deep exome sequencing on mixed populations of cells. However, they faced a significant challenge: while they could generate high-quality DNA sequencing data, identifying the specific composition of the T and B cells within their samples was problematic:

• Heterogeneous Cell Populations: The cell populations analyzed contained a mixture of cell types, making it difficult to discern which immune cells were present.

• Data Utilization Gap: The team had generated extensive somatic genomic data, but needed a solution to fully understand the immune context of their samples.

• Lack of Viable Tools: Existing tools failed to deliver actionable insights, especially when it came to characterizing TCR and BCR sequences.

“We had deep exome sequencing on a collection of cells, but didn’t have high confidence in the different cell types present. Could we use the data we were already generating to get an answer?”

Solution:

Quotient Tx adopted MiXCR, the leading immune analysis software, to help bridge the gap between raw DNA sequencing and immune cell characterization. MiXCR enabled the team to:

• Quantify T and B Cell Populations: By extracting and assembling T cell receptor (TCR) and B cell receptor (BCR) clonotypes directly from their deep exome sequencing data, the team was able to accurately estimate the relative abundance of T and B cell. This approach provided quantitative immune cell composition data without additional assays, enabling direct correlation of immune repertoire diversity, clonal expansion patterns, and somatic mutation profiles, deepening the understanding of the immune response.

• Characterize Diversity: MiXCR provided insights into the polyclonal nature of the immune repertoire, which is crucial in understanding biological complexity and therapeutic targeting.

“The MiXCR pipeline was computationally lightweight, adding very little to our overall execution times.”

Results & Impact

To validate the results, Quotient Tx ran benchmark tests comparing MiXCR-derived data to RNA-Seq datasets. The results confirmed:

• Strong concordance between MiXCR and RNA-Seq deconvolution: Correlation plots comparing immune cell fractions quantified by MiXCR (y-axis) and RNA-Seq deconvolution via BayesPrism (x-axis) across multiple LCM samples showed high alignment, indicating consistency between DNA- and RNA-based approaches. (Figure 1)

• Validation of MiXCR for immune repertoire profiling: These results support the utility of MiXCR as a dependable tool for immune cell quantification and repertoire characterization using DNA sequencing data.

“Our experience with MiXCR has been very positive. Even for a unique use case like ours, it worked well. We’d recommend it to anyone looking to characterize immune repertoires.”

- Matthew Young, Head of Target Data Science

By unlocking immune repertoire data from their existing exome sequencing workflows, Quotient Tx was able to unlock critical immunological information without the need for additional experiments—saving both time and resources.

Ready to unlock faster, deeper immunology insights?
Now you can access MiXCR on Platforma, a user-friendly bioinformatics platform, empowering your team to move seamlessly from raw NGS data to actionable results—without writing a single line of code. Platforma is free for academic use, or contact us at licensing@milaboratories.com to get started.

Get started today

At MiLaboratories, we’re revolutionizing how researchers work with NGS data. Created by scientists with a passion for innovation, our mission is to make advanced computational tools intuitive and accessible. Our technology is used by thousands of researchers globally, rooted in the values of open knowledge, collaboration, and empowerment.

We recently launched Platforma, a UI-first, bioinformatics platform that empowers biologists to take ownership of their own data analysis and accelerate raw data to insights without the coding bottleneck. As an ambassador, you'll have the unique opportunity to foster connection, drive engagement, and champion our mission worldwide.

What will I do as a Platforma Ambassador?

• Organize local events — host hands-on workshops, user groups, or hackathons

• Connect globally — collaborate with fellow Platforma ambassadors around the world

• Accelerate your growth — enhance your resume, expand your network, and develop new skills along the way

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What are the benefits?

1. Make an Impact - Help grow the Platforma community, elevate your personal brand, and increase your influence across social platforms.

2. Support - Access dedicated budgets for your events, plus opportunities for travel grants to help you reach your community.

3. Skill Development - Receive 1:1 bioinformatics training, career coaching, resume guidance, and introductions to our professional network.

4. Insider Access - Get behind-the-scenes insight into Platforma initiatives, upcoming features, and new developments.

5. Recognition & Networking - Be featured in our “Ambassador Showcase,” earn exclusive merch and referral rewards, and join a community of next-generation change-makers.

What We Expect from You

• Engage in local and virtual community outreach through workshops, meetups, webinars, or hackathons

• Commit to hosting 2+ events per year

• Post about Platforma on social media at least once per quarter

• Keep us updated on your progress via email

Who We’re Looking For

Ideal candidates are those who:

• Are current MiXCR or Platforma users

• Are enrolled as a Masters, PhD, PostDoc, or Researcher at an academic institution

• Are enthusiastic about representing Platforma for at least one year

Does this sound like you?

Just complete our application (10 minutes max). After we receive your submission, you'll hear from us soon to chat through the next steps.

Our timelines:

September 9 - 21 : Applications open

September 22: Ambassadors notified by email

October 7th: Kickoff and onboarding meeting

If you have any questions, please reach out to support@milaboratories.com

If you're analyzing T-cell and B-cell receptor repertoires, you already know the stakes: choosing the wrong tool can lead to inaccurate results or weeks of delays. The core challenge isn't just the data complexity from V(D)J recombination and somatic hypermutation—it's navigating a fragmented landscape of bioinformatics tools.

This guide cuts through the confusion by comparing the leading tools at each stage of your analysis:

• Upstream Analysis (Clonotyping Engine): the algorithm that reconstructs receptor sequences from raw data — accuracy here is foundational

• Downstream Analysis (Discovery Environment): where you turn clonotypes into biological insights

We'll show you the data-driven path to the best solution for your research, backed by both vendor benchmarks and independent academic studies.

Part I: Upstream analysis - a head-to-head comparison of clonotyping engines

The reliability of any repertoire study depends on your clonotyping engine's performance. Inaccuracies at this step affect all downstream analyses [1]. This section provides a data-driven comparison of the leading engines for both bulk and single-cell sequencing.

1.1 Bulk sequencing analysis: speed and accuracy matter

For bulk V(D)J sequencing, the main command-line tools are:

• MiXCR: https://github.com/milaboratory/mixcr

• Immcantation suite: https://github.com/immcantation

• TRUST4: https://github.com/liulab-dfci/TRUST4

While they all aim to solve the same problem, benchmark data reveals critical differences.

Computational Speed

In large-scale studies, processing time is crucial. MiXCR completes analysis of a 20-million-read dataset in under 2 hours, while Immcantation requires over 10 hours — making MiXCR up to 6-fold faster [2].

Accuracy and Sensitivity

More importantly, you need correct clonotype identification (sensitivity) while avoiding artifacts (specificity). Using simulated datasets, MiXCR shows the highest sensitivity — an advantage that grows as sequencing error rates increase [3].

MiXCR excels at minimizing false positives. Analysis of monoclonal hybridoma datasets (where only a few B-cell clones should exist) showed MiXCR correctly identified minimal clones. In contrast, TRUST4 reported ~20x more clones, and Immcantation reported 100-200x more.

Feature comparison

Beyond speed and accuracy metrics, practical features determine a tool's real-world utility. The following comparison table shows key differences in capabilities, from basic functionality to advanced features like custom allele discovery or phylogenetic analysis.

1.2 Single-cell analysis: getting the most from every cell

For single-cell V(D)J analysis, the goal is reliably linking paired receptor sequences to transcriptomic profiles. The primary comparison here focuses on MiXCR versus the platform-default 10x Genomics Cell Ranger. While TRUST4 technically supports single-cell data [5], we exclude it from this comparison because it lacks noise filters — resulting in reports of ~10x more "cells" that are actually technological artifacts rather than real cells. This makes meaningful performance comparison impossible.

Cell Detection Efficiency

The key metric is how many cells yield productive, paired receptors [1, 6]. On public 10x datasets under standard conditions, MiXCR and Cell Ranger perform comparably, identifying similar numbers of T and B cells with productive receptors.

Both tools demonstrate robust performance when sufficient sequencing depth is available.

However, MiXCR's true advantage emerges with low-read data. When computationally downsampled to 50% of original reads, MiXCR maintains significantly higher cell detection rates than Cell Ranger, whose performance degrades substantially. This robustness is critical for real-world applications, allowing researchers to multiplex more samples per run and significantly reduce per-sample costs without sacrificing data quality.

Feature comparison

Single-cell V(D)J analysis demands specialized capabilities beyond standard clonotyping. The following comparison highlights key differences in platform support, species coverage, and advanced features that directly impact experimental design and analysis options.

1.3 Reference libraries: the hidden factor in analysis accuracy

Accurate V(D)J annotation depends entirely on germline reference quality. Most tools rely on IMGT, which has known limitations: slow updates, population bias and incomplete allele coverage. Using an incomplete reference causes cascading errors —germline polymorphisms get misidentified as somatic mutations, and novel alleles force incorrect alignments.

How tools handle references

• MiXCR: built-in curated library with continuous updates, automatic novel allele discovery (findAlleles), and custom library creation (buildLibrary)

• Immcantation: TIgGER for allele inference, but requires manual reference management [4]

• Cell Ranger: mkvdjref for custom references, limited to 10x data

• TRUST4: User-provided references only, no discovery capability

The difference is critical: studies show that using population-matched references with allele discovery can recover 15-20% more productive sequences than static IMGT-only approaches. For non-European populations or rare alleles, this difference determines whether you capture real biological diversity or introduce systematic bias.

Part II: Downstream analysis - from clonotypes to discovery

Accurate clonotypes are just the beginning. Real discoveries come from downstream analysis: identifying tumor-reactive clones, tracing B-cell evolution, or linking clonotype to cell phenotype. Here, researchers face a choice between powerful but complex command-line tools and more accessible graphical interfaces.

2.1 Command-line pipelines: power with complexity

Command-line interfaces offer unmatched control. MiXCR and the Immcantation suite (multiple R/Python packages) allow granular analysis and custom scripting [4, 7]. However, for bench scientists, this requires coding skills in multiple languages. Even basic tasks like merging V(D)J data with gene expression require custom scripts — a major bottleneck [1].

2.2 Graphical user interfaces: accessibility vs. functionality

General-purpose suites like Geneious offer point-and-click interfaces, lowering barriers for non-coders. However, they face critical limitations for NGS analysis: lack of dedicated backend infrastructure means datasets above ~100,000 sequences cause performance degradation or crashes. The platform requires manual data transfer between modules, lacks specialized immune analysis functions (no built-in clonotyping engine, V(D)J assignment, or clonality analysis), and cannot handle the millions of sequences typical in modern NGS experiments.

Web-based platforms like Galaxy provide web-based access to command-line tools, including immune repertoire pipelines like IGGalaxy and pRESTO implementations [8, 9]. While more accessible than pure CLI, functionality depends entirely on which tools administrators have installed. Processing large NGS datasets requires significant server resources - a standard Galaxy instance struggles with datasets >1GB. Additionally, users must chain together multiple independent tools manually, creating opportunities for errors and increasing analysis time.

2.3 The integrated solution - Platforma's unified approach

The ideal solution combines a top-tier clonotyping engine with a seamless, no-code environment designed for immunologists. Platforma achieves this by integrating the benchmark-proven MiXCR engine with an intuitive workflow for complete repertoire analysis.

Platforma's innovation is its ecosystem of interconnected "Blocks" that transform fragmented workflows into fluid, interactive discovery. Each block handles specific analysis tasks while seamlessly passing data to others, creating fluid workflows impossible with traditional tools.

Deep downstream analysis

Where other tools stop at basic clonotype counting, Platforma enables sophisticated biological discovery. Its Clonotype Space block reveals repertoire architecture and functional clusters through interactive high-dimensional analysis. For antibody researchers, automated SHM tree construction traces maturation pathways and identifies key mutations driving affinity improvement. Clone tracking across timepoints, tissues, or treatments becomes straightforward with integrated metadata management — no more Excel gymnastics to follow clonal dynamics.

AI-powered discovery tools

Platforma uniquely includes built-in machine learning models that transform repertoire analysis from descriptive to predictive. Specificity prediction identifies likely antigen targets without experimental screening, while affinity estimation ranks candidates by predicted binding strength. For therapeutic development, developability assessment flags potential manufacturing issues early, and tumor reactivity scoring prioritizes TCRs likely to recognize cancer antigens.

Effortless V(D)J Analysis - Watch Platforma transform millions of clonotypes into publication-ready plots in seconds. See dynamic grouping across 60 samples, real-time customization, and statistical analysis revealing immunization peaks — all with just a few clicks.

Conclusion: making the right choice for your research

TCR/BCR repertoire analysis demands both accurate upstream clonotyping and efficient downstream discovery tools. The data shows MiXCR as the best engine for speed, accuracy, and versatility. When choosing a complete solution, consider your analysis environment equally.

While command-line tools offer power to coding experts and generalist GUIs provide basic accessibility, Platforma represents the evolution of repertoire analysis. By integrating the leading MiXCR engine into a comprehensive, no-code discovery environment with unique AI capabilities, it provides the most powerful and accessible end-to-end solution — accelerating the journey from raw data to immunological discoveries.

Platforma is free for academic researchers - get started today.

References

1. Irac, S. E., Soon, M. S. F., Borcherding, N., & Tuong, Z. K. (2024). Single-cell immune repertoire analysis. Nature Methods, 21(5), 777–792.

2. Bolotin, D. A., et al. (2015). MiXCR: software for comprehensive adaptive immunity profiling. Nature Methods, 12(5), 380-381.

3. Barennes, P., et al. (2020). Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nature Biotechnology, 38(10), 1215-1224.

4. Gupta, N. T., et al. (2017). Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics, 31(20), 3356-3358.

5. Song, L., et al. (2021). TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data. Nature Methods, 18(6), 627-630.

6. Andreani, T., et al. (2022). Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data. NAR Genomics and Bioinformatics, 4(3), lqac049.

7. Vander Heiden, J. A., et al. (2014). pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics, 30(13), 1930-1932.

8. Afgan, E., et al. (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses. Nucleic Acids Research.

9. van der Loo, B., et al. (2021). Antigen Receptor Galaxy: A User-Friendly, Web-Based Tool for Analysis and Visualization of T and B Cell Receptor Repertoire Data. The Journal of Immunology.

10x Genomics’ Universal 5’ Gene Expression products generate full-length, paired V(D)J sequences from individual cells, providing rich insights into the clonal diversity of immune receptors. While Cell Ranger and Loupe Browser are powerful tools for analyzing 10x Genomics data, incorporating MiXCR into your workflow can unlock even deeper immunological insights.

MiXCR comes equipped with advanced computational capabilities, allowing you to:

• Perform advanced error correction for PCR and sequencing artifacts

• Detect cross-cell contamination

• Support individual or strain-specific allele inference

• Enable construction of B cell somatic hypermutation trees

• Integrate with bulk RNA-seq and repertoire sequencing data

• Support a broad range of species beyond human and mouse

• Accommodate custom transcriptome references

• Facilitate analysis of unconventional immune chains, including gamma delta (γδ) TCR repertoires

MiXCR is available as a command-line tool, or as a block on Platforma - UI‑first bioinformatics platform designed for analysis and visualization of sequencing data without any coding (figure below). Either option allows you to analyze 10x Genomics Universal 5’ V(D)J data with simple, ready-to-use presets on a laptop, server, or in the cloud.

MiXCR workflow

The MiXCR workflow consists of three parts:

Part 1: Upstream analysis

• Assemble contigs - Longer consensus chains are assembled from short reads in the FASTQ files.

• Alignment - Consensus sequences are aligned to the reference.

• Filtering - Alignments are filtered based on reads per UMI and UMI per cell distributions, lowering artificial diversity.

• Sequencing and PCR error correction - Multiple error correction steps reduce PCR and sequencing errors.

• Clonotype assembly - Full-length clonotype sequences are reconstructed.

• Chain pairing refinement - Cross cell contamination is removed and multiplets are resolved.

Part 2: Quality control

• QC report generation - Comprehensive text reports are created for every step of the pipeline.

• QC plot generation - Percent alignment, chain usage, and UMI/cell barcode distribution plots assessing quality of one or more samples are created.

Part 3: Downstream secondary analysis

• Somatic hypermutation trees generation

• Allele inference

• Link metadata to samples

• CDR3 characteristics analysis: strength, hydrophobicity, volume, charge, and more.

• Multiple diversity measures: Normalized Shannon-Wiener, Chao1, Gini Index, and more.

• Segment usage analysis

• Pairwise distance analysis

The MiXCR workflow starts with the raw sequencing data, usually a FASTQ file from your sequencing vendor of choice. MiXCR is equipped to analyze all 10x Genomics VDJ chemistries, including the GEM-X (5’ v3) chemistry - just select the already available 10x Genomics Preset. Upon initiation, MiXCR performs the upstream analysis (Part 1) and QC steps (Part 2) as summarized above. It then generates QC reports and a list of clones for paired TCRα/β or IG heavy/light chains. Afterwards, you can proceed with the downstream secondary analysis (part 3).

Advantages of MiXCR

MiXCR is the leading software for immune repertoire analysis, trusted by 47 of the top 50 research institutions and 8 of the top 10 biopharma companies, thanks to its ease of use, comprehensive features, exceptional speed, and high accuracy. It has been used to analyze over 10 million samples and is cited in more than 1,700 academic publications.

Easy to use

Performing secondary analysis on 10x Genomics Universal 5’V(D)J data often requires the use of multiple tools and coding proficiency. However, using MiXCR on Platforma streamlines the entire process, including secondary analyses, into drag-and-drop blocks. Use dedicated presets for 10x Genomics: mixcr analyze 10x-sc-xcr-vdj or mixcr analyze 10x-sc-xcr-vdj-v3, making analysis simple and accessible even to those without coding skills.

Rich with features

MiXCR offers many analysis features to process all on market bulk or 10x Genomics Universal 5’ V(D)J data. Beyond core functionalities, here are some features you can use in MiXCR:

• Multimodal analysis: Integrate bulk, single cell, or Sanger data.

• Custom reference library: Align data to IMGT database, built-in curated library, or custom-made library.

• Supports many species: Analyze data from nonstandard species using custom library.

• Secondary analysis options: Take advantage of a comprehensive set of secondary analysis tools.

Fast processing speeds

Computational efficiency is crucial for immune system profiling, particularly when handling large single cell datasets. In benchmarking tests, MiXCR was faster, more sensitive, and more accurate than other popular tools like TRUST4 and Immcantation. Additionally, MiXCR allows for easy scalability of resource usage (e.g., CPU and memory) as per your data processing needs.

Highly accurate

Accuracy is paramount when working with complex immune sequencing data. MiXCR delivers highly accurate results with negligible error rates, even in the most challenging samples. The MiXCR analysis pipeline incorporates multiple error correction steps that eliminate errors from PCR, sequencing, and UMI artifacts. You can see examples of MiXCR’s accuracy in the benchmarking tests.

Getting started with MiXCR

MiXCR is available as a block (application) on Platforma. To get started, download Platforma (free for academia). You can also visit the Platforma Getting Started page, which walks you through multiple deployment options.

Requirements

Compatible with all Mac, Linux, and Windows. The minimum configuration to run on the desktop is 8 GB of RAM, 4 CPU cores, and 20 GB disk space.

Support

To ask questions and interact with the Platforma community, check out our community forum or speak directly to the support team at support@milaboratories.com.

Read more: Analysis guide from 10X Genomics

Overcoming these challenges calls for bold, integrated solutions that unite cutting-edge technology, rigorous science, and open collaboration to drive real progress in health outcomes.

Challenge #1: Complexity and Heterogeneity of the Immune System

Our immune system is extraordinarily diverse, composed of millions of immune receptors of dozens of cell types, each with different activation states, memory phenotypes, and functional roles. Even within one cell type, behavior can differ based on location, disease state, or stimulation.

Solution: This is where computer science meets biology: from classical algorithms to AI-driven models, modern tools are essential for analyzing complex, high-dimensional immunological data. These tools help uncover patterns, simulate immune responses, and support both research and clinical decision-making. Platforma - a leading immune analysis software free for academia - hosts apps with deep learning models trained on the large public datasets to predict antibody affinity or TCR binding against specific epitopes.

Challenge #2: Computational Challenges with terabytes of data 

We generate massive datasets that often exceed computational capacity, especially in genomics and single-cell studies. Processing this data requires resource-intensive pipelines and a steep learning curve in computational expertise. To unlock the full potential of these datasets, equal investment in scalable, interoperable data infrastructure and accessible tools is essential.

Solution: Using efficient data formats (like Parquet) and high-performance tools like MiXCR can significantly speed up data processing and analysis, enabling researchers to process millions of sequences quickly. By removing the need for coding or Linux expertise, user-friendly tools empower biologists to finally utilize the powerful computational resources, like HPC clusters, that already existed within their organizations but remained out of reach. This democratization of access fosters broader collaboration, accelerates discovery, and ensures that insights are no longer limited to those with specialized computational training.

Challenge #3. Standardization and Reproducibility Issues

Reproducibility challenges in immunology research stem from the field's inherent complexity combined with inadequate standardization across multiple experimental dimensions, creating a cascade of factors that undermine result consistency and scientific progress. For example, single-cell sequencing introduces variability due to differing preprocessing, normalization, and clustering methods. Without standardized pipelines, the same dataset can yield conflicting results across studies.

Solution: Implement standardized protocols and quality control measures, use validated reagents and reference materials, and adopt community-validated workflows for preprocessing, normalization, clustering, and statistical testing, especially in single-cell and immune repertoire analysis. Utilizing reproducible frameworks designed for project-level integrity ensures true end-to-end consistency, making the entire research workflow — not just individual pipelines — reproducible by default. A great example of this in practice is a recent BCR-SEQC consortium led by the FDA, in which MiXCR played a key role to support.

Challenge #4. Integration of MultiOmic and Longitudinal Data

Today, we rarely see a single study using only one type of data. With so many modalities becoming more wide-spread, each study is multiomic now. Integrating multiomic and longitudinal data in immunology is complex due to the inherent heterogeneity of data type, each with distinct structures, scales, and noise profiles. Longitudinal studies add further complications, including inconsistent sampling intervals, missing data, and temporal variation in immune responses. Most analytical tools are optimized for single-omic or static data, making it difficult to derive cohesive, time-resolved insights across layers.

Solution: Choose multiomic integration frameworks such as MOFA, Seurat v4, or Harmony, which model shared and unique signals across omics layers. Rigorous preprocessing steps, including quality control, normalization, and batch correction, are essential for harmonizing data across time and platforms. Visualization techniques such as UMAP enable intuitive interpretation of complex datasets. All of these available in Platforma, with an all-in-one user-friendly interface, breaking barriers across modalities.

Challenge #5: Collaboration Gaps and Data Siloes

Despite the collaborative nature of science, immunology research is often hindered by fragmented efforts and siloed datasets, with individual labs sometimes operating in complete isolation. Data is frequently stored in isolated institutional, national, or proprietary repositories, limiting accessibility and interoperability. This lack of coordination slows progress, leads to duplicated efforts, and prevents community-wide knowledge sharing, creating substantial bottlenecks that prevent researchers from fully leveraging existing information.

Solution: Fostering interdisciplinary and multi-institutional collaborations is crucial for advancing immunology, as exemplified by centers like Northwestern's Center for Human Immunobiology (CHI),  leading to groundbreaking discoveries like the hemifusome. Promoting open science and data-sharing platforms is essential to break down barriers. Platforma, for example, addresses this through its highly modular design. It provides a software development kit (SDK) that empowers individual scientists to contribute their own specialized tools as self-contained 'blocks'. The platform's core architecture then seamlessly integrates these diverse components, enabling them to work in concert to democratize access to sophisticated and customizable bioinformatics applications.

Built by the same creators of MiXCR, Platforma breaks through immunology’s biggest bottlenecks by providing an ecosystem that makes high-quality immunological data accessible, analyzable, and actionable for the global research community:

✓ No coding needed – Intuitive interface designed for biologists 

✓ Hours, not weeks – From raw data to top 10 antibody/TCR candidates in a few clicks

✓ Powered by MiXCR - Harness the power of MiXCR, the gold standard for immune repertoire analysis used in 47 out of the top 50 research institutions and 8 out of the top 10 biopharma companies

Ready to accelerate your research? Platforma is free for academia

Start today with Platforma