Partners Archives - Decode Science

AgriPrep™ Library Prep Kit

June 18, 2026/in Partners, SeqWell/by Harshita Sharma

From DNA to Sequence-Ready Libraries in 100 Minutes — Built for Agricultural Genomics at Scale

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seqWell AgriPrep™ Library Prep Kit

The seqWell AgriPrep™ Library Prep Kit is a purpose-built NGS library preparation solution for plant, animal, and human low-pass whole genome sequencing and SKIM-sequencing workflows — designed from the ground up for the throughput, simplicity, and cost efficiency that population-scale agrigenomics demands. Powered by seqWell’s one-step ExpressPlex workflow, AgriPrep combines tagmentation and amplification into a single step, with built-in auto-normalisation and early sample pooling, taking you from extracted genomic DNA to sequence-ready libraries in just 100 minutes with only 30 minutes of hands-on time. It is the simplest library prep workflow on the market for its scale — and the numbers back that claim.

AgriPrep supports up to 3,072 unique index combinations, is available in 96-well and bulk reagent formats, and is compatible with 384-well and Echo-compatible plate formats for ultra-high-throughput operations. Its auto-normalisation of both mean insert size and read depth eliminates the need to individually quantify and dilute samples before pooling — reducing labour, reducing consumable use by up to 95%, and reducing sequencing costs by removing the need to over-sequence to bring your lowest-coverage samples up to target depth. As your authorised Australian and New Zealand distributor for seqWell, Decode Science can advise on kit format selection, throughput optimisation, and compatibility with your sequencing platform.

Harvest actionable insights using simple, scalable, cost-effective sequence exploration

One-Step Workflow — 100 Minutes, 30 Hands-On

Tagmentation and amplification are combined into a single reaction step, followed by auto-normalisation and early pooling. The result is the most streamlined library prep workflow available for low-pass WGS at scale — no polymerase, primers, or barcodes to source separately, all are included in the supplied ready reaction mix. At 100 minutes total and 30 minutes hands-on, AgriPrep removes library prep as a bottleneck for high-throughput genomics programmes.

Auto-Normalisation Eliminates Individual Sample Quantification

AgriPrep's auto-normalisation of mean insert size and read depth across a 20–100 ng input range removes the need to individually quantify and dilute samples before pooling. Across a 24-plex barley gDNA library pool (10–200 ng input), mean insert size CV was 6.9% and read depth CV was 10.5% within the normalisation range — delivering the coverage consistency needed to hit per-sample read depth targets without over-sequencing.

Up to 95% Fewer Consumables and Dramatically Lower Sequencing Costs

Pre-plated reagents, single-step chemistry, early pooling, and sequential reagent addition combine to use fewer than two pipette tips per sample — reducing consumable costs by up to 95% versus standard library prep approaches. Auto-normalisation of read depth further reduces sequencing costs by minimising coverage variance between samples: in a head-to-head comparison, AgriPrep required 5× less sequencing than a non-normalising prep to bring all samples to ≥100,000 reads from a pool with variable DNA inputs.

Scalable to 3,072 Indexes Across 384-Well and Echo-Compatible Formats

Up to 1,536 unique index combinations are available off-the-shelf, with up to 3,072 available via custom ordering. AgriPrep is automation-friendly and compatible with 384-well plate and Echo-compatible plate formats for ultra-high-throughput operations — making it practical for population genomics, genomic selection programmes, and large-scale agrigenomics studies where per-sample cost and throughput are the primary operational constraints.

Running low-pass WGS or SKIM-sequencing at scale and want to know if AgriPrep fits?
Our team can help you assess throughput requirements, index combinations, and format compatibility for your programme.

Product Data: Performance at Scale

Auto-Normalisation Delivers Consistent Coverage Across Variable Inputs

A 24-plex library pool prepared from 10–200 ng barley gDNA and sequenced on an Illumina MiSeq i100 demonstrated consistent performance across the full working range. Within the 20–100 ng normalisation range, mean insert size CV was 6.9% and read depth CV was 10.5% — confirming that AgriPrep’s auto-normalisation reliably equalises coverage across samples with variable starting inputs.

80% Reduction in Required Sequencing Versus Non-Normalising Methods

In a direct comparison using 8 samples with variable DNA inputs (20–100 ng), AgriPrep with auto-normalisation achieved ≥100,000 reads per sample in a pool sequenced to 1.2 million reads. The non-normalising comparator required 6 million reads — 5× more sequencing — to bring the lowest-represented sample to the same threshold. For large-scale programmes, this difference in sequencing cost is substantial.

Fewer Than 2 Tips Per Sample

Pre-plated reagents and sequential reagent addition reduce consumable use to fewer than 2 pipette tips per sample — a reduction of up to 95% versus standard library prep workflows. At population scale, this translates directly to lower per-sample reagent cost, reduced waste, and faster processing.

Ready to simplify your agrigenomics library prep workflow?

Request a quote or ask about bulk format pricing — our team will respond within one business day.

Why It Matters to You?

Because Population-Scale Genomics Programmes Live or Die on Per-Sample Cost

Low-coverage whole genome sequencing is transforming agrigenomics — enabling genomic selection, genotype-by-sequencing, and population-scale variant discovery at costs that make large cohort studies feasible. But the library prep step has remained a persistent bottleneck: labour-intensive, consumable-heavy, and prone to coverage variance between samples that forces over-sequencing to compensate.

AgriPrep is designed specifically to remove this constraint. It is most relevant for:

Genomic selection and breeding programmes

Large populations of plant or animal samples need to be processed consistently, affordably, and at throughput that matches breeding cycle timelines. AgriPrep’s 100-minute protocol, auto-normalisation, and 3,072-index capacity support exactly this.

Aquaculture and livestock genomics

Validated for high-throughput low-pass WGS in aquaculture applications. AgriPrep’s scalability and simplified workflow make it practical for fisheries and livestock genomics programmes processing hundreds to thousands of samples per run.

Plant genomics and crop science

Validated across plant gDNA inputs including barley, with performance benchmarked against fragmentation-ligation workflows for genomic selection using lpWGS.

Core laboratories and high-throughput sequencing facilities

384-well and Echo-compatible plate format support, automation compatibility, and bulk reagent availability make AgriPrep a practical choice for facilities processing large sample volumes across multiple projects.

AgriPrep Specifications

Primary Applications	Genotyping-by-sequencing (GBS), Low pass sequencing, SKIM-seq
Sample Input types	Plant, animal, and human genomic DNA
Transposase	TnX – Next generation engineered transposase
Kit format	96-well Custom dispense
DNA Input	Working range: 10-200 ng Normalization range: 20-100 ng
Total Library Prep Time	100 minutes (30 minutes hands-on time)
Indexing Method	Combinatorial Dual Indexing
Number of Unique Index Combinations	Up to 1536 (off-the-shelf); up to 3072 via custom ordering
Batch Size	8-96 samples; bulk reagents support 384-well & Echo-compatible plate formats

Resources

Download AgriPrep Brochure For More Details

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FAQs

What applications is AgriPrep optimised for?

AgriPrep is purpose-built for low-coverage whole genome sequencing of plant and animal genomes — including genotyping-by-sequencing (GBS), SKIM-sequencing, and genomic selection programmes. It has not been validated for RNA-seq. Contact Decode Science if you’re considering it for other applications requiring ≤50M clusters, such as microbial WGS.

What is the recommended DNA input range?

The working range is 10–200 ng (2.5–50 ng/µl), using 4 µl of purified DNA per reaction. Auto-normalisation performs optimally within the 20–100 ng range (5–25 ng/µl). Inputs below 2.5 ng/µl are not recommended due to increased risk of failure. If your samples are all above 50 ng/µl, dilute to an average of approximately 10 ng/µl before processing.

Do I need to individually normalise samples before pooling?

No — this is one of AgriPrep’s key advantages. The kit is formulated to tolerate up to a 20-fold difference in sample input (10–200 ng), so individual sample quantification and dilution before pooling is not required. Fluorometric methods such as Qubit or PicoGreen are recommended if quantification is needed.

Are all adapters, indexes, and amplification reagents included?

Yes. AgriPrep includes all indexed adapters, amplification master mix, and amplification primers needed to produce dual-indexed, Illumina-compatible libraries. No separate polymerase, primers, or barcodes are required.

How many samples can I process per run, and how many index combinations are available?

The standard 96-well kit supports 16–96 samples per plate. Up to 1,536 unique index combinations are available off-the-shelf; up to 3,072 via custom ordering. Bulk reagent formats support 384-well and Echo-compatible plate operations for ultra-high-throughput workflows.

Can I save unused reagents if I process fewer than 96 samples?

Yes — provided no DNA or indexing reagents have been added and no incubations have been run. Use a razor blade to cut the plate seal up to the number of wells being processed, peel only those wells, proceed with the protocol, then cover used wells and store remaining reagents at −20°C for future use.

Is AgriPrep compatible with automation?

Yes. AgriPrep is well-suited to automated liquid handling platforms. Note that plate seals are not pierceable — they must be peeled back before automated access. Contact Decode Science for guidance on validated automation methods and available automation guides.

What is the expected library fragment size?

Typically 300–2,000 bp, depending on sample input. If input DNA is shorter than 1,000 bp, the resulting library size distribution will reflect this. Fragments above 1,200 bp do not affect clustering or data quality, but should be accounted for in library quantification — use region analysis on a TapeStation or Fragment Analyser to determine the proportion of sequenceable fragments before calculating loading concentration.

Do I need to spike in PhiRx™ Indexed Control before sequencing?

PhiRx is included with every AgriPrep kit and is strongly recommended. While AgriPrep libraries have highly diverse base composition and don’t strictly require PhiRx for read diversity, a 1–2% spike-in provides a useful internal sequencing control. If sequencing on XLEAP-SBS chemistry or fewer than four plates simultaneously, a 5–10% spike-in is advised to support colour balancing.

What sequencing platforms is AgriPrep compatible with?

AgriPrep libraries are compatible with Illumina iSeq, MiSeq, MiniSeq, NextSeq, HiSeq, and NovaSeq systems, using the same sequencing primers as Nextera® libraries. Note that TruSeq v3 Cluster kit primers are incompatible — the TruSeq Dual Index Sequencing Primer Box is required for older Illumina systems. Many users have also successfully used conversion kits for non-Illumina platforms.

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Parse Biosciences Evercode™ WT FFPE

May 13, 2026/in Evercode, Parse, Partners, Seminar/by Harshita Sharma

Parse Biosciences Evercode™ WT FFPE

Whole Transcriptome Single-Cell RNA-Seq From the Archived Samples You Already Have

Evercode™ WT FFPE brings whole transcriptome single-cell RNA sequencing to formalin-fixed, paraffin-embedded tissue — unlocking retrospective cohorts that have historically been out of reach for discovery-driven transcriptomics. Unlike existing FFPE single-cell methods that rely on targeted probe panels and limit your analysis to a predefined gene list, Evercode WT FFPE uses reverse transcription-based chemistry to capture the full transcriptome directly from archived tissue. The result is unbiased, discovery-scale snRNA-seq from the clinical samples you already have in storage — without restricting what you can find before you start looking.

Designed for cohort-scale studies, Evercode WT FFPE supports processing from a single sample up to 96 samples per run, with cell throughput scaling from 10,000 to 5 million cells across four kit configurations. Combinatorial barcoding is built into the workflow — multiplexing requires no additional steps or protocols, minimising batch effects and technical variability across large sample sets. The platform is instrument-free and automation-compatible, supporting straightforward adoption and scale-up across standard and high-throughput laboratory environments. As your ANZ distributor for Parse Biosciences, Decode Science can advise on kit selection, experimental design, and workflow integration for your specific cohort.

Key Features: What makes FFPE Popular?

Whole Transcriptome Profiling — Not a Probe Panel

Every targeted FFPE single-cell method makes the same trade-off: you get clean data on the genes you chose, and nothing on the ones you didn't. Evercode WT FFPE captures the full transcriptomic landscape — including transcript isoforms, SNPs, and long non-coding RNAs — without restricting discovery to a predefined panel. For researchers working on cell state identification, regulatory biology, or exploratory cohort analysis, this is the difference between confirmation and genuine discovery.

Reliable Gene Expression Data From Degraded RNA

FFPE fixation fragments RNA, and fragmented RNA has historically degraded the accuracy of gene expression measurements. Evercode WT FFPE uses reverse transcription chemistry specifically optimised for fragmented RNA, enabling consistent gene expression detection and confident cell state identification from archival material. The resulting data are directly comparable with fresh tissue datasets — preserving biological signal across sample types and supporting cross-cohort integration without correction artefacts.

Instrument-Free and Automation-Compatible Across Four Kit Sizes

Evercode WT FFPE requires no dedicated instrument for the core workflow, lowering the barrier to adoption and making it accessible to labs without specialised single-cell infrastructure. Four kit configurations — Mini, Standard, Mega, and Penta — scale from 10,000 cells and 1–12 samples up to 5 million cells and 1–96 samples, supporting everything from pilot experiments to large-scale clinical cohort studies. The workflow is compatible with laboratory automation for high-throughput operations.

Not sure which kit configuration fits your cohort size?

Our team can help you match the right Evercode WT FFPE kit to your sample numbers, cell input, and experimental design.

Watch How Evercode Technology Works

Why It Matters to You

Because Your Most Valuable Samples Are Already in the Biobank

Fresh tissue single-cell studies are powerful — but they’re prospective by design. The patients you’ve already treated, the tumours you’ve already resected, the longitudinal samples you’ve already collected — those are locked in FFPE blocks, largely inaccessible to the transcriptomic methods that would extract the most value from them.

Evercode WT FFPE changes that calculus. Its relevance is clearest in these settings:

Retrospective cohort studies

Clinical biobanks represent years of carefully annotated patient samples. Whole transcriptome snRNA-seq from these collections enables discovery-driven analysis of disease progression, treatment response, and cellular heterogeneity at a scale and clinical depth that prospective studies take years to replicate.

Tumour microenvironment and cell state analysis

FFPE tissue preserves spatial and cellular context. Evercode WT FFPE recovers full transcriptomic complexity from these samples — including lncRNA expression and regulatory transcript diversity — enabling characterisation of rare cell states and microenvironmental programs that targeted panels miss entirely.

Multi-site and multi-cohort studies

Built-in multiplexing and instrument-free operation make it straightforward to harmonise sample processing across institutions and study sites, reducing the batch effects that complicate cross-cohort comparison.

Integration with fresh tissue data

Evercode WT FFPE data are directly comparable with fresh tissue snRNA-seq datasets, enabling combined analysis across sample types within a single study and supporting meta-analyses that span preservation methods.

Product Data: Performance From Archived Tissue

Whole transcriptome single cell profiling from FFPE samples

Accurate Cell State Identification From FFPE

Evercode WT FFPE enables confident identification and annotation of distinct cell populations from archived FFPE tissue, with gene expression measurements that support direct integration with fresh tissue reference datasets. Cell state resolution is maintained across sample types, preserving biological signal through the variability introduced by fixation and archival storage.

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lncRNA and Regulatory Transcript Detection

Whole transcriptome capture recovers biologically meaningful regulatory RNA species that targeted panels cannot access. Long non-coding RNA expression — including transcripts such as TUG1, LINC00993, and LINC00472 — is detectable across annotated cell populations, enabling analysis of regulatory programs within specific cell states. In TNBC tumour epithelial cells, for example, differential lncRNA expression across proliferating and non-proliferating states reflects the complex regulatory landscape across breast cancer subtypes — a finding that probe-based methods cannot surface.

Ready to sequence your FFPE cohort?

Request a quote or talk through your experimental design — we’ll respond at the earliest. Our team can help you match the right Evercode WT FFPE kit to your sample numbers, cell input, and experimental design.

Cohort-Scale Multiplexing With Minimised Batch Effects

Processing multiple samples within a single combinatorial barcoding experiment reduces technical variability and supports the statistical power needed for meaningful biological comparisons. Large retrospective cohorts can be processed together without introducing the batch effects that arise from sequential single-sample runs — a critical requirement for clinical cohort studies where sample-to-sample consistency directly impacts interpretation.

Download Product Sheet >

Scalable Throughput Across Four Kit Configurations

Kit	Cells per Run	Samples per Run
Evercode WT FFPE Mini	Up to 10,000	1–12
Evercode WT FFPE	10,000–100,000	1–48
Evercode WT FFPE Mega	100,000–1,000,000	1–96
Evercode WT FFPE Penta	1,000,000–5,000,000	1–96

Resources

Download Product Sheet Instantly!

Evercode™ Whole Transcriptome v4

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Comparison of Evercode™ WT v3 and Chromium™ GEM-X Single Cell 3’ Kit v4

FAQs

What makes Evercode WT FFPE different from other FFPE single-cell methods?

Most existing FFPE single-cell approaches rely on targeted probe panels, which means your analysis is restricted to genes included in the panel at the time of design. Evercode WT FFPE uses reverse transcription-based chemistry to capture the full transcriptome, enabling unbiased discovery — including regulatory transcripts, lncRNAs, and transcript isoforms that probe-based methods cannot recover.

What tissue types has Evercode WT FFPE been validated on?

Evercode WT FFPE has been validated across a range of FFPE tissue types including tumour tissue. Contact Decode Science for tissue-specific guidance relevant to your sample type and storage conditions.

Do I need a dedicated instrument to run Evercode WT FFPE?

No. The core Evercode WT FFPE workflow is instrument-free and compatible with standard laboratory equipment. It is also compatible with laboratory automation for high-throughput processing.

How many samples can I process in a single run?

This depends on your kit configuration. The Mini kit supports 1–12 samples; the standard kit supports 1–48; and both the Mega and Penta kits support 1–96 samples per run.

Can Evercode WT FFPE data be integrated with fresh tissue snRNA-seq datasets?

Yes. Evercode WT FFPE is specifically designed to produce data directly comparable with fresh tissue datasets, supporting integration and cross-cohort analysis within a single study.

What sequencing platform is required?

Evercode WT FFPE libraries are compatible with Illumina sequencing platforms. Contact Decode Science for guidance on sequencing depth and read length requirements for your application.

What is the minimum RNA quality required?

Evercode WT FFPE uses reverse transcription chemistry optimised for fragmented RNA and does not require intact RNA. Contact Decode Science for guidance on tissue age, fixation duration, and input quality thresholds relevant to your samples.

Where can I access protocols and technical documentation?

Full protocols and technical documentation are available from Decode Science on request.

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May 7, 2026/in NGS, Promotions, Twist/by Harshita Sharma

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Whole transcriptome single cell analysis for FFPE tissues

April 1, 2026/in News, Parse/by Harshita Sharma

Whole transcriptome single cell analysis for FFPE tissues

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Single Cell Whole Transcriptome Analysis of Breast Cancer FFPE Samples Across Tumor Subtypes

Key Takeaways

Whole transcriptome single cell profiling from archival FFPE breast cancer samples resolves tumor, stromal, and immune compartments in a single experiment
Distinct epithelial programs are identified across clinically relevant subtypes, including ER+, ER/PR+, HER2+, and TNBC
Subtype- and proliferation-associated lncRNA expression patterns are captured, highlighting the value of unbiased RNA profiling in FFPE samples

Experimental Design:

Formalin-fixed, paraffin-embedded (FFPE) tissue represents a vast source of clinically annotated samples, but has been difficult to use for whole transcriptome single cell analysis. In this dataset, nuclei isolated from 4 archived breast cancer FFPE samples were profiled using Evercode WT FFPE’s reverse transcription–based workflow designed to capture whole transcriptome expression from degraded RNA, profiling over 100,000 nuclei.

These results demonstrate that FFPE samples enable whole transcriptome profiling that captures meaningful cell types and tumor subtype biology across multiple donors while preserving cellular heterogeneity.

Results:

Whole transcriptome profiling resolved epithelial tumor populations alongside stromal and immune compartments, including CAFs, endothelial cells, VSMCs, myeloid cells, pDCs, B cells, NKT cells, and mast cells.

Cells cluster strongly by donor, with clear differences in gene expression programs across samples. Subtype-specific expression patterns distinguish ER+, ER/PR+, HER2+, and TNBC tumors, with TNBC remaining particularly distinct after integration.

Figure 1: UMAP of human breast cancer FFPE nuclei. Tumor and proliferative epithelial states are resolved together with stromal and immune populations from FFPE samples.

Subtype- and state-associated lncRNA expression

Whole transcriptome profiling enables detection of biologically relevant lncRNAs across breast tumor cell states, revealing patterns linked to both tumor subtype and functional cell state.

LINC00993, a lncRNA associated with tumor-suppressive activity in breast cancer, is enriched in luminal epithelial populations. In contrast, the oncogenic lncRNA TUG1 shows higher expression in TNBC and proliferating epithelial states. These patterns are consistent with known subtype-associated biology and highlight how lncRNA expression reflects underlying tumor programs.

Figure 3: Proliferation-associated lncRNA expression.

Expression of TUG1 and additional lncRNAs across proliferating epithelial populations marked by Ki67 protein expression. Proliferating tumor cells show increased expression of specific lncRNAs, linking noncoding RNA activity to cell cycle state.

Proliferating epithelial populations show coordinated expression of TUG1 alongside proliferation markers, indicating an association between lncRNA activity and cell cycle progression.

Together, these results demonstrate that whole transcriptome FFPE profiling captures both coding and noncoding features of tumor biology across subtype and cell state.

Figure 2: Subtype-associated lncRNA expression across breast tumor cell states.

To further connect lncRNA expression with functional and clinical measures of proliferation, lncRNA expression was assessed using Ki67 positivity, as determined by prior immunohistochemistry protein staining.

Dot plot showing expression of LINC00993 and TUG1 across annotated cell populations. LINC00993 is enriched in luminal epithelial populations, while TUG1 is elevated in TNBC and proliferating epithelial populations. Dot size indicates the percent of cells expressing each transcript and color indicates average expression.

As the official distributor in Australia and New Zealand, Decode Science makes accessing genomics solutions straightforward. Our role is to connect your lab with advanced technologies, ensuring you get the right solution for your sequencing projects—delivered locally with support when you need it.

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MGISTP-B1000

February 20, 2026/in Automated Systems, MGI, Partners, Sample Transfer/by Harshita Sharma

MGI STP-B1000

The STP-B1000 is designed for laboratories that require accurate, repeatable separation and transfer of blood components without compromising traceability or efficiency. It precisely recognizes plasma, buffy coat, and red blood cells within centrifuged blood collection tubes and transfers each component with high positional accuracy, reducing manual handling and the risk of cross-contamination. Integrated barcode tracking ensures every transfer remains fully traceable, preventing sample mismatches and data integrity issues in high-throughput workflows.

Operation is intentionally streamlined… users define only the component type, transfer volume, and number of transfers before initiating the process with a single click. This simplicity minimizes training requirements while delivering consistent, standardized blood processing suitable for clinical, biobanking, and research applications.

Accurately Identify Blood Components & efficiently recover the buffy coat

As the official distributor of MGI in Australia and New Zealand, Decode Science is providing local access to STP-B1000 solutions with region-based technical and application support. Simply talk to me and we can discuss your research needs.

Product Components & Software Functions

Integrated Scanning, Identification, Transferring

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Product Specifications

Indicators	Parameter
Pipettor	Pipette Range 1 μL–1000 μL
	Pipette Accuracy 1 μL: CV≤8%, accuracy: ±10% 50 μL: CV≤1%, accuracy: ±2% 200 μL: CV≤1%, accuracy: ±2% 1000 μL: CV≤1%, accuracy: ±1%
	Independent 8-channel Pipettor
Detection Mode	pLLD, cLLD
Throughput	1–192 samples/run
Size	1420 mm (L) × 1010 mm (W) (without door handle) × 1120 mm (H)
Weight	~250 kg