Introduction

Trailmaker™ is Parse Biosciences’ cloud-based single-cell RNA-seq analysis platform that streamlines the journey from raw sequencing data to biological insight through an intuitive, no-code workflow. The platform combines automated processing of FASTQ files generated using Parse Evercode™ technology, together with interactive downstream tools for quality control, integration, clustering, visualization, differential expression, cell type annotation, immune repertoire analysis, and plot customization. In addition, Trailmaker includes a Sample Loading Table module that helps researchers prepare for the combinatorial barcoding part of the Parse workflow that is upstream of data generation, reducing setup errors and simplifying experiment tracking. Together, these capabilities make Trailmaker a comprehensive environment for planning, analyzing, and sharing single-cell studies across teams.

Select from the following 4 entry points below for detailed instructions on how to get started with Trailmaker:

1. Sample Loading Table: to help accurately load your round 1 barcoding plate

2. Data Repository: to start exploring the platform features and functionality with public data

3. Pipeline module: to process Parse Biosciences Evercode FASTQ files

4. Insights module: for downstream data exploration and visualization

Sample Loading Table

For users who are about to start the combinatorial barcoding process, the Sample Loading Table module is available to support the accurate loading of your round 1 barcoding plate. This module currently supports Evercode WT (Mini, WT, or Mega), Evercode TCR, Evercode BCR, and CRISPR Detect for Evercode WT.

Our user guide provides full step-by-step instructions for setting up your Sample Loading Table.

To get started with the Sample Loading Table module, navigate to: https://app.trailmaker.parsebiosciences.com/sample-loading-table

Data Repository

For users who are new to Trailmaker and want to explore the features and functionality of the Insights module, the data repository is the best place to start.

Exploring a dataset from the repository allows you to take a deep dive into the Insights module, where processed data files generated after FASTQ file processing (such as cell-by-gene count matrices, Seurat objects, etc.) are input for downstream analysis and visualization using either Seurat or Scanpy workflows. Explore the advanced filtering, quality control and integration options, data visualization and clustering customization options, cell set annotation, differential expression and pathway analysis, and plot customization for the generation of publication-ready figures.

The dataset repository contains ~65 publicly available datasets, totalling >11.5 million cells. Some specific datasets to draw your attention to are:

The Parse Biosciences “Human PBMCs - Evercode v4” from the Performance of Evercode™ WT v4 in Human Immune Cells (PBMCs) dataset showcases the data quality of our new v4 chemistry Evercode kits
The fully integrated multi-technology Comparison of Evercode™ WT v4 and Chromium™ GEM-X Single Cell 3’ Kit v4 in Human PBMCs. Explore the integrated data in Trailmaker: “Evercode WT v4 vs 10x Chromium 3prime”.

Datasets in the repository can be explored quickly using the View option within the Explore menu. This option is particularly useful if you want to take a quick look at the features available in Trailmaker Insights module or to validate gene expression or cell type presence in a particular dataset (e.g. tissue) of interest.

Alternatively, datasets can be copied to your account for independent control using the Copy option within the Explore menu. This option is particularly useful in cases where you want to take a deep dive into a specific dataset of interest that’s relevant to your own study, or if you want to integrate your own data with a relevant dataset from the repository in order to increase the power of your results.

For full explanation of the View and Copy options, see our support article: How to explore demo datasets from Trailmaker’s dataset repository.

To select a dataset from the repository, navigate to: https://app.trailmaker.parsebiosciences.com/repository

Pipeline module

For users who are looking to process FASTQ files generated using Parse Biosciences’ Evercode technology, the Pipeline module is what you’re looking for.

The Pipeline module processes FASTQ files generated using Parse Biosciences’ Evercode technology. Our FASTQ processing pipeline handles essential tasks such as barcode correction, read alignment, read deduplication, and transcript quantification. These quantified transcripts are then used to generate a cell-by-gene count matrix for each sample that is then used for downstream analysis.

Our Guided walkthrough: Pipeline module set-up support article provides an overview of the input requirements for the Pipeline module, while our user guide provides full step-by-step instructions for setting up your Pipeline run.

To get started with the Pipeline module, navigate to: https://app.trailmaker.parsebiosciences.com/pipeline

Insights module

For users who are looking to perform downstream analysis and visualization of processed data files such as cell-by-gene count matrices or a Seurat object, the Insights module is your solution.

The Insights module is where processed data files generated after FASTQ file processing (such as count matrices, Seurat objects, etc.) are input for downstream analysis and visualization using either Seurat or Scanpy workflows. This module offers advanced filtering and data cleanup, integration of multi-sample datasets, customization of data visualization and clustering, cell set annotation, differential expression and pathway analysis, and plot customization for the generation of publication-ready figures.

The Insights module supports:

Parse Biosciences Evercode data: The outputs from the Trailmaker Pipeline module are automatically sent to the Insights module for downstream analysis. Alternatively, outputs from running the Parse pipeline locally can be uploaded directly to the Insights module. For whole transcriptome data you should have 3 data files per sample (all_genes.csv; cell_metadata.csv and count_matrix.mtx or DGE.mtx), while for TCR or BCR immune profiling data you should have an (additional) 3 data files per sample (clonotype_frequency.tsv, barcode_report.tsv and either bcr_annotation_airr.tsv or tcr_annotation_airr.tsv).
Seurat objects: Processed data in the form of a Seurat object (.Rds file) can be uploaded directly to this module.
10x Genomics^TM Chromium^TM data that have been processed using Cell Ranger^TM: Count matrices can be uploaded as 3 data files per sample (barcodes.tsv, features.tsv or genes.tsv, and matrix.mtx). Alternatively, H5 files can be uploaded in the matrix.h5 file format.
BD Rhapsody^TM data can be uploaded in the expression_data.st file format.

For optimal functionality within Trailmaker, we recommend uploading cell-by-gene count matrices.

Our Guided walkthrough: Insights module set-up support article provides you with additional explanation of this module, while our user guide provides full step-by-step instructions for setting up your Insights project.

To get started with the Insights module, navigate to: https://app.trailmaker.parsebiosciences.com/data-management