LatchBio

Visualize key insights from top differentially expressed genes via MA plots, heatmaps, and volcano plots in Latch's DESeq2 report. Analysis on Latch Plots is easily customizable with accessible and modifiable underlying Python code. Try out the Latch Plots experience yourself using this interactive demo! https://lnkd.in/gCA3axGU

Latch provides a native Nextflow integration that enables developers to use the existing nf-core/rnaseq codebase with minimal modifications and customize an intuitive graphical interface for scientists. Scientists can use interactive downstream visualizations, such as volcano plots, MA plots, and heatmaps, to understand their data. Read more below:

Latch Registry provides error-validated metadata capture for wet lab scientists and bioinformaticians to create a single source of truth for their experiments and bioinformatics results. Read more below.

Bulk RNA-seq continues to be the workhorse assay for most of the biotech industry. However, the data infrastructure for the storage, analysis and reporting of results is often lacking for this experiment. Disproportionate attention is placed on the bioinformatics workflows that process sequencing reads into gene counts, at the expense of the upstream and downstream infrastructure necessary for collaboration, compliance and future analysis. This includes: - Central and structured capture of data and experimental metadata - Reports that are accessible and usable by scientists - Links between raw experimental data, bioinformatics workflows, processed counts and final reports Bulk RNA-seq analysis is as much a human engineering project as a software problem and demands infrastructure where each component is centralized and accessible to different wet and dry lab teams. Here we present the full analysis lifecycle of this experiment on LatchBio to answer the following biological questions: - What are the gene counts in my sample? - What are the differentially expressed genes between conditions? - What are the key functions, pathways or ontologies associated with key genes? Link to a long form description of the lifecycle - https://lnkd.in/dgmvw5bU

It was great to talk with Colin Ng, VP of AtlasXomics yesterday during our webinar! We’ve been fortunate to partner with AtlasXomics Inc. to create a toolset that makes their customers’ data analysis automated and accessible without sacrificing flexibility.  If you couldn’t make it to our live webinar yesterday, you can still watch the recording here: https://lnkd.in/gvdvc3nm

Last chance to register for our webinar tomorrow at 9am PST with Colin Ng, VP at AtlasXomics Inc.! Register here to learn more about their breakthrough spatial epigenome solution: https://lnkd.in/gMPmWATC

Because many classes of analysis problems in biology involve searching, manipulating or otherwise moving around large amounts of file data, progress in systems programming will have a large impact on therapeutics development and basic biology research alike. But while the pace of data generation from experimental techniques is increasing, there are actually few assays where the computational step is the bottleneck in the end to end research workflow. In industry, high quality open source tools and widespread access to cloud infrastructure are often sufficient to distill GBs of raw data into small tables of values and plots with clear biological interpretation. If we consider bulk RNA-seq and genetic sequencing as two representative examples, both have well-maintained aligners with strong consensus amongst computational biologists. So even when the data size becomes quite large, as deep WGS can yield raw files 100s of GBs in size, the use of computers is a small component of a workflow dominated by the wet lab Spatial single cell epigenetics is one such experimental technique where the existing software tools simply break and computational steps are rate limiting towards interpretable results. This assay provides the biomolecular state of tissue with geometric structure, building beautiful pictures of cell state progression, gene programs and mechanisms that elucidate disease pathogenesis and basic biology. Unfortunately, the multi-modal nature of spatial epigenetics and the volume of data generated from modern kits is actually breaking the fragmented ecosystem of tools that has emerged to process it. Most analysis workflows of this data type lean heavily on core objects and operations defined in the Seurat or Scanpy packages, popular frameworks for the exploratory analysis of single cell data. These codebases were born in academic biology labs and started as small Python and R projects for the early days of single cell biology. Spatial assays routinely generate >1M cells worth of sequencing data and this amount is doubling every few years as assay developers are shrinking the feature size on tissue slides with parallels to semiconductor manufacturing decades prior. Working with this volume of single cell data is very difficult and reveals the seams in the existing academic codebases. We are sitting down with AtlasXomics Inc., a talented group of scientists and engineers that are building these kits. If you are a strong programmer and want to make a contribution to the pace of experimental biology in practice, this is a good place to look. If you are building a biotech platform, or working in the ivory tower, want to understand what state-of-the-art spatial workflows look like and the rich biology they uncover, please tune in. https://lnkd.in/gEVjCmG2

Announcing full support for Fluent Biosciences single-cell kits on LatchBio 🧬 Over the past 6+ months, we’ve partnered with the Fluent team to empower their customers to self-serve data analysis for all single-cell kits, proving out: 2k cells 10k cells 20k cells 100k cells 1000k cells In our largest experiment, a Morris Lab PhD candidate at Washington University, St. Louis ran 1 million+ cell sequencing for cellular reprogramming. This involved processing 7 billion reads in under 72 hours on latch 🤯 (more on this soon...) Scientists are seeing a decrease in time to insight and processing costs of up to 80-95% across all kit sizes. And wet lab biologists are actually able to use it. :) Today, anyone can run PIP-Seq analysis in a way that is easy to use and requires no coding. See the link and case studies in the comments below 👇 We’re excited to continue supporting Fluent as they pioneer single-cell biology at Illimuna. Thank you for continuous support from Fluent BioSciences Inc., Kristina Fontanez, Sadie VanHorn, Yigal Agam, Aaron May-Zhang, PhD, Robert Meltzer, Jessie Matakis, Rahul Desai, Kenny Workman, Emma Krivoshein, and Aidan Abdulali. All of your involvement, development, and feedback made this possible. We're committed to continue driving down costs and making single cell biology - and bioinformatics - scalable and accessible to everyone.

AtlasXomics Inc. is creating beautiful maps at cellular resolution to reveal the epigenetic underpinnings of disease development. Creating these maps generates 100s of TBs of data and requires a vast computational infrastructure. Tune in to our webinar on Thursday, August 1st to hear from Colin Ng, VP of AtlasXomics, about how they partner with LatchBio to overcome this data challenge and deliver quicker insights to their customers. Register here to join: https://lnkd.in/gMPmWATC

We are thrilled to host a webinar with one of our partners, AtlasXomics Inc.! As a pioneer in spatial biology technologies, AtlasXomics offers the first and only comprehensive spatial epigenome solution at a cellular resolution. Their platform is now used by over 50 labs to uncover the epigenetic drivers behind key disease areas. Join us on Thursday, August 1st to hear from Colin Ng, VP at AtlasXOmics, as he discusses the development of their breakthrough platform and how they’ve utilized LatchBio to unlock key insights for their customers. We hope to see you there! Register here to tune in: https://lnkd.in/gMPmWATC