KNIME for reproducible cross-domain analysis of life science data

Experiments in the life sciences often involve tools from a variety of domains such as mass spectrometry, next generation sequencing, or image processing. Passing the data between those tools often involves complex scripts for controlling data flow, data transformation, and statistical analysis. Such scripts are not only prone to be platform dependent, they also tend to grow as the experiment progresses and are seldomly well documented, a fact that hinders the reproducibility of the experiment. Workflow systems such as KNIME Analytics Platform aim to solve these problems by providing a platform for connecting tools graphically and guaranteeing the same results on different operating systems. As an open source software, KNIME allows scientists and programmers to provide their own extensions to the scientific community. In this review paper we present selected extensions from the life sciences that simplify data exploration, analysis, and visualization and are interoperable due to KNIME's unified data model. Additionally, we name other workflow systems that are commonly used in the life sciences and highlight their similarities and differences to KNIME.publishe

Metabolomics Data Processing Using OpenMS

This chapter describes the open-source tool suite OpenMS. OpenMS contains more than 180 tools which can be combined to build complex and flexible data-processing workflows. The broad range of functionality and the interoperability of these tools enable complex, complete, and reproducible data analysis workflows in computational proteomics and metabolomics. We introduce the key concepts of OpenMS and illustrate its capabilities with a complete workflow for the analysis of untargeted metabolomics data, including metabolite quantification and identification

Open Source Tools for Biological Image Analysis

Visiting the Bio Imaging Search Engine (BISE) (Bio, BISE, Engine, http://biii.eu/, Imaging, Search) website at the time of writing this article, almost 1200 open source assets (components, workflows, collections) were found. This overwhelming range of offer difficults the fact of making a reasonable choice, especially to newcomers. In the following chapter, we briefly sketch the advantages of the open source software (OSS) particularly used for image analysis in the field of life sciences. We introduce both the general OSS idea as well as some programs used for image analysis. Even more, we outline the history of ImageJ as it has served as a role model for the development of more recent software packages. We focus on the programs that are, to our knowledge, the most relevant and widely used in the field of light microscopy, as well as the most commonly used within our facility. In addition, we briefly discuss recent efforts and approaches aimed to share and compare algorithms and introduce software and data sharing good practices as a promising strategy to facilitate reproducibility, software understanding, and optimal software choice for a given scientific problem in the future

High-Content Analyses of Vaccinia Plaque Formation

Vaccinia virus plaque assays are employed for quantification of virus titer through serial dilution of virus on a monolayer of cells. Once the virus titer is diluted enough to allow for only few cells of the monolayer to be infected, clonal spread of infection can be detected by observing the lesion in the cell monolayer or using virus specific staining methods. Beyond simple titration, plaque formation bares priceless underlying information about subtle virus-host interactions and their impact on virus spread during multiple rounds of infection. These include virus infectivity, the mode of virus spread, virus replication rate and spatiotemporal spread efficacy. How this underlying information can be harnessed using a high-content imaging setup is discussed here

Indirect comparison of linvoseltamab versus teclistamab for triple-class exposed (TCE) relapsed/refractory multiple myeloma (RRMM)

7560 Background: No head-to-head clinical trials have compared effectiveness of anti-BCMA×CD3 bispecific antibodies for TCE RRMM. This analysis compared efficacy of linvoseltamab vs teclistamab via an unanchored matching-adjusted indirect comparison (MAIC). Methods: A MAIC was deemed feasible after excluding 10 patients (pts) with prior BCMA antibody–drug conjugate exposure from LINKER-MM1 (linvoseltamab) to match MajesTEC-1 (teclistamab) criteria. Pt-level data from LINKER-MM1 (107 pts receiving 200 mg in Phase 1/2, data cut-off [DCO] 9/2023, median follow-up 11.1 months [mos]) and published data from MajesTEC-1’s efficacy population (150 pts, DCO 11/2021, median follow-up 9.8 mos) were analyzed. LINKER-MM1 pts were weighted to match key baseline characteristics in MajesTEC-1 (cytogenetic risk, age, refractory status, ISS stage, ECOG score, extramedullary disease/plasmacytoma status) selected via a prespecified algorithm (Kumar et al., 2023). Objective response rate (ORR), very good partial response or better (≥VGPR), complete response or better (≥CR), and minimal residual disease (MRD) negativity (- [at 10-5 threshold]) rates, duration of response (DOR), progression-free survival (PFS), and overall survival (OS) were compared. Odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs) were reported before and after matching; a sensitivity analysis included all LINKER-MM1 200 mg pts (n=117). Results: Effective sample size for linvoseltamab was 82 after matching and baseline characteristics were balanced with MajesTEC-1. Before and after matching, linvoseltamab exhibited higher ORR, ≥VGPR, ≥CR, and MRD(-) rates, with significant differences in ≥CR. Linvoseltamab had significantly longer PFS and a trend toward longer OS and DOR (Table). Sensitivity analysis results were similar. Conclusions: The results suggest potentially greater efficacy for linvoseltamab vs teclistamab for all outcomes, highlighting its potential as a highly effective treatment option for TCE RRMM. [Table: see text