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Mölder, F.

Publications and source records attributed to Mölder, F..

2 recordsLinked to original sources

Datavzrd: Rapid programming- and maintenance-free interactive visualization and communication of tabular data

Tabular data, often scattered across multiple tables, is the primary output of data analyses in virtually all scientific fields. Exchange and communication of tabular data is therefore a central challenge. We present Datavzrd, a tool for creating portable, visually rich, interactive reports from tabular data in any kind of scientific discipline. Datavzrd unifies the strengths of currently common generic approaches for interactive visualization like R Shiny with the portability, ease of use and sustainability of plain spreadsheets. The generated reports do not require the maintenance of a web server nor the installation of specialized software for viewing and can simply be attached to emails, shared via cloud services, or serve as manuscript supplements. They can be specified without requiring imperative programming, thereby enabling rapid development and offering accessibility for non-computational scientists, unlocking the look and feel of dedicated manually crafted web applications without the maintenance and development burden. Datavzrd reports scale from small tables to thousands or millions of rows and offer the ability to link multiple related tables, allowing to jump between corresponding rows or hierarchically explore growing levels of detail.

bioinformatics↗

Rapid T cell receptor interaction grouping with ting

MotivationClustering of antigen-specific T cell receptor repertoire (TCRR) sequences is challenging. The recently published tool GLIPH aims to solve this problem. However, clustering large repertoires takes several days to weeks, making its use impractical in larger studies. In addition, the methodology used in GLIPH suffers from several shortcomings, including non-determinism, potential loss of significant antigen-specific sequences or inclusion of too many unspecific sequences. ResultsWe present an algorithm for clustering TCRR sequences that scales efficiently to large repertoires. We clustered 26 real datasets with up to 62 000 unique CDR3{beta} sequences using both GLIPH and an implementation of our method called ting. While GLIPH required multiple weeks, ting only needed about one hour for the same task. In addition, we found that in naive repertoires, where no or very few antigen-specific CDR3 sequences or clusters should exist, our method indeed selects fewer sequences. AvailabilityOur method has been implemented in Python as a tool called ting, using numpy and NetworkX. It is available on GitHub (https://github.com/FelixMoelder/ting) and on PyPI under the MIT license. Contactfelix.moelder@uni-due.de or sven.rahmann@uni-due.de

bioinformatics↗