Performing these tasks requires the installation, integration, and tuning of multiple software packages, which is not trivial even for groups with extensive bioinformatics expertise. As a result, most studies rely on ad hoc pipelines based on custom scripts and intensive manual analyses, making it difficult to reproduce or extend analysis results and hampering collaboration.
useful text
level of improvement provided by MetAMOS over other assembly tools is highly dependent on the specific characteristics of the dataset being assembled
aggressive assembly approaches sometimes result in more contiguous assemblies, but often introduce errors of the most severe kind (chimeras)
There are trade-offs. - Could we ask the user if the tool should err on more contiguous vs chimeras with a set % - or summarize results from both and ask the user to choose..?
motivates our focus on fast end-to-end analysis and inclusion of multiple assembly methods, allowing the user to tailor the pipeline to their data
This could be iterated by easily with user inputs
the installation, integration, and tuning of multiple software packages, which is not trivial even for groups with extensive bioinformatics expertise.
laborious work of setting up multiple packages
the advantages of computational pipelines over ad hoc scripts, even for simple tasks, are all more apparent with increasingly complex datasets and the use of parallel processing.
the advantages of computational pipelines over ad hoc scripts, even for simple tasks, are all more apparent with increasingly complex datasets and the use of parallel processing.
why pipelines vs ad hoc scripts - track dependencies (statically inferred, DAGs) - rules reused for many files (parallelization) - data tracking (rapid development in subsets of pipeline ~ changing parameters,ie. avoid duplicate work when resuming workflows.)
Run a generative AI chatbot on Jetson Orin Nano Super Developer Kit. This chatbot features Ollama with Open WebUI, a widely used, open-source, chatbot server interface that connects to locally running LLMs.
deploying Omi - Open WebUI could be used to run a local LLM through API calls on T8 server?
Omi: Bridging the Bioinformatics Expertise Gap with a Natural Language-Driven Metagenomics Co-pilot
:😄 Monday, April 28 RAD Microbes Boot Camp 2025
AI can be a powerful tool for helping scientists dig into results and more quickly identify interesting patterns
Touch on this for introduction
omi feature idea: minor CLI tools - not pipelines
Thought process: What does this tool need as input: MSA.
Can this CLI tool make the MSA as well if the user tells it stuff? That’s too specialized -- would be nice to make an LLM tool like omi for that though
I think omi can beat seqera AI and chatGPT in this space where we identify and wrap essential CLI tools to be run by text prompts
Leave the nextflow part to seqera AI :: if it’s good enough for running pipelines
the advantages of computational pipelines over ad hoc scripts, even for simple tasks, are all more apparent with increasingly complex datasets and the use of parallel processing.
why pipelines vs ad hoc scripts - track dependencies (statically inferred, DAGs) - rules reused for many files (parallelization) - data tracking (rapid development in subsets of pipeline ~ changing parameters,ie. avoid duplicate work when resuming workflows.)
the installation, integration, and tuning of multiple software packages, which is not trivial even for groups with extensive bioinformatics expertise.
laborious work of setting up multiple packages
Eine von 30 NGOs ausgearbeitete Studie zeigt, dass die Schiffstransporte sich auf das Klima so negativ auswirken wie der Luftverkehr. Hintergrund der Veröffentlichung ist die Sitzung der Welt-Schifffahrts-Organisation MPI zur Dekarbonisierung der Schiffahrt.
Studie: https://seas-at-risk.org/publications/the-state-of-shipping-oceans-report/