🧵🧵 THREAD: #Bioinformatics applications - a deep dive into #pipelines and #workflows 🧵🧵
1. #Bioinformatics is the application of #computational techniques to the #analysis of #biological #data, such as #sequences, #structures, and #interactions, and is an essential field of modern biology and medicine.
2. #Bioinformatics has many exciting applications in various fields, such as #genetics, #genomics, #proteomics, and #metabolomics, that can provide new insights into the workings of living systems, and can help to advance science and society.
3. #pipelines and #workflows are a critical component of many #bioinformatics applications, as they allow the #automation, #organization, and #reproducibility of complex and multi-step analyses, that can involve multiple data sources, tools, and algorithms.
4. It can be implemented and executed using different #frameworks, languages, and #paradigms, such as #Nextflow, #Snakemake, and #Common #Workflow #Language, that can provide specific features, advantages, and limitations, depending on requirements.
5. Also, it can be deployed and run on different computing platforms, such as cloud, cluster, and desktop, that can provide different levels of scalability, flexibility, and performance, depending on the size, complexity, and cost of the data and analyses in the project.
6. The workflows you build can be shared and reused, through different channels, such as version control, containerization, and publication, that can provide different levels of accessibility, interoperability, and reproducibility, depending on the goals, needs, and policies.
Join us on Twitter @cloudgenomics for more information and updates on #bioinformatics #pipelines and #workflows, and be part of our community of experts, learners, and enthusiasts. #bioinformatics #computationalbiology #pipelines #workflows

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More from @CloudGenomics

Dec 11
🧵🧵 THREAD: 9 tips for successful #bioinformatics #projects 🧵🧵
1. Start by defining the aim, scope, and objectives of your project, and by identifying the #biological and #computational questions you want to answer.
2. Identify the #data sources, types, and formats, that are relevant and available for your project, and evaluate their quality, quantity, and suitability.
Read 11 tweets
Dec 10
🧵🧵 THREAD: #Bioinformatics and #AlphaFold 🧵🧵
1. #AlphaFold is a deep learning system developed by #DeepMind, to predict the 3D #structure of #proteins from their #aminoacid sequence.
2. #AlphaFold has been applied and tested extensively in the biennial #CASP (Critical Assessment of protein Structure Prediction) experiment, and has achieved state-of-the-art performance.
Read 9 tweets
Dec 9
🧵🧵 THREAD: 10 reasons why #bioinformaticians should use #Nextflow 🧵🧵
1. #Nextflow is an open-source platform for #bioinformatics #pipelines and #workflows, designed to make them #scalable, #reproducible, and #portable.
2. #Nextflow allows #bioinformaticians to write their #pipelines and #workflows in a simple and expressive domain-specific language, called #Nextflow Script.
Read 11 tweets
Dec 8
🧵🧵 THREAD: The role of #cloud #computing in #bioinformatics 🧵🧵
1. #Cloud #computing allows #bioinformaticians to access and analyze large and complex #biological #data sets in a fast, scalable, and cost-effective manner.
2. The #cloud provides a flexible and dynamic #infrastructure that can be easily adjusted to the changing needs and demands of #bioinformatics projects.
Read 8 tweets
Dec 7
🧵#Bioinformatics applied to genomics, a THREAD🧵🧵:
#Genomics is the study of the entire #genetic makeup of an #organism, including its #DNA sequence, #gene structure, and regulation of #gene #expression.
#Bioinformatics is the application of computational techniques, such as #machine #learning and data mining, to the analysis of large datasets of biological information, such as #genomic, #proteomic, and #metabolomic data.
Read 9 tweets
Dec 5
Are you interested in getting started in #bioinformatics but not sure where to begin? Here are some tips to help you get started on your journey. A THREAD🧵🧵:
Start by learning a high-level #programming language, such as #Python or #R, and familiarizing yourself with data structures and #algorithms commonly used in #bioinformatics. The #BioPython and #Bioconductor libraries are great resources for this.
Next, learn about #genomic data formats and standards, such as #FASTA, #FASTQ, and #GFF. This will allow you to effectively manipulate and analyze large-scale #genomic #datasets. The #NCBI SRA and #EBI ENA databases are great places to find real-world data to work with.
Read 12 tweets

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