🧵🧵 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.
3. #Nextflow enables #bioinformaticians to easily and quickly run their #pipelines and #workflows on any compute #infrastructure, including #cloud, #cluster, and local.
4. #Nextflow allows #bioinformaticians to easily and quickly scale their #pipelines and #workflows to large data sets and parallel executions, using tools, such as #AWS Batch and #Google #Cloud Dataproc.
5. #Nextflow enables #bioinformaticians to easily and quickly share and reuse their pipelines and #workflows, using tools, such as #GitHub and #Docker Hub.
6. #Nextflow allows #bioinformaticians to easily and quickly monitor and troubleshoot their pipelines and workflows, using tools, such as #Nextflow #Tracker and #Nextflow #Logs.
7. #Nextflow integrates seamlessly with other tools and services, such as AWS and Google Cloud, to provide a complete and powerful bioinformatics solution.
8. #Nextflow offers a rich ecosystem of community-contributed plugins and modules, to extend the capabilities and functionality of the platform.
9. #Nextflow is actively developed and supported by a vibrant and growing community of users and contributors, to ensure its continued evolution and innovation.
10. Join the cloud genomics community on Twitter @cloudgenomics and learn from our experts and users how to use Nextflow for your bioinformatics projects. #bioinformatics #computationalbiology #nextflow

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More from @CloudGenomics

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.
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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.
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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
Dec 4
🧵#Bioinformatics applications, a THREAD🧵🧵:
One of the key areas of #bioinformatics is the application of #computational techniques to the analysis of large #datasets of biological information, such as #genomic, #proteomic, and #metabolomic data.
#Genomic data provides information about the entire genetic makeup of a #biological system, including the #sequences of all its genes and the #regulation of their e#xpression.
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Dec 3
Thread explaining a complete pipeline for #RNA-seq analysis 🧵
RNA-seq is a powerful technique that allows researchers to study the expression of genes at a global level. The RNA-seq analysis pipeline typically involves several different steps, including:
1. Quality control and filtering of the raw RNA-seq data
2. Alignment of the reads to the reference genome
3. Assembly of the aligned reads into transcripts
4. Quantification of gene and transcript expression levels
Read 8 tweets

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