News: Python version of COCACOLA is available now!

Address of the bookmark: https://github.com/younglululu/COCACOLA

Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, Kåre L. Nielsen and Per .H. Nielsen

Nature Biotechnology 2013, doi: 10.1038/nbt.2579

See the associated online guide for detailed information.

https://github.com/MadsAlbertsen/multi-metagenome

Address of the bookmark: https://github.com/MadsAlbertsen/multi-metagenome

Address of the bookmark: https://chunlab.wordpress.com/tetra-nucleotide-analysis/

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. You can find a more detailed description of the new version of the pipeline in NCBI Handbook chapter. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/genome/annotation_prok/

Understanding Large Language Models

LLMs are AI systems trained on extensive datasets of natural language. Their ability to model context, identify patterns, and generate coherent language has proven invaluable across domains, including bioinformatics. By fine-tuning these models on biological datasets, researchers can unlock insights into molecular biology, systems biology, and beyond.

Key Applications of LLMs in Bioinformatics

1. Annotating Biological Data

Annotating genomic and proteomic data is fundamental yet labor-intensive. LLMs streamline this process by extracting functional annotations from literature and databases, predicting gene and protein functions, and providing automated insights.

2. Mining Scientific Literature

The exponential growth of publications presents a challenge for researchers to stay updated. LLMs can process large volumes of text to extract key findings, summarize papers, and identify trends, thereby facilitating efficient literature reviews.

3. Predicting Gene and Protein Functions

By leveraging sequence data and annotations, LLMs can predict the functions of uncharacterized genes and proteins. This capability is particularly useful for studying non-model organisms and orphan genes.

4. Drug Discovery and Repurposing

LLMs enable pattern recognition across chemical, genomic, and clinical datasets, identifying novel drug candidates and repurposing existing drugs for new therapeutic targets. They can simulate interactions between drugs and biological molecules, accelerating the discovery pipeline.

5. Generating Hypotheses for Research

LLMs analyze complex datasets to propose testable hypotheses. For example, they can predict protein-protein interactions, identify regulatory motifs, or model evolutionary processes in genomes.

Advantages of LLMs in Bioinformatics

• Scalability: LLMs process massive datasets rapidly, reducing the time required for data analysis.

• Versatility: These models adapt to diverse bioinformatics tasks, from genomic annotation to network analysis.

• Contextual Insights: By synthesizing information across disparate datasets, LLMs provide integrative insights into biological systems.

Challenges in Applying LLMs

Despite their promise, LLMs face limitations:

• Data Quality and Bias: Inaccurate or biased datasets can affect model predictions, necessitating rigorous data curation.

• Interpretability: Understanding the decision-making process of LLMs remains a critical challenge, especially in high-stakes fields like genomics and medicine.

• Resource Intensity: Training and deploying LLMs require substantial computational power, which can limit accessibility.

• Ethical Concerns: Handling sensitive genomic data raises privacy and security issues, emphasizing the need for ethical guidelines.

Future Prospects

The continued development of LLMs tailored for bioinformatics promises exciting advancements. Specialized models trained on omics data, open-access platforms, and interdisciplinary collaborations will expand the utility of LLMs. Moreover, integrating LLMs with other AI technologies, such as graph neural networks and reinforcement learning, can unlock deeper biological insights.

Conclusion

Large language models are revolutionizing bioinformatics by addressing longstanding challenges in data annotation, literature mining, and function prediction. Their ability to analyze complex biological datasets efficiently positions them as indispensable tools for modern research. As bioinformatics embraces AI, the synergy between LLMs and biological sciences holds the potential to unravel the complexities of life with unprecedented precision and scale.

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

https://csgillespie.github.io/efficientR/

https://r-graphics.org/

https://rstudio-education.github.io/hopr/

https://r-pkgs.org/

https://r4ds.had.co.nz/

Address of the bookmark: https://r-graphics.org/

Features

• Linear representation of several segments of DNA
• Comparisons represented by areas between the segments (like Artemis, for example)
• Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
• Plot several subsegments of the same segment on the same line, separated by a //
• Automatic or manual placement of the segments on the plot
• Add annotations to all the lines
• Create smart, automatic annotations for genomes, based on gene names
• Add a user-generated tree
• Add a global scale or a scale to each line
• Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

Address of the bookmark: https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

• Free!
• Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
• Presentation quality graphics
  • Save as a png, pdf or svg
• History
  • What you do can be saved for the next time you use R.
  • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

• Lack of a comprehensive graphical user interface, but two do exist: However some do exist: R commander: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ and Limma-gui (microarrays) : http://bioinf.wehi.edu.au/limmaGUI/

Preparation

• (Optional) Download and save the tutorial data set from
  • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
  • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

• Project Home page
  • http://www.r-project.org/
  • Check out the ‘introduction to R’, which is a much more in depth guide .
  • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

• Mac/Linux: this is the directory where you typed in R
• PC: Change using the change working directory option