AI Tools for Scientific Research | Differential Expression Analysis

Differential Expression Analyses & Bioinformatics
Scientific Literature Review - AI Tools
Research | Science | Writing | Everyday - AI Tools
Data Analytics- AI Tools
Bioinformatics- Multiomics-AI Tools

Differential Expression Analysis on Array Data in Limma R

  1. https://sbc.shef.ac.uk/geo_tutorial/tutorial.nb.html#Introduction

Analyzing data from GEO - Work in Progress |Mark Dunning | Last modified: 30 June 2020

  1. https://kasperdanielhansen.github.io/genbioconductor/html/limma.html#more-on-the-design

  1. https://bioconductor.org/packages/release/workflows/vignettes/maEndToEnd/inst/doc/MA-Workflow.html

    An end-to-end workflow for differential gene expression using Affymetrix microarrays

    Bernd Klaus1 and Stefanie Reisenauer2

    1EMBL Heidelberg, Meyerhofstrasse 1, 69117 Heidelberg, Germany, bernd.klaus@embl.de
    2EMBL Heidelberg, Meyerhofstrasse 1, 69117 Heidelberg, Germany, steffi.reisenauer@tum.de

  2. https://bioconductor.org/packages/release/bioc/html/limma.html https://bioconductor.org/packages/release/workflows/html/arrays.html

    https://bioconductor.org/books/release/

    https://bioconductor.org/help/package-vignettes/

    https://bioconductor.org/help/course-materials/

    https://bioconductor.org/help/community/

https://support.bioconductor.org/t/Tutorials/

https://bioinf.wehi.edu.au/limma/

https://www.youtube.com/@bioconductor/featured

  1. https://gtk-teaching.github.io/Microarrays-R/ Introduction to gene expression microarray analysis in R and Bioconductor

  2. https://online.stat.psu.edu/stat555/node/12/ Lesson 7: Linear Models for Differential Expression in Microarray Studies

  3. https://www3.nd.edu/~steve/Rcourse/Lecture11v1.pdf

  4. https://pluto.bio/blog/differential-expression-analysis-techniques-and-benefits Differential Expression Analysis: Understanding the Techniques and Benefits in Research

  5. https://rdrr.io/bioc/limma/man/06linearmodels.html 06linearmodels: Topic: Linear Models for Microarrays In limma: Linear Models for Microarray Data

  6. https://rdrr.io/bioc/limma/ limma: Linear Models for Microarray Data | Data analysis, linear models, and differential expression for microarray data.

  7. https://rdrr.io/bioc/limma/man/selectmodel.html selectmodel: Select Appropriate Linear Model In limma: Linear Models for Microarray Data

  8. https://rdrr.io/bioc/limma/man/modelMatrix.html modelMatrix: Construct Design Matrix In limma: Linear Models for Microarray Data

  9. https://support.bioconductor.org/p/77543/ Processing two-colour array data from GEO using limma when no raw data are available

    Differential Expression Analysis on RNA-seq Data in Limma R

  10. https://ucdavis-bioinformatics-training.github.io/2018-June-RNA-Seq-Workshop/thursday/DE.html Differential Expression with Limma-Voom 2018 UC Davis Bioinformatics Training

  11. https://ucdavis-bioinformatics-training.github.io/2022-April-GGI-DE-in-R/data_analysis/DE_Analysis_with_quizzes_fixed#:~:text=Differential%20Expression%20Analysis%20with%20limma,data%20for%20use%20with%20limma. Differential Expression Analysis in R | UC Davis Bioinformatics Core 2022

  12. https://bioconductor.org/packages/release/workflows/vignettes/RNAseq123/inst/doc/designmatrices.html

    A guide to creating design matrices for gene expression experiments

  13. https://star-protocols.cell.com/protocols/931 Analysis workflow of publicly available RNA-sequencing datasets 2021

  14. https://www.ebi.ac.uk/training/online/courses/functional-genomics-ii-common-technologies-and-data-analysis-methods/rna-sequencing/performing-a-rna-seq-experiment/data-analysis/differential-gene-expression-analysis/#:~:text=There%20are%20different%20methods%20for,when%20choosing%20an%20analysis%20method

    EMBL-EBI Training | Functional Genomics II | Differential Expression Analysis

  15. https://academic.oup.com/nar/article/43/7/e47/2414268 Limma powers differential expression analyses for RNA-sequencing and microarray studies 2015 Matthew E. Ritchie, Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, Gordon K. Smyth

  16. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-91#Sec7 A comparison of methods for differential expression analysis of RNA-seq data 2013 Charlotte Soneson & Mauro Delorenzi

  17. https://www.nature.com/articles/s41598-023-43686-7#data-availability Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits 2023 Shizhen Tang, Aron S. Buchman, Yanling Wang, Denis Avey, Jishu Xu, Shinya Tasaki, David A. Bennett, Qi Zheng & Jingjing Yang

  1. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02337-8#Sec13 Best practices on the differential expression analysis of multi-species RNA-seq 2021 Matthew Chung, Vincent M. Bruno, David A. Rasko Christina A. Cuomo, José F. Muñoz, Jonathan Livny, Amol C. Shetty, Anup Mahurkar & Julie C. Dunning Hotopp

  2. https://www.sciencedirect.com/science/article/pii/S200103702100221XRobustness of differential gene expression analysis of RNA-seq 2021

    Author links open overlay panelA. Stupnikov a b 1, C.E. McInerney b 1, K.I. Savage b, S.A. McIntosh b, F. Emmert-Streib c, R. Kennedy b, M. Salto-Tellez b, K.M. Prise b, D.G. McArt b

  1. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190152 2017

  2. RNA-Seq differential expression analysis: An extended review and a software tool |Juliana Costa-Silva, Douglas Domingues, Fabricio Martins Lopes

  3. https://blog.devgenius.io/differential-gene-expression-analysis-using-limma-step-by-step-358da9d41c4e

    Differential gene expression analysis using Limma-step by-step

    Data GOAT |Published in Dev Genius | 7 min read | Feb 11

  4. https://bioconnector.github.io/workshops/r-rnaseq-airway.html Count-Based Differential Expression Analysis of RNA-seq Data

    Differential Expression Analyses : Multiomics

    1. https://rpubs.com/jrgonzalezISGlobal/transcriptomic_analyses

    Transciptomic analysis using limma and limma + voom pipelines

    Juan R Gonzalez1*

    1Bioinformatics Research Group in Epidemiology, Barcelona Institute for Global Health, Spain

    *juanr.gonzalez@isglobal.org

    May, 2021

    2. https://pnnl-comp-mass-spec.github.io/proteomics-data-analysis-tutorial/dea-with-limma.html

    https://pnnl-comp-mass-spec.github.io/proteomics-data-analysis-tutorial/

    Proteomics Data Analysis in R/Bioconductor Tyler Sagendorf May 27, 2022

    3.https://www.researchgate.net/publication/311242925_RNA-seq_analysis_is_easy_as_1-2-3_with_limma_Glimma_and_edgeR

    RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

    November 2016 DOI: 10.12688/f1000research.9005.2 Charity W Law, Monther Alhamdoosh, Shian Su, Show all 5 authors Matthew Ritchie

    4.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6554642/

    Differential Expression and Functional Analysis of High-Throughput -Omics Data Using Open Source Tools 2019 Moritz Kebschull, Melanie Julia Fittler, Ryan T. Demmer, and Panos N. Papapanou

Resources on Limma R

  1. https://bioconductor.org/packages/devel/bioc/manuals/limma/man/limma.pdf Package ‘limma’ December 8, 2023 Version 3.59.1 Date 2023-10-30 Title Linear Models for Microarray Data Description Data analysis, linear models and differential expression for microarray data.

  2. https://academic.oup.com/nar/article/43/7/e47/2414268?login=false 2015 limma powers differential expression analyses for RNA-sequencing and microarray studies Matthew E. Ritchie, Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, Gordon K. Smyth

  3. https://rdrr.io/search?q=limma

  4. https://rdrr.io/bioc/limma/man/ Man pages for limma | Linear Models for Microarray Data

  5. https://rdrr.io/bioc/limma/api/ API for limma | Linear Models for Microarray Data

  6. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7873980/ 2020 A guide to creating design matrices for gene expression experiments

  7. https://www.youtube.com/watch?v=Hg1abiNlPE4 R Tutorial - Limma Package - DataCamp

  8. https://web.mit.edu/~r/current/arch/i386_linux26/lib/R/library/limma/html/01Introduction.html Introduction to limma package

Differential Expression Analysis - Data Visualization

  1. https://bioconductor.org/packages/devel/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html

EnhancedVolcano:

publication-ready volcano plots with enhanced colouring and labeling

Kevin Blighe, Sharmila Rana, Myles Lewis 2023-10-24

  1. https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html Publication-ready volcano plots with enhanced colouring and labeling

  1. https://github.com/kevinblighe/EnhancedVolcano/blob/master/vignettes/EnhancedVolcano.Rmd

  2. https://github.com/kevinblighe https://github.com/kevinblighe/EnhancedVolcano vignettes/EnhancedVolcano.Rmd

  3. https://www.rdocumentation.org/packages/pheatmap/versions/1.0.12/topics/pheatmap

    pheatmap (version 1.0.12) pheatmap: A function to draw clustered heatmaps. A function to draw clustered heatmaps where one has better control over some graphical parameters such as cell size, etc.

  4. https://davetang.org/muse/2018/05/15/making-a-heatmap-in-r-with-the-pheatmap-package/ Making a heatmap in R with the pheatmap package

Extra Resources

  1. https://swcarpentry.github.io/shell-novice/index.html Introduction to the Unix environment

  2. https://www.r-tutor.com/r-introduction Basic R concepts

  3. https://support.bioconductor.org/

  4. https://rpubs.com/

  5. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03932-5 2021 GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

  6. https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-021-07370-2 2021 GCSscore: an R package for differential gene expression analysis in Affymetrix/Thermo-Fisher whole transcriptome microarrays

  7. https://genviz.org/module-04-expression/0004/02/01/DifferentialExpression/ Differential Expression with DEseq2

Extra Reading

  1. https://ologyjournals.com/beij/beij_00001.php Commonly used statistical methods for detecting differential gene expression in microarray experiments 2017

  2. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04438-4 Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data Takayuki Osabe, Kentaro Shimizu & Koji Kadota

  3. https://academic.oup.com/bioinformatics/article/37/Supplement_1/i34/6319701 2021 Statistical approaches for differential expression analysis in metatranscriptomics Yancong Zhang, Kelsey N Thompson, Curtis Huttenhower, Eric A Franzosa

  4. https://assets.researchsquare.com/files/rs-2018316/v2/40430b2b-b23d-4116-872b-8fdd90e4bbb5.pdf?c=1663090151 Systematic benchmarking of statistical methods to assess differential expression of circular RNAs

    Alessia Buratin University of Padova Department of Molecular Medicine: Universita degli Studi di Padova Dipartimento di Medicina Molecolare https://orcid.org/0000-0003-2461-750X Stefania Bortoluzzi University of Padova Department of Molecular Medicine: Universita degli Studi di Padova Dipartimento di Medicina Molecolare https://orcid.org/0000-0001-8240-3070 Enrico Gaffo (  enrico.gaffo@unipd.it ) University of Padova Department of Molecular Medicine: Universita degli Studi di Padova Dipartimento di Medicina Molecolare https://orcid.org/0000-0001-6338-7677

  5. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-603#Sec2 2010 Content-based microarray search using differential expression profiles

  6. https://gist.github.com/ahmedmoustafa/5520526 Gene Expression using R

  7. https://docs.rc.fas.harvard.edu/wp-content/uploads/2012/11/Microarray_with_R_and_bioconductor.pdf 2012 Microarray Analysis with R Bioconductor

  8. https://www.youtube.com/watch?v=NGCjdsXu3bk Microarray normalization and Differential Expression using R

Other Resources

  1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055218/ Dream: powerful differential expression analysis for repeated measures designs 2021 Gabriel E Hoffman1,2,3 and Panos Roussos1,2,3,4

  2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8754388/ 2022 Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform Itamar José Guimarães Nunes, Mariana Recamonde-Mendoza, and Bruno César Feltes

  3. https://www.mdpi.com/2227-9059/11/4/1230 2023 OMyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders by Daniel Pérez-Rodríguez, Roberto Carlos Agís-Balboa, Hugo López-Fernández

  4. https://deepblue.lib.umich.edu/bitstream/handle/2027.42/146768/12859_2018_Article_2531.pd?sequence=1 miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase

  5. https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad093/7221568?login=false 2023 circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging Markus Hoffmann, Leon Schwartz, Octavia-Andreea Ciora, Nico Trummer, Lina-Liv Willruth, Jakub Jankowski, Hye Kyung Lee, Jan Baumbach, Priscilla A Furth, Lothar Hennighausen ... Show more

  6. https://www.bioinformatics.babraham.ac.uk/training/10XRNASeq/R%20packages%20for%20SCRNA.pdf Analysing Single-Cell RNA-Seq with R v2022-05 Simon Andrews simon.andrews@babraham.ac.uk

  7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8308052/ 2021 CircIMPACT: An R Package to Explore Circular RNA Impact on Gene Expression and Pathways Alessia Buratin,1,2 Enrico Gaffo,2 Anna Dal Molin,2 and Stefania Bortoluzzi2,3,*

  8. https://academic.oup.com/nargab/article/3/4/lqab117/6481213?login=false 2021 miRetrieve—an R package and web application for miRNA text mining

    Julian Friedrich, Hans-Peter Hammes, Guido Krenning

  9. https://www.frontiersin.org/articles/10.3389/fgene.2020.00548/full 2020 Rcirc: An R Package for circRNA Analyses and Visualization

  10. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4788200/ 2016 MiRComb: An R Package to Analyse miRNA-mRNA Interactions. Examples across Five Digestive Cancers Maria Vila-Casadesús, Meritxell Gironella, and Juan José Lozano

  11. https://github.com/topics/snrna-seq

  12. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7285417/ 2020 FcircSEC: An R Package for Full Length circRNA Sequence Extraction and Classification Md. Tofazzal Hossain, Yin Peng, Shengzhong Feng, and Yanjie Wei

  13. https://academic.oup.com/bioinformatics/article/37/20/3604/6276428?login=false 2021 TimiRGeN: R/Bioconductor package for time series microRNA–mRNA integration and analysis K Patel, S Chandrasegaran, I M Clark, C J Proctor, D A Young, D P Shanley

  14. https://link.springer.com/protocol/10.1007/978-1-0716-1581-2_22 2021 Bioinformatic Analysis of Circular RNA Expression

    Enrico Gaffo, Alessia Buratin, Anna Dal Molin & Stefania Bortoluzzi

  1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3654997/ 2013 MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data Guy N Brock, Partha Mukhopadhyay, Vasyl Pihur, Cynthia Webb, Robert M Greene, and M Michele Pisano2,3

  1. https://www.mdpi.com/2079-7737/11/10/1411 2022 CiberAMP: An R Package to Identify Differential mRNA Expression Linked to Somatic Copy Number Variations in Cancer Datasets

  1. https://academic.oup.com/jxb/article-abstract/74/17/4949/7189801?redirectedFrom=fulltext&login=false 2023

    JustRNA: a database of plant long noncoding RNA expression profiles and functional network Kuan-Chieh Tseng, Nai-Yun Wu, Chi-Nga Chow, Han-Qin Zheng, Chin-Yuan Chou, Chien-Wen Yang, Ming-Jun Wang, Song-Bin Chang, Wen-Chi Chang

Scientific Literature Reviews - AI Tools

Several excellent AI tools can help researchers conduct scientific literature reviews in 2023, each offering unique features and functionalities. Here's a breakdown of some of the best:

For Efficient Literature Search and Discovery:

Dimensions.ai:

  • Strengths: Covers a vast database of research publications (over 130 million) with comprehensive connections to grants, datasets, clinical trials, patents, and policy documents. Offers powerful search features, including full-text, filters, and citation analysis. Free for personal use.

  • Weaknesses: The interface can be complex for new users. AI capabilities primarily focus on data analysis and visualization, less on literature summarization.

Connected Papers https://www.connectedpapers.com/

  • Strengths: Creates a visual map of research connections, allowing you to explore related papers and identify key research trends. Offers filtering and search options. Free for personal use.

  • Weaknesses: Limited database compared to some other tools. Focuses primarily on visualizing research connections, less on summarization or analysis of individual papers.

Semantic Scholar https://www.semanticscholar.org/

  • Strengths: Powerful AI engine that analyzes the full text of research papers and identifies key concepts, relationships, and trends. Offers advanced search and filtering options.

  • Weaknesses: Limited access to full-text papers. Can be overwhelming for new users due to the advanced features.

Paper Digest https://www.paper-digest.com/

  • Strengths: Provides concise summaries of research papers, allowing you to quickly grasp the key findings. Offers personalized recommendations and integrates with other research tools.

  • Weaknesses: Limited database compared to other tools. Focuses primarily on summarization, lacks advanced features like citation analysis or visualization.

Elicit.ai:

  • Strengths: Combines AI with human expertise to provide personalized research assistance. Offers services like literature review, competitor analysis, and grant writing.

  • Weaknesses: Paid service with varying pricing plans. Limited access to some features without a subscription.

Scite.ai:

  • Strengths: Analyzes citations and research trends to help you discover relevant papers and understand their impact. Offers "semantic search" for nuanced queries. Integrates with other research tools.

  • Weaknesses: Limited database compared to some other tools. Focuses primarily on citation analysis and research trends, less on individual paper summarization.

Consensus.ai:

  • Strengths: Analyzes the scientific literature to identify emerging trends and areas of consensus. Offers visualization tools to explore research landscapes.

  • Weaknesses: Relatively new tool with limited features compared to others. Focuses on identifying consensus, less on individual paper summarization.

Scite.ai

This groundbreaking AI tool analyzes citations and research trends, helping you discover relevant papers and understand their impact within the broader scientific landscape. Its "semantic search" feature allows for nuanced queries, making it easier to find specific information.

ResearchRabbit https://www.researchrabbit.ai/

This tool acts as a personal research assistant, providing literature recommendations and alerts based on your interests. Its "music streaming" feature lets you quickly skim through abstracts and identify relevant papers.

Iris.ai

This AI-powered platform helps you find relevant research articles and navigate complex scientific topics. It utilizes natural language processing to understand your research interests and deliver personalized results.

Bard https://bard.google.com/

Google ScholarSummarization, writing assistance, information extraction, Keyword search, filters, Leverage Google Scholar's vast database, summarize and assist writing. Limited access to full text, still under development. Generate summaries of research papers on a specific topic.

Research, Writing, and Everyday Use - AI Tools

For Summarization and Analysis of Research:

Scholarcy https://www.scholarcy.com/

This tool reads and summarizes research papers, book chapters, and reports, saving you valuable time and effort. It also allows you to assess the relevance of documents quickly.

Quillbot https://quillbot.com/

This AI-powered writing assistant can summarize and paraphrase research papers, helping you quickly grasp the key findings. It can also improve your writing style and ensure clarity.

Trinka: https://www.trinka.ai/

This tool offers comprehensive research assistance, including summarizing articles, identifying relevant sections like methods and results, and checking for plagiarism and citations.

For Citation Management and Collaboration:

Mendeley https://www.mendeley.com/

This popular reference management tool allows you to organize your research papers, annotate them, and collaborate with colleagues. It integrates with Scite and other AI tools to enhance your research workflow.

Zotero https://www.zotero.org/

This free and open-source reference manager allows you to store and organize your research papers, generate bibliographies, and collaborate with others.

ReadCube https://www.readcube.com/en/

This AI-powered platform helps you organize your research and collaborate with colleagues. It integrates with various research tools and offers features like literature search, annotation, and citation management.

Additional Tools to Consider:

Lexalytics Semantria https://www.lexalytics.com/

This tool analyzes the sentiment and tone of research articles, helping you understand the author's perspective and potential biases.

Writefull https://www.writefull.com/

This writing assistant uses AI to help you paraphrase sentences, rephrase sentences, and check for plagiarism, ensuring your writing is clear and original.

Other helpful Tools for Researchers:

Academic Phrase Banks:

For Everyday use:

Grammarly Premium:

  • Grammar and spelling checking: This tool helps ensure your writing is free from grammatical errors and typos, which can be crucial for scientific writing.

  • Clarity and conciseness suggestions: Grammarly can suggest ways to improve the clarity and conciseness of your writing, which is important for scientific communication.

  • Plagiarism detection: This feature helps prevent accidental plagiarism, which can be a serious offense in academia.

Monica AI:

  • Summarization of scientific papers: This tool can help you quickly grasp the main points of a scientific paper, saving you time and effort.

  • Citation management: Monica AI can help you keep track of your references and generate citations in the correct format.

  • Research topic suggestions: This tool can help you generate new research ideas based on your existing interests and reading history.

Sider AI:

  • Literature search and recommendation: This tool can help you find relevant scientific literature for your research topic.

  • Automatic data extraction: Sider AI can extract key data points from scientific papers, saving you time and effort.

  • Data visualization: This tool can help you create clear and concise data visualizations to communicate your findings.

ChatGPT4Google:

  • Generating text based on your prompts: This tool can help you quickly generate drafts of research proposals, grant applications, and other scientific documents.

  • Answering questions about scientific concepts: ChatGPT4Google can provide summaries and explanations of complex scientific concepts.

  • Paraphrasing and summarizing text: This tool can help you rephrase and summarize scientific text in your own words.

Max AI:

  • Research collaboration platform: This tool helps researchers connect and collaborate on research projects.

  • Data analysis and visualization: Max AI provides tools for analyzing and visualizing scientific data.

  • Grant writing assistance: This tool can help you write and submit grant proposals.

Frontdoor AI:

  • Scientific literature review automation: This tool can automate the process of reviewing scientific literature, saving you time and effort.

  • Research topic identification: Frontdoor AI can help you identify new research topics based on your existing research interests.

  • Research paper writing assistance: This tool can help you write scientific papers more efficiently and effectively.

  • https://hemingwayapp.com/

  • https://www.powerthesaurus.org/

  • https://prowritingaid.com/

  • https://www.wordtune.com/

  • https://www.scribens.com/

  • For basic clarity and conciseness: Hemingway Editor

  • For vocabulary expansion and precision: Power Thesaurus

  • For comprehensive grammar and style checks: ProWritingAid

  • For quick rephrasing and generating new ideas: Wordtune

  • For professional editing and proofreading: Scribens

Data Science AI Tools

EinBlick https://www.einblick.ai/

Einblick is an AI-native data notebook that can write and fix code, create charts, build models, and more. It allows users to mix Python and SQL in one workflow, and its AI agent, Einblick Prompt, can solve data problems based on natural language requests. The platform features a visual canvas for data science, interactive components, and a cloud-based environment to save time and resources. Einblick also supports collaborative work and offers secure and scalable solutions for teams of all sizes, with SOC 2 Type II compliance.

TinyBio https://ai.tinybio.cloud/

tinybio is a collection of tools designed to assist scientists. The platform offers a chatbot for assistance with bioinformatics complexities, tools to enhance workflow, and a smart lab notebook for organizing the computing environment without the need for migration.

The chatbot in AI by tinybio helps scientists by providing assistance with ideation, debugging, and navigating bioinformatics complexities. It serves as a helpful tool for scientists to overcome challenges and find solutions related to their work in the field of bioinformatics.

RTutor - https://rtutor.ai/

RTutor is an AI-based app that translates natural language into R code for data analysis. Users can upload a data file and request analyses in English, receiving results as an HTML report. The app uses OpenAI's language model and allows for voice input. However, the generated code may contain errors, so caution is advised. RTutor is primarily for individuals with some R coding experience and aims to expedite the coding process. It is not intended to replace statisticians or data scientists. The app is freely available for non-profit organizations and has a monthly usage limit.

Chatlize- https://chatlize.ai/

The AI chat system allows users to upload data files of any format and interact with the data through prompts. Users can request a snippet of the data, receive information about the data, and request summary statistics. The system can analyze tabular data, list columns, and specify categorical ones. Users can progressively analyze the data in subsequent prompts and download a report, prompts, source code, or data objects upon completion. For more details, users can refer to the YouTube video or file bug reports on GitHub. For collaboration, they can email gexijin@gmail.com.

https://rosalind.info/problems/locations/ Rosalind- Educational Community

Rosalind is a platform for learning bioinformatics and programming through problem solving. It offers different tracks for learning, including the Python Village for beginners, the Textbook Track for accompanying Bioinformatics Algorithms book, the Bioinformatics Stronghold for algorithm implementation, the Bioinformatics Armory for using existing tools, and the Algorithmic Heights for introductory algorithms. Each track provides exercises and challenges to help users learn and practice bioinformatics and programming skills.

KDB.AI

is a vector database that enhances Generative AI applications with contextual search at scale. It provides a simple and scalable solution for handling complex data, allowing developers to turn unstructured data into actionable insights with accuracy and speed. KDB.AI offers intuitive querying options, scalability for billion vector searches, optimized prefiltering, and real-time processing capabilities. It is especially beneficial for language models, as it extends knowledge bases for LLMs and enables the development of enterprise-grade Generative AI solutions. KDB.AI offers fast performance, integration with popular tools, time-aware analysis, filterable metadata, support for multi-modal data types, and efficient data compression without the need for GPU.

Microba https://microba.com/microbiome-research/bioinformatics/

Microba's advanced bioinformatics uses metagenomics to accurately identify species, genes, and pathways in the microbiome. Their bioinformatics leverages a curated genome database and achieves superior accuracy in species and functional profiling. It can identify more species and has high-resolution signals, capturing the diversity of the microbiome using the GTDB taxonomy. The solution has been validated against competing metagenomic profilers and offers an end-to-end analysis platform for microbiome research.

Microba's bioinformatics achieves superior accuracy in species and functional profiling through several key factors. They leverage a curated genome database, which allows for highly accurate taxonomic and functional profiling, microbial genome recovery, and strain tracking. Their approach has been validated against competing metagenomic profilers and has shown superior performance in species identification with a significantly lower false discovery rate compared to other classifiers[^1^]. Additionally, Microba's bioinformatics can identify more species, achieving an average coverage of 85% and up to 95% of sequencing reads assigned to species in a typical fecal sample. This high-resolution identification is made possible by capturing the diversity of the microbiome using the GTDB taxonomy.

Bioinformatics AI Tools in 2023: A Landscape Overview

Bioinformatics, the intersection of biology and computer science, is experiencing a surge in AI-powered tools, revolutionizing research and development across various areas. Here's a glimpse into the current bioinformatics AI landscape:

Drug Discovery and Development:

  • DeepChem: This open-source platform utilizes deep learning for small-molecule drug discovery, optimizing lead identification and target selection.

  • AtomNet: This AI tool predicts protein-ligand binding affinities, aiding in drug design and repurposing.

  • BenevolentAI: This platform combines AI with bioinformatics to identify promising drug candidates and predict their clinical outcomes.

Genome Analysis and Interpretation:

  • DeepVariant: This Google AI tool utilizes deep learning for high-accuracy variant calling from DNA sequencing data.

  • Sentieon: This platform applies AI algorithms for variant calling and analysis, accelerating genomics research.

  • Interpret Genomics: This AI-powered platform helps researchers understand the functional implications of genomic variants.

Protein Structure Prediction and Function:

  • AlphaFold: This AI tool from DeepMind predicts protein structures with remarkable accuracy, opening new avenues for protein engineering and drug discovery.

  • RoseTTAFold: This open-source platform utilizes protein structure modeling algorithms to predict protein structures, accelerating functional characterization.

  • ProtTrans: This AI tool predicts protein-protein interactions, crucial for understanding cellular processes and disease mechanisms.

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BigOmics Analytics: Omics Data Analysis Software

Website: https://bigomics.ch/

Overview:

BigOmics Analytics is a software platform designed for facilitating the analysis of omics data. It offers various tools and functionalities for researchers to:

  • Import and manage large datasets: Supports various data formats commonly used in omics research, including genomics, transcriptomics, proteomics, and metabolomics data.

  • Perform exploratory data analysis: Provides tools for data visualization, statistical analysis, and dimensionality reduction.

  • Build and run data analysis pipelines: Allows users to chain different analysis steps together for efficient workflows.

  • Integrate with external tools: Offers plugins and APIs for integrating with other bioinformatics software and databases.

  • Generate publication-ready results: Produces high-quality figures and reports for presentations and publications.

Key Features:

  • User-friendly interface: Designed to be accessible to researchers with diverse technical backgrounds.

  • Omics-specific tools: Includes pre-built analysis tools tailored to specific omics data types.

  • Scalability: Capable of handling large datasets with high efficiency.

  • Reproducible analysis: Enables users to document and share their workflows for reproducibility.

  • Open-source core: Encourages community development and customization.

Benefits:

  • Reduces time and effort in data analysis: Streamlining workflows and automating tasks.

  • Improves data analysis accuracy: Utilizing pre-built tools and reducing manual steps.

  • Facilitates data exploration and discovery: Providing powerful visualization and analysis capabilities.

  • Enhances scientific collaboration: Enabling sharing of data and workflows.

Target Users:

  • Researchers working with omics data in various fields, including genomics, proteomics, metabolomics, and systems biology.

  • Bioinformaticians and computational biologists developing data analysis pipelines.

  • Scientists in academia and industry involved in drug discovery, personalized medicine, and other areas.

Pricing:

BigOmics offers both free and paid plans with different levels of features and support. The free plan provides access to basic functionalities, while paid plans offer additional features such as larger data storage, increased processing power, and priority support.

Overall, BigOmics Analytics is a powerful and versatile platform that can significantly enhance the efficiency and accuracy of omics data analysis. It is a valuable tool for researchers in various fields who are looking to gain deeper insights into their data and accelerate their research progress.

Here are some additional resources that you may find helpful:

Seqera: Modern Biotech Stack for Running Robust, High-Throughput Computing

Website: https://seqera.io/

Overview:

Seqera is a platform designed to facilitate the development and execution of data pipelines for scientific research. It's particularly well-suited for biotechnology and life sciences applications involving large datasets and complex workflows. Seqera offers a range of features and benefits, including:

  • Simplified pipeline development: Provides a user-friendly interface for building and managing data pipelines, making it accessible to researchers without extensive programming experience.

  • Scalable and efficient data processing: Utilizes next-generation technologies like containers and GPUs to ensure efficient data processing even for large workloads.

  • Collaboration and reproducibility: Enables secure sharing and collaboration on pipelines among researchers, promoting transparency and reproducibility of research results.

  • Compliance and auditability: Provides predictable and auditable pipeline execution, ensuring adherence to regulatory requirements.

  • Open-source foundation: Built on open-source technologies like Nextflow, promoting community engagement and customization.

Key Features:

  • Modular and flexible pipeline construction: Enables researchers to easily build and customize pipelines with various components and tools.

  • Cloud-based execution: Leverages the scalability and flexibility of cloud resources for running pipelines on demand.

  • Integration with diverse tools: Offers built-in support for popular bioinformatics tools and databases, allowing integration with existing workflows.

  • Version control and reproducibility: Tracks changes to pipelines and allows for easy reproducibility of research results.

  • Community support: Provides a vibrant community of users and developers contributing to the platform's growth and improvement.

Benefits:

  • Accelerates scientific research: Streamlines data analysis and interpretation, freeing up researchers' time for innovative pursuits.

  • Reduces costs and complexity: Eliminates the need for specialized infrastructure and simplifies pipeline management.

  • Improves collaboration and transparency: Enables researchers to share and reproduce results efficiently.

  • Enhances data security and compliance: Ensures adherence to regulatory requirements and protects sensitive data.

Target Users:

  • Biotechnology researchers: Scientists involved in genomics, proteomics, transcriptomics, and other data-intensive research areas.

  • Bioinformaticians and computational biologists: Developers who build and execute complex data analysis pipelines.

  • Pharmaceutical and healthcare companies: Organizations working on drug discovery, personalized medicine, and other applications.

Pricing:

Seqera offers various pricing plans tailored to different needs, including a free tier for individual users and academic research.

Overall, Seqera is a valuable platform for researchers and organizations seeking to streamline scientific discovery. Its user-friendly interface, powerful features, and open-source foundation make it a compelling choice for running robust, high-throughput computing in the modern biotech era.

Here are some additional resources that you may find helpful:

DeepChem: Democratizing Deep Learning for the Life Sciences

Website: https://deepchem.io/

Overview:

DeepChem is an open-source Python library designed to facilitate the application of deep learning and machine learning to chemical and biological data. It provides a comprehensive set of tools and functionalities for researchers to:

  • Load and prepare chemical data: Supports various data formats commonly used in cheminformatics, including SMILES, SDF, and PDB.

  • Build and train machine learning models: Offers various algorithms and architectures for tasks such as molecule property prediction, reaction prediction, and virtual screening.

  • Evaluate and analyze model performance: Provides tools for visualizing and interpreting model results, ensuring their reliability and accuracy.

  • Deploy models for real-world applications: Enables integration with other tools and platforms for practical use in drug discovery and materials science.

Key Features:

  • Large collection of pre-built models: Offers various pre-trained models for common tasks, allowing researchers to get started quickly.

  • User-friendly API: Provides a Python-based API for building and manipulating models, accessible to researchers with diverse programming backgrounds.

  • Customizable and extensible: Allows users to integrate their own algorithms and functionalities for specific needs.

  • Cloud-based deployment: Offers a cloud-based platform for running DeepChem models on demand, eliminating the need for local infrastructure.

  • Active community and support: Supported by a vibrant community of developers and users who contribute to its development and provide assistance.

Benefits:

  • Reduces time and effort in model development: Pre-built models and user-friendly API accelerate model creation and optimization.

  • Improves prediction accuracy and interpretability: Deep learning models can achieve superior accuracy compared to traditional methods, while offering interpretability tools for understanding how they work.

  • Facilitates discovery and innovation: Enables researchers to explore new areas and accelerate research progress in drug discovery, materials science, and other fields.

  • Lowers barriers to entry: Open-source nature and cloud-based platform make DeepChem accessible to researchers with limited resources.

Target Users:

  • Chemists and biochemists: Scientists working with chemical and biological data in various research areas, including drug discovery, materials science, and computational chemistry.

  • Machine learning engineers: Developers interested in applying machine learning to chemical and biological data.

  • Pharmaceutical and biotechnology companies: Organizations seeking to leverage deep learning for drug discovery and development.

DeepChem is a powerful tool that democratizes deep learning for the life sciences, empowering researchers to tackle complex challenges and accelerate scientific discovery. Its open-source nature, user-friendly interface, and extensive features make it a valuable resource for individuals and organizations across the field.

Here are some additional resources that you may find helpful:

AlphaFold: AI for protein structure prediction

AlphaFold is a deep learning artificial intelligence (AI) system developed by Google DeepMind to predict the structure of proteins. It has been hailed as a breakthrough in the field of biology, as it can accurately predict the 3D shape of proteins from their amino acid sequences. This is a significant achievement, as protein structure is essential for understanding how proteins function and how they interact with other molecules.

How AlphaFold works:

AlphaFold uses a deep learning model called a convolutional neural network (CNN) to predict the 3D structure of a protein. CNNs are a type of artificial intelligence that are particularly good at identifying patterns in images and other data. AlphaFold is trained on a massive dataset of protein structures and amino acid sequences. This allows it to learn the relationship between the sequence of amino acids in a protein and its 3D structure.

When a new protein sequence is input into AlphaFold, the CNN predicts the 3D structure of the protein. The predicted structure is then evaluated using a scoring function. The scoring function takes into account the physical and chemical properties of the protein, as well as the known structures of other proteins. If the predicted structure is consistent with the scoring function, it is then considered to be accurate.

Benefits of AlphaFold:

AlphaFold has the potential to revolutionize our understanding of biology. By accurately predicting protein structures, AlphaFold can help us to:

  • Develop new drugs and treatments for diseases

  • Design new enzymes for industrial applications

  • Understand the mechanisms of disease

  • Create new materials with desired properties

Applications of AlphaFold:

AlphaFold has already been used to predict the structures of many proteins that were previously unknown. These include:

  • The protein that causes the Ebola virus

  • The protein that is responsible for cystic fibrosis

  • The protein that is the target of many cancer drugs

AlphaFold is still under development, but it has the potential to be a powerful tool for scientific research and development.

Here are some additional resources about AlphaFold:

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