วีดีโอ

Thank you ☺️

yes you can calculate logFC from abundance values

Glad you like it

@@Mrindia-ek1z i am planning, thanks for suggesting a good topic

Whats the error

After installation its not opening..

Hi plot or ChiPlot?

Thanks canva

@@asifmolbio l need the vidio please ❤️‍🩹

I also won a RFIS on the same topic i already started to investigate during my postdoc. You can present preliminary results and propose future experiments

Wslam off course you can

@@asifmolbio Ok sir thank you so much

Can you share its link

Okay

Thank you ☺️ you are welcome to ask me anything

You can download from NCBI GEO database to download any examples for your practice

Thanks for suggesting a good topic, i will record a video on this

Welcome

Search medical in the search

There are other heat map options available in this website, try them

Its up to you. no hard and fast rule

This is only designed to for dataset, as shown in example

Welcome

Seeing differential metabolite will work

@asifmolbio thanks to reply i have done all ,but how to do it this and pathway analysis is remaing

Welcome

Yes

In iDEP?

I never tried for this You can try

Thank you, Dr. Asif for your kind reply. I have tried it, but it is not working; I'm trying it now with metaboanalyst. Thank you once again.

That is gene enrichment count. You can use this for your data

Thank you dear

Will upload a video later

th-cam.com/video/Tn7yw6y3UgY/w-d-xo.html

Try this method

JAZAK ALLAH U KHAIR brother

Yeah that’s not discussed here

@asifmolbio So, Sir, can you please explain how to install them through wsl- linux

No it is usually done after acceptance

for only one analysis, or giving errors for all analyses?

@asifmolbio software is not working form the start ,

i used the file you shared in your video that one is also not working

@asifmolbio for all

You can install wsl in windows for this

How to create a Linux sub-system in Windows for running RNA_seq pipeline | Trimmomatic and HISAT th-cam.com/video/I1xiNtEDncI/w-d-xo.html

What is UGC? I never heard about it

Glad you like it

PCR primers designing using Primer Premier 5 | Tutorial and Questions | By Dr. Asif Mol Biology th-cam.com/video/BKUPVhCKXU8/w-d-xo.html

You can use this tool

@ isn't this paid?

How to calculate fold change FC, log2FC, Pvalue, Padj, Up and down regulated genes th-cam.com/video/HH3Mll4W5WE/w-d-xo.html

This video will be helpful

Okay, thank you, Dr. Asif... I will check it out.

Usually it don’t take long time check your protein sequence again and upload again

@@asifmolbio can you suggest another website or software because sequence in fasta files are correct from ncbi buy after waiting a long time it stops on 3rd or 6th step before results

I want to find the paralogous genes of my candidate gene in soybean

Orthofinder

You can still use the GWAS data in your paper if you explain the context clearly. For example, you could: 1. Discuss the relevance of the identified transcription factors: Even without direct SNP associations, transcription factors can still be pivotal in understanding gene regulation, plant traits, or other biological phenomena. 2. Mention limitations: Address the fact that while SNPs were not found, the GWAS results might still be informative about genetic regions or pathways of interest. 3. Explore other associations: You might want to look into the regulatory regions around the transcription factors and whether there are indirect genetic markers or other variants that could explain their role. Using the data in your paper is valid, but ensure you frame the findings carefully and highlight their significance in the broader context of your research.

Yes you can choose top 30

asifalikalas@foxmail.com

if not, please make video on how to upload short sequences of targeted genes for first report studies

Did you mean mean that sequence just varies for a few base pairs than published genes? If so then nucleotide archive databases will be suitable

@@asifmolbio yes brother. the protocol will be the same?

Thanks

Agriculture and Plant sciences journals with no APC (article processing fee) th-cam.com/video/SwtpxjLHric/w-d-xo.html

Check this

Sure i will do record in the future

To study the effect of a metabolite on a protein related to a disease in silico, you would typically use various computational biology tools and approaches. Here’s a step-by-step guide on how to approach it, including tools you might use: 1. Protein and Metabolite Selection • Select your protein of interest related to the disease (e.g., a receptor, enzyme, or signaling protein). • Select the metabolite you want to study. It could be a substrate, cofactor, or a molecule that can bind to or affect the protein’s function. 2. Structural Data Retrieval • Protein Structure: You need to obtain the 3D structure of the protein, either through experimental methods (e.g., X-ray crystallography, NMR) or predicted models. • Tools: • Protein Data Bank (PDB): If the protein structure is available experimentally (www.rcsb.org/). • Homology Modeling: If the structure is not available, you can predict it using tools like Modeller or Swiss-Model. • Metabolite Structure: Similarly, you will need the 3D structure of the metabolite. • Tools: • ChemSpider or PubChem to find the metabolite structure (www.chemspider.com/ or pubchem.ncbi.nlm.nih.gov/). 3. Docking Study (Protein-Ligand Interaction) • You can perform molecular docking to see how the metabolite interacts with the protein. This helps predict the binding affinity and potential impact on the protein’s function. • Tools: • AutoDock Vina or Dock for docking the metabolite to the protein (autodock.scripps.edu/). • PyRx: A graphical user interface for AutoDock Vina (pyrx.sourceforge.io/). 4. Molecular Dynamics Simulation • After docking, you can run molecular dynamics (MD) simulations to observe the binding stability of the metabolite with the protein over time and how it influences the protein’s conformation. • Tools: • GROMACS: A widely used MD simulation tool (www.gromacs.org/). • AMBER or CHARMM: Other MD simulation tools, depending on your system (ambermd.org/ or www.charmm.org/). 5. Binding Free Energy Calculation • To quantify how strongly the metabolite binds to the protein, you can calculate the binding free energy using methods like MM-GBSA or FEP. • Tools: • GROMACS or AutoDock Vina for energy calculations. 6. Pathway and Network Analysis • After understanding the interaction, you can explore how this interaction might affect biological pathways related to the disease. This could include the activation or inhibition of certain signaling pathways. • Tools: • Cytoscape: For network and pathway analysis (cytoscape.org/). • Reactome: A database of biological pathways (reactome.org/). 7. Prediction of Mutations and Effect on Function • If you suspect mutations in the protein might alter its interaction with the metabolite, you can use SIFT or PolyPhen to predict the effect of mutations on protein function (sift.bii.a-star.edu.sg/ or genetics.bwh.harvard.edu/pph2/). 8. Visualization • PyMOL or Chimera: To visualize the structure, the docking results, and molecular interactions (pymol.org/2/ or www.cgl.ucsf.edu/chimera/). Workflow Example: 1. Retrieve the protein structure from PDB or model it using Swiss-Model. 2. Download or create the structure of the metabolite. 3. Use AutoDock Vina for docking the metabolite to the protein. 4. Perform MD simulations to check the binding stability. 5. Analyze the protein-ligand complex and calculate binding energies. 6. Use Cytoscape to analyze how the metabolite-protein interaction might affect disease-related pathways. Final Thoughts: The tools mentioned above will help you simulate and analyze the effects of a metabolite on a protein in the context of a disease. For practical use, you may find tutorials and documentation provided by these tool websites helpful for learning how to apply them to your specific problem.

Thanks ..... جزاك اللهُ‎

@@mahi1654 welcome

@@asifmolbio i don't have any bioinformatics background so finding it very difficult but your videos r quite helpful regarding basic information

You are welcome

To analyze your data and create figures, here’s a general approach: 1. Data Preparation: • Understand the content of the files: The OTU table contains the abundance of microbial taxa across samples, the taxonomic file links OTUs to their taxonomic classifications, and the mydata file holds metadata like sample information or experimental conditions. 2. Data Exploration: • Summarize the data to check distributions of OTUs across samples. This will give you a basic idea of the microbial composition in your samples. 3. Diversity Analysis: • Alpha diversity measures diversity within each sample (e.g., richness, evenness). • Beta diversity compares diversity between samples (e.g., how different samples are from one another based on their microbial composition). 4. Visualization: • Use bar plots to show the relative abundance of taxa across samples. • Use PCA or NMDS plots to visualize how samples group based on their microbial communities. • A heatmap can show patterns of OTU abundance across samples. 5. Statistical Testing: • Perform statistical tests to identify which microbial taxa differ significantly between conditions (e.g., disease vs. control). This could be through methods like ANOVA or PERMANOVA. 6. Interpretation: • Analyze your visualizations to find trends, such as which taxa are enriched or depleted in different conditions, and relate these findings to the disease or condition you’re studying. This workflow allows you to analyze the data, visualize microbial patterns, and identify meaningful differences. If needed, you can use additional tools for more complex analysis or pathway predictions.

Here is one video

How to interpret results of Microbiome analysis | alpha beta diversity th-cam.com/video/BNZl8NmSLmQ/w-d-xo.html

@@asifmolbio thank you sooo much dr asif for your wonderfull response and helpfull information, thanks alot once again

Glad you like it

Thank you ☺️

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