Hi, thank you for the video. Which proteins are more reliable to analyze out of reviewed or unreviewed sets?

The very relevant aspects for visualization, thank you for this training.

This is insightful.

Even though a vcf can easily be above 50 mb, ensembl only keeps a 50 mb limit when using their vep, is there another platform that takes vcfs for pathogenicity analysis which can take more import data?

hello ! please How can I find motifs of a protein on UniProt ?

Could you please let me know the detailed tutorial of chembl API

Currently I'm working with schisostoma mansoni, thank you so much!

I would like to cancel this nonsense as I didn't apply for it

great explanation. thank you for posting

Thanks for sharing. I am Computer Science background, am doing PhD title on "An automated decision-making system to identify T2DM (type - II diabetes Miletus ) based on DNA sequences". i got data from which i have to figure it out diabetic variant from genomic dataset and i am stuck with it. i will be extremely glad if you can provides me some help in my research. Can you please drop some thing to contact you.

Thank you for this series! ❤

SRSF1 gene = Serine and Arginine Rich Splicing Factor 1.

MT-CO1 gene = Mitochondrially Encoded Cytochrome C Oxidase I.

I love the explanation of endosymbiont theory here. 🤍

“This gene encodes an enzyme involved in blood pressure regulation and electrolyte balance. It catalyzes the conversion of angiotensin I into a physiologically active peptide angiotensin II. Angiotensin II is a potent vasopressor and aldosterone-stimulating peptide that controls blood pressure and fluid-electrolyte balance. This angiotensin converting enzyme (ACE) also inactivates the vasodilator protein, bradykinin.” - National Institutes of Health, USA.

IL6 = interleukin 6. From “leukocyte” and the Greek language, leuk- “white”, cyt- “cell”.

“Fat mass and obesity associated (FTO) was the first gene found to be associated with obesity in three independent genome-wide association studies.” -NIH USA Gene full name: FTO alpha-ketoglutarate dependent dioxygenase.

“Cystic fibrosis is an inherited disease caused by mutations in a gene called the cystic fibrosis transmembrane conductance regulator (CFTR).” - National Institutes of Health, USA

18S rRNA = 18S ribosomal RNA.

“The TP53 gene provides instructions for making a protein called tumor protein p53 (or p53). This protein acts as a tumor suppressor, which means that it regulates cell division by keeping cells from growing and dividing (proliferating) too fast or in an uncontrolled way.” -MedlinePlus Genetics

XIST gene: X inactive specific transcript.

TTN is so big, what if we renamed “titin” into “titan”? 😅

HBB: Hemoglobin subunit beta gene. 🌬🩸

00:59 A gene is “a region of genome that makes a particular protein or functional RNA.”

Was useful. Many thanks

Nice presentation, Alex. Although I have got to work on coming to terms with the novel techniques you talked about. especially the use of drep on MAG. All the same, it was interesting to learn from you. Thanks!

Great vidéo from Julia 🥰

My targets are not from CHEMBL but in other sources with their identifiers. How can I convert those identifiers of hundreds of targets into CHEMBL IDs?

pchembl_value__gte=5? under the threshold of: less than 10 um of potency? pchembl = - log10(10) =-1, if potence is great than 10 um ( ie. < 10 um), the pchembl should be >= -1. Right?

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Terrific information! Thank you.

😮😮😮

Dream of working there?

Is ttn gene really fatal for newborns especially ?

Thank you so much

👍

Is there a good tutorial on how to analyze/visualize .raw files for people without proteomic experience?

Anyway to get technical support to get my 16S datasets? i find that difficult to follow.

Can we annotate our fungus whole genome sequence here

How to upload my 16S dataset?

good video

Thanks for the presentation! I've been doing GO and other functional analysis of proteomic data for several years, and still came by several useful tips and tricks in this video :).

@18.27 isn't specific parents gives to broader children, since arrow is pointing towards biological processes?

Greetings from Ethiopia 🇪🇹

unclear accent

💘 "Promo SM"

Very informative - thank you. I have had success the conda package for VEP: conda create -n VEP109 conda activate VEP109 conda install ensembl-vep=109.3 (latest at time of installation) conda install perl-compress-raw-zlib=2.202 An additional step was required (suggested during installation of the above) to install cache data. Here I installed human GCRh38: vep_install -a cf -s homo_sapiens -y GRCh38 -c ~/.conda/envs/VEP109/ ~/.conda/envs/VEP109/GRCh38/ --CONVERT --PLUGINS all

Thank you so much. Greetings from Molecular Biology, Environment and Cancer Research Group at Universidad del Cauca, Colombia.

Thank you EMBL - EBI for this useful video. Greetings from a bioeng graduate student.