Відео

Thanks, and yes I'm a big fan of free and open education and software. I can code all day and write one tool, but if I teach others, we'll have the many tools we need much faster.

You're welcome 🤗

Sure, just drop me an email and I can see how I can help you out.

Yep, it's just labels being put on things. For all fields (neuroinformatics & computational neuro) you just need to code and be an expert in neuro biology. General bioinformatics will give you a basis in both fields (coding & biology), after which you can specialize yourself more into the neurobiology field.

The main differences: PAM is based on global alignments of closely related proteins. BLOSUM is based on local alignments. BLOSUM matrices are based on observed substitutions from local alignments in the Blocks database, in contrast PAM1 is based on observed data of global alignments of closely related proteins, but all higher PAM matrices are extrapolated. Next, is that the numerical coding is inverted, a higher BLOSUM number means the substitution matrix is made by including more related protein sequences (more related), for PAM higher numbers mean more evolutionary time has occured for sequences to diverge (less related). The way I think about it, if you want to score a human protein versus a chimp use high BLOSUM or a low PAM. A human against a spider low BLOSUM or a high PAM. The following article which goes into great detail in how both PAM and BLOSUM matrices are computed: cs.rice.edu/~ogilvie/comp571/pam-vs-blosum/ Hope this helps.

I always advise people to pick up the "introductory statistics with R" book. It's a great resource for learning statistics and gives good examples that you can directly play around with in R

@@DannyArends Will do, Thanks!

Yes and no, in general introns are under less selective pressure than the coding regions of proteins (CDS), but are under more selective pressure than intergenic regions. However, this last part depends on the type of intron and splicing mechanism relative to what the function of the intergenic region is. In short most DNA is under selective pressure, the strength of this selection is very variable, e.g. the wobble bases in the CDS feel much less pressure than the first two bases of a codon. Selective pressure is furthermore heavily influenced by which protein you look at (and the environment), a ribosomal protein feels much more selective pressure than a pigmentation gene.

@ Thank you for the insight! Appreciate the response Professor

My guess is that most of the algorithms will be already available in R: Ishikawa diagrams, are provided by the qcc library (they're called cause.and.effect plots) The other 3 algorithms are harder to search for on google, since to me it is unclear to which area of research they are used in.

Much appreciated! I think it's a cool method

You are welcome!

Glad you enjoyed it!

With only 3 samples per group you cannot really detect or deal with batch effects, since they are highly likely to be aligned with the sample groups. The only thing you might be able to do is add a quantile normalization step after RMA, to ensure that every sample has a similar expression level.

​@@DannyArendsThank you Prof. for your time and effort.

If the website isn't working, just drop me an email. You can find my email address on the about page.

Yeah, phylogeny visualized by a tree is a nice way of visualization but it has its downsides.

You're welcome, thanks for leaving a comment.

Yes, you can get all of the assignments, data, and answers at: dannyarends.nl/bioinfo/ For the lecture slides, they are available on dannyarends.nl/bioinformatik/

@@DannyArends Thank you very much hats off to you sir

Longer videos can't go without them, it's just easier to navigate through the content.

Many thanks!

You're very welcome, my email is on the "about" page of the channel, feel free to reach out.

Glad it was helpful!

You're welcome, enjoy learning about the wonderful world of bioinformatics

If you are subscribed to the channel, you'll be informed about upcoming live streams. Generally I post the stream announcement ~ 1 week before the actual stream takes place, so people can plan to attend.

rebooted the server, but it seems like someone is using a DDos attack on my website, send me an email (my email is on the about page) and I'll drop you a zip file with the assignments.

@@DannyArends Wow, thanks for the quick answer! I seem to have been a bit impatient, it's working again today :) All the best!

Good question, we generally assume that a perturbation to a homeostatic system (e.g. add a chemical to cell media) will change the system. However, from gene expression you only get a list of genes that are up/down regulated. Gene ontology tries to be an abstraction on top of this to provide a more global overview of what is happening. You could image adding glucose to the media will cause small changes across the whole system, but this won't teach you what the main effect of glucose is. Gene ontology groups genes in different groups, and from that you would expect the system most affected would be the glucose uptake, and the downstream pathway. The same goes for the other non-pathway ontologies. e.g. cellular localisation. If we see that most genes responding to a treatment are located in the mitochondria this will give us a very clear hint that the main effect of the treatment will cause changes to the mitochondria, this can be very helpful to know. Hope this explains it a little bit

Glad it was helpful! Thanks for leaving a comment.

PDFs are on my website: lecture PDFs at: dannyarends.nl/rlectures/ Assignments, Data, and Answers at: dannyarends.nl/r2022/ If you cannot download it for some reason, do send me an email and I can send you a zip file

@@DannyArends thank you so much professor i can download all , thank you very much .

My email address is on the about page of the UA-cam channel.

The ribosome translates a single mRNA molecule into a protein at a time. However to do this it uses different ribosomal associated RNA molecules, the ribosome is a complex of several proteins and several rRNAs that work together. In the animation we see the crystal structure of just the ribosome (proteins & rRNA), no mRNA is present. Q1: Only a single mRNA is translated into a protein at a given time Q2: No, see answer Q1 You can learn more at en.wikipedia.org/wiki/Ribosomal_RNA (section: "Subunits and associated ribosomal RNA") for the different types of associated rRNA molecules and how they differ between prokaryotes and eukaryotes.

Thank you! Just to clarify, what is those half helix appeared at 37:14?

Sorry missed the reply, those helix structures are probably ribosomal subunit associated RNAs

​@@DannyArendsNo worries. For me, it takes time to absorb the wiki content. Thanks!

You're welcome, the idea (and software implementation) was the foundation of my PhD thesis

@@DannyArends Yes, it seems like lots of hard work.

You're welcome, got some new things I'm working on.

@@DannyArends Excited about it. Looking forward to it.

Many thanks!

No bother, yeah It seems a newer version was released, you can always get the older versions from the archives, a direct link to the 11.5.0 netinst image: cdimage.debian.org/mirror/cdimage/archive/11.5.0/amd64/iso-cd/debian-11.5.0-amd64-netinst.iso

@@DannyArends thanks a ton

In that case just download trimmomatic v0.39 from here: www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.39.zip and extract it. Make sure to update the script to reflect that you're using 0.39 not 0.40-rc1

@@DannyArends thank you so much and also the virtual box version what you have used in the tutorial and the one in the pdf is different, is it fine?

The version of virtual box should not matter, the important part is to use the same Debian version

This error means that the trimmomatic jar file wasn't found at the path specified. Use the debian file browser to confirm that the file is really there.

For smaller data sets and genomes, 16 Gb will be enough (e.g. Yeast, Bacteria, Bees, some Plants). For Mouse or Human, 16 Gb is probably not going to be enough, and 32 / 64 Gb is going to be the minimum.