![Danny Arends](/img/default-banner.jpg)
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Danny Arends
United Kingdom
Registrace 3. 10. 2011
World-class geneticist, Lecturencer, ESPRIT Fanboy, and Associate Professor in Bioinformatics at Northumbria University. I live-stream my lectures (R programming & Bioinformatics) for MSc and PhD students on CZcams. I create educational streams about my work on genetics and multi-omics analysis using amongst others DNA and RNA sequencing techniques. Occasionally, you can find me playing games on my Twitch.tv channel.
I live together with my girlfriend Anna ❤️ and Oscar our 🐈 in Newcastle upon Tyne. For my work I develop new methodologies and create (open-source) software for use in (systems) biology research 🔬. I contribute to open-source software development 🖥️. If you are interested in my work, read my publications 📝, or check out my software contributions on GitHub.
Thanks for taking an interest in my CZcams channel 😄 Support me by giving a like (👍) or subscribing (🔔).
I live together with my girlfriend Anna ❤️ and Oscar our 🐈 in Newcastle upon Tyne. For my work I develop new methodologies and create (open-source) software for use in (systems) biology research 🔬. I contribute to open-source software development 🖥️. If you are interested in my work, read my publications 📝, or check out my software contributions on GitHub.
Thanks for taking an interest in my CZcams channel 😄 Support me by giving a like (👍) or subscribing (🔔).
Dutch German Friendship - Viewer Reward 4
Danny draws a Dutch-German Friendship sketch as a viewer reward during the Twitch live stream. I make sketch drawings with the best intentions, but always end up asking myself: "Where did it go wrong ?"
Let me know in the comments !
Thanks for taking an interest in my channel 😄I do lectures on bioinformatics and R programming. Subscribe to my CZcams, and/or join me during my live streams Thursday afternoons on Twitch @ www.twitch.tv/dannyarends
#SketchDrawing #DannyDraws #friendship #Dutch #German #GoodIntentions #Drawing #TwitchLecture #Art #viewerspickthetopics
Let me know in the comments !
Thanks for taking an interest in my channel 😄I do lectures on bioinformatics and R programming. Subscribe to my CZcams, and/or join me during my live streams Thursday afternoons on Twitch @ www.twitch.tv/dannyarends
#SketchDrawing #DannyDraws #friendship #Dutch #German #GoodIntentions #Drawing #TwitchLecture #Art #viewerspickthetopics
zhlédnutí: 270
Video
30min PhD thesis - Correlated Trait Locus (CTL) mapping
zhlédnutí 740Před 2 lety
30min PhD thesis - Correlated Trait Locus (CTL) mapping
The next R course - Your Feedback and Suggestions!
zhlédnutí 355Před 2 lety
The next R course - Your Feedback and Suggestions!
Summary and Example Exam Questions (Bioinformatics S15E2)
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Summary and Example Exam Questions (Bioinformatics S15E2)
Course Summary - (Bioinformatics S15E1)
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Course Summary - (Bioinformatics S15E1)
Citations, Reference Managers, and Version Control (Bioinformatics S14)
zhlédnutí 276Před 2 lety
Citations, Reference Managers, and Version Control (Bioinformatics S14)
Volcano plot in R - (Bioinformatics - Answers S12)
zhlédnutí 1,8KPřed 2 lety
Volcano plot in R - (Bioinformatics - Answers S12)
DNA Metabarcoding of eDNA/eRNA (Bioinformatics S14E1)
zhlédnutí 4,9KPřed 2 lety
DNA Metabarcoding of eDNA/eRNA (Bioinformatics S14E1)
Standards for Analysis (Bioinformatics S13E1)
zhlédnutí 309Před 2 lety
Standards for Analysis (Bioinformatics S13E1)
An R package in 15 minutes (Bioinformatics S13E2)
zhlédnutí 407Před 2 lety
An R package in 15 minutes (Bioinformatics S13E2)
Camera Trap Image Analysis at the Chinko Nature Reserve (Bioinformatics)
zhlédnutí 730Před 2 lety
Camera Trap Image Analysis at the Chinko Nature Reserve (Bioinformatics)
Gene Ontology and mRNA visualization (Bioinformatics S12E2)
zhlédnutí 675Před 2 lety
Gene Ontology and mRNA visualization (Bioinformatics S12E2)
Gene Expression Analysis (Bioinformatics S12E1)
zhlédnutí 2,2KPřed 2 lety
Gene Expression Analysis (Bioinformatics S12E1)
Answers S11 - MSA Assignment in R (Bioinformatics)
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Answers S11 - MSA Assignment in R (Bioinformatics)
Multiple Sequence Alignment (MSA) in R (Bioinformatics S11E2)
zhlédnutí 6KPřed 2 lety
Multiple Sequence Alignment (MSA) in R (Bioinformatics S11E2)
Sequence Alignment, Scoring, and Analysis (Bioinformatics S11E1)
zhlédnutí 2,4KPřed 2 lety
Sequence Alignment, Scoring, and Analysis (Bioinformatics S11E1)
Answers S10, PubMed, biomaRt, and BLAST - (Bioinformatics S11E0)
zhlédnutí 326Před 2 lety
Answers S10, PubMed, biomaRt, and BLAST - (Bioinformatics S11E0)
SNP chip data, PCA, and biomaRt in R (Bioinformatics S10E3)
zhlédnutí 886Před 2 lety
SNP chip data, PCA, and biomaRt in R (Bioinformatics S10E3)
Databases and biomaRt (Bioinformatics S10E2)
zhlédnutí 361Před 2 lety
Databases and biomaRt (Bioinformatics S10E2)
Databases and biomaRt (Bioinformatics S10E1)
zhlédnutí 500Před 2 lety
Databases and biomaRt (Bioinformatics S10E1)
Primer Design for RNA/DNA amplification (Bioinformatics S9E3)
zhlédnutí 338Před 2 lety
Primer Design for RNA/DNA amplification (Bioinformatics S9E3)
Primer Design for RNA/DNA amplification (Bioinformatics S9E2)
zhlédnutí 516Před 2 lety
Primer Design for RNA/DNA amplification (Bioinformatics S9E2)
Primer Design for RNA/DNA amplification (Bioinformatics S9E1)
zhlédnutí 963Před 2 lety
Primer Design for RNA/DNA amplification (Bioinformatics S9E1)
Correlated Trait Locus (CTL) mapping (Bioinformatics S8Ex)
zhlédnutí 47Před 2 lety
Correlated Trait Locus (CTL) mapping (Bioinformatics S8Ex)
QTL mapping and GWAS (Bioinformatics S8E2)
zhlédnutí 2,8KPřed 2 lety
QTL mapping and GWAS (Bioinformatics S8E2)
QTL mapping and GWAS (Bioinformatics S8E1)
zhlédnutí 2,7KPřed 2 lety
QTL mapping and GWAS (Bioinformatics S8E1)
Answers S6 - Pathway analysis (Bioinformatics S8E0)
zhlédnutí 384Před 2 lety
Answers S6 - Pathway analysis (Bioinformatics S8E0)
Introduction into R - Regression (Bioinformatics S7E3)
zhlédnutí 409Před 2 lety
Introduction into R - Regression (Bioinformatics S7E3)
Introduction into R - Basics 2 (Bioinformatics S7E2)
zhlédnutí 403Před 2 lety
Introduction into R - Basics 2 (Bioinformatics S7E2)
sorry for the disturbance, the link that you have provided for debian is 12.6.0, but what you have used in the video is 11.5.0, can you please provide the link for 11.5.0?
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
What to do if the compilation for trimmomatic has mot been done?
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
Hi , there's a problem in running trimmomatic, it says unable to access jarfile dist/jar/trimmomatix-0.40-rcl.jar
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.
Hi Danny, i have 16gb RAM memory in my laptop, will i be able to do RNA seq?
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.
Thank you very much Dr. Arends for the tutorials. Just wanted to add something for Ubuntu users, because the folders that created in Ubuntu are kind of different from centos linux. In "Ubuntu", the PATH for "./vdb-config --interactive" or "./fasterq-dump" will be similar to this "/software/sratoolkit/sratoolkit.3.1.1-ubuntu64/bin".
Thanks for the info, every Linux flavor is slightly different indeed.
@@DannyArends True, Dr. Arends. Thank you for providing the chance for learning and sharing our experience.
Hi Professor, First of all, I want to thank you for this course. It's really challenging to find R programming courses that focus on statistical analysis, so I truly appreciate it. I'm currently majoring in statistics and computer science and looking to advance my knowledge in both statistics and analysis. While working on the 2021 version of the course (I'm still on the first lecture), I discovered the 2022 version. I wanted to ask if the 2022 version is an upgrade of the 2021 course. Should I switch to the 2022 version, or continue with the 2021 one?
Hi Hector, both versions are essentially similar, the 2022 version has some improvements based on student feedback, but globally topics discussed are the same. You could just switch between lectures, e.g. after 2021 lecture 3, you could jump to 2022 lecture 4 without any issues.
On video, knock out results in heterozgyous. It means... Phenotype was: aa now: Aa Gamete "a" is knocked out and replaced by "A".
The IMPC generates preferential homozygous knockouts (aa to AA, here big A means the gene is removed), so in a homozygous knockout the gene is removed completely from both genomes. In some cases this isn't possible, since no gene leads to a lethal phenotype (e.g. hemoglobin without it no oxygen can be transported). In those cases heterozygous knockouts are made (aa to Aa), so there will only be 1 working copy of the gene to test its effect on the phenotypes.
Do the Moodle still running? I would to do the assignment so as to have a better understanding of the subject. Thanks 😊
The moodle is not available anymore (and was restricted to people with a university account), you can get the assignments, answers, and data from my website: dannyarends.nl/bioinfo/ In case this doesn't work (my website is down sometimes,, and sometimes files get corrupted during download) just send me an email (my email address is on the about page of the channel)...
thank you! 😊
18:02 figure on the top left corner with 2 pairs of chr. Parent 1 is a heterogeneous alleles. It means one chromatid got "B" allele, the another chromatid got "b". Q: DNA is formed by complementary base pairing. So, each side of chromosome would be looked exactly the same. How come the chromatids look different?
Since every diploid animals has 2 copies of each chromosome, each of these copies have 2 strands of DNA (coding strand and template strand), so in a haploid sperm cell you have 1 copy of a chromosome, which is 2 strand of DNA (which are mirror-identical). However in a diploid individual cells with 2 copies can have SNPs between the two copies of the chromosomes (e.g. on the first chromosome we find A:T, while the second chromosomes has C:G at this position) we always write down SNPs relative to the coding strand so this individual would be genotyped as A/C at this SNP.
So, why do the chromosomes of parent 1 look different?
Because it has 2 different copies of the same chromosome. Unless, I'm misunderstanding your question.
@@DannyArendsI think I need more biology background information so as to absorb the topic. Could u recommend any youtube channels or books that can help? 😊
@@sueyue6650 Well a basic introduction to biology / genetics at A-levels could perhaps be advisable if you didn't do biology in high school, for books e.g.: "Introducing Genetics" by Steve Jones "Foundation Biology" by Cambridge Modular Sciences On CZcams, @missangler has very well done introduction videos on Genetics, which might be useful to get into genetics, and @ProfessorDaveExplains has an introduction video on genetics as well.
8:00 regarding the distant between w and m gene which is 37%. It is appreciated if the author can shed some lights on the following questions. Q1: What is mean by independent? If it means far apart on the same chromosome, or, separate chromosomes. Q2: The video stated "If locates on different chromosomes, then we would have expected half of individuals to be recombinant". Why half means the genes (w and m) is not linked? Whats is the logic behind the statement? Thank you!
Hiya, Regarding Q1: independent means separate chromosomes. Q2: we assume 2 copies of each chromosome. Then here, the female only has aa and bb gene versions, as such the female egg cell (contains 1 copy of each chromosome) always has ab in them. The male has aA and bB, so male sperm cells come in 4 flavors (ab, Ab, aB, AB). (2 out of 4 are recombinant: Ab and aB) Since the egg cells are always the same only the male sperm cells are recombinant. Each sperm has a 25% chance to occur when gene a and b are on different chromosomes (i.e independent). If gene a and b would be on the same chromosome then a and b (as well as the A and B) version of sperm would be more common, since the small a,b and big A,B would be physically linked by being on the same chromosome. Hope this helps, try drawing a punnett square with 1 row and 4 columns, and look at the resulting combinations. ab x ab = aabb (looks like mom) ab x Ab = aAbb (recombinant) ab x aB = aabB (recombinant) ab x AB = aAbB (looks like Dad)
why the F1 have to be aabb, instead of AABB, AAbb, or aaBB?
Due the the genotype of the parents used in a 2-point cross (AaBb x aabb), the offspring genotypes cannot be AABB (since the mother does not have the big A and big B alleles. only the 4 possibilities listed above can occur, the frequency at which they occur teaches us about the genetic linkage between locus a and b.
Awesome course! Very helpful and informative ❤️ Too bad the assignments are no longer available, though..
The assignments should be available from my website. It might be down, please drop me an email (on the about page) and I'll send them to you by email.
Hi Danny, thank you so much for taking the time and effort in creating these video! I have a stupid question. At 41:00 on the top diagram, white=3/8 (38%), red=3/8, pink=2/8(25%), why it says white and red 25% and pink 50%?
Ahh, I see how this can be confusing. I made a highlight of the image which can be found here: 1drv.ms/i/s!AtYWSYRMmSHZh7x1ExuYBku3hA_pvQ?e=U053HN to be able to talk about the example more easily The child flowers are inside the orange square, there are 4 of them. The "2 flowers" in the yellow circles aren't flowers, they are "parental alleles" so inside each box we see 2 alleles which belong to 1 parent flower, e.g. the father flower is on top (represented by it's 2 alleles), the mother flower is on the left (also represented by the two alleles). Since we have 2 copies of each gene. In the additive case, both parents will look orange (since they have both a white and a red allele) In the dominance case, both parents will look red (since they have a while and a red allele, but red is dominant) In the next slide at 46:00 this is explained, but indeed the graphic is confusing, since I use the same image for flower and allele For a more detailed explanation see: en.wikipedia.org/wiki/Punnett_square
Thank you for the reply. I never expect such detailed explanation. It help a lot for a 0 beginner. Now i understand more about the Punnett square. The 1st row and 1st column are parent genotype (circled in yellow). The offspring genotypes are in orange circle.
Indeed, I'm happy it's clear now. If I present the slide again in the future I'll update it to avoid confusion.
Hi thanks for your teaching sessions, could you please record a video about finding outliers between cancer subtypes microarray samples by hierarchical clustering and pca?
Thanks for leaving a comment, and I'll put your request on the list of topic ideas for the future
Hi Danny, I could not extract this data despite using WinRaR to try and do so. The file appears to be corrupted. What can be done to remedy this. Best, Samuel
Hey Samuel, this sometimes happens due to connection issues. Normally another browser can help. Alternatively, drop me an email (my address is on the about page), then I can send you a copy.
👏 👏 👏
I'm working on a project that uses some of these concepts; this video was super helpful for understanding metabarcoding in a simpler way!
Very good to hear it was helpful, thanks for leaving a comment
I had statistics, programing
R will be for you then, combining stats, programming, and data visualization
interesting regards
Thanks for leaving a comment, R is an interesting language to learn
auto scale is like calculating a zscore?
Indeed 👍
here it sounds good... clear and loud haha
Hope it's not too loud 📢
Can you build a heatmap for this data using fold change right?
Yes you can use the heatmap function on the normalized expression data after filtering down to the probes which are differentially expressed
Hi bro. Can you explain this kind of selection item "[". Just wondering if the split <- strsplit(GPL201[probe, ontology], "///")[[1]] it is not hard coded because GPL201 or it is a general code for the ontology of affy? Thanks
when you want to select from a list, which at each entry in the list has vectors of elements, you can use: lapply(list, "[", 1) # for all vectors in the list return the first element of the vector GPL201 is just the variable holding the GPL201 annotation file, you could change it to another affy annotation file, since all of them use the same way of storing GO data.Probably would have been better to call the variable GPL (without the 201), or AffyAnnot to be more generic.
Respected Dr. Arends, first of all thank you for sharing your knowledge and expertise through this tutorial. I ran into an issue while trying to use ReadAffy() function: "Error in read.celfile.header(as.character(filenames[[1]])) : Is GSM28627.CEL really a CEL file? tried reading as text, gzipped text, binary, gzipped binary, command console and gzipped command console formats". I have no idea how to deal with it, searched through the web but was unable to find any solution. I followed some suggestions that hinted unzipping the files might help, that's why the error shows .CEL instead of .CEL.gz.
Hmm, the error indicates that the file is not a valid.cel file. My suggestion would be to delete the files downloaded and make sure that there are no other files in the working directory except the cel files. The ReadAffy function generally is smart enough to figure out the files are gzipped when the extension is .CEL.gz but perhaps one of the files got corrupted during downloading. If you extract them you're going to need 7zip or another tool that deals with gz giles under windows, but plain cel files can be opened in an editor since they're plain text.
Thank You So Much!
Glad it helped!
Good day, thank you for the video, very informative. I just want to know if i can use the vitual machine for human genome? I will be checking for gene expression levels between two groups and i figured that is a large data set. I will appreciate your guidance in this. thanks
For a human genome it would be advisable to run it in Linux, or even better on a university high performance compute cluster. The data size is going to be orders of magnitude more than the example S. Cerevisiae RNA sequencing dataset.
Much appreciated, Dr. Danny!
You're welcome, thanks for taking the time to comment really appreciated 👍
wonderful! thank you good sir.
Thanks for sharing such an exciting video on Enrichment Analysis. I thoroughly enjoyed your video. Clear and very informative explanations!
Good to hear that you enjoyed it!
+1
nice explanation. qtl still wanted))
Thanks, and thanks for taking the time to leave a comment
Thank you prof, How CTL deal with Epistasis phenotype?
The idea behind CTL Analysis is that it can help you identify epistatic phenotypes (they will share a CTL). The second/follow-up step is to combine it with QTL data to figure out the causality between the two phenotypes.
@@DannyArends thank you prof
This is very informative. Thanks 🙏.
Glad it was helpful! thanks for leaving a comment
Would be nice to start this course with someone else.looking for accountability partner
That is a good idea, to have someone to keep you on track.
Thank you prof
You are welcome
Thanku so much for this!!
You're welcome, thanks for taking the time to leave a comment.
This is damn amazing. All your lectures are so easy to comprehend. I appreciate more...
Thanks for the compliment, making more is on the schedule. But with the new job time is lacking.
This is a great resource!! Thank you for making this resource available, Dr Danny!
My pleasure!
Thank you so much for the lecture I learn some thing new! Question: any advice on testing package in R? what is your favorite (if you have one) and why?
I'm not quite sure what you mean by testing package in R ? You can use the command line R CMD check command to test packages for a number of issues regarding data, code, and documentation issues. If you plan on submitting your package to CRAN, add the --as-cran flag to make sure that additional requirements for CRAN are tested.
In python we have UnitTest or pytest libraries to test our code. Do we have something similar in R? For example I develop R package and now I want to test it, and instead of manually write everything I can utilize some other packages to perform testing.
Ahh, no R provides two ways of testing code, the examples section in the R documentation are run every time you compile your package as kind of a unit test. Integration tests can be written in the test/ folder in your package and are executed on R CMD check. So R has a test runner integrated but you'll have to write the tests yourself (or have chatGPT write them for you)
Thank you! I really appreciate your responses! :)
You're welcome
Great vidoe! question: why did you choose the threshold of 25%? (32 min) Is it from reference literature?
Nope, it's just the threshold at which the number of hits is manageable. Better would be to do a proper permutation to get 1% and 5% thresholds by randomly reassigning labels.
Thank you so much for these courses( this and R ).
You're welcome, thanks for the comment and enjoy learning bioinformatics/R
Dr.Arends, Thank you for sharing the assigments. I am not a student but It is fun to do them! :) And, yes, Huge Thank you for the interesting series on bioinformatics! Perfect balance of programming and biology! I appreciate all your work!
Thank you for the kind words, and for leaving a comment.
Thank you for sharing this on youtube!
My pleasure, thanks for taking the time to leave a comment.
I'm very excited for the rest of the lecture series! Thanks for this, I've been very interested in pursuing bioinformatics for some time now!
You're welcome, I hope you'll enjoy the lectures and learning about bioinformatics.
Thank you so much for this amazing lecture! so helpful.
"Thank you, Professor. I have a question about the Poisson distribution. Could you please explain how the Poisson distribution is implemented in the context of EMS mutagenesis in Arabidopsis seeds? For example, if I treat Arabidopsis seeds with EMS, what is the probability of obtaining a desired mutant in the M2 plants?"
My guess would be that the Poisson distribution is the one that models your response to EMS treatment on seeds. Since mutations are random across the genome, we would expect genes to be hit at random. However, since generally we do random mutagenic treatment, but look/are interested in a single phenotype for differences in the M2, we expect only a very small amount of mutations to be hitting genes that influence it. So if we do this many times (e.g. 100 rounds of mutagenic treatment,), we would see that often (e.g 85%) we have no single mutant which shows an mutant phenotype. sometimes (e.g. 10% of the treatments) we see 1 mutant, very seldom (e.g. 4.9% of the treatments) we see 2 mutants, and we almost never (< 0.1 %) observe >3 mutants. The distribution (of how many mutants do we expect in the M2) most likely follows a Poisson distribution as above. Hope this helps.
@@DannyArends thank you prof
in ubuntu you need to run the vdb-config --interactive in the /bin that is at the root of your extracted file that should be in sratoolkit folder if you have mkdir one otherwise it's going to be in root of your /software folder. (Maybe because it'd my machine, but it is the most annoying program ever)
Thanks for the info, I tend to run a debian based OS.
@@DannyArends No worries it's very similar. Had to interrupt myself because it was a very long install and my day started, tomorrow I'll resume and try part 2. Thanks for the great work!
Thank you for making this content available for everyone, you made everything really clear!! Can I ask for the pdf of these slides, lecture 12 is not included in the attached link.
Sure, just drop me an email and I can send them. My email is on the about page of the channel.
Hi Professor, i think your website might be down. Could you reboot it? Greetings from Austria
Sorry about that, I should really put something in place that auto-restarts it when the server dies. Should be up and running now
@@DannyArends Thank you so much, love your courses as a psychology student.
That's great to hear, R coding is very universal in that sense, everyone in academia can use it.