Human Genetics - Science topic

Just because something is "common in the field" does not mean that it does make sense.

If you have a good estimate of a reference distribution, then you can give the quantile of the observation according to that reference distribution. If you have only a vague estimate of the reference distribution, then you cannot say anything useful. With n=3, even with n=30, you won't get a reasonably good estimate of the reference distribution. Even if you like to employ Chebychev's inequality you'd need a good estimate of the variance, what again requies a rather large sample.

I agree genetic relationship is a key issue, but I think there is more important taxonomic detail available today than just the raw genetic similarity. So the Last Common Ancestor of humans and rats (or mice - or rabbits for that matter) was probably a stem member of euarchontoglires, about 90 million years ago. In other words, mice, rabbits and rats are roughly equally related to humans (so choosing between them might be based more on other characteristics such as sociality, cost etc.).

By contrast, say for dogs, the LCA with humans belongs to the boreoeutheria, with an LCA time perhaps 10-20My earlier, so dogs are somewhat less well matched; and in the other direction, the LCA for old world monkeys and humans would be the stem group of the catarrhini, about 35 Mya, and for chimps maybe around 6Mya (making chimps and monkeys by far the most related, but leading to correspondingly greater ethical issues).

By the way, it's worth noting that there are two sources of indefiniteness in the dates. One is lack of knowledge, heavily affecting the older dates (maybe 10My or so), subject to improvement as more fossils are discovered and better dating is applied. But for the more recent dates, the greater difficulty is that taxonomies on the fine scale are actually networks rather than trees, so that even the definition of LCA is a bit fuzzy. For chimps, for example, a reasonable value could be anywhere between about 4Mya (last exchange of genetic information) and 7Mya (when the trees fully merge). This source of indefiniteness is unlikely to disappear.

To elaborate and improvize on my my part, three tools can be used to aptly cater your needs as mentioned:

1) use appropriate and widely practiced tool to annote the variant (i sugget you to use etfSNP (review for spelling) if you are dealing with SNPs. There is an automated system to identify the vriant and its possible effects known VEP i.e. Variant Effect Predictor.

MOST IMPORTANT FACTOR: Seuencing plays a pivotal role. Genetic Sequencing (i suggest you to use Sangers procedure if you have shorter reads and Illumina if you have longer reads). But make sure you perform the sequencing as per the required scenario (don't over spend making it less cost-effective).

Database gives you the apt results but make sure to incorporate databases menat for specific event such dbSNP is a databases of SNPs having genomic annotations. I would ClinVar to check for clinical relevance and pathogeneity of the variant indentified making it helpful to formulate molecular or genetic therapeutic interventions. People usually refer to genomeAD which have database of allelic frequencies which can help to cater the needs which aims to analyze Population of particular race or ethnicity giving a better understanding of Population Genetics. I rather prefer to use ExAC.

DNA plays a crucial role in the genetic process in humans by carrying the genetic information that determines characteristics and traits. This process involves several key steps:Replication: DNA replicates itself during cell division, ensuring each new cell receives identical genetic information, Transcription: Information encoded in DNA is copied into messenger RNA (mRNA) by RNA polymerase in the nucleus. mRNA carries the genetic information to ribosomes in the cytoplasm, Translation: mRNA is decoded by ribosomes, and amino acids are assembled into proteins based on the genetic code. and

Gene Expression: Genes are expressed through transcription and translation, synthesizing functional gene products like proteins. Gene expression is regulated by various factors.

Inheritance: DNA contains genetic instructions passed from parents to offspring through sexual reproduction, contributing to the unique traits of offspring.

Reading a bit late in the game, but I'm actively using "Hapl-o-mat" - it's pretty good, easy to use and open to a range of applications: https://github.com/DKMS/Hapl-o-Mat

No specific recommendation, however, I have recently come across this resource: https://gnomad.broadinstitute.org/

Oliver Boakye Danquah Thank you very much

I have VCF file and want to filter only SNP using Ms Excel. How should I do it

Brian Thomas Foley. I had tried that I'm getting some hits. Thank you for your suggestion.

José Germán Serrano-Gamboa

@ Assembly will be done in a Linux workstation.

Well, on the browser page in hg19, there are a lot of track options, find the TFBS and choose the way to see them, refresh and you'll see it in the browser.

all the best

fred

I do find this article belong to this research question.

The way that the media reports Black civil-rights protests has contributed to the long delay in reckoning with anti-Black racism, argues media researcher Danielle Kilgo. Kilgo and her colleagues used linguistic analysis to quantify narratives from newspapers, websites and television, mainly in the United States. The results reveal that civil-rights protesters are the least likely to have their concerns and demands presented substantively, compared with protestors focusing on other issues, such as women’s rights or gun control. “Less space is given to protesters’ quotes; more space to official sources,” she writes. “The dominant narrative accentuates trivial, disruptive and combative actions.”

Basim Mahmood interesting question and am sure people and experts from your domain would definitely look at this and will have some sort of discussions with you, however my core is Biogas and anything related to Biogas i would happy to collaborate

Genetic diseases can run in families. Genetic testing helps in identifying the underlying cause in many families. Before testing we need to try to find out the possible inheritance pattern from family history or constellation of clinical findings. This would help in making a specific diagnosis or keep few differentials and go for advanced testing. Secondly, find out which test really needs to be done? For example, if child with intellectual disability or autism comes, the preferred test would be a chromosomal micro-array; as per prevailing recommendations. Thirdly, appropriate counseling needs to be done regarding the expectations and limitations of test. Fourth, if cause is identified and disorder causes significant morbidity and mortality, a prenatal diagnosis in next pregnancy or rarely pre-implantation diagnosis can be planned to facilitate prevention in the family.

Cons: Genetic testing may not be able to give an appropriate cause or diagnosis in some cases. In such cases, maternal illness during pregnancy may be responsible in few cases, or pregnancy or delivery complications may be underlying reason. Further advanced evaluation may be required in few cases which may be financially not feasible. In these cases also appropriate genetic counseling and alternative options and follow ups should be advised. Sometimes, a positive report may add to the anxiety especially in adult onset pre-symptomatic testing; and good counseling and consultation with psychiatrist may be required

I agree with @Pascal Rihet

CNVs are usually detected by techniques like chromosomal microarray and MLPA. But nowadays, advanced NGS technologies enable also CNV detection along with SNV detection. The Read depth and Pair end mapping/End-pair mapping are usual algorithms used for CNV detection using several statistical models or clustering approach. Markov model is also used. CNVs can affect 15% of the human genome and lengths can vary from 50 bp to 1 Mbp. They can also be intragenic. Decrease in copy at particular locus is deletion and increase in copy can be duplication or triplication..

There is an interesting preprint showing this:

They show that there are around 21,306 protein-coding genes and 21,856 noncoding.

product release this month.

It is reported that there are many "RR type" players in measurement items that require instantaneous power such as back muscle strength and long jump.

I think Rutger A. Vos is very right when he said: "usually yes, unless something else is going on". This "something else" is quite important, because there are several possible reason why we might see or not see IBD (as already mentioned). You have to take the demographic history of your populations into consideration, when interpreting the results.

I saw that some people suggested to do a Mantel Test and I feel the need to intervene. One has to be very very careful, when using the Mantel Test to test for IBD and/or IBE. It only gives reasonable results under very specific circumstances (see Guillot and Rousset(2013) or Meirmans (2013)).

A much better approach is redundancy analysis (RDA). Although, when using RDA a few things have to be considered as well (see Gilbert and Bennett(2010) or Meirmans (2013)).

Guillot G., Rousset F. (2013). Dismantling the Mantel tests. Methods in Ecology and Evolution 4(4). doi: 10.1111/2041-210x.12018.

Meirmans P. (2015).Seven common mistakes in population genetics and how to avoid them. Molecular Ecology 24(13). doi: 10.1111/mec.13243.

Gilbert, B., and Bennett, J. R. (2010). Partitioning variation in ecological communities: do the numbers add up?. Journal of Applied Ecology, 47(5). doi: 10.1111/j.1365-2664.2010.01861.x

Thank you very much.

Antonino Colanzi , Institute of Protein Biochemistry, National Research Council, Naples, 80131, Italy; Telethon Institute of Genetics and Medicine (TIGEM), Naples, 80131, Italy.

Song-Tao Liu , Department of Biological Sciences, University of Toledo, Toledo, OH 43606, USA.

Xiaomei Lu , Clinical Medical Research Institute, First Affiliated Hospital of Xinjiang Medical University, Xinjiang Uygur Autonomous Region, Urumqi, 830011, People's Republic of China.

Most methods will assume independence among sites as a part of the underlying model. Building on Jamie's example, the likelihood of assignment for a two-site multilocus genotype of A₁/A₁ , B₁/B₂ to either population "X" or "Y" could be calculated like so:

If "X" has frequencies:

A₁: 90%

A₂: 10%

B₁: 60%

B₂: 40%

And "Y" has frequencies:

A₁: 30%

A₂: 70%

B₁: 20%

B₂: 80%

P(genotype| ind is from X) = P(A₁|X) * P(A₁|X) * P(B₁|X) * P(B₂|X) = 0.9*0.9*0.6*0.4 = 0.1944

P(genotype| ind is from Y) = P(A₁|X) * P(A₁|X) * P(B₁|X) * P(B₂|X) = 0.3*0.3*0.2*0.8 = 0.0144

So, the probability of e.g. genotype A₁/A2 being sampled from population X is the probability of sampling an A₁ allele multiplied by the probability of sampling an A₂ allele. We do the same for locus B, but this assumes that the probability of sampling an allele at locus B doesn't depend on the genotype of locus A. If the occurence of alleles at any two sites is biased e.g. by their physical linkage, then the assumption inherent to this calculation is broken. Note that another assumption we are making is that the probability of sampling a given allele just depends on its frequency in the population. (Also, this is a simplified version for the sake of demonstration)

Some methods consider LD among sites, such as the more recent version of Structure (Falush et al 2003), although as Binod mentioned, genetic drift can drive spurious results (Kaueffer et al 2007). As a result, most studies of this kind will sub-sample a single SNP per locus, and test for LD pairwise among loci. Some methods derive information from among-site LD though (Malinsky et al 2018). Final takeaway is that you'll need to think about the assumptions being made by a given analysis.

As an update, I'm not sure these will ever be 100% reliable. For us, it's been mostly reliable. That's why we genotype at weaning and after sacking, just to make sure. Originally, we were using the suggested PSEN1 primers with the internal controls about 4 years ago. I got us to switch to APP when the PSEN1 stopped working reliably. It's a struggle that's compounded by issues we were having with DNA extraction. I think the key is having a stable positive and negative control, as what a "positive" band for the 5XFAD mutation is not 100% consistent from week to week sometimes. Our main issue was that the internal control wouldn't show up in every lane, preventing us from knowing what a WT was and what reaction just didn't work. Another issue I found is that you can get a positive 5XFAD band (in a WT animal) if you simply load in too much APP/PS1 primer into the reaction which again, is why the positive and negative controls are so key. For what it's worth, this is the protocol we're using:

5xFAD primers:

oIMR3610 5’ AGGACTGACCACTCGACCAG 3’ - APP

oIMR3611 5’ CGGGGGTCTAGTTCTGCAT 3’

oIMR1644 5’ AATAGAGAACGGCAGGAGCA 3’ - PSEN1

oIMR1645 5’ GCCATGAGGGCACTAATCAT 3’

oIMR7338 5’ CTAGGCCACAGAATTGAAAGATCT 3’ - IL-2 Precursor (internal control)

oIMR7339 5’ GTAGGTGGAAATTCTAGCATCATCC 3’

Prepare PCR master mix (X # of samples + 1-2) (5XFAD APP)

4.0µL Water (PCR Grade)

10µL RED Extract-N-Amp PCR Reaction Mix (Sigma R4775) (Kit – XNAT2)

1.0µL Internal Control Primer (IL2) For (oIMR7338) (75µM stock)

1.0µL Internal Control Primer (IL2) Rev (oIMR7339) (75µM stock)

1.0µL APP Primer For (oIMR3610) (30µM stock)

1.0µL APP Primer Rev (oIMR3611) (30µM stock)

Add 18µL master mix to 2µL extracted DNA solution

Use 5XFADAPP program on PCR

94°C 2 min

10 cycles of: 94°C 20s, 66°C 15s, 72°C 10s

25 cycles of: 94°C 15s, 66°C 15s, 72°C 10s

72°C 2 min

Hold at 4°C

Prepare PCR master mix (X # of samples + 1-2) (5XFAD PSEN1)

4.0µL Water (PCR Grade)

10µL RED Extract-N-Amp PCR Reaction Mix (Sigma R4775) (Kit – XNAT2)

1.0µL Internal Control Primer (IL2) For (oIMR7338)

1.0µL Internal Control Primer (IL2) Rev (oIMR7339)

1.0µL PSEN1 Primer For (oIMR1644)

1.0µL PSEN1 Primer Rev (oIMR1645)

Add 18µL master mix to 2µL extracted DNA solution

Use 5XFAD program on PCR

94°C 3 min

35 cycles of: 94°C 30s, 58°C 1min, 72°C 1min

72°C 2 min

Hold at 4°C

Hi Mahmud Hassan ,

Indeed the kind of experiment you use is too specific for general domain predicting tools (unless you know that there is a particular protein domain already observed to play a role in this process).

Despite that, testing deletions in silico is not so simples, especially if you want to make several deletions. One way I can think of is only possible if you have your protein PDB available. You can use Cytoscape [https://cytoscape.org/], and the RIN Analyzer plug-in [https://rinalyzer.de/]. RIN Analyzer created an interaction network between residues, allowing you to observe your protein as a network of nodes and edges. You need to read about the plug-in because it has tons of features and works with different other tools.

Then, you can use Interference [https://apps.cytoscape.org/apps/interference]. Interference allows you to create virtual knockouts, which reflects on a given set of topological parameters (You might need to send an e-mail to get it from the developers).

Please, check the version of Cytoscape to use both plug-ins because Interference is old. I also highlight that the use of these tools is an old protocol I tested, which provided some good results in a Protein-Protein Network, but I didn't test it in a Residue-Residue network. I don't know how it holds nowadays or if the developers still provide access to Interference. Nevertheless, this is a method I know, which is fast after you understand the tools.

Honestly from my experience, your email is too long, professors are too busy and usually wont read a long email. A simple 3 line sentence is sufficient and if they are taking new students they will email you for more information. This is what I wrote and recommend to my friends:

Subject: Prospective Master Student in XXX

Dear Dr.XXX,

I hope this email finds you well. 

I am writing as a potential graduate student at the University of XXX.  I am interested in your research involving XXXX. I was wondering if you were taking on any new students for the Fall 20XX academic year. 

I look forward to hearing from you.  

Thank you.

- I hope that helps!

You can visit this website http://www.sciomics.de/services/microarray-printing. Hope this helps

Regards

Yes, it definitely could be CYP2D*1/*4N4

It's quite important to list all tested SNPs. If rs3892097 wasn't genotyped then we couldn't distinguish *1 from *4

dear Ireneusz Stolarek

thank you for your help

Please see that my understanding of the issue is true or not.

Admixture LD means that in the ancestry admixture process the nearby markers are probably inherited from a common ancestor. Models such as RFMix for ancestry inference use Admixture LD to Relationship detection of markers or windows. As in Equation.

background LD is a within-population process that investigates the linkages between the nearby markers that are of the same population.

but I do not know how background LD in the Models such as SABER is used for the ancestry inference.

Best regards

Dear Mr and Dear Hatem Zayed

You've helped me a lot, Thank you.

I have another question. Is the acceptance of the article in this field that related to human genetics and data mining is complicated and difficult or is normal?

Best regards

Hi Andrea,

RNA found in both plasma and serum is normally fragmented RNA or small RNA , mainly miRNA, and is either bound to proteins or contained inside extracellular vesicles. If your target is to purify mRNA from plasma/serum then you have to be aware that this mRNA may not be the full length, original RNA, that you may be getting when isolating RNA from whole blood.

I personally recommend for you using one of Norgen's Plasma/Serum RNA Purification kits ( https://norgenbiotek.com/sites/default/files/resources/Plasma-Serum-RNA-Purification-Kit-PI55000-5.pdf). These kits covers a sample volume range from 50ul up to 5mL without using any phenol or carrier RNA. Unlike other purification kits, Norgen's kits uses Silicon carbide as its separating matrix instead of silica. Some literature has mentioned that silica-based technology with/without phenol has a bias towards binding large RNA sizes as well as a bias towards binding RNA with sequences containing high GC contents whereas Norgen's Silicon carbide doesn't have such a bias.

For the isolation of RNA from blood, this should be a straight forward isolation but you have to use a different isolation method for this. I also recommend using Norgen's Total RNA Purificiation kit (Cat. 17200) for this.

When isolating RNA from plasma/serum you should be expecting low RNA amounts (normally in the picogram range) therefore you can't quantify you plasma/serum RNA using conventional methods such as regular spectrometer or Nanodrop since they are not sensitive enough to quantify such low RNA amounts. I recommend using Agilent Bioanalyzer RNA Pico Chip for quantifying plasma/serum RNA. Also don't relay on the RIN values from the bioanalyzer chips cause it will be low and won;t reflect the quality of the purified RNA. RIN values are calculated based on the ratio of the 28S rRNA and the 18S rRNA and since plasma/serum doesn't have cells therefore the purified RNA won't have these two bands which will lead to a very low RIN value. The best way to evaluate the quality of the purified RNA is to amplify a highly abundant small RNA target such as the 5s rRNA or to amplify a highly abundant miRNA such as miR-21. I believe that what Norgen does when performing small RNA sequencing services. https://norgenbiotek.com/services/small-rna-and-microrna-next-gen-sequencing

Sorry for the long message but I just want to give you some detailed information so you don't face any issues through out you project.

I hope this answered all of your concerns.

Best Regards and Good luck.

You can use gnomAD (https://gnomad.broadinstitute.org/) to find variants and mutations found in a large dataset of whole exome and whole genome sequencing.

You can search in ClinVar for any variants/mutations with clinical significance (https://www.ncbi.nlm.nih.gov/clinvar/?term=nrcam%5Bgene%5D)

How I can isolate WBC from whole blood ?

Hello, see below info good example.

Good luck !

It look likes that the second method is more reliable. When the haplotype frequency equals to zero in cases and/or controls, empty value would be appeared.

Dear Martin and Ana do you think the number of repeats (adjacent to mutation) between patients and healthy candidates can help. If yes how many missing STR repeats (due to slippage) per generation possible?

The Cas9 protein is commercially available and works in in vitro reactions.

A couple drawbacks though, the specificity is not perfect, sometimes even with up to 3/4 mismatches, efficient cut can occur and what is responsible for this lack of specificity is not yet fully understood. your cut site has to be adjacent to a PAM motif (NGG with the classically used Cas9 from S. pyogenes) and you would likely have to produce an sgRNA for each of the cut site you want to use.

You should find updated data at this adress: hive.biochemistry.gwu.edu/review/codon

See:

Well, I have solved this problem by reading papers.

1. Doing genome-wide PCA requires merging the files together. The good news is that SNPs used in PCA should be pruned or "thined". So typically only about 20,000 SNPs are used, making the merged file small.

2. PLINK file is recommended to be converted to EIGENSTRAT format using convertf. The convertf tool accept PLINK format as input, but smartpca does not.

Hello, to start with Rst calculations might be a bit problematic with Y-STRs for a number of reasons... To start with, all those haplotypes with (a) null alleles, (b) partial alleles (e.g. DYS458*18.2, etc), and (c) multi-allelic patterns (e.g. allelic duplications), etc have to be removed prior to analyses. This is something automatically done with the YHRD AMOVA/MDS tool, and one will have to to do something parallel with Arlequin. This would still be fine with some population datasets, but consider a population with a higher frequency of partial alleles, say Semitic populations with the DYS458*.2 variants, which mostly correlate with te J1 haplogroup, and which may make up to nearly 50% of the given population if not more. Do you really want to cut the population dataset in half or even more prior to the Rst analysis, even if this would mean cutting out perhaps the most relevant haplogroup for such populations? Alternatively one can remove the entire DYS458 locus which would be a more conservative approach, but which would in turn reduce the data resolution. As a result of problems like these and others, we moved to the use of Spagedi software for Rst calculations at one point, and ultimaly we now normally prefer to do allele frequency-based Nei's DA distance calculations instead of Rsts. In our hands, Nei's DA distance based estimate and visualisation via phylogenegtic trees and MDS plot seem to yield more reliable results with Y-STR datasets, though we still do calculate Rst at times, and only after cutting down our datasets to reduce above explained 'Rst unfriendly data'

An individuals genetic sequence will not change as a result of exercise training or even a lifetime of a particular type of training. However, over evolutionary time populations living where their environment requires them to adapt to specific physiological conditions, particularly those of interest to exercise genetics, will take place. For a recent example read this paper: https://www.cell.com/cell/fulltext/S0092-8674(18)30386-6.

They show that in a population that require aquatic breath holding to survive (i.e. hunting for food etc.), adaptations in a gene responsible for increased spleen size providing them with a larger reservoir of oxygenated red blood cells (PDE10A gene) were naturally selected and would thus be a useful gene to study in the content exercise generics for endurance athletes maybe.

However, it is well know that any change in the cellular environmental conditions will result in gene expression changes to adapt to that particular environment at that particular time i.e. muscle expression during resistance exercise. But according to the "central dogma of molecular biology" you need the beneficial blueprint (DNA sequence) to elect the beneficial expression (as a result of a specific training stimulus) and get the beneficial result (optimal athletic performance).

Hope that helps,

Shane

Dear Sir, this is very useful source about DNA evidence I would like to share with you. https://www.nij.gov/topics/forensics/evidence/dna/basics/pages/identifying-to-transporting.aspx

Briefly, if you refer to human mitochondria, the most accepted hypothesis is that only the head and part of the neck of the spermatozoon enters the egg in the fertilization. The head is mainly composed by a compact nucleus and a very small cytoplasm. After the neck, the middle piece is found: here lie paternal mitochondria, which do not enter the egg, avoiding paternal mitochondria inheritance.

From an ecological point of view, the strategy of spermatozoa is to produce a lot of gametes with the minimum cost (reducing size and removing/modifying non-useful organelles) and try to improve the probability of fertilize an egg. In the case of the egg cells, only few are produce with a big cost. They are big (0.15-0.20um) and provide with all the cellular machinery and nutrients needed for the zygote, which includes mitochondria.

I concur with Abhijeet: No.

It will not express embryonal, or tissue-specific genes (Except of course those from the specific differentiation state these cells are in). And of course some genes are only expressed upon stimuli (radiation, virus infection, IFN, LPS) if you are looking for a broad source of expressed genes, I would probably recommend whole mouse embryos (and even there......(see above)). However at VERY low levels you might find almost every gene in almost every tissue (And I mean V E R Y low)

Thomas :-)

Animal cloning from an adult cell is much more difficult than from an embryonic cell. So when scientists working at the Roslin Institute in Scotland produced Dolly, the only lamb born from 277 attempts, it was a major news story around the world.

To produce Dolly, scientists used an udder cell from a six-year-old Finn Dorset white sheep. They had to find a way to 'reprogram' the udder cells - to keep them alive but stop them growing – which they achieved by altering the growth medium (the ‘soup’ in which the cells were kept alive). Then they injected the cell into an unfertilised egg cell which had had its nucleus removed, and made the cells fuse by using electrical pulses. The unfertilised egg cell came from a Scottish Blackface ewe. When the research team had managed to fuse the nucleus from the adult white sheep cell with the egg cell from the black-faced sheep, they needed to make sure that the resulting cell would develop into an embryo. They cultured it for six or seven days to see if it divided and developed normally, before implanting it into a surrogate mother, another Scottish Blackface ewe. Dolly had a white face.

From 277 cell fusions, 29 early embryos developed and were implanted into 13 surrogate mothers. But only one pregnancy went to full term, and the 6.6 kg Finn Dorset lamb 6LLS (alias Dolly) was born after 148 days.

There are 20-25000 protein coding genes in human and we are able to manage with fewer genes due to multitasking by these genes. Multitasking is facilitated by formation multiple transcripts from the same gene. Multiple transcripts are formed by alternate splicing, alternate promoters and alternate poly A sites.

In addition to protein coding genes there are hundreds of small RNA coding genes which are regulatory RNAs (example miRNAs)

CRISPR is Clustered Regularly Interspaced Short Palindromic Repeats. These are the hallmark of a bacterial defense system that forms the basis for CRISPR-Cas9 genome editing technology (a technique).

Attached figure is a sgRNA (“s” for single, they’re the same thing). sgRNAs were artificially made by humans and don’t exist in nature.

This gRNA design is based off of the crRNAs and tracrRNAs which naturally exist in nature. Nucleotides 1–32 is the naturally-occuring crRNA. Nucleotides 37–100 is the naturally occuring tracrRNA. Briner et al. added a GAAA linker between the two pieces to make them one single RNA piece. The main purpose of this is to simplify the CRISPR system so you don’t have to express three things (i.e. Cas9, tracrRNA, and crRNA). Instead, you just need Cas9 and sgRNA). This simplification is important when you start dealing with CRISPR applications; the less moving parts the more efficient the system.

Source: https://www.quora.com/Whats-the-difference-between-crRNA-tracrRNA-and-gRNA-in-CRISPR-Cas9

Depends on what you mean by activation.

When you have the naive T cell, the APC and the relevant antigen present (in vitro): If measuring activation by secretion of IFN-g, it takes 24-48 hours to see a measurable response ( by CBA, ELISA or ELISpot). If measuring CD107a expression, you would see a response within 6 hours. If looking at T cell proliferation as a measure of activation, it takes 5-6 days.

In any case, the time required for activation would depend on a multitude of factors including the reactivity of the donor T cells, immunogenicity of the antigen ( strong antigens need less time), the nature of the antigen (peptides take less time compared to full length proteins), the antigen presenting capability of the APCs and so on.

I am not sure in animals. In plants, we use colchicine for chromosome doubling. For example, a developed haploid line (ex. developed from anther culture) can be induced into double-hapolid (DH) line through chromosome-doubling agents. DH lines are important sources for plant breeding.

I also found this online:

How do you know if you have identical twins?

Your ultrasound technician may be able to tell whether your twin babies are fraternal or identical by looking at the placenta. If you're having twins that are fraternal, they will have their own placentas.

So, even before they were born, one still have a chance to tell whether they are identical twins if ultrasound was taken.

The nature of RNA transcript and the fact it works under different environemntal forces in cells, that makes the transcript liable to different secondery as well as tertiary structures such as hair pins, inner loops or circular loops, as the internal complementarity plays a role here, we usually study thermodynamics of RNA sequence to give us insights about RNA folding.

there is no general problem using short sequencing primers ( pyrosequencing works well with them) so long as they only anneal at the right position and have no looping self complementarity and can anneal at the extension temperature of the sequencing kit but how short are you thinking. If the problem is getting as much sequence as possible from a short template then clone it and use vector primers to sequence and get the whole template sequence

Dear Mushtak,

your question sounds very broad and general. Most of coevolution between plant and pathogen happens in pathways related to defense and anti-defense mechanisms. Half of the answer you can find in recent work devoted to Solanaceae: Moghe GD, Leong BJ, Hurney SM, Daniel Jones A, Last RL. Evolutionary routes to biochemical innovation revealed by integrative analysis of a plant-defense related specialized metabolic pathway. Elife. 2017 Aug 30;6. pii: e28468. doi: 10.7554/eLife.28468.

Alex

I think that one major difference is the amount of work then analysis of results required between the two techniques. Homozygosity mapping is based on a mutation happening in a founder person on one chromosome. The change will be passed on to the children along with half of the founder genome but still linked to close marker alleles. With each generation the uninteresting part of the genome is halved to the next generation but the change and closely linked markers will always stay together. If we have an inbreeding situation 6 generations down then the change will be inherited from both parents and the child will be affected ( if heritance is recessive.). The close markers will also be inherited so a region of homozygosity by descent is created and the change is in that region.

In this case and assuming the change is rare in the population then the generations of the family have done most of the work and after 6 generations 1/(2exp6) of the genome may contain the gene and 63/64 has been excluded so not too many more markers ned to be run in areas of interest which are obvious by visualisation since they are homozygous for the markers nun. Homozygosity mapping can be carried out on affected individuals only without needing other relatives but exclusion mapping has an absolute need for parenal dna and other family members dna to establish linkage

Exclusion mapping excludes areas where the gene is not so hundreds of markers have to be analysed on a number of families for linkage and some of these may be linked but not informative and the analysis is more complicated . Recent chip technology and NGS has changed this greatly as the amount of information gained on each sample is huge

Hi Emerson

Codon optimization is always a good practice when you are dealing with such amount or rare codon species. So, I will definetively advice you to use codon optimization. Many companies that do gene synthesis also perform codon optimization by propietary algorithms (See for instance Genescript: https://www.genscript.com/codon-opt.html). However the application that you cited is very good and reliable.

Many other bottlenecks can appear in your protein expression experiments, and it is not so easy to describe all of them. If you have access to several E.coli strains, do a expression screening in all of them and check how your protein behaves. If everything goes wrong, try to low the expression temperature (to 20-25 ºC) and do longer expressions (10h to ON). Also it is advisable not to use very high IPTG concentrations for induction. It is amazing but tac-lac promoters are almost saturated with 0.2 mM IPTG, and you will easily see "good" papers claiming that they induced at 0.5-1 mM. IPTG is toxic for the cells, and at those concentrations you can see nasty effects.

Other options that you can explore (specially if you are as lazy as I am), is the use of autoinduction media.

Hope it helps. Best

Paco

Such researches are not at all required on human.

You can perform many genetic researches on birds and animals (as their life span is short) to get the trend which may be found in human. Many studies were performed in the following manner.

1. Half sib and full sib mating ( cross between brother x sister).

2. Test cross.

3. Back cross ( father x daughter, mother x son cross) etc.

By many such studies, it was clear that offspring of close relatives have very high chance of gathering of bad genetic characters ( as homozygous recessive Gene gatheration ) and so chance of delivering abnormal offspring is more.

IT IS SCIENTIFICALLY WRONG TO MARRY IN CLOSE BLOODS.

Yes HWE is necessary to test. If the alleles you are testing are in HWE, it represents the real population and your results are justified and correct.

Dear Mary,

I would be happy to help you with this! You can contact me at l.evers@nimagen.com

Thank you Dr. Masuma. I received it now from the company Anatolia Gene Works.

Best Regards.

@Saad Alaraji

Is it clinically accepted that somatic mutation profile can be used to detect breast cancer?

Can you provide some clinical reports regarding that?

 Thank you Umair.....your suggestions help a lot

The next questions might give a hint to the answer.

What are the variant/variants in question?(are these from genome build 37?). Please provide one or more examples.

How did you get the CADD annotations?

Is your gene annotated in Ensembl 75 at http://grch37.ensembl.org/index.html ?

Ming Tang,

sorry for delay.

Here

 Hello,

For known clinical significance: you need to screen detected variants against Clinvar database. Clinvar is a experimentally repository of clinically important mutation.

For novel variants: if detected variant is novel, then you can still assessed it's clinical significance based on several insilico tools, such as SIFT, Polypjen2, Mutation tester, Mutation assessor etc.

Matej, Abel, 

Thank you, I'll try those out!

Dear Dr Mahmood

Your question reminds me of what used to be done before sequencing became the standard practice. This method used to be known as clamped homogeneous electrical field electrophoresis.

Your choice of endonculease would be important here. If it were to cut at too many places the gel will be very difficult to understand. If it cuts at two few you will probably miss things.

It may be that you would be better off with an endonuclease that cuts at methylated bases. Others here may be able to better advise you on this point

Hi Rami,

Please see attached the instructions and an example input file I wrote a decade ago for Haploview input file generation:

1) PDF file: Instructions for generating Haploview input files

2) XLS file: an example of raw data and input files (check out all tabs)

I hope this helps.

Best,

Naser

What exactly do you need? There is plenty of data published in PubMed. Or you need certified or FDA approved examples of tests? 

Thank you all for your valuable replies and suggestions.

Vikas