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Microsatellites - Science topic

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Through Arlequin, I'm hoping to learn how to study population genetics (e.g., Fst values, Hardy-Weinberg analyses) using DNA sequences. Perhaps you could give screenshots of the files you have or a step-by-step guide on how to perform it. All I know is how to use Arlequin to analyze microsatellite data.
Your response will be greatly appreciated!
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One easy way is to load the entire FASTA (or Nexus) file in DnaSP software and define sequence sets. Defininf sequence sets simply means grouping samples (in this case sequences) into the defined populations. To do this in DnaSP, go to Data<Define sequence sets, then assign sequences to their respective populations. After this, you need to save the defined sets as *.hap and *.arp files. Then load the *.arp file into Arlequin, select the parameters you are interested in, and run. The results (look for file named _main.htm) are written in the folder with the *.arp file. Good luck!
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Hi dears,
Hope everyone doing best. Anyone has access to Excel Microsatellite Toolkit (Park, 2001).
I need it for analysis of SSR markers data.
Thanks, and Regards
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GenAlEx (https://biology-assets.anu.edu.au/GenAlEx/Welcome.html) is a famous excel add-in that can analyze the microsatellite data. For an example, check our paper:
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if samples are limited, e.g., less than 10 samples per population, which molecular markers are suitable(Microsatellites or SNPs generated by RAD-seq)? If you can provide some references for your opinion, it would be better. Thank you for your attention!
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I will go for microsatellites
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The STR markers I am working on are linked to Cystic Fibrosis Transmembrane (CFTR) gene and I wonder where should I check if these STR have previously been reported or not.
Cheers,
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Katie A Burnette I realized that the NCBI Probe database is not available anymore. Through the UCSC Genome Browser, I managed to find di- and tri-nucleotide microsatellites.
Have you got any clue how can I find tetranucleotide STR markers that have previously been reported for my gene of interest, CFTR?
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We are working with DNA samples extracted from faeces, so we are trying to find the optimal PCR conditions to amplify microsatellite markers, as the nature of the samples require quite an effort.
On the image with the stuttering zones is represented the second test we performed in order to see what conditions are optimal.
We used the same PCR conditions with the expection of the following:
-Finan Extension was a)68 oC b)72 oC for both for the duration of 30min as stated by the Qiagen PCR Multiplex Kit protocol
-T annealing both times was 60 oC
What could cause such an utter mess? Is it possible the higher temperature on the final extension step do this?
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Hi Nikos,
You may have a problem of primer specificity, particularly problematic in a mixture of DNA from different sources. >40 cycles seems excessive, assuming the major source of DNA in the faeces is from R.r.balcanica. Such a high number of cycles is allowing for amplifications of ‘contaminants’, eDNA that matches your primers at lower specificity, but still enough for showing up at >40 cycles.
The fact that the problem in also getting good profiles in hair samples points to a poor pair of primers. If they don’t work properly in normal samples after optimization, you should probably consider avoiding it. If possible, try to use tri or tetra repeats, as di are often a source of confusion.
In any case, the 2nd profile you post here is strange, I would not also exclude some technical issue. You should also try again or in a different sample. Run always a positive control with a good DNA sample from the target species.
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I would like to construct a dendrogram by microsatellite data, I have progeny trials that consist of 72 individuals, 2 from location A, 2 from location B, 63 from location 5 from location D. Is that okay to run the analyses to omit the tree, if possible is there any justification?
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In general, yes, a samples size of 2 individuals is not enough to accurately estimate allele frequencies (if it was genomic data, that would be a different story).
However, you can also estimate genetic distance between individuals instead of populations and create a dendrogram based on those distances. See some of the functions in the poppr R package for examples (https://grunwaldlab.github.io/poppr/reference/diss.dist.html).
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Hi, I have a study in which I have approximately 250 samples (plant material). Microsatellite makes have already been developed and tested for this species, and I would like to know if anyone have experience with sending away material for isolation and sequencing, and if yes - the name of the company/institution? I just don't have the time to do it myself, and also no time to train lab-tech for the task...
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Will you send your samples to India?
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Hello, I am doing a microsatellite PCR and the bands appear with a few definition, the electrophoresis conditions are the following, TBE buffer 0.5X pH 8.4 (according to Sambrook, Molecular Cloning), 45 mA constant or 100 V constant (I've try both conditions), 8% polyacrylamide gel (according to Sambrook, Molecular Cloning) (8 cm long), loading of pPCR 3uL + 1uL Gel Red [1: 2000]. What can be my mistake that the bands are thick and have little definition?
Thank you.
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Make sure that the gel is cast with freshly made reagents especially the APS. Wash out the wells thoroughly with buffer before loading the sample. Use less dna ( smaller volume and lower amount of dna) in the sample and make sure that the sample and loading dye are well mixed before loading
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I am running DIYABC on microsatellite data and all the data that was accumulated over the last 8 days still exists (the project file is over 25GB now), but the program is not recognizing the last run.
It has crashed before, and restarted without a problem. Now, when I re-open the microsatellite project it is starting me from 0 "Number of already simulated data sets in the reference table". Has anyone encountered this before? Any ideas?
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So sad and frustrating to hear that! Have you already try to import your reference table? I mean, clone your project with the same parameters and try to combine your biggest ran with another smaller...
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I have made detection for microsatellite SSR markers in patient with plasmodium...
for statistical analysis, I have used genalex 6.5 ...
I still need to make confirmation with another software...
can anyone help me within this point?
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Arlequin and GenePop are a good softwares.
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unlike amplifies coding region or gene via PCR which shows simple single band pattern in single well but microsatellite is some what confusing that shows multiple banding pattern while screening on gel electrophoresis. so how can i interpret the screening results of ssr via gel electrophoresis. as the attached image shows multiple band in same well.
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Sorry for the delay. Check my earlier answer now it is clear. In my case it was different and had clarity the moment you see the gel you can identify the polymorphism.
You have not mentioned what you are working, if you cannot share publicly you cans send msg, so I only can see that and try to advice.
apart from that you mentioned Primer A to F. Are they different primer sets!!!
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I am having an assembled fasta file (~3.7 GB in size). I tried using the web based MISA v2.1, but it accepts fasta upto 2Mb only. Is there any other alternate software which can be used for a large size dataset for filtering SSRs?
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Dear Nazima, You may consider GMATA tool.
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I am interested in assessing the inter- and intra-specific variations in fish using genetic markers. RAPD, cytochrome b, COI, microsatellites are mostly used for the evaluation of genetic variations. Can you please recommend me the best marker to be used.
Thank you.
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Good, definitely you can see variation exists. But need to check with different Primers as some of the primers do not show any variation.
Probably you might have started screening.
Good Luck!
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Hello,
I have developped 4 markers to detect microsatellite instability in a cancer type using digital PCR.
I would like to determine the discriminatory power of each marker, meaning the cut-off (mutant allelic frequency) to classify a tumor as microsatellite instable (MSI) or microsatellite stable (MSS). For each marker, I performed ROC analysis using 7 MSI tumors and 20 MSS tumors but I was wondering if ROC is the right analysis for this purpose. Could you help please ?
Best,
Khadidja
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Go and check the guidelines for interpretations of forensic samples typed by microsatellites. They have done huge anount og experimental work to be able to separate artifacts like stutter and pull up from true alleles in samples with more than one contributor and where the amount of DNA is different from the contributors. This is analog situation to your samples with normal tissue and cancer tissue.
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I am running DIYABC for both microsatellite and mtDNA data and testing 3 scenarios. Once I start the analysis I get the following error message:
"Something happened during the reftable generation :
Program of thread 'Species_ABC reference table generation' exited (with return code -6) unsuccessfully.
not priormusmoy.fixed"
Does anyone new the reason and a way to fix it?
Thank you
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Hi Catarina,
I am experiencing the same issue with DIYABC as you described here; no matter how I adjust the parameter settings, the program run keeps stopping and giving me that error message. Do you remember more specifically what you did with the defined priors for Ne that solved this issue for you?
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Assume a hypothetical microsatellite region with the sequence AGTA. Towards the middle of the set of repeats, there is a repeat where the first A is replaced with a T.
Would that insertion split the microsatellite region into two separate regions, one with the original AGTA sequence and one with GTAA?
...AGTA-AGTA-AGTA-AGTA-AGTA-AGTA-AGTA-AGTA...
...AGTA-AGTA-AGTA-T-GTAA-GTAA-GTAA-GTAA-GTA...
I've seen literature regarding how to count repeat numbers in a situation like this, but I can't remember if there was a biological justification for the conclusion it drew (to only consider the longer remaining piece as the original microsatellite region) or if it was a shortcut to make computations easier.
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Well, two points in here, one about your question and another particularly about your example, but first things first.
Microsatellites might be classified according to their motifs as perfect, imperfect, interrupted, composite or a combination of some cases.
Perfect microsatellites is not interrupted by any base not belonging to the motif (e.g. TATATATATATATATA); Imperfect microsatellites have bases between motifs strange to the motif sequence (e.g. TATATATACTATATA); Interrupted microsatellites have small sequences between a single motif (e.g. TATATACGTGTATATATATA); Finally, a composite microsatellite contains two distinctive motifs (e.g. TATATATATAGTGTGTGT GT).
You might also have some combination of it like TATATA-GCTG-CACACA, maybe an imperfect-composite microsatellite, but for me still the same locus and therefore, the same microsatellite.
If I got it right you probably tried to point out a situation where a perfect microsatellite became a composite one (two motifs). However, if you read that again you'll find out the original motif just a couple of bases after the point mutation:
AGTA-AGTA-AGTA-T-GTA-AGTA-AGTA-AGTA-AGTA
Therefore, just an interrupted one... So in this particular case, same microsat with a point mutation. Moreover, if you are just counting repeats (like in a gel) you will never find the difference between the mutant and the wild version, since they have the same length. Otherwise, you might be able to evaluate the sequences mutation just like any other sequence analysis, considering not just the length but bases that constitute it.
Hope you find it helpful.
Cheers
Wilson
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selection DNA fragment for application microsatellites technicians, will be association in improvement performance of domestic population layers.
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Dear Majid
You can follow my page, because l am using many programs
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How to score microsatellites for analysis through NTSYS pc? Should I use some other program? which one is the best and available on line? Please guide me in details.
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Besides 0 & 1 format, you can directly write the exact allele size (band size). You may use them for genetic diversity analysis with the available softwares. Furthermore, band size based scoring will be useful for population structure analysis, association mapping analysis, marker profiling analysis, AMOVA etc.
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I have with me diploid data(genotypes)which is microsatellite markers.Can you suggest any software to produce the same fig,with pie chart as attached?
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For minimum spanning tree, you can use EdeNetworks.
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I'm working with sequence data from a set of closely related species and along with other indels and SNPs there are microsatellites regions in the sequences.
I was planning on building a character matrix using complex indel coding, which from my understanding requires the sequences to all share a 5' or 3' end. Where I'm running into trouble is that there is some variation in the repeats as well, with the occasional insertion or deletion giving me trouble aligning (ex. the repeats are mostly AT but there's the occasionally extra A or T).
I can't only rely on the microsatellite data, so is there a way to build a character matrix that differentiates the taxa based on the repeat number and the variations in them (insertions, deletions, and substitutions) rather than just depending on the length?
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Hi,
I am not sure if it fits your goal but you can check Müller 2006
Hope it will help.
Best regards,
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In my investigation on microsatellite I always get the results from Fig. E (the peaks around 160).
It looks like I have stutter pieks before the highest peak, but also after.
Before the highest peak there is a rising trend (normal). After de highest piek is a downward trend (not normal).
I used a tail-PCR. A forward primer with a tail at the 5' (around 40 bp), a reverse primer (around 20 bp), a fluorescent primer (around 20 bp). Each microsatellite has an other primerpair (I designed them).
I have done a PCR in simplex and a multiplex-PCR (with 3 fluorescent primers). Most microsat of both PCR's had the pattern of Fig. E.
Because this is a rare pattern, I was wondering how this can happen. What are possible causes?
Your sincerely
Thomas Raps
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Thomas, in general, the longer the repeat element, the fewer such problems with it.
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I have amplified the rpl32-trnL spacer region for five different grass species (trnL (UAG) and rpl32-F primers). Instead of seeing one consistent amplicon size, I have some fragments that differ by about 150 bp. Has anyone seen something similar in plants or grasses in particular? Could there be a microsatellite region that is being amplified that is causing this size difference?
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Hi Clarke,
I dont know anything about the locus you amplified, but I think it is not unusual that you get diverse fragments for different species.
I would say they have a simple length polymorphisms. Did you blast your primers to compare the sequences between the primers for these species ?
Regards
Stephan
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There is three molecular pathway for sporadic colorectal cancer is Microsatellite instability (MSI), Chromosomal instability pathway (CIN), and CPG island methylation CIMP.
So, how can we know their diagnosis?
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Hi Ohoud,
the methylation of SEPT9 promoter region has been proposed as a diagnostic tool for CRC. However, I think you still need to verify the results with a colonoscopy. Here is some literature:
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From my understanding, microsatellite repeats should be consistent (e.g. di-, tri-, tetra-); therefore, allele sizes should be consistently separated by 2, 3, or 4 nucleotides. I am working with a new locus that seems to have multiple repeat lengths. For example, there are repeats of 2 nucleotides, followed by repeats of 3, and then back down to 2. Has any one else had this experience with microsatellites? Is it a biological phenomenon, or likely just scoring error?
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This is microsatellite instability. Look https://doi.org/10.1038%2Fnpg.els.0000840
It is so called compound SSR.
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I am trying to estimate the null allele frequency in my Microsatellite data. For this purpose, I used Steven Kalinowski's software ML-Null.
You will find it here:
Has anyone ever used it? If yes, can anyone suggest it for publication purpose?
The software is quite easy to operate; uses a Genepop input file. However, it lacks the manual or description about wich approach or model is used for Null allele freq. calculation.
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Haven't used this one, but if it works fine for detecting null allele freqs I see no problem why it should not be suitable for publication purposes as well. But maybe double check the outcomes to be sure, e.g. using the R package PopGenReport (which specifies approaches in the documentation), e.g. see here: https://rdrr.io/cran/PopGenReport/src/R/null.all.r
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Dear all,
I am preparing a population genetics study at the moment, specifically a microsatellite-based approach to identify fly specimens from different regions. Currently I am at the step of establishing all of the microsatellite markers to assess which are suitable for the study - this requires a sufficient quantity of DNA to test all primers on the wild fly specimens.
As the wild fly samples are the most valuable in this experiment, I need to get as much yield as possible from the DNA extraction.
I have tried 2 protocols so far:
- Zymo research quick g-DNA miniprep kit: It delivers a low DNA yield when extracting from 6 fly legs; treatment with proteinase K for 1 hour before the standard protocol increases the yield around 5-fold to 30-40ng per μl. This would be barely enough for all PCRs, so I am looking for a way to increase the yield even more.
- in-house prepared homogenization buffer, consisting of TRIS-HCl, EDTA and NaCl. Following a quick and easy protocol to extract crude, non-purified DNA from single legs in 80 μl homogenization buffer, the leg is crushed, buffer added. The solution is vortexed and spun down, then 1.7 μl Proteinase K is added. The sample is incubated at 37 degrees C for at least one hour, followed by a short inactivation of Proteinase K at 98 degrees C. From this method, I could successfully amplify DNA with 1 μl template DNA from a fresh fly leg. However doing the same with a preserved wild fly leg, I don't receive the same result, even when using 4 μl template DNA.
The wild fly samples are conserved in propylene glycol as well as Ethanol. Before the extraction I take out the fly, let it dry and rehydrate the leg.
Any suggestions on how to enhance the DNA yield for one of those protocols?
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@Elena Sharova
Thanks for the reply. I crush the legs to a Powder using liquid nitrogen and a micropestle.
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Hi everyone, How can i input microsatellite loci data into nexus file? Or maybe theres a way to convert popgen file into nexus?
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You should check POPTREE2.
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I want to construct a NJ tree for my microsatellite data which should be supported with bootstrap values. I downloaded the software Populations 1.2.31. I did not find any manual for that so tried to deal with the short guidlines section in the software webpage but couldnt get any result. even I could not open the input into the software. Can sombody help me to get through this?
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You can use FigTree software to open your output file (saved in newick format [.nwk] ) to view your tree. There are some online tools as well such as ITOL (https://itol.embl.de) but you can edit your tree easily with FigTree.
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Hi! I'm analyzing microsatellites to hybrid fish (triploid, sometimes even tetraploid). I'm not sure if I do enough good. I send two pictures. Do you agree with my analysis? I think that the first specimens on the left are residuals.
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Hi Daniel
The scoring of genotypes using microsatellites is a tricky process.
I am sure you must be running multiple samples and will have to ascertain yourself whether the peaks (you referring to as residuals) are consistent in all your samples or in a few. Do consider the allele range while scoring. There is a very high chance that if a single peak is appearing consistently in all your samples, it might be an unwanted residual. Second, compare the structure of the peak (as Filipe Pereira suggested) with a reference peak. Third, do triplicate scoring for a few samples. It will help you avoid unwanted residuals and help identify the correct peaks.
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I am currently trying to create a dendogram from my microsatellite data, which consists of alleles from nine strains of nile tilapia using nine primers/loci. It has been recommended to me to use Phylip to infer the trees and then using either Tree Explorer or Dendroscope to make a reasonable image. Does anybody know if there is a better/newer method of creating dendograms or am I better off sticking with this software? Thanks!
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I following the best answer.
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I look for databases concerning morphometric measurements or goat microsatellites, either a database in EXEL form or a site from which I find what I'm looking for
📷
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Actually we've collected morphometric data only for our goats as research materials according to the research purposes. So very apologìzecally we aren't able to serve the morphometric data bases.
Further we haven't collececting and having microsattelite date bases.
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Fst is used in many genetic studies which involve different markers (mtDNA, microsatellites, SNPs...) as methodologies and next-generation genetics improve. I was wondering if it would be correct to compare Fst values obtained from microsatellites with Fst obtained by SNPs or rather should simply compare results qualitatively.
Thank you very much.
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Most of my microsatellite markers are dinucleotides, therefore they show stuttering. In regular stuttering is easy to identify the allele, which is the highest peak on the right, after the shorter stutter peaks (Fig. A). But in some samples, usually in larger alleles, the highest peak varies the position or all peaks show similar highs (Figs. B, C, D, E). In these cases, what is the allele: the highest peak (independently of position) or the peak on the right (following the position pattern)? Fig. E is an extreme case, with confusing peaks. In a specific marker, some samples show a central highest peak with two other shorter peaks: one ~30 pb smaller and the other ~30 pb larger (Fig. F). Is one of the shorter peaks a true allele or are both a type of artifact ("ghost peaks")? Finally, can I consider weak amplifications? Probably these shorter peaks are because of low quantity/quality of DNA or due to large allele dropout (Fig. G). I am scoring microsatellite data manually in Peak Scanner 1.0 (labels show height (H) and size (S) of peaks). In the PCRs, was used Platinum Taq DNA Polymerase, cycles with 30 min of final extension and primers forward tailed with M13. Due to limitations of time and resources, we won't be able to rerun the fragment analysis. Thanks for your attention.
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Thanks for sharing the links!
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Which is one is better in between 16S rRNA and microsatellite phylogentic study?
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16s rRNA study
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I am trying to determine if a highway is too much of a barrier for shrews to cross and breed freely. What I have to work with is ~1080 bp of the mitochondrial cytochrome b gene (we did not do microsatellites which most researchers do use and I think it is the source of a lot of my troubles using common programs and R packages). I have 3 sites north and 3 sites south of a highway and I want to see if the Fst values I have calculated through DNAsp are significantly different from zero or each other. Does anyone suggest a way I can organize my data so that I can put it in R to determine the significance or another route I should take? Thank you for your time!
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I hope this tutorial may help you (with FST estimation and other analyses):
*I may also suggest you to look for the snp-sites tool (or any other that may help you transform your sequences into other formats, such as .vcf, or .gen):
That will give you the freedom to analyze deeply with other tools (Arlequin, Genepop, Genodive...)
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I ran a three scenario analysis using DIYABC. Some output files attached. PCA looks ok and the 95% PP clearly indicates that scenario 3 is the most supported one, with no CI overlap. However, either I am not doing it well but the error is lower for scenario 2 than 3. Any idea why this happens when the remaining analyses clearly indicate that scenario 3 is the best one?
Thank you in advance
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Do you mean that Type I and II errors are lower for scenario 2? Could you provide details on the prevalence of these errors for each scenario? As Jose Alberto Lopez-Aleman suggests, looking at the regression and posterior plots might give some clues.
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I am using a trinucleotide SSR primer (169 pb). I expected that the allele sizes would be in 3 pb (e.g. 157/166) but GeneMapper software also detected sizes 156/165. More details about the allele sizes can be viewed in the attached file.
Should I round the allele sizes? Could anyone help me how to score this?
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How can I score the accurate microsatellite allele size?
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Male birds (30 generations of selection) belonging to three homozygous genotypes (DD, CC, BB) at ADL0176 microsatellite and females (4 dams to each sire) having corresponding homozygous genotypes were crossed avoiding mating between full and half-sibs with the help of pedigree record. The pullets raised out these straight-run chicks (progeny) when genotyped revealed 7 genotypes (AC, AD, BD, CC, CE, DD, EE) at ADL0176 locus. What could be the explanation behind the appearance of extra A and E alleles in the progeny?
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Good Answer Frederic Lagarce
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I have Microsatellite Data of buffalo and i want to associate with milk yield. Please guide me the exact model for this analysis?
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The association of the genotype to the phenotype will really depend on whether the markers(primers) have been developed from the gene/s or QTL controlling milk production and thats a proper association or else if your sample size is diverse a simple correlation of phenotype and genotype data works but doesn't tell you anything about using that particular marker to select for milk production
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I've run a couple hundred samples using a microsatellite panel, and recently some of the runs show very exaggerated stutter peaks smaller than the true allele size. To troubleshoot the issue, I've rerun the same PCR product multiple times on our ABI 3500. The problem appears to arise from the fragment analysis, rather than the PCR step, since the same product produces very different results from run to run. Some samples look fine in one run but the same PCR product shows the mystery peaks on a second run. Since some of them are showing up in each run, and the peaks are often much larger than the true allele peak (sometimes the true allele is even absent), this makes accurate scoring impossible.
I've posted three examples from three different samples and 3 different loci. The lower run for each is the one depicting the correct alleles, the upper run shows the large stutter peaks and small true peaks. In one case, the ghost peaks are 4bp smaller for a 4bp repeat, but in the other two they are 10.5bp smaller and 7bp smaller for a 3bp and 4bp repeat, respectively.
I've never had this problem before, but also am relatively new to running the ABI machine myself. I'm loading:
7ul of MClab orange size standard in SuperDi
1ul each of 3 PCR products (topped with mineral oil). They are diluted 2x first before loading.
the product was stored for ~1wk in the fridge before loading.
I've tried heating the plate to 95deg for 5 min then chilling and loading, to no avail. I've also tried re-running PCRs and the problem returns. Some plates look fine, and others are all bad.
Any suggestions on what is going on or how to fix it?
Thanks!
Kevin
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SOLVED: I believe i've figured this out. I tried again with fresh PCR product, my old size standard and formamide, and a different batch of size standard and formamide. Fresh formamide + size standard fixed the problem. The old size standard/formamide mix also worked if I denatured the plate at 95 degrees C for 5 minutes followed by snap cooling at 4 deg c for 5 min.
I found this issue described here, for other's future reference, on slide 58:
Basically, it looks like my DNA was hybridizing, probably because the old formamide was bad and couldn't sufficiently denature the fragments. Looks like I just rediscovered a common problem, but hopefully this'll help someone else more quickly find the solution.
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I have calculated the distance matrix in populations software. The data which i used is microsatellite data. I saved the distance matrix file in .txt format.But when I chose this same file to develop phylogenetic tree with distance matrix, showing "Matrix file format is not recognized.....".
Could somebody help me to sort this issue.
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Hi,
You can try cophenetic.phylo package on R program to compute the pairwise distances between the pairs of tips from a phylogenetic tree using its branch lengths. You’ll find it useful to visualize dissimilarity in the whole dataset using heatmap.
Best regards,
Saeed
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Previously for genotyping from mouse tissue, I have used an extraction method which involved SDS-proteinase k, followed by RNAse A and Isopropanol, then resuspension of remaining pellet in buffer for PCR. We never had an issue with amount of DNA with this method.
My current lab uses a phenol-chloroform extraction, which seems like 'overkill' for DNA extraction for the purposes of PCR and microsatellite analysis. Plus, there are the health and environmental impacts of using these materials.
Has anyone tried both methods - are there additional drawbacks or considerations to think about?
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Enzymatic method
Pros - clean, easy
Cons - expensive, cmparative lower yield, inhibitors might still be present
Phenol-chlorofom-isoamyl method
Pros - cheap, higher yield, removes inhibitors, most popular
Cons - needs more expertise, longer duration protocol, significantly hazardous
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statistical analysis with microsatellite data markers
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Ameur Ameur Abdelkader just sent you.
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I'm studying the polymorphism of a microsatellite related productive performance in 4 goat breeds and I would like to represent the distances between them according to its polymorphism (alleles and genotypes), which method is the most appropriate: PCoA, dendrogram,...? . Thank you in advance
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For me both are fine. As you want to show distance based on polymorphism, so you must show results in both graphs. It would be very nice presentation.
But I will prefer PCA over dendrogram, if you want to choose one of them.
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Hi All, we have a bioinformatics challenge and we would love any help this community can offer. We have data from a target-enrichment experiment that was supposed to capture certain microsatellite motifs. The three enriched libraries were sequenced in a rapid run on Illumina Hiseq 2500 (paired end mode) and our data is in the standard illumina fastq output. Our three libraries come from three different sources. The first library is developed from fresh fish tissue; the second one is mammal tissue; and the third one is the same mammal species but from fecal samples. For the fecal samples, we need to somehow filter out sequences belonging to the mammal only (i.e. not prey or microbiome). We have a reference genome for the mammal, but not for the fish. The data has been demultiplexed already (so for the fish we have 40 individual fish each with its own .fq file containing all the read data). Now, we are facing the challenge of how to deal with this data. Although we are familiar with most basic bioinformatic tools and analyses we do not have advanced programming skills. We need to find a way not only to find and identify the length of our microsats within the reads but also (for the fecal library) somehow be able to identify unique flanking sequences that would correspond to our mammal, in such a way that the reads of other species in the fecal libraries can be excluded. Would anyone have a suggestion on what approach(es) we could use? We have already (unsuccesfully) attempted to tackle this with SSR_pipeline. Thank you in advance for any help you can offer - it is very much appreciated! Daniel & Vania
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Vania Carolina Fonseca da Silva You may want to consider a pangenomic approach whereby you use K-mer count sets with calculable probabilities of a faecal K-mer set originating from a fish species or group of species.
Because you have sampled enrichment you only have a probability of sampling any given SSRs in each species, and it is difficult to determine that probability for each fish species if the genome is unknown.
My suggestion is to take each set of fish reads and do a K-mer analysis, maximising the K-mer size until you obtain a distribution such that – guestimate – 10% of K-mers have coverage of between 10 and 100 copies
The guestimate of 10 is because you are expecting some coverage at the sampled SSRs and less than 10 could be indicative of sequencing errors. My guestimate of 100 is because you are looking to discard the SSR repeat polymerics and are looking for K-mers likely to be unique both intra and inter fish species. Hopefully for each fish species you will end up with many thousands of identified K-mers accepted within the copy number constraints and of maximal size up around 50% of your read lengths. You can’t use K-mer sizes up around read lengths simply because most K-mers will be discoverable through partial read overlaps, not full length overlaps.
Repeat iteratively over all fish species, adjusting the K-mer size until some minimum number of K-mers per species is reached, and build a matrix containing columns: fish species, K-mer sequence, count of that K-mer present in faecal sample reads – initialise count to 0.
With your faecal sample reads use a sliding window of the K-mer size you built the final accepted matrix with, and slide this window along each read.
For each K-mer from the sliding window check if that K-mer is present in the mammal genome – if so then discard and try next window.
If K-mer not in mammal genome then search matrix for a matching K-mer sequence and for every fish which matches then increment that fishes faecal K-mer match count.
When all K-mers in the faecal sample reads have been processed you can process the counts in the matrix and assign probabilities of the mammals individual fish species (or groups of fish) consumption.
I suggest that you discard counts of less than some threshold – say 10 - as these could represent sequencing errors.
The foregoing approach will need much optimisation to become practical, but could be applied even when the prey consumers genome has yet to be assembled providing you have reads from the consumer species. K-mers from the consumer reads are added to the matrix and so if count more than some threshold then discard that K-mer from further analysis!
I am interested in your research and may be able to provide some bioinformatics expertise ( https://github.com/kit4b ), do you have further details of your experiment you can share?
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I have read that sequence based data are not powerful for analyze isolation by distance between population. However, I want to know if is possible to analyze the effect of environmental variables as elevation, temperature or land use on the genetic differentiation between lineage or cryptic species using sequence based data as mitochondrial DNA and nuclear DNA (ITS2)?
If yes. Which metric should I use as a measure of genetic differentiation between lineage or cryptic species?
If not. I know that ITS data can have microsatellites, it is possible to use these microsatellites as a source of genetic information for landscape genetics?
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selecting the markers for landscape genetics depends on the level of divergence of the putative lineages. For example, cytb of mtDNA can capture the lineages separated around 1 to 20 million years. If you need to capture the differentiation very recently, d-loop could be a better option. Microsatellite can capture the differentiation in very recent times even within a species. I used microsatellite markers for landscape genetics of mosquitofish in coastal Louisiana to understand genetic differentiation due to temperature, salinity and salt water intrusion. . I used cytb for understanding genetic differentiation between 1 to 2o million years.https://scholarworks.uark.edu/etd/3170/.
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When scoring a couple of tetranucleotide microsatellite loci, I noticed in a few samples the allele length is inconsistent with the repeat modulus. For example, I have sizes 369-373-377… up to 417 following a nice 4bp repeat pattern. Then, the next alleles are 422-430-435-439 and 447 (verified by 2 different software). Question is…should I keep these sizes as is or do I fix them to fit a 4bp-repeat motif? Thanks.
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Totally agree with Samuel: microsats often have alleles that just don't want to follow the pattern. For the most part it shouldn't be a problem and most population genetic software just treats them as different alleles. The only issue would be analyses that incorporate a microsat-specific mutational model that assumes alleles follow a specific pattern. Those are mainly coalescent-based analyses and for most other stuff it won't matter.
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In which cancer patients do you determine the microsatellite instability (MSI)? What are the new studies in this area?
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For Lynch syndrome screening universal screening testing is recommended for patients with colorectal cancer. Other Lynch spectrum tumours should also be tested if there is clinical suspicion of Lynch syndrome due to early age of onset (esp before 50 yo), family history of cancer, or presentation with multiple tumours. Tumour types include but are not limited to endometrial carcinoma, gastric carcinoma, small intestinal carcinoma, upper urothelial tract carcinoma and sebaceous neoplasms.
For immunotherapy we are testing all patients with stage 4 colorectal cancer or any other gastrointestinal tract tumour.
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Can someone suggest any research paper for a complete understanding of genotyping by microsatellites in fungus?
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Hello,
I have an excel sheet with microsatellite data, I would like to make a NJ tree out of it. I have a basic level in bioinfo.
Can someone please guide me through the steps to get the tree?
Thank you
Tamara
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Thank you Zarrin for the suggestions.
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I'm working on optimizing three panels of sand lance for multiplex PCR. I'm essentially starting from scratch and first ran gradients on each individual marker to determine annealing temperature and size range, and I then created a multiplex plan.
The first panel went really well, and all the samples worked, which is why I can say that the DNA is fine.
For the second panel, I used the same samples, and am getting variable results. Some samples work pretty well, others have no amplification for any of the three markers within the panel. I'm not sure what my next step would be. This is my second try on this panel. The first was a standard PCR at the determined annealing temp, and the second time I tried a touchdown protocol and lowered the concentration of primers a bit.
Anyone have any suggestions on where to go from here?
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Dear Kirby,
I think this is a really good paper for Multiplex PCR optimization.
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I am conducting a population genetic study on several species of fishes using microsatellite alleles. I have used BayesAss (http://www.rannala.org/software/) to estimate migration percentages to and from each population in my datasets for each species. Superficially, the outputs provided seem to be apparently correct and adhere to pre-conceived hypotheses. However, the output data do not provide me with p-values to indicate if the estimates are statistically significant. For each estimate (mean) the SD is provided in the output file, and after reading the generated Trace file (https://www.beast2.org/tracer-2/ ) several other outputs are calculated (SE, SD, variance, upper and lower 95% CIs) but no p-value. Could anyone help me either a) identify the p-value in the output that I am somehow missing? or b) calculate the p-value from the outputs provided? Unfortunately statistics aren't my strong suit so I feel like the answer might be obvious with the results I've been given but I can't figure it out from what I've looked up so far.
Thanks!
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Hi,
first of: I assume you have decided on the "best" run using the trace files (reviwed for example in the Tracer software) as well as a comparison of runs by their Bayesian Deviance (which is thankfully easily calculated with the R script provided by Meirmans (2013) himself (see article below).
If so, you can calculate the 95% confidence intervalls as alternative to a p-value (actually the concept of p-values is more and more under critical review lately), by multiplying the standard deviation with 1.96. You will find various good explanations online for this if you look for "calculate confidence intervall from standard error". Then, a common approach would be to interpret migration rates as significant if their 95% confidence intervall does not include zero.
Hope this helps,
Cheers
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What is the relevance of Microsatellites or Simple Sequence Repeats (SSRs) based markers in the era of genomics for their utilization in plant molecular breeding ?
What proportion of plant molecular breeders are using them (SSRs) for their breeding activities ?
Are there any better DNA molecular markers systems evolved apart from SNPs ?
Did the development of SNPs marker system significantly reduced the use of SSR based markers in plant molecular breeding ?
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One of the adavantages of using SSRs is its cost effectivity for plant breeder. SSR markers are also advantageous incase of orphan and underutilised crops plants, where SSRs can be designed from transcriptome data as well, moreover one can link them to traits , diseases using basic molecular biology experiments. These advantages do come really handy for breeders.
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Dear researchers,
I am currently using BEAST to infer TMRCA  from 10 microsatellite loci that I have genotyped for one population where I have cases and controls, so my question is:
What is the best way to estimate the TMRCA of the cases considering the 10 loci all together for the taxa previously described? How do I do this?
Thanks in advance!
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Hola Uxia Saraiva-Esperón , lograste correrlo en beast2 o que estrategia seguiste? Se que es viejo el post pero tengo la misma incógnita y tu consulta es de lo primero que ha aparecido, tengo experiencia con beast y paquetes adicionales pero no he aplicado aun msat + beast.Gracias
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I have analyzed the length of microsatellite PCR products by means of capillary electrophoresis and, basing on the results, I estimated the number of shorts tandem repeats (STRs). Then I created a matrix, where number of STRs are placed in columns (values 1-33) and names of strains are put in rows. Which clustering method would the best to create a distance tree? I've been thinking of UPGMA algorithm with Euclidean similarity index. But please advise me anything better if I'm wrong. So far I've worked with PAST Software. Thank you in advance!
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Katarzyna Roeske you may want to be cautious; STRs (MSATs) do not mutate in the same way as INDELs / Transitions / Transversion. STRs may be lost as well as gained, so it is likely at least some STR alleles are the result of homoplasy, not by descent, and therefore they are generally unsuitable for making conclusions about genetic distance, and most useful for evaluating contemporary relatedness of individuals and populations.
In considering the above statement, I will freely admit I have zero familiarity with Yeast STRs.
Interested to hear other opinions.
Useful background info:
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There's this PCR product on all my samples of one species (first image) that amplified and is detectable but does not look the way the other alleles look so I don't know if I should be including it as an allele for the analyses or is this how a null allele looks? And if that's the case, should I include it with my dataset? The second image shows the PCR products for the second species I'm analyzing. You can see that the same product amplifies but looks almost exactly as the other alleles.
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Hello I think you have a confusion a null allele is an allele that can not be amplified, for example if you have an individual that is heterozygous for a certain microsatellite locus, but only one allele was amplified, in your electropherogram you would have only one peak, it was believed to be a homozygous but it is not. Then the null alleles you can not see.
The null alleles cause an overestimation of homozygotes and exist programs to verify the presence of null alleles, such as MICROCHECKER software and then analysis to know if the presence of null alleles significantly modify your results FREENA. If you see peaks in your electropherograms that do not have the allele form of that microsatellite, you may stutter and do not consider it. Guiate by the way
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Hello,
I'm doing PCR and gel electrophoresis on tetranucleotide microsatellite repeat of a species of diploid salamander. The gel results yield three bands in two sample (attached; red arrows), and I'm confused about how should I interpret this. Other samples (not shown) also display three bands. I only expect from one to two bands in diploid organism. There are a lot of non-specifics, but the microsatellite alleles are highly pronounced.
Thank you
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Hello
Well, there could be several things. They may be nonspecific amplifications, if so, you would have to perform tests to improve the PCR (eg raise the anneling temperature), with microsatellites it is relatively normal that you have to standardize per sampling site. Another option is that you effectively have more than two alleles.
One way you could make sure is to perform the capillary electrophoresis in a sequencer, and having the results and according to the shape of the alleles you can know if they are indeed alleles or stutter.
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This is all the data I have for PCR products of 102 samples for this specific locus. I'm not sure if I should be counting the fragments on that range (more that 175 bp) because it is too far from the original one reported for the microsatellite. How do I know if the articles that have been published for my species actually use alleles that amplify over the top range number?
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Don't assume that previous studies have completely captured all the genetic variation possible for a species. Our lab routinely tests microsatellites in which we find alleles out of the supposed "range" (still happens with loci we have used for a decade). Have others tested these microsatellites on this population or geographically proximate ones? Is there the potential for introgression?
Unfortunately studies do not typically list the alleles they observe per locus. Maybe check for an allele frequency table. 175 really is not that far off from the reported range and those peaks look like real alleles to me, especially since they have been observed in multiple individuals. The peaks look just like those of the smaller alleles. Have you tried re-running the samples to confirm? If you really want to test it you could run the sample on agarose, cut out the alleles, and then sequence them.
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Hello,
I'm using PCR to assess the differences between the banding patterns of a microsatellite in different salamander species. In my resulting gel, there's extra unclear bands underneath the clear bands in the A.texanum and A. laterale samples, and above A. jeffersonianum's clear bands. What do these unclear bands indicate?
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OK, thanks
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I am working on a seascape genetics study, and I am using 30 microsats. I know Fst can be skewed by highly polymorphic loci. I have ran an analysis with interesting results, but I want to make sure it is not the metric. Is there a rule of thumb about the other genetic distance metrics that are better for microsats? I recently read a paper about AFD, but I do not think there is software available to calculate it.
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Dear Anthony,
I suggest you to see links and attached file on topic.
Evolutionary and statistical properties of three genetic distances
An Introduction to Molecular Ecology
Genetic Diversity Analysis of South and East Asian Duck Populations ...
Best regards
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Hi community,
I have a dataset of individuals of a species (several populations) genotyped at 9 microsatellites.
I would like to ask if (and how) I could simulate that STR data forward in time, so to implement different scenarious (e.g. cut-off of one population, varying migration rates between populations, etc)?
Ideally, the simulated data (forwarded for X years/generations) would be in allele frequencies or even STR data format as well, so that I can compare the initial and the forward-simulated data using standard procedures of population genetics. It seems to me that some programs are able to simulate STR-data forward in time (e.g. R package skeleSim using Rmetasim), yet as initial data they also use simulated data. So I think it should be able to use real STR data instead of the simulated one? But please correct me if I am wrong here.
Your help is appreciated,
Florian
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Hi Ronaldo,
thanks for your answer.
Does this mean you have some experience about forward-simulation? If so, could you please share it with us?
Cheers Florian
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Dear RG community,
I am currently analyzing a microsatellite dataset with individuals from two different species. Some of the individuals from both species are sharing a few alleles at one/several markers. I am trying to discriminate putative hybrids from individuals sharing alleles because of homoplasy. Does anybody know a way to do that?
Many thanks in advance,
Alex
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I need to score the data obtained through genotyping process using microsatellite markers in population genetics study. In the data i am getting two peaks , three peaks and also four peaks in one loci of samples what i collected. But i don't know the ploidy level this species as it is really useful while scoring the data. Whether this species is diploid or polyploid?What should i consider? Please help me......
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Dear colleagues,
Unfortunately, the “Permanent Genetic Resources” of the journal “Molecular Ecology Resources” are not permanent, because the database (http:// tomato.biol.trinity.edu) no longer exists. The journal editor could not help with that issue, it seems that all the “Permanent Genetic Resources” published in papers of the journal “Molecular Ecology Resources” that have been deposited in this database are lost. This raises questions about the current trend of relying solely on online publications, but that’s a different story.
I am looking for information on the described microsatellites and primers of the species Thais(ella) chocolata from the following publication:
Permanent Genetic Resources added to Molecular Ecology Resources Database 1 December 2010–31 January 2011. Molecular Ecology Resources (2011) 11, 586–589
The microsatellite sequence is published in GenBank, but not the primers.
We have contacted already one of the authors, but we didn’t receive the primer sequences.
My question is, if perhaps someone had retrieved detailed information about the microsats and primers from Thais(ella) chocolata before the database was shut down.
Thank you very much in advance.
Best regards,
Marc
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How much of the sequence is published? If its just the microsatellite region, then you might be out of luck. If there is sequence flanking, you can design your own.
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I'm trying to develop a bunch of microsatellite primers and am trying to use an alternative to the classic Thermo dyes. Has anyone had success with this? I found an unpublished document "MULTIPLEX MICROSATELLITE ANALYSIS WITH 5’ DYE-LABELLED MARKER SETS USING FAM, YAKIMA YELLOW, ATTO 550, ATTO 565, AND ATTO 633 ON AN ABI 3130XL GENETIC ANALYZER" saying it works but that they changed the dye matrix. Does it work with the classic dye set 33?
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What about the prices? Is it really worth trying this dye set economically?
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Hello everyone!
I'm studying kin biases in macaques, and I establish parentage from 17 microsatellite loci. I'm now checking that all loci are in Hardy-Weinberg equilibrium, but different softwares give me different outcomes. Does anyone have experience with this? I know they use different algorithms, but what program is most trustworthy?
Any insights on this topic are more than welcome!
Thank you :)
Delphine
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I suggest you to calculate the deviation for HW by regular estimation methods. And then check the softwares. You can either try the GenALEX.
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How many cells per face will you consider?
Will you left a hole face without cells or will you just put less to accommodate access connectors or other similar things?
How many in series and how many in parallel and why?
I know it depends on the mission but what is, in your experience, the most common or most beneficial configuration?
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Which configuration you recommend for the solar panels of a 3U CubeSat?
Wiring to the minimum. For a 3U I can imagine the amount of solar panels can increase dramatically compared to a 1U. I would recommed a configuration ensuring a solar panel on each side of the Cubesat, so even with a bad attitude control you´ll have some power available. Of course it depends on your mission (if you have to prioritize available power, or maximum voltage, or max current, thermal requirements, etc.). The overall idea is to minimize the amount of wiring you´ll end it inside your cubesat. Solar panels with 2 cells/panel all over your cubesat might lead to a crazy about of wiring, bear in mind.
How many cells per face will you consider?
2 cells per panel is the standard among most providers. If you have no prior experience, go for this one. Then accommodate as many panels as you need/wish/mission requires (wiring will become a thing).
How many in series and how many in parallel and why?
This is related to your Voltage and Current requirements. Standard configurations mount 2 cells/panel and give you around 5V@0.5A (round numbers). Voltage and Current combination will determine in the end how quick you'll be able to charge your Power system battery. If you need to charge your battery at a rate of 5V@2A, then you do the math. If you don't have a battery, then things get a bit trickier. I would stick with most standard voltage outputs when selecting solar panels and then you decide how many of them you need to put in series to charge your battery properly.
I know it depends on the mission but what is, in your experience, the most common or most beneficial configuration?
At least 1 panel in all sides plus wiring to the minimum. I have seen some 3Us with a lot of solar cells/panels, it was beautiful to see such amout of power available, yet a nightmare to integrate all panels.
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Can we use binary data of SSR markers for diversity analysis using PowerMarker 3.25 software. What is data format?
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While using Power marker for SSR data analysis, binary data format (0 or 1) is not appropriate. Genotypic data format (AA, AB, AC, BB, BC, CC) similar to that required for co-dominant marker analysis using Popgene software is appropriate.
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Hi everyone,
I'm running different models in DIYABC using both microsats and mtDNA. The models run fine as well as the analysis, however I get some unexpected results when I estimate posterior distributions of parameters.
This is my scenario
N1 N4
0 sample 1
0 sample 2
ti varNe 2 Ni
t1 merge 1 2
t1 varNe 1 N3
And one of the conditions set is ti < t1. I point out that for both time parameters I'm using very relaxed and broad prior values with considerable overlap.
When estimating posterior distributions of parameters I would expect time parameters to be constrained by the conditions so that ti < t1. However ti is consistently higher than t1 (see picture below).
Am I missing something here?
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Not completely sure but If your markers don't fit the model based on your priors, you simply can't make them fit. Have you ticked the box at bottom of the parameter set page to run until all parameters are met? There's two options at the end of that page..Also, how many times do you run it?
Perhaps email the authors and ask them. People are usually happy to reply to queries about their programs.
It might be something very simple.
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Hello all,
I wish to determine pairwise relatedness and eventually reconstruct pedigrees for a population of dolphins using high-density microsatellites (>2000 loci/individual). However, most genome-wide estimators of IBD are optimized for SNPs. Is anyone aware of a program that can compute IBD/kinship coefficients from multiallelic genome sequence data? Alternately, does anyone have experience entering microsats into PLINK or KING?
Ideally I would like to be able to use a program like PRIMUS or CLAPPER to reconstruct pedigrees.
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Gregoire Leroy Thanks a mil
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Hi guys, I need some insights here. I have a microsatellite data for two populations of O. rufipogon and O. sativa for comparison. I just want to compare their genetic diversity population structure of the two wild rice populations. I initially used 128 SSR markers but only used 48 after removing loci that are monomorphoc, non-amplifying, and with more than 5% missing bands. I computed the Nei‘s genetic distance (1973) and constructed from it a neighbor-joining tree. I am actually happy with the result as it confirms that the two populations of rufipogon are unique indeed based on the used loci. However, I saw one strain from one population (nap13) which was grouped closely together with the O. sativa genotypes (see attached photo). I am wondering what causes this phenomenon and how to deal with it. Your answers will be highly appreciated.
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I would also recommend using dapc analyses through the R environment.
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To detect microsatellites using primers labeled in applied biosystems platform. I will use multiplexing (3 dyes) same specie. I need to make it as cheap as possible, in sequencing equipment (serie)
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