Conservation Genetics - Science topic

Ireneusz Stolarek Thanks for your thoughts, very helpful.

Please find attached

Best wishes

In this instance, The AMOVA percentage of variance can be explained as:

Among subpops: 1.69 (significant)

This seems Ok.

Among inds within subpop: -2.43 (not significant)

Negative valuesare taken as zero.

Within inds: 100.73 (not significant)

More than 100 , is explained as: as sum of all 3 above values is 100, and as among inds within subpop: -2.43 is taken as zero, so

Within inds is (100- 1.69) 98.31.

Yes I have measured root exudates. In solution culture, but the roots did exude phytosiderophores and we could measure them by HPLC after derivatizing them. https://www.researchgate.net/publication/11538514_Reversed-phase_liquid_chromatographic_determination_of_phytometallophores_from_Strategy_II_Fe-uptake_species_by_9-fluorenylmethyl_chloroformate_fluorescence

In addition to the software that you already commented you can use Barrier.

This is old, but since I have helped people with the same problem several times already I'm going to leave my answer here. Rohit's answer works because the problem is usually with the line endings and/or other unseen editing options in the file. Always edit such files in proper programming editing tools, such as Komodo Edit, and, with Micro-checker, beware when using other operational systems such as Linux or OSX to edit files, micro-checker only reads files with Windows line endings. So if you edited the files in linux or someone sent you files and you are using their files check in the file preferences whether the line endings are Unix line endings or the Windows line endings. I haven't encountered the same problem in other population genetics tools, however, similar effects can happen with the tab. Some input files have to have all tabs removed and replaced for spaces. Good luck to everyone reading this.

I figured out getting the data format accepted now sorting through the results of boot.ppfst and the $vc.per.loc tables.

I'd like to add a word of caution here. To test whether there is isolation by distance, you need several data points along a geographical range (either several populations or individuals at a range of distances). Because you are testing only two populations, you cannot infer there is a pattern of isolation by distance--your regression line is just connecting the two populations.

It will depend on how much genetic variation is left in the extant population. If there are several isolated populations left, you might have a chance, but if there is just one small population remaining, probably not.

Use Vortex. You can use your pmx files as input and also provide microsatellite data. We did some simulations assuming different founder assumtions. Worked quite well.

Hello,

yes, you can do genotyping with Miseq, as you can see here.

Also read about the use of Illumina in forensics (Illumina offical website), I found it very useful to see what can be done with low quality human DNA, I guess it should work with any DNA and the rigth PCR primers.

Best, Meta

You’ve identified an urgent problem for virtually all species that aren’t local endemics, and it’s particularly important for agricultural species where the stakes are high for conserving native genetic biodiversity. Fortunately, many of the core elements of conservation strategies are broadly similar across species, and scientific communities within individual countries aren’t necessarily limited by political ideologies or borders. There are good examples of scientific collaboration and exchanges between countries with conflicting ideologies or frosty relations: examples include collaborations between American and Soviet scientific communities on paleontology, fisheries, and climate-related research during the Cold War.

Developing sound conservation strategies is possible as a joint or collaborative scientific endeavor – implementation and/or monitoring programs will likely be much more challenging, but should still be feasible if a reasonable case can be made for a country’s self-interest. In some cases, being able to show the biological value of a country’s biotic resources has been useful for international negotiations, such as Equador’s proposal in 2007 to not develop oil reserves in Yasuni National Park in order to protect the rich local biodiversity, in return for funding from the international community. Even though it didn’t pan out in the end, the recognized value of the local biodiversity was a key driver for the idea. Getting a monitoring program up and running may at first be limited to local efforts through the scientific community, but as their value becomes apparent, larger-scale collaborative efforts are possible. A good example of this is the decline of American eel (Anguilla rostrata) across the species range – observations by scientists, published in the literature and communicated to jurisdictional management agencies, led to a collaborative effort across the species range to try to slow and (hopefully) reverse the species’ decline.

An important facet for building multijurisdictional collaboration is establishing a core set of shared data metrics, preferably collected using agreed-on / standardized sampling methodologies. With that, each study’s (or jurisdiction’s) data can be compiled to build a comprehensive picture from the individual pieces. If you’re looking at genetic biodiversity, there are several good examples in the fisheries literature, especially for Pacific salmon in North America and Russia and the multinational FishPopTrace consortium in Europe.

My pressure Chrystelle, good luck with that!

Gregoire, its why I strongly recommend to use R for it, because you just need to add the equations (maybe is a little hard to do, but you'll do it just once) and then run any data set without problem. But of course, the other way (like in a lot of softwares) is to switch to WinXP.

Best!

Alex.

Thank you Jacob, very good comments and additions indeed! Herewith a few reflections on your points.

I certainly would agree that a global fund, through whatever sources filled, would be sufficient to ensure adequate conservation and presence of sufficient genetic diversity in the production systems. We will need other approaches such as the ones you suggested, but possibly also political frameworks that allow that to happen! And indeed, global public goods are different from local resources, in many respects (other than the biological dimension). Both will require proper support and stimuli.

Your point about 'linking conservation with local identity, ownership and economic opportunities' is indeed a critical one and is of crucial importance to ensure sustainable conservation approaches and to ensure the required links between 'development' and genetic resources. Consequently, proper representation of the community and consumer level stakeholder groups in any national/governmental conservation efforts and in national, regional or global conservation and use networks seems to be indispensable. Conserving genetic diversity is also conserving and respecting cultural diversity, stakeholders' identity and accepting their traditions and certainly their interests.

Your point on the importance of information and communication is indeed a key aspect to allow and facilitate efficient conservation and use, in particular through the sharing of experiences, knowledge and linking initiatives and players as well as to allow proper linking of the resources with its users. The provision of adequate tools and means to the communities, rural and urban, would allow these opportunities to be used, including to facilitate the needed exchange of knowledge and experiences gained through 'citizen science'.

Yes, we need more possibly a rethink too on how to create more flexible and certainly more dynamic formal and informal networks. Too heavy handed and top-down functioning networks are not the solution and are also not sustainable. Allowing indeed the 'power' over planning and decision-making to the lowest possible level in the society is a key prerequisite. Facilitating rather than dominating is another aspect that is crucial to allow the creation of dynamic, inclusive and open PGRFA (and other) networks!

Andrey, thanks so much! I'm using them...

I´m looking for Tocantins sequences where were described the Holotype

This artificial "treshold" depends on the marker and the taxonomical group you are working on: you should probably be more specific to have the beginning of an answer.

However, never forget this treshold actually... does not exist. :)

You can empirically measure mean genetic differentiations between species you have defined a priori, but in no wise you can use it to systematically "delimit the species". In particular, since you mentioned a mitochondrial gene (COI), note:

Unfortunately, taxonomy is not (only) about measuring genetic distances. At most, sequences divergence can pinpoint "unconfirmed candidate species" (see Padial et al. (2010): https://doi.org/10.1186/1742-9994-7-16). You then need other types of evidence (nuclear DNA, phenotypical/ecological characters...) to validate whether or not "these two species/individuals are of distinct species".

Best regards.

Hi João 

Thank you for the paper. I sen you two papers with regard to genetic differentiation which may be helpful.

Good Luck

@Shafagat Mahmudova: thanks a lot 

@ Rosalia Pineiro: I am not able to open the TESS page from github. Thanks anyways

You are mixing together several different concepts in these papers.  

Jost (2008) claimed that Fst was a useless or perhaps misleading measure of genetic differentiation, a paper that initially caused quite a stir.  Jost was not clear about core assumptions such as biallelelic loci and also fundamentally misunderstood the role of a model of population arrangement such as the finite island model imbedded in Gst.  See the paper by Ryman and Leimar 2009. 

Hedrick 2005 presented the idea that the maximum Gst (or more properly an estimate of the parameter Fst) is a function of allele frequency distribution and may not be 1.0 such as when heterozygosity is very high.  Using a well worn approach (e.g. the standardized coefficient of linkage disequilibrium, D'), he suggested Gst' to standardize an estimate of Gst by its maximum value so that it can be interpreted as a percentage of the maximum.  This approach can be taken with pretty much any statistic where one can compute the maximum value for a given data set.

Sequence data and allelic data are different in some ways, but they can be thought of as a collection of multiple loci if the sites are independent.  At the other extreme, they are a collection of many completely non-independent sites and therefore constitute a single locus.  Both allelic data and sequence data can be treated similarly from a conceptual point of view, but data type usually differ in the types of statistical estimators are used to obtain an estimate of a certain quantity.

What is your main hypothesis of interest? Start there and then ask what types of statistics would provide quantities related to testing that hypotheses. The last thing to think about is the implementation (i.e. software) you need to estimate the statistic of interest. 

I think there is not an "absolute number" however in a simulation it was shown that after 20 specimen heterozygosity does not change much. (doi:10.1371/journal.pone.0045170).

Perhaps it is true for earthworms as well.

Hi Dragos,

The attached file help me much. Thank you....

Emilio Rolán-Alvarez has given the perfect answer. Additionally though, there are several drawbacks associated with the use of mtDNA in phylogenetic studies which nuclear loci do not suffer from. The following two papers would be a good start to get an overview of these:

Balloux, F., 2010. The worm in the fruit of the mitochondrial DNA tree. Heredity 104, 419-420.

Galtier, N., Nabholz, B., Glemin, S., Hurst, G.D., 2009. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Molecular Ecology 18, 4541-4550.

@Vivek Vaishnav

You provided a beautiful art work.  Although the density-unweighted algorithms that you aforementioned have been wildly recogniced and used in population genetic studies, these algorithms may not be suitable in many cases of RAPD studies.  There are two specific computational biology prerequisites for use of the density-unweighted algorithms in the studies such as RAPD: (a) all matched DNA pairs in the electrophoretic lanes being compared must have essentially the same densities, as illustrated in the book provided; and (b) all DNA amplicons must be well separated from the adjacent DNA moieties with similar molecular weights and conformations by electrophoresis.  

Ni et al. 2014 (Am J BioMed Sci, 2014, 6(2): 82-104) devolupped the density-weighted algoritms, which is mathematically general with no specific prerequisites and governs all conditions, in contrast to the density-unweighted algorithms that narrow the specific cases under the strict prerequisites (a) and (b) described above.  

Best,

Josh

Hi Paras

You should be aware that cross-species SSRs many times have null alleles, and might suffer other amplification problems.

If you insist to use them, be sure to check the efficiency of your RT-PCR before going on.

Hope this helps

Pablo

Yes, this paper used weighted averages of landscape variables to compare least cost path distances between sites: Rangewide landscape genetics of an endemic Pacific northwestern salamander, Molecular Ecology (2013) doi: 10.1111/mec.12168

It appears that recent real cases of economically significant biopiracy are actually relatively rare. Of the four cases cited by Efferth in the first reply, one represented within-country (cross-cultural) 'piracy' (not applicable to international law), two were for products not yet proven to have any significant economic value, and then there is the decades-old poster child, Madagasar phytophora. The $5.4 billion quoted by the UN is mostly for long past issues (worth resolving, but not relevant to current piracy).

What is rarely discussed is the massive cost to legitimate researchers that result from now-widespread export permit polices that are likely to have very little actual desired impact, and are based more on anecdote and paranoia that on actual cases. For example, current restrictions on bio-material export would have no effect on the ability to patent tumeric (cited by more than one paper above), a cat that got out of that bag centuries ago.

Someone needs to produce a database of (all?) proposed biopiracy events of the last 20 years (since it is current piracy that these policies are meant to prevent), along with estimates of the actual economic loss associated with each, not just someone putting in for a patent that ends up going nowhere (which is the fate of most of the bio-material patents). Maybe such a list is already out there; if so please share it here.

Sorry I am not working with genetic aspects of coalescence.

Hi,

a high Fst can occur with K=2, if the two clusters are well represented and (sub-) populations have low diversity. A method I have used some years ago to verify the numbers of K is to run an AMOVA with the regions defined as the clusters of STRUCTURE. Then see the PhiPT and compare its values for each K. Nowadays I used adegenet (as mentioned above) and Geneland (R). The latter uses a similar algorithm as Structure and defines the optimum K after multiple runs and also can incorporate spatial parameters in the model.

Good luck

Poppr should work for your needs. There are other packages you may want to explore as well though; 'PopGenome', 'Pegas', and 'Adegenet' depending on your needs. A bit more explanation might help narrow it down.

Dear Mohamed

There are several softwares to use, as STRUCTURE for population structure analysis, genepop, FSTAT and popgene for basic statistics, and also GeneAlex for ANOVA or PCoA analysis, also a excel add-in called MStools will help you to transfer your original data into input format for several softwares. Tassel also could be used for building trees and doing some correlated analysis.

Good luck.

Another alternative would be the "PresenceAbsence" package.

If you have a file called "validate_data" with this structure: 

ID     Observed     Predicted

1      0                 0.15

2      1                 0.86

3      1                 0.92

4      0                 0.26

Just write: 

auc (validate_data) # Get the AUC value. You can specify the sd. 

auc.roc.plot (validate_data) # Plot the ROC

Hi Sayantani,

I'm not very comfortable with human data I must admit, but I would state that it is better indeed to keep only markers that are in equilibrium in your control population. If you are doing genome-wide association, you could have a look at this publication : http://onlinelibrary.wiley.com/doi/10.1002/gepi.20335/epdf that suggests an other way to estimate HWE when dealing with case-control studies, based on a likelihood ratio test. Apparently this method works best for markers that are actually associated with the disease. You can look at the articles citing the susmentioned one for additional information and amelioration of the methods.

I hope this helps in any way :) Maybe someone else could complete my answer ?

You have to collect the fresh dung samples for better result. Fresh samples (less than 24 hours freshness) has higher DNA quality than older samples. But you can consider collecting 2/3 days old samples too. You have to collect the dung's outer layer and store in 95% ethanol. 

Hi Jia Zhou , I am doing something similar so if you ever came up with an answer could you let me know the methodology that you followed. Thanks 

Hi

You can get binary data, such as for dominant markers like AFLP, into splitstree using this format

#NEXUS

BEGIN Taxa;

 [&&SPECTRONET SN_BLOCKNAME Taxa_2]

 DIMENSIONS NTAX=4;

 TAXLABELS tax1 tax2 tax3 tax4;

END;

BEGIN Characters;

 [&&SPECTRONET SN_BLOCKNAME Characters_1]

 DIMENSIONS NCHAR=10;

 FORMAT DATATYPE=STANDARD GAP=- SYMBOLS = "012345";

 MATRIX

tax1 01010 01010

tax2 11001 00101

tax3 01110 11001

tax4 10101 10101;

END;

For co-dominant markers, you can code the data as binary data by recording the presence/absence of each allele - but this is not a particularly good approach. If you want to combine dominant and co-dominant markers together it will function.

For co-dominant markers alone, it is generally better to use a population genetic software package to generate a distance matrix for the samples/populations and then get that into Splitstree using the format you can find in the examples folder supplied with the software in the file south.nex. Be careful to use an appropriate genetic distance measure in your population genetic software! My experience would suggest being very wary of models that do not treat all alleles as equally related. Typically the Nei (1972) model is a good start.

To run SAMOVA you need two files, one with microsatellite data, and another one with geographival positions of your populations studied. Please, see attached files. Order of populations in the geo file has to be the same like in the arp file. 

It really depends what your data looks like, and what statistics you are looking to use for comparing landscape variables with genetic and geographic distance. I just included a short list here based on the most common methods I have seen, but there are lots more. 

If you are looking to partial geographic (or riverine) distance out, the most common statistics are Mantel tests (typically performed in the R packages ecodist or vegan) and multivariate stats such as redundancy analysis or distance-based redundancy analysis (usually performed in vegan again). Partial mantel tests are subject of debate over their precision and accuracy, so be sure to read up on that debate if you use them. 

Riverine systems also lend themselves well to graph theoretic techniques (see Garroway et al. 2008) and network analyses. These types of analyses are often performed in the R package igraph or the specialized genetics programs mentioned in Garroway. I typically use igraph though, does the same thing. 

I also like to use spatial regressions and mixed models, but these techniques depend on your genetic variables being independent or including an effect for repeated sampling. Spatial regressions can be found in spdep (R package) and mixed models can be performed in many R packages, I prefer lme4. 

There are many other techniques such as random forests and Bedassle, but again, which technique you use depends on your data, and what variables you ultimately derive (i.e., how do you represent landscape variables and choice of genetic variable). Hope this helps. 

Liz

Hi Husam,

I'm most familiar with the presence-only SDMs like Maxent.  In general for SDMs, you'll want to focus on biologically-relevant environmental factors as your predictor variables.  So feasibly, you could include some sorts of habitat suitability index (HSI) metrics as predictor variables; however, you would need those HSI metrics calculated for every unit you want to predict the species distribution into.  This is why almost every example simply uses larger, publicly-available datasets with large spatial coverage.  If you are interested in using Maxent, I would highly recommend Elith et al. 2011:  

The 2nd option is probably more likely to give a quality answer. If you have the time/computing power I think you should look at the larger dataset, too. If your clustering combines particularly disparate sequences you may be thinning out your taxa distribution and inviting more opportunity for long branch attraction. I would consider comparing the topologies and look for places where lots of relative diversity was collapsed in one area but not in other areas of the tree. If it is even throughout, the smaller dataset is probably much more likely to give a fair answer.

Hi,

despite your msats were developed for the species, it is still likely that some loci can have null alleles. There are a vast number of studies confirming that. If homozygosity excess is repeatedly observed for certain locus (or loci), then null alleles (or allelic drop-out) is more than likely. Also, sometimes short alleles are preferentially amplified during PCR, leading to unbalanced product amount. Then, it is easy to mistakenly interpret heterozygotes as homozygotes. Besides technical problems, mating between relatives may produce significant inbreeding. Another potential source of homozygosity excess is the so-called Wahlund effect, i.e. the effect of treating different gene pools as a single panmictic unit. However, I’m not sure if biparental inbreeding or Wahlund effect is relevant to your species or sampling design, respectively.

Hi there,

since I've received several requests for the HAT-RAPD protocol I will just post it here.

We first published the use of the protocol (without the fancy name :-) ) in  Eimert et al. (2003). Since that journal does not allow the distribution of copies I attach here some later papers describing it.

Basically, the trick is in using decamer primers with higher GC content (70-90%) and or di- or trinucleotide microsatellite-like composition. That gives them kind of inter-simple sequence repeat (ISSR)-anchored primer (Zietkiewicz et al. 1994) or SPAR (Gupta et al. 1994) like properties and allows for the higher annealing temperatures (we usually use TA around 45-50°C). We bought the primer sets 1,4 and 8 from the University of British Columbia for the above mentioned reasons. They don’t sell them anymore, but I attach the primer list here so you can order them from your local suppliers. I also attach a short list of primers we tested successfully for several species (Asparagus, Heuchera, Euonymus, Malus, Phalenopsis, Prunus, Ribes, Vitis, Zinnia) and which turned out to work in most of those reasonably well (some better in one species then in the other). You might want to start with those amplifying well in several species.

You will probably have to optimize the Primer/Mg/TA combination for your species of interest. Using thermocyclers with programmable ramp times, we get very reliable results over different labs and operators (repeated checks after several years give the same patterns). One thing you still have to keep tightly controlled is the enzyme – using different brands of (Taq-) polymerase may result in changes in fingerprint patterns (mostly in intensity, but still … - better not to change suppliers during a running experiment).

Good luck to all of you and feel free to ask any questions :-) !

Klaus

Helpful (hopefully :-) literature:

Eimert et al. (2003) J Agric Sci 141:73–78

Zietkiewicz et al. (1994) Genomics 20:176–183

Gupta et al. (1994) Theor Appl Genet 89:998–1006

And attached:

Ruangsuttapha et al. (2007) Gen Res Crop Evol 54:1565–1572

Eimert et al. (2012) Plant Syst Evol 298 609–618

Thank you for your suggestion, but unfortunately the software does not perform cluster analyses.

best regards

fabienne

The paper by Atkinson et al (Proc Roy Soc B, 276:367-373, 2009) shed light on the problem addressed in my question. The answer is that Bayesian Skyline Plots are truncated to the median estimate of each population's TMRCA. Problem fixed! Thanks!

"Whether the separate or exclusive breeding has an impact on the natural population genetics is the question?"

1. If they are really bred separately and you have evidence for this, than it is a separate species and no colour variation any longer. 

2. In my groups the colour variation depends on photoperiodism: length of day and night during their photosensitive phases. In these group there is no trend to be separated as different species.

Some of these species:

Pachynematus clitellatus Serville: Tenthredinidae, insect spec. of seed grasses, sometimes reported on wheat. 3 generation per day, the larval colours are different, very likely due to the photophasis-scotophasis during their photosensitive phase (early larval instars. )

Eutomostethus ephippium Panzer: southern colour variation with red thorax and northern colour variation entirely black. probably this colour differences is also caused the difference between night and day in South and North.Sawfly living on grasses. 

Cimbex femoratus L. has black, brown and gold colour variations. probably the same situation since the flying period pretty long It needs further research.  

Hi André,

An inbreed population can certainly lead to issues with your data, like elevated linkage among loci, and it can be difficult to characterize this without a reference population. However, you can assess the quality of your marker set for your application by generating statistics like probability of identity (PID) and by using distance measures that don't assume HW equilibrium and are appropriate for individual to individual comparisons (Rousset’s distance). For paternity assessments, your greatest power generally comes from excluding a potential parent rather then assigning a parent with 100% confidence.

Hope that helps, and best of luck with your project,

-TAY

Yes, you can, but...

My recommendation is you first make the map without the distorted markers and then add them to the available linkage groups.

Hope this helps

LD results from a non-random association among alleles at 2 or more loci. This may be a result of physical linkage: the alleles are on the same chromosome, perhaps very close together, or in some cases, 2 loci considered to be different are actually the same (b/c two differing primer sets were constructed that amplify the same locus). Assuming physical linkage is not the cause, LD can arise through drift, selection, and/or gene flow. Popgen programs like STRUCTURE assume that data is in Linkage equilibrium, and loci that are in LD can lead to the program overstating popgen structure. See: http://www.nature.com/hdy/journal/v99/n4/full/6801010a.html

Yes, I wanted to determine the relative degree of conservation e.g. the Human eEF2 protein is 76% identical to Yeast eEF2 protein while Human DNA polymerase III protein is 82% identical to Yeast DNA polymerase III protein?

I've got a long list of human proteins-of-interest. So manually identifying their ortho- or paralogs across various eukaryotic species and doing alignments seems a bit insane. 

I was hoping there might be an online tool with such functionality.

Re: determining evolutionary age: I have used 'ProteinHistorian' to estimate the evolutionary age of the protein set ... it's a great tool.

Dear Sir

For further surprises please read following papers which show S. apetala found to be naturally regenerated in coastal shores of Bangladesh like in Sitakunda, Mirersarai and Hatia.

Latif MA, Del Castillo RA. 1990. Growth and yield of keora (Sonneratia apetala) in coastal Plantations of Bangladesh. Bangladesh J Forest Sci. 19:11–18.

Haque SMS, Hossain MK, Kabir MA. 2000. Performance of some common mangrove species in Sitakunda and Mirersarai Forest Ranges under Chittagong Coastal Afforestation Division. The Chittagong Univ J Sci. 24(2):1–10

Uddin MM, Hossain MK. 2013. Status of coastal plantations and its impact on soil PH, salinity and land stabilization at Nolchira range of Hatiya Island, Bangladesh. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS). 3 (4):7–15

Siddiqi NA, Khan MAS. 1990. Growth performance of mangrove trees along the coastal belt of Bangladesh. Mangrove Ecosystem Occasional papers. No. 8 UNDP/UNESCO/RAS/86/120. Thomas Press, Delhi, pp. 5–14.

Thank you.

Mr. Siddiq

I'm not sure if this is what you are looking for, but I recently read a paper by Geraldes et al. (http://onlinelibrary.wiley.com/doi/10.1111/evo.12497/abstract) that used SNP chips to genotype Populus trees. They had a nice supplementary figure (S3)  that presented Fst and Hs from each of their target populations in a pretty intuitive way. (http://onlinelibrary.wiley.com/store/10.1111/evo.12497/asset/supinfo/evo12497-sup-0001-FigureS1.pdf?v=1&s=838895939296cf9680278d71d2284b79cdec9da8).

I thought this review article was useful:

Estoup, A., & Guillemaud, T. (2010). Reconstructing routes of invasion using genetic data: why, how and so what?. Molecular Ecology, 19(19), 4113-4130.

Interesting question! Basically there should be no fundamental difference in utility for terrestrial vs. marine systems. However, keep in mind the long - standing notion (assumption) that there *tends* to be more gene flow, fewer obvious physical barriers to gene flow in the three dimensional marine ecosystem, especially for vagile and/or broadcast spawning taxa. 

But recently, the role of mitochondrial DNA (mtDNA) sequences in taxonomy and phylogenetic inference has become quite contentious:  two extreme viewpoints have

emerged. One which criticizes the use of mtDNA because the marker suggests 'misleading patterns of variation'; specifically, phylogenies that

are inconsistent with those derived from nuclear gene

sequences in the context of species relationships among

closely related taxa (Ballard and Whitlock, 2004; Shaw,

2002). The other extreme, the DNA “barcode” movement,

espouses the sole use of small fragments of a single

mtDNA gene, cytochrome c oxidase I (COI), to identify

most of life (Hebert et al., 2003).

In a review paper my colleague Dan Rubinoff and i published a few years back we present the disadvantages of these two extreme

viewpoints and argue for an integrated role for

mtDNA, one that takes advantage of mtDNA’s strengths

but also accounts for its shortcomings by using it in concert

with other independent data sources (e.g., nuclear

DNA, cytosystematic, morphological, behavioral). We

are against the abolition of the use of mtDNA in phylogenetics

but also against its narrow use in barcoding as currently

defined. We try to show why neither viewpoint

is particularly productive and emphasize how analysis

of mtDNA can be an important tool in the context of both

taxonomic and phylogenetic studies, and I would say equally for marine versus terrestrial organism systematics.  (for our review on this debate in general see:

Rubinoff, D. & B.S. Holland. 2005.  Between the two extremes: Mitochondrial DNA is neither the panacea nor the nemesis of phylogenetic and taxonomic inference. Systematic Biology, 54(6): 952-961). 

For a couple of additional papers that used mtDNA markers to examine and evaluate  systematic boundaries in closely related marine taxa:

Bird, C.E., B.S. Holland, B.W. Bowen & R.J. Toonen. 2011.  Diversification in broadcast-spawning sympatric Hawaiian limpets (Cellana spp.). Molecular Ecology. 20: 2128-2141

Bird, C.E., B.S. Holland, B.W. Bowen & R.J. Toonen. 2007. Contrasting phylogeography in three endemic Hawaiian limpets (Cellana spp.) with similar life histories. Molecular Ecology, 16(15): 3173-3187

It is possible that you still need to do some adjustment of the PCR conditions for samples that do not amplify. You should try to move/change each componenet of your PCR. You should try to change e.g. amount of template DNA, number of cycles, amount of polymerase, try also different polymerases and so on. If you will not notice any improvement, then it is probable that you have „null alleles“ in those species for those loci. It is possible that there is a mutation on the primer-site region (one or more nucleotides are changed and the primers are not perfectly complementary to DNA strands). Than the PCR is not successful. If this is your case, you will not get the products from those loci. Or, you can try to design new primers with several degenerate nucleotides at different position.

The ratio has no effect. How could it?

A lower ratio indicates that the DNA sample does not only contain DNA (or, more precisely: nucleic acids) but also things that have an absorption maximum above 260 nm. Typical suspects are proteins (aromatic amino acids, especially phenylalanine, absorb maximally around 280 nm) and phenole (often residing from the extraction buffers). Protein contaminations are no problem. Often, the PCR works even better when some proteins are present. Many PCR mixes contain BSA (some protein mixture without enzymatic activities) to enhance the PCR. Phenol is the bad guy, since this inhibits most of the polymerases. The DNA sample should be free of contaminations of phenole (and other phenolic components). As far as I know, the OD 260/280 ratio is usually done to check for contaminating phenole.

If I removed proteins using a phenole-chlorophorm extraction, a low ratio likely indicates residing phenole that will casue problems in a subsequent PCR.

In contrast, if I have a phenole-free solution that may contain proteins, then this presumably has a low ratio, but this won't mean trouble for a subsequent PCR.

Dear P.K Panday

    I am using the primer to amplify the DNA of  mammal and the primers used are that of a reptilia. amplyfying cyto b gene.

    I dont collect the feces from the zoo.they were collected in wild.

   In my past work i got amplification from the samples but not now...

i solved the problem  replacing / with comma (5000,50000) in  naming the parameter set

you may want to use ecoprimers http://www.grenoble.prabi.fr/trac/ecoPrimers

Check out the metabarcoding.org page for more info. 

Some tissue death is to be expected, especially at the center of the bombarded area.  You could try conditions used for onion epidermal peels (a delicate sample type) as a starting point and modify the protocol from there. 

If you're  not set on doing particle bombardment you could try transient expression by Agro. infiltration.  It may result in less tissue damage.

Coulson et al 2011 (DNA barcoding of Canada’s skates, Molecular Ecology Resources (2011) 11, 968–978) noted that here was a lack of genetic differences between Amblyraja hyperborea and Amblyraja jenseni. However, while very similar in appearance, morphology and most meristics, upper jaw tooth count is different between these "sibling" species. They also appear to have different growth and maturity and I am presently examining that aspect. They are largely geographically separate and what we may be observing is a recent separation. I agree that DNA bar coding is a useful tool but is not in itself definitive in defining species.

I think its probably degraded RNA. Treating with RNase will sort this out. You can just add say 4 microlitres of 100 mg/ml RNaseA to 100 ul of DNA extract for a few minutes and then stick it the sample through a column clean up then run some more on the gel to see what it looks like. Good luck!

Thank you for your answer. What I mean with poor results is that when I make the slices the ant cuticle is almost always broken and the tissues are torn. In some slices the ant tissue separates of the rest of the slice (there is a hole in the place where the ant should be). I think it is because of the hardness of the cuticle. I also used a pepsin to get a softer cuticle, but it doesn't work very well. I use the usual procotol: the ants are frozen and then I embed them in tissue tek and make the slices.

I would recommend considering PeerJ (peerj.com).

They are fully open access, fast (~ 3 weeks to respond) , inexpensive (pay a one time $99 fee and publish for free for the rest of your career), indexed (pubmed etc) . No impact factor yet (started last year) but rapidly growing.

Not sure what you mean. there's no reason the markeers should work in turtles but not in deer. However, if you wonder whether you can use the same primers to amplify the region: try to align the primers with the mt genome of the deer (or any species closely related to your target species) and make adjustments where necessary.

well what i say is there may be a homologous sequence in platyhelminthes or in other trematodes try to look for it. It must not be the case that all of sudden the sequence via mutation or gene transmission got into nematode. if you are right annotation is a problem in doing phylogency if you are working in something new.

Hi, are you working on population study of endangered species? Non invasive samples have been a quite good source for DNA genotyping when collected fresh. You can see some examples on the elephant study by Lori Eggert (University of Missoury).

Hello Inayat.

I guess the non-invasive samples are feces. Starch (as well as BSA) reduces the quantity of bile acids and bile salts, that inhibits PCR. Sometimes it works, sometimes it does not. There are kits to specifically extract DNA from feces, that use to work well. Whatever you follow for DNA extraction, my advise is to do PCR (if you have to) with Hemo Klen Taq (New England Biolabs). I use Hemo Klen Taq when working on feces. This enzyme is not inhibet by bile, heglobin or heparine.

It really depends on your objective (microsatellites, mtDNA, etc.) and, of course, your sample. You are already using non-invasive samples, therefore you are already expecting DNA with lower quality, yield and concentration. Additionally, faeces can also have different DNA outputs depending on their preservation, before and after collection. Taking this into account, most companies, including Qiagen, have specialised kits for this kind of extractions, but you also have to be more careful with contaminations. If you do not have good results with a standard kit, either you try to optimize the protocol or you try more specialised but usually more expensive kits.

Good luck!

Dear Ahasan,

I suggest you to use "Isolation by Distance Web Service" (http://ibdws.sdsu.edu/~ibdws/). Together with the matrix of geographic distances, you can give raw sequences or genetic distance matrix as input file. Examples of input files are available on that page. Many statistical options are also available. Good luck!

You reminded me about a recent article in BMC Evolutionary Biology. In this case the authors wanted to know how the strategy of the larvae cycle (dispersive planktonic or benthic non-dispersive larvae) can influence temporal genetic structure in polychaete. As Farhat suggests, these authors work with microsatellite analyses. I hope it's useful to you. Anyway it's an interesting paper:

Temporal genetic structure in a poecilogonous polychaete: the interplay of developmental mode and environmental stochasticity

Jenni E Kesäniemi, Marina Mustonen, Christoffer Boström, Benni W Hansen, K KnottBMC Evolutionary Biology 2014, 14:12 (22 January 2014)

Regards,

Antoni

As you are working on population genetics I agree on the use of microsatellite markers and/or mtDNA. With regard to the use of AFLPs, these are rarely used in animal population genetics when one aims to publish their data. Nevertheless, quit a few published articles in the last year measured the levels of genetic diversity and delineated population genetic structure in several elasmobranch species using AFLPs. Then again as said by our colleagues it depends on your research question.