Molecular Phylogeny - Science topic

The Phylogenetic Handbook ( https://www.cambridge.org/core/books/phylogenetic-handbook/A9D63A454E76A5EBCCF1119B3C56D766 ) includes sample data sets. But searching for those data sets now, it seems that they are not currently available online at the URL given in the book (www.thephylogenetichandbook.org ).

thank you for your valuable response. Hope this would help me.

Yousef Shahwan

The main issue with calculating phylogenies or genetic distances including insertions and deletions, is that insertions and deletions come in many different types. A whole gene or other large chunk of DNA can be inserted or deleted in a singe event and should not be "measured" the same way as an large number of single-base substitutions. Insertions and deletions of transposable elements are more frequent than similar sized regions that are not known to be transposable. The smaller in/dels also come in many types, such as those that are flanked by inverted repeats making a hairpin in the DNA or RNA temple, variable numbers of tandem repeats, etc... But in many cases it is again more likely for an insertion or deletion of n bases to happen at one time, than for n number of single base events to have happened.

The algorithm for CLUSTALW works by calculating the similarity scores as the number of k-tuple matches between two sequences, accounting for a set penalty for gaps. The more similar the sequences, the higher the score, the more divergent, the lower the scores. While The MUSCLE algorithm proceeds in three stages: the draft progressive, improved progressive, and refinement stage. In this first stage, the algorithm produces a multiple alignment, emphasizing speed over accuracy. This step begins by computing the k-mer distance for every pair of input sequences to create a distance matrix. UPGMA clusters the distance matrix to produce a binary tree. From this tree a progressive alignment is constructed. The 2nd and 3rd stage is focused on obtaining a more optimal tree by calculating the Kimura distance for each pair of input sequences using the multiple sequence alignment obtained in Stage one, and creates a second distance matrix. UPGMA clusters this distance matrix to obtain a second binary tree. A progressive alignment is performed to obtain a multiple sequence alignment like in 1st Stage , but it is optimized by only computing alignments in subtrees whose branching orders have changed from the first binary tree, resulting in a more accurate alignment.

Further, in terms of accuracy both MUSCLE and CLUSTALW show greater accuracy in alignment of multiple sequences but MUSCLE is advantageous in handling more complex dataset and sequence length greater than 1000 bp.

To code the indels I used GapCoder, which is not available anymore. But I think there are other programs that do the same work.

To test the partition models you can use PartitionFinderMorphology, however it has only two binary models (BINARY+G and BINARY+G+A) and you can test them only once at a time.

I run both and kept the one with the highest score. I leave you the comand and also a .cfg as example

PartitionFinderMorphology.py partition_finder.cfg --raxml

Dear Atena,

You should bare in mind that relationships among taxa in haplotype networks are not necessarily equivalent to their relationships in phylogenetic trees (such as Bayesian, maximum likelihood or parsimony). This is because a haplotype network is usually calculated based on a distance matrix, thus it clusters individuals according to their genetic distances. This may or may not reflect the 'true' phylogeny.

So the short answer is that the two are not easily comparable.

I think Roman Bohdan Hołyński is a little bit too negative about this paper. There apparently was no prior phylogenetic hypothesis, and the classification was based on complementary characters, such as long/short penial flagellum. Obviously, one of those two states must be a symplesiomorphy with respect to the other, and so it is no surprise that one of the groups diagnosed by that feature appears as paraphyletic. Since most systematists now support the idea that genera should be monophyletic, it is perfectly reasonable for the authors to propose some new genera to solve problems of paraphyly. Of course, it would be nice to include data from both molecules and morphology, but as it stands, this molecular tree provides a framework for reinvestigating morphological characters, which can lead to "reciprocal illumination" and refinement of our understanding of the morphological features in this group of snails. The tree could be wrong and some of the new names not warranted, but if that is so, then the generic names can be synonymized, and there is no permanent damage done.

Hello All,

Great to see this question being brought up again!

As I mentioned in the question itself, the branching was evident clearly in the natural samples and it was lost while sub-culturing in the lab. I think it is more related to the environmental factors, though it has also been induced in the lab too. This particular sample under study was from the Czech Republic.

Branching has been reported previously too by few workers.

We eventually described the isolated cyanobacterium as a new species of Nostoc using a polyphasic approach (so definitely not a new species on the basis of branching!). I attach the published work herein as it does have detailed story.

In our case, we could not attach taxonomic importance to this character simply because it was lost in the lab. Strong and consistent morphological traits should be given weightage in the polyphasic approach but then unique observations like this too can find just a mention appropriately. It will be great if the branching aspect could be studied in detail in Nostoc's. I do not think anyone tried some detailed study with particular focus on this occasional branching.

-PS

Hello

The DNA barcode fragment depends on taxa. For example for terrestrial invertebrates there is usefully the COI as spiders, butterflies, moths, ants, wasps and bees. But in vertebrates I think is better the CytB (for Aves and mammals) it , as exploratory and comparative DNA fragments but to get information for a publication, it is recommended, we will use more than a fragment (a mitochondrial and a nuclear, at least). My best regards

Use this code in R to concatenate genes

setwd("D:\\project\\snake\\sequences")

library(ape)

ex1 <- read.dna("Cytb.fasta", format="fasta")

ex2 <- read.dna("c-mos.fasta", format="fasta")

ex3 <- read.dna("16s.fasta", format="fasta")

output <- cbind(ex1, ex2, ex3, fill.with.gaps=TRUE)

write.dna(output, "Cytb_c-mos_16s.txt", format="fasta")

I think you should give more detail if you want this kind of proposal to be successful, whether on here or in direct correspondence with researchers.

You want to make a phylogeny of a salamander. OK, but why? Phylogenies by themselves are fairly pointless things - most of the time they tell us what we already know: things are related to other things. So, put your question into a context which might lead to wider insight. What can your phylogeny tell us about evolution? About evolutionary history? About other species? About ecologies? About genes or genomic information?

Same with methods. Outline what methods you already have insight on, what issues they have, and why your project can address those issues. What recent research is relevant to your salamander? How can that research be improved upon?

And most crucially - why should anyone care? If you can't find a good reason to get other people interested in your salamanders, find another topic.

Good luck.

I have read the question carefully and what you have wrote in brackets does not mean singleton species.

Most journals (and funding sources) require you to make sequence data publicly available when you publish in a peer-reviewed scientific journal. Part of the collaborative nature of science, adding to the known data collection, etc. It's unprofessional to not make your data available.

A thesis isn't technically a peer-reviewed publication so you don't have to publish your sequences. But you can't cite your unpublished thesis.

That's what's normal for my field of research. Other fields might have different conventions.

Hi Jan, it's likely that your priors for TMCRA were not used or your settings were not write to .xml file. Please have a check for you xml file or try to use other version BEAST to exclude such possibility. It appears to me that BEAST v1.7.4 and v1.8.4 are stable.

Relics of Phylowar II can be found in this thread.

Hello, can't be evidence of ascorbate peroxidase in fungi. Peroxidase structures have significantly increased our understanding of the evolutionary and functional relationships within the plant peroxidase superfamily.

Three distantly related structural classes have emerged:

1. mitochondrial yeast cytochrome c peroxidase, chloroplast and cytosol ascorbate peroxidases, and gene duplicated bacterial peroxidase class I

2.secretory fungal peroxidases classII

3. classical, secretory plant peroxidases (class III).

So ascorbate peroxidase is class I peroxidase enzyme and catalyse the H2O2-dependent oxidation of ascorbate in plants, algae and certain cyanobacteria but not fungi. For this type of peroxidase your culture shuld be photosynthetic. My be we will see this activity in future in Radiotrophic fungus.

Good luck!

There is no single short answer to this question. It depends on what type of protein-coding gene you are studying, the distances between the taxa, etc. Some proteins are under rather uniform very strong negative selection pressure, some are under rather uniform positive selection pressure, some are rather neutral, some have regions of negative pressure and other regions under positive pressure. When the sequences are very nearly identical to each other, such as studying human, chimp, gorilla or populations of humans, there are very few changes and we need every bit of data we can get, so we would need to use all 3 codon positions. When comparing fish, mammals, birds, snakes and other vertebrates it may be that the third codon positions are saturated with mutations and the first and second sites are relatively more informative.

I like to use DAMBE to study the ratio of transitions to transversions vs pairwise distances in my data set. It is easy in DAMBE to make a plot for each of overall, first, second and third codon positions to get a feel for how the data looks.

Hi Dr. Iqbal,

I fully agreed with Prof. Marc. You must get an accession number from either IF or MB. I have experience on this type of case. I had to submit my dissertation before I submit my monograph to FUDI. But if you check them in IF, you can see the names have been cited from FUDI. We must publish them in peer-reviewed journals.

Hi Sian,

I'm glad your problem has been fixed, and I thought I might throw in an additional comment. Any method that estimates branch lengths to achieve a max/min or optimum of some sort imposes compensation across branch length estimates. The error in each estimate propagates across all the other estimates, and these methods approximate a configuration of errors that is locally best across some sampling of the space of possibilities. I think your insight about correlations in branch length estimates was a good one.

Cheers,

Guy

New facts or new interpretations are presented every year. (Some of the new advances, however, soon proved to be erroneous.) As a starting point safe, I suggest a good textbook – e.g., Vertebrate life (Pearson, 2013, 9 ed.), by F H Pough et al.

95% HPD Interval

--------------------

Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+

--------------------------------------------------------------------------------------------------

TL 1.061980 0.000095 1.042879 1.080876 1.062098 451.19 601.10 1.001

r(A<->C) 0.103976 0.000009 0.098559 0.110387 0.103935 281.42 309.01 1.016

r(A<->G) 0.360533 0.000027 0.350404 0.370181 0.360801 150.72 161.41 1.003

r(A<->T) 0.071054 0.000006 0.066360 0.075745 0.071203 289.18 321.59 1.001

r(C<->G) 0.020114 0.000002 0.017775 0.022591 0.020091 317.71 326.55 1.000

r(C<->T) 0.346303 0.000028 0.336815 0.357066 0.346073 144.77 178.70 1.000

r(G<->T) 0.098020 0.000008 0.092461 0.103101 0.097997 223.08 252.54 1.000

pi(A) 0.247380 0.000009 0.241770 0.253749 0.247389 263.70 273.91 1.005

pi(C) 0.247447 0.000010 0.242003 0.253900 0.247466 223.47 293.12 1.003

pi(G) 0.254260 0.000009 0.248221 0.259791 0.254200 246.65 248.82 1.005

pi(T) 0.250914 0.000010 0.244960 0.257107 0.250921 260.87 265.54 1.004

alpha 71.419755 35.677437 59.166980 82.053450 71.338790 728.65 739.83 1.001

--------------------------------------------------------------------------------------------------

Run Arithmetic mean Harmonic mean

--------------------------------------

1 -96849.51 -96921.21

2 -96844.91 -96921.99

--------------------------------------

TOTAL -96845.59 -96921.67

--------------------------------------

Overlay plot shows good plateauing.

Thanks!

Can I use a fungus as an outgroup for my Gram positive bacterial phylogenetic tree?

If you multiple those values per 100 you will get the percentage of difference (0.7% and 25%).

The DAAD (Deutscher Akademischer Austauschdienst) offers grants to this kind of projects, usually for no more than 6 months in German Universities. There are several types of grants, so, you must look in the DAAD website for further information. The process is competitive and you must be in contact with a German professor in advance, which support and approves your project (a support letter is compulsory before to apply). If you do not have the support letter of acceptance by a German professor, your application will not be accepted.

Good luck!

Thanks @Omar, have you tried further with,

1) decompress

2) compare two files

I am getting error message at decompress (>Error#435)

Hi. Jmodeltest is best for your work, Ofcourse Mega6 or 7 version has Modeltest and based on your sequences, you can select the best model.

You can use Alienness:

Publication available here:

It works for HGT or any origin to any destination.

If you need help, with this tool, do not hesitate to ask.

Check the attached articles.

This question certainly requires specification. The answers depend heavily on what you are actually want to now.

(1) Morphological evidence considered in total evidence studies; A wide range and the selection depends mostly on the morphological disparity of the lineage inferred. Thus, the answer depends on the lineage of taxa inferred.

(2) Morphological evidence considered in the discussion of studies employing molecular phylogenetic: Again, the answer depends on the organisms studied.

(3) Material used to extract DNA for molecular phylogenetic studies: Again, the answer depends on the organisms studied and the question asked.

The hypothetical answers has one thing in common: it depends very much on the organisms studied.

AMAS: a fast tool for alignment manipulation and computing of summary statistics.

Sequence Matrix:

I recommend watching this 5 minute tutorial/explanation video, to see if you think the SequenceMatrix tool suits your needs:

https://www.youtube.com/watch?v=Adwr-CZdNOM 

Hi,

See the links below.

Good luck. 

A quick look through Saxena and Trivedi's Catalogue of Tertiary spores and pollen from India for Dicolpopollis spp (dispersed pollen of Rattans) reveals many records from the Middle Eocene onward, but virtually no records from the Paleocene or Early Eocene (a couple of records such as 'Dicolpopollis spp, Paleocene-Eocene' for instance need to be checked out for both the actual age of the sediments and the attribution to Calamoidae) .

Rattan pollen first appears in the Maastrictian of the Horn of Africa, and is also present in the Paleocene of Sarawak, so the possibility needs to be raided that Calamoids dispersed into India from Asia following collision, and might therefore be an 'into India' immigrant!!  

The oldest records of Calamoid pollen in India therefore needs to be carefully checked to comfirm or refute the possible Middle Eocene first appearance

Parsimony is outdated. Go for both Maximum Likelihood (ML) and Bayesian analyses.

For ML analysis can use Garli, RaxML or IQTree. For Bayesian try MrBayes or BEAST.

Hi Shiran, your mutation can be inserted into the wt DDR1 and the efect on kinase activity compared in cell assay with the activity of wt DDR1 or other known mutants. We have tools for that in the lab. 

Best, 

Pavel Krejci (krejcip@med.muni.cz)

Thank You for Your answers! I have dug through litearture and currently there is practically no way to deal with compund morphological data. Albeit polymorphisms can be simply encoded as dummy variables, there are completely different models for continuous and categorical data. I have decided to use Gower's dissimilarity index - just a simple phenetic but there is no other option if You are unwilling to categorize Your data. 

There is an R package for this called treeclust

Barcode gap: http://wwwabi.snv.jussieu.fr/public/abgd/. The basic idea is that, when looking at the distribution pairwise genetic distances, we can define "clusters" (=species?) as groups of individuals which have lower distances than to individuals in other clusters. The linked program is very easy to use and does this automatically.

Theta: A decent estimate of population mutation rate can be derived by calculating the maximum intraspecific distance (see above and below)

Minimum/maximum inter/ intra- specific distance: Just get a distance matrix get the values needed from it (e.g. maximum intraspecific distance for species A is the largest distance calculated from pairwise comparisons of conspecifics for species A). I think the linked program ABGD (above) will give you a distance matrix

Tree height, coalescence rate, etc: https://github.com/joaks1/pyule Use this nice and easy command-line program.

Thanks Iqbal!

Thanks for the interesting links. The greatest interest is the question of the distinction between types of relationship A. acutiloba, A. monticola, A. sarmatica etc. (poorly distinguished species groups). It is not clear we are dealing with microspecies or intraspecies variability of one species. For example: A. acutiloba-A. tichomirovii-A. vorotnikovii.

Hi José. Usually polytomies indicate lack of resolution in your data.  A hypothesis of hybrid origin (introgression) cannot be rulled out, but the short terminal branches may also indicate weak support for a hypothesis of introgression. Why do you think that your particular tree indicate a hybrid origin of the ITS2? With a single marker it is easy to manually verify the origin of each mutation. For each polytomy select three specimens representing the suspected parent (monoclonal) populations, and the suspected hybrid, and visually compare their aligned sequences. Make a table classifying the various types of nucleotide similarities: positions with nucleotides unique to a each specimen, and positions with pairs of nuceotides shared by each of the three possible pairs of sequences. For ambiguous base calls (presumed polymorphic positions) consider the individual nucleotides comprising the ambigous call (but check the chromatograms to make sure that the polymorphisms are real and not the result of poor quality chromatograms).  If the frequency of pairs of shared nucleotides are large and close to 50%, 50%, 0%, the hypothesis of hybrid origin is preferred; otherwise alternative hypotheses, such as lack of ancestral resolution or stable polymorphism may be preferred.

One more 

Algorithms for computing parsimonious evolutionary scenarios for genome evolution... Mirkin et al. BMC Evolutionary Biology, 2003

Thanks David, that is an extremely helpful response.  I was able to open the alignment file in a MASE and determine based on a similarity threshold what areas are most variable. This should help greatly in designing any primers for metagenomic studies in the future.

Dear Arthur Burzynski,

Thank you very much for the orientation and contribution. I find this kind of discussions helpful for my science, although there may some occasional misunderstandings occur.

This problem is frequent for people who work in plant sciences (molecular biology, biochemistry). Most plant pigments are phenolic compounds and we get rid of them with PVP (soluble or insoluble) during lysis steps. These are polymers which chelate phenolic compounds before they make links with nucleic acid (and reduce enzymatic activities). I'm not familiar with animal tissues but I just read that some of them contain pigments with phenolic radicals (melanin). Moreover, hemoglobin-derivated pigments can be fixed on Polyvinylpyrrolidone. And these pigments are also known to reduce qPCR efficiency.

I think the use of insoluble Polyvinylpyrrolidone is the easiest (and cheapest) way to chelate phenolic or hemic pigments, you can use it during the lysis step of commercial extraction kits : the chelated pigments sediment with cellular by-products and solubilized nucleic acids can be processed during further column-based or beads-based purification steps.

Jai Ghosh points another way to improve nucleic acid purification : it seems that invertebrate contains proteic pigments and in theses cases, a PVP-based step would be inefficient.

Hi Ashwini,

I really don`t know how to get the std dev for each node, but as you noted already you can get the 95%hdp from figtree

Hi there,

If you have experience in R, you could try using the "pml" function in the phangorn package. Here you can specify the proportion of invariant sites, the model as well as the gamma distribution. 

Have a look here: http://www.inside-r.org/packages/cran/phangorn/docs/optim.pml

What do you mean by linkage inheritance? Genes are linked and are inherited together both in plants and animals.

Please check the following link for said query.

Hi Sora,

After the clarification you provided let me ensure you that there is no clear case can answer your question in a general form. You have to test it first, the easiest way is to search for the ITS and/or matK sequences of the species under study at any nt database, then do a simple analysis to determine whether the species posses an intra-specific variations enough for your study. The other way is to select few of the most different samples (e.g. different locations, morphotraits, collection period... etc.) and  sequence the ITS and/or matK before proceeding with all your samples.

I may also recommend combining a cpDNA region (e.g. ycf, rbcL...etc.) along with the nuclear ones as the genetic variation and phylogeny are something a bit different from DNA barcoding.

Best of Luck! 

Hi!

You can concatenate the two matrix on Mesquite software (http://mesquiteproject.wikispaces.com/home). This software is a very easy platform available on Windows and Mac OS X. It is important to obtain a topology for COI and another one for 16S RNA, then to compare the topologies and to obtain a tree with both markers together.

These trees (for separate markers and both together) can be calculate by a distance method as NJ (on MEGA software) or by a phylogenetic method as Parsimony (TNT or PAUP software), Maximun Likelihood (on RaxmML and RaxMLGUI software) or Bayesian Inference (on Mr. Bayes software).

It is important to have a criterion for a substitution model in your sequences before to make your analysis. You can infer it on JModel test software.

In Youtube are a lot of tutorials for this kind of analysis. You can looking for this information for a exploratory aproximation.

*All software mentioned is free except PAUP.

Best regards from Mexico.

Fresh samples always  are the best. The more you store the more DNA gets degraded. But, if it is possible to store in liquid nitrogen, that has no parallel. Still, if the stored ones are all we have, Proteinase K treatment, high molar NaCL precipitation, repeated Isoamyl-alcohol extraction, Ribonulease incubation and finally, a salt precipitation by any commercial kit should be the best way I feel to get a about 1.8 reading in 260/280 and an un-smeared gel. But, things change a lot with time, more protocols may be available. What I mentioned is inexpensive and easy to handle for a large-scale extraction work. All the best.

Hi Bihua Chen,

I think it depends on which group you are working on. For plants, the evolutionary rate of the mitochondria is very low, then the chloroplast genome might be a better choice. In addition, I agree with Manolo Perez, sample nuclear genome will avoid inappropriate interpretation based only on the cytoplasmic genomes. Hope this helps.

Xiangqin

As long as you do not want to do any fancy things like doing your own post analysis of the markov chain of hypotheses, only the consensus tree is of interest for you.

I do not understand what the problem is with the two sequences being separated from the rest of the tree. Each group of two sequences are separated from the rest of the tree. Your NJ tree is a cladogram without branch lengths. Therefore the amount of separation cannot be compared among the two trees.

Best Christoph

what is the length of sequence? If longer than 1400 bp is good for species identification. you can use the server eztaxon  for this purpose. Eztaxon only includes the sequences from type strains, so you will clearly know which species your strains belong to. Hope it helps. Thanks.

 I am quoting gene bank

"As part of the standard submission process, GenBank staff review submissions for biological accuracy and assist authors in providing accurate annotations. If GenBank staff is unable to verify the accuracy of the submitted sequences and/or annotations, they may now add a comment to the record stating that the sequence is unverified. Until the submitter is able to resolve the issues, such sequences will have the word ‘UNVERIFIED:’ at the beginning of their definition lines and will not be included in BLAST databases."

Shefali

The main point, as I understand was "what percentages should be used for functional OTUs distinction"? My answer will be: each gene/protein has its own number if it could be determined at all.

16S OTUs are based on high scale comparison of 16S identities and genomic DNA hybridizations (in vitro or in silico) of the same organisms. It appeared, that 96% of ANI (or 70% of in vitro genomic DNA hybridization) ~ 97% 16S identity  => since 70% of hybridization was initially chosen for a species border, the 97% of 16S rRNA gene identity became a species border too. Thus, all this is based on huge amount of data, coming from comparison of genomic DNA and this particular gene. Indeed, you can use few functional genes/their products for phylogenetic analysis in some particular cases (eg. McrA, RecA, RpoB), but to link these phylogenies with an assignment of real taxonomic units you will need the same comprehensive comparison of identities of these genes/proteins and the genomic DNAs of the same organisms (or with 16S since we know the border for 16S). I`m not sure, but probably, for McrA or may be for RpoB thess kind of works were done before.

As for HGT, duplications etc. this is a real problem but also it is correct for 16S (16S rRNA gene, actually, are often duplicated and even multiplicated). All, what I said before is right only for functional genes, suitable for phylogenetic analysis, what means they are also characterized by low HGT and duplications/deletions rates.

The best thing would be to do the analysis in a program were you can add information that makes it possible to calibrate the phylogeny as input, e.g. BEAST this makes it possible to estimate te uncertainty of the age estimates. However, I assume you tree is scaled to some kind of genetic distance, if you know the mutation rate for the data you use, you can calculate point estimates for the time of nodes. It should be noted that this is not a good approach as it do assume that you know the exact rate for, and that it is the same on all branches.  

Posada & Crandall on TEE produced a review that can be a start ref. https://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&ved=0ahUKEwjX55Guy7PMAhUMJJoKHR8xAPQQFgg7MAM&url=http%3A%2F%2Fdna.ac%2Ffilogeografia%2FPDFs%2Fnetworks%2FPosada%2526Cran_01_networks%2526geneal.pdf&usg=AFQjCNHpGa2fWz8RgZT4117cI3KspM01AQ&sig2=OvUXIXrjjUrqQkxDbMd0kg&bvm=bv.120853415,d.bGs&cad=rja; Mardulyn in 2012 reddraw it for further contribution with more refs in http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2012.05622.x/pdf

The base number is apparently 2n=26

You might want to have a look in:

http://e-journal.biologi.lipi.go.id/index.php/berita_biologi/article/viewFile/2019/1898 at page 116

Sarkar & Datta (1985) Cytology of Calamus L. as an aid to their taxonomy. Cell and Chromosome Research 8: 69-73

Sarkar, A. 1986. Karyomorphological studies of Calamus L. (Palmae) to ascertain their taxonomic affinities. Proc. Indian Sci. Congr. Assoc. 73(3–VI): 157

Hi Martin,

The R package diveRsity may be useful for this, specifically the basicStat function which will do Hardy-Weinberg Equilibrium tests to look at homozygosity/heterozygosity excesses. Alternatively, the Hwxtest R package might also do the job. More information on these is available in the links below.

Cheers,

ML

Dear Yordanis, 

Thomas' suggestion would be very helpful. If you need ITS sequences of species related to one organism of interest, you can BLAST its ITS sequence and you will get a list of the closest relatives represented in GenBank. BLAST search results include links to download the sequences of all closest relatives. 

If you are interested in a particular group of organisms, e.g. the fungal genera Alternaria, Cladosporium or Trichoderma, big revisions of them have been published in Studies in Mycology and other journals, and the alignments used in the phylogenetic analyses have been deposited in TreeBASE (https://treebase.org/treebase-web/home.html). You can download the complete alignments from TreeBASE with many species, without the need to compile the sequences one by one as you would need to do from GenBank.

Success with your research!

Hugo 

HI Gregiore, 

This is a topic that has taken my attention for sometime, morphological vs molecular. With many people holding polarized views. 

It also comes from what your view of a species is. There are cases of morphologically distinct but molecular identification and vice versa. 

Certain organisms are classified as different 'species' but have the same DNA barcode and also the converse. 

If one is to go down the molecular route than it is a basic requirement to use mitochondrial and nuclear genes but certain fungi do not contain mitochondria. 

If going down the morphological route it is suggested that habitat preference ought to be taken into account also. Then there is the possibility of subspecies, ecotypes etc but not everybody agrees with. I'm a little on the fence at the moment (after having been solidly in the molecular camp). 

Very interested to hear your views and those of others. Hopefully this will provide for a fruitful discussion. 

paper kasa upload karaycha plz tell me

The % expression is an expression of the molecular divergence.  It should be read as "per 100 base pairs".   Also, the rate of molecular divergence should be the same regardless of how it is calculated.  It does not matter if you call it divergence rate or substitution rate.  It is all the same thing.  If one rate is about half of the other rate, then someone has miscalculated.  Of course, the date estimates of fossils have a large amount of error.  This should be taken into account in proposing dates.  When possible, multiple fossil calibrations should be used.  

If you calculate a rate from the MRCA, as represented by a fossil, then you need to divide the observed divergence between the OTUs in half.  (This would be the same as using the node height on an ultrametric tree.)  If you are using someone else's rate, you can get the divergence time by dividing the divergence by the rate (all units cancel except time); in that case, you don't divide in half.  

You are right, this is easily confusing.  It looks like the authors, editors, and reviewers of that 2005 paper were confused as well.  The question is: Is the error in how they used the term "node height" , or is the calculated rate inflated by 100%?  Their figure defines node height as substitutions per site.  But, we don't know if this is an estimate from the MRCA or a divergence between the OTUs.  You could check this by using their NS model to calculate divergence between the OTUs, using sequences from GenBank.  Also, you could ask them.  

The distance matrix contains 18 Gb? Well then you definitely need more than 4 Gb. A process can't access all of the memory available on the machine due to usage by the OS and other necessary processes, so I would say you should probably find a machine with 32 Gb of memory. You don't quite need that much but usually you don't see anything in between 16 Gb and 32. 

Thank you!

 I will keep trying! Thank you very much!

Hmm . Good question. I am wondering whether the anal scent glands shared with some carnivores could be plesiomorphic.

Essentially, the outgroup helps you to "root the tree", which aligns the other branches of the tree with the flow of time so that you not only know the evolutionary distance, but also the predicted order of branching in time. The outgroup sequence(s) should be from species that are known to have branched from the ingroup before any other branchings in the tree, but not very distant. Usually you would choose one or a few members from sister clades known from previous research. http://labs.eeb.utoronto.ca/murphy/PDFs%20of%20papers/2010_A-Rong_outgroup.pdf

Hello Chia Hao Hsu,

of course the used input data (marker, gene, bio molecule) influences the topology of the resulting phylogeny. In an ideal world all input should lead to the same output, but unfortunately there are mechanisms leading to incongrunece and homoplasy. Any phylogenetic tree gives you as a result by definition the relative order of evolution. This means from any phylogeny you can see which species evolved when, relative to the other species.

The usage of fossils is a common method for dating phylogenetic trees. Just search for "dated phylogeny" or "divergence time estimation" and you will find hundreds of articles. If you are interested not only in the relative order, but also in the absolute time the species diverged from each other this is one way to go. There are several problems linked to the usage of fossils: usually you can't use molecular methods to find out where they belong on the phylogeny. Fossils are rare. Morphological interpretation of the fossils/ recent species may be bias by over- or underestimation of certain traits etc. There are reviews and books out there dealing with these problems, you may also search for these.

Kind regards.

So far I know, you could do that using Dispersal-Vicariace Analysis. That uses a phylogeny of your species and some information about geographic ocurrence of each one of these species. There is a software (S-DIVA, Yan Yu , AJ Harris  and Xingjin He, 2010 doi:10.1016/j.ympev.2010.04.011) to do that. There is another options called Lagrange, a python script that make something similar to S-DIVA.

Hope this helps.

Different genes evolve at different rates.  It is possible to build trees from a mix of data such as you are describing, but the results need to be understood in the context of the data used.  With the explosion of data available in databases today, it is quite common to see researchers gathering large but sparsely populated data matrices from which to build phylogenies.    The resulting trees quite often have some species on long branches because only rapidly evolving genes (mitochondrial genes for example) were available from those species while other species are on short branches because only more conserved genes were available, and I have seen authors claim that this indicates different rates of evolution of the species as a whole when in fact it does not.

Most phylogenetic methods require that there be at least some overlap of comparable sites across all species in the set.  And it may be more instructive to build a tree from each gene and then compare the trees, than to attempt to build one supertree when the  larger set is a sparsely populated matrix.

It is a little difficult to give advice in general about such things, because not all types of organisms have similar evolution.  For example some species have more geographical or other barriers preventing gene flow between populations as they evolve.  Are you dealing with viruses, plants, fungi, bacteria, vertebrates, insects?

One example I am familiar with, which has their complete data matrix available in TreeBase so you can explore it, is a study of avian evolution by Hackett et al 2008:  http://www.sciencemag.org/content/320/5884/1763    http://treebase.org/treebase-web/search/study/summary.html?id=11811

Hi,

Thank you for your answer. .cvs file is generated from excel?