That is the correct sequence for 2019-nCov. Coronavirus is of course an RNA virus and in fact, to my knowledge, every RNA virus in Genbank is present as cDNA (AGCT, i.e. thydmine) and not RNA (AGCU, i.e. uracil).
The reason is simple, we never sequence directly from RNA because RNA is too unstable and easily degraded by RNase. Instead the genome is reverse ...
The scenarios are impossible and would be laughable if they were not so serious. The evidence is in the phylogenetic trees. Its a bit like a crime scene when the forensics team investigate. We've done enough crime-scenes often going to the site, collecting the pathogen, sequencing and then analysis - (usually neglected diseases) without any associated ...
Most sequencing experiments, be it Illumina-based next-generation-sequencing or Sanger sequencing uses DNA as template, not RNA. Even if this virus is RNA-based it would be reverse-transcribed prior to any sequencing experiment. Therefore the output is DNA and this is what NCBI provides here.
I think you can try dendextend, in this manual there is an example of coloring the branches. I don't think it is exactly like your coloring, but with a little tweaking you might get your colorscheme in there.
The manual mentions an argument called color_lines for the function tanglegram():
# The `which` parameter allows us to pick the elements in the list ...
This paper claims that FastTree is almost as accurate as RAxML, while being much faster. You just have to be careful, however, that the support values output by FastTree are not bootstrap values, they are based on the Shimodaira-Hasegawa test. (Also, see this comment for the case you have very short branch lengths). [update: However, according to the ...
UPDATE: The article has now been withdrawn with the following note:
This paper has been withdrawn by its authors. They intend to revise it
in response to comments received from the research community on their
technical approach and their interpretation of the results. If you
have any questions, please contact the corresponding author.
This is very ...
I would not look for a package for this, but instead build a small pipeline calling external tools with something like the following workflow:
Cluster the ~100 sequences with CD-HIT-EST/PSI-CD-HIT or many other options
Take all the sequences that form one individual cluster and build a multiple sequence alignment (MSA) with MAFFT/ClustalOmega or similar
Normally "inserts" used in the manuscript are "indels" in protein alignments, short for insertions and deletions.
What I think has happened is a group investigating indels in HIV env noticed indels in 2019-nCov. Essentially I think the correlation is spurious - but I haven't test it, but the area of research in understanding indels is certainly valid and ...
The central focus of the tree is to highlight the key biological concern of the new coronavirus, 2019-nCov. The key concern is the genetic similarities to SARS epidemic, and relates to the SARS receptor.
SARS background SARS is endemic in bats (your BioRxiv tree partly shows that and this tree definitely shows it) and in the 2002 epidemic infected ...
There are models that take into account compositional heterogeneity both under the maximum likelihood and Bayesian frameworks. Although the substitution process is not time-reversible, the computations are simplified by assuming that the instantaneous rate matrix can be decomposed into an "equilibrium frequency vector" (non-homogeneous) and a symmetric, ...
No. A perfect phylogeny is one such that all characters evolve on the tree with no homoplasy, i.e. for a binary character, changes occur once from 0 -> 1 but never from 1 -> 0. A maximum parsimony phylogeny may produce a perfect phylogeny, but typically for real datasets some degree of homoplasy is required to explain character patterns.
The goal of a phylogeny is to estimate the "expected" number of mutations between all taxa in the analysis and their hypothetical common ancestors. A cluster-analysis will only identify the "observed" mutations and "expected" and "observed" mutations can be majorly different due to the major artefact of reversion mutation. This is particularly true of ...
There isn't a vaccine for any coronavirus, and your question is generally about targeted attentuation, which is a complex area.
The basic building blocks for any vaccine development is virological understanding of the proteins involved in pathogenesis. I will focus on covid-19 as an example here.
The majority of bioinformatics work is based around the ...
They are both lentiviruses and share a distant common ancestor.
HIV-1 and HIV-2 are descendents from simian immunodeficiency virus (SIV). The following tree shows the relationships very clearly, from Wertheim and Worobey (2009) Dating the Age of the SIV Lineages That Gave Rise to HIV-1 and HIV-2 PLoS Comp Biol. here. The evolution of HIV-1 is heavily mixed ...
Even with inbreeding and other genetic phenomena that might mask actual evolution of these cultivars, any phylogenetic methodology would be capable of determining relationships accurately.
Try creating a Neighbour Joining tree with MEGA, which is one of the simplest methods available. This should give you enough to check the relationships of the cultivars.
using something like for col in column(alignment):
I may get the number of columns which have all the same letters (amino acids)
len(set(col)) == 1
The number of columns which have at least 2 different amino acids (with no dashes):
len(set(col)) > 1 and '-' not in col
The number of columns which have any single dash:
'-' in col
or if you really ...
A site is simply an individual discrete position, normally a single nucleotide (or amino acid). For example, consider a toy alignment:
# ^ arrow points to the third site
Here there is 1 mutation in 4 sites, so 0.25 substitutions per nucleotide site.
I'm not sure where the term originated, but probably one of the ...
I finally managed to do it in R. Here is my code:
beast_output <- read.annot.beast('beast_output.trees')
beast_output_rooted <- root.multiPhylo(beast_output, c('taxon_A', 'taxon_B'))
unique_topologies <- unique.multiPhylo(beast_output_rooted)
These are cytotoxic T-cell HLA alleles. HLA genotyping is very common and easy to do, so Genbank is the repository.
A*11:01 has very high frequency in Aborigine populations here . You can explore the population genetics and the past, present and current population genetics distributions per population of HLA at http://www.allelefrequencies.net/
A great question, though a little ambiguous. I don't know what "general clustering algorithms" refer to. For biological sequences, building a tree can be thought as a way of clustering. Anyway...
There are different tree building algorithms. Max parsimony (MP), max likelihood (ML) and bayesian algorithms directly take sequences as input. They are distinct ...
For a species complex nucleotide phylogenies all the way.
The reason is neutral mutation, which is not observed at the amino acid level because these mutations are part of the degenerate code which don't induce amino acid substitutions. Such mutations are commonly referred to as the third codon wobble, although occasionally the first codon can mutate and ...
To expand on the comment by marcin:
fetch downloads files in mmCIF format by default (https://pymolwiki.org/index.php/Fetch). Not all PDB entries have PDB format files, e.g. due to too many chains. Presumably this is why the change was made, though mmCIF files tend to be larger and hence download slower.
When calling save with the .pdb file extension the ...
It is recommended you learn the degenerate nucleotide code. In sequencing it can signify poor quality sequence data, but in primer design it is useful. R (mutation within purines) and Y (mutation within pyrimidines) are common. K, a purine to pyrimadine or pyrimadine to purine mutation is, in my opinion, rare. I would treat a K mutation with caution and ...
The evolutionary related group (clade) of betacoronaviruses you have identified share an amino acid homology of 85% and include SARS. I know this from the underlying tree published on BioRxiv of a broader group of betacoronaviruses, i.e. your data is a defined subset of the betacoronaviruses which all share a unique, single common ancester.
Lets call this ...
If this were [cDNA], the end of the true mRNA sequence would be ...UCUUACUGUUUUUUUUUUUU, or a "poly(U)" tail.
A cDNA sequence, maybe confusingly, refers to the coding strand of the cDNA (despite being called “complementary”). So while cDNA is the result of reverse transcribing RNA into DNA, by convention it has the same strandedness as the original RNA. ...
In summary, the authors are saying the complete opposite of "human intervention".
While the analyses above suggest that SARS-CoV-2 may bind human ACE2
with high affinity, computational analyses predict that the
interaction is not ideal and that the RBD sequence is different from
those shown in SARS-CoV to be optimal for receptor binding.
You can accomplish this using the ggtree package available on Bioconductor.
First you will need to combine your tree with the data.
ftree <- tree$edge %>%
mutate(Node_number = 1:n()) %>% # finds edge numbering
right_join(data, by = "Node_number") %>% # find internal node ...
Extract desired gene sequences using standalone blast
Simply provide a reference database with your desired output.
Set up your command and away you go. You can set the search up with a for loop for a batch of sequences. Command may look like
for f in *.fasta; do
f=$(basename $f .fasta)
-outfmt "6 sseqid qseq %" \
-query $f.fasta \