There area few different influenza virus database resources:
The Influenza Research Database (IRD) (a.k.a FluDB - based upon URL)
A NIAID Bioinformatics Resource Center or BRC which highly curates the data brought in and integrates it with numerous other relevant data types
The NCBI Influenza Virus Resource
A sub-project of the NCBI with data curated ...
I found a post useful for this topic.
It explains the difference of coverage and depth. It also has a useful explanation on how to calculate coverage and depth.
Here is a copy of what the link says just encase the post is removed:
Depth of coverage
How strong is a genome "covered" by sequenced fragments (short reads)?
Per-base coverage is ...
There are several questions in your post I'll try to answer each one:
Is there any way to calculate how deep the sequencing is ?
See gringer's answer. TLDR: The depth of the sequencing is how many times each position has been sequenced.
What should be the optimum depth to get reliable data ?
The optimum depth depends on what you want to do with that ...
Sequencing depth is typically calculated as the number of total bases sequenced divided by the number of bases in the target genome. An Illumina sequencing run with 2x125 bp reads and 500 million read pairs sequenced would be a sequencing depth of about 40X (assuming my calculations are correct for a 3 billion base genome).
The sequencing depth depends on ...
It seems to be explained right there in the image you posted:
So, the three strains were classified based on three specific variants:
Strain S, variant ORF8-L84S: a variant in the gene "ORF8" which changes the leucine (L) residue at position 84 of the gene's protein product to a serine (S).
Strain G, variant S-D614G: a variant in the gene "S&...
Most read aligners will report unaligned reads as well, which presumably will include your viral sequences. I would ask them to formally confirm that the BAM files will contain unaligned reads before choosing that option.
R is a reference to R0, otherwise known as the basic reproductive rate, and means the number of new cases from a single patient infection. R0 has to be above 1 for a disease to persist. It is a classic equation which account for the probability of transmission against immunity. The y-axis is the number of infecteds, very poorly presented.
The idea is that ...
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 ...
I can only speak of drug design (and even then I am terrible at turning down the jargon).
In the case of drug design, this is pretty much plan C. Namely, none of compounds that entered clinical trial at the start of the year work (let's call this plan A) and none of the vaccines that are entering now clinical trial work (let's call this plan B although ...
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 ...
I don't know of any transcript-to-transcript aligners that are able to do this, but LAST can align transcript queries to protein reference sequences using a specified frameshift cost. Here's the specific documentation for that option:
Align DNA queries to protein reference sequences, using the specified
frameshift cost. A value of 15 ...
Since you are already using the Broad Tools sets you can use Picard FastqToSam to make the conversion
As far a clipDb I am unfamiliar with that and a quick google search and look at the trimmomatic manual were unhelpful
This may not strike most as a bioinformatics, but getting the key clinical outcome is essential in understanding the molecular basis of pathogenicity.
I think the mortality rate is over-reported. This is not to say the situation of 2019-nCov is not serious - it is very serious.
The two essential factors missing in your equation for 2019-nCov are:
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 ...
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.
Making a consensus sequence tends to involve some arbitrary decisions. In this case, the common practices are:
Use the ancestral allele (this isn't always apparent)
Use the most common allele
I think 1 and 2 are generally preferred, with 2 being easier to implement.
To add a more complete answer: the current coronavirus is closely related to the SARS virus that caused the outbreak in 2004, and on which much research has been done.
Here is a general review of the coronavirus epidemiology, life cycle etc.
I haven't found yet any materials about the RNA structures in the translatable region, however the structures in the ...
Please look up flavivirus 'double loops' as you described them previously (post for "Coronavirus RNA') and associated RNA secondary structure anomalies for dengue virus and associated vaccine (Butantan) and the yellow fever virus and its vaccine (17D). If you are aware of "double loops", we must be aware of the association of RNA secondardy structure and ...
R, the reproductive number, relates to the average number of (new) people that will get sick (infected) per person that is already sick.
For instance, if $R=2$ and you start with a single infected person, then the next generation will be 2 people, those 2 people will make 4 people sick, those 4 people will make 8 people sick, and so on.
Is R a fixed ...
If you are interested in a particular viral sequence, it seems wasteful to request bams that are aligned to the human genome, but you could request bams aligned to the human genome with unaligned reads retained. As an alternative, you can generate unaligned bams, where all of the same reads are present that would be present in a fastq but in a bam format. ...
It is a crystallisation artefact. Namely, the protein are placed in a condition where they fall out of solution without aggregating a gloop (as happens in most well in xstal trial) and to pack as a crystal they need to be placed nice and orderly. It is coordinated by water so is not relevant. A lot of structures have these —DMSO and ions are the most ...
Your understanding of FASTA format is about right. The type of basic problem you're eluding to we term "sequence alignment"- edit distance might be okay for teaching but in practise we use other algorithms, e.g. you might be interested in the Needleman–Wunsch or Smith–Waterman algorithms. Richard Durbin et al. wrote a great book that covers these ...
Note that the protein sequence of ORF8b is not contained within the protein sequence of protein N. That means that they are translated from different frames (though they are in the same orientation). Indeed, if we just subtract the start coordinates we do not find a multiple of 3.
Thus, they cannot be results of the same translational event. This means that ...
Alright, so there are a number of problematic patterns in your code - as far as I understand what you are trying to do. Next time, try to post a reproducible example that people can use and more people will be willing to help.
combination_labels = 
combination_counts = 
for lineage in lineages:
Declaring these two lists before the loop, then ...
Getting SNPs from whole-genome data is usually done via mapping sequencing reads on the reference (bwa-mem or bowtie are two popular mappers) and then using one of variant calling tools (like freebayes, samtools, or GATK). However, I would also look up if there is something virus-specific.
According to this answer, you could get variant calls of a specific ...
Try MACSE v2 (https://academic.oup.com/mbe/article/35/10/2582/5079334) will align multiple protein-coding nucleotide sequences based on their amino acid translation while allowing for the occurrence of frameshifts