The very basic difference between a local and a global alignments is that in a local alignment, you try to match your query with a substring (a portion) of your subject (reference). Whereas in a global alignment you perform an end to end alignment with the subject (and therefore as von mises said, you may end up with a lot of gaps in global alignment if the ...
Horizontal gene transfer
Don't expect to have a tree! Horizontal gene transfer happens and therefore we would end up with a network, not a tree.
Different DNA sequences have different evolutionary histories. See, in particular, the question of incomplete lineage sorting. This means that one may compute a tree for a given DNA sequence that must ...
There exists a bunch of population genetics forward and backward (coalescence) simulation platforms. Here is a non-exhaustive list. They all differ and you'll have to go through their manual to see what is more adapted to your needs.
List of softwares
Here is a long list of such platforms. The list might arguably be a little bit outdated today though and ...
It is naïve to think that the extent of protein similarity is sufficient to determine what is the best animal model for a human disease. The physiology of the animal and the question of compensatory genes are all factors that contribute.
Indeed, if the protein is functionally similar, it may be irrelevant if it has diverged in other regions. And, of course,...
Global alignment is when you take the entirety of both sequences into consideration when finding alignments, whereas in local you may only take a small portion into account. This sounds confusing so here an example:
Let's say you have a large reference, maybe 2000 bp. And you have a sequence, which is about 100 bp. Let's say that the reference contains the ...
compare the amino acid sequence of protein 1 with nine homologous proteins and make a multi-alignment of the sequences.
EBI have a portal for many MSA tools and there are also other MSA tools available elsewhere.
In research, it's good practice to use several alignment techniques and look at which generates sensible indels. Usually, this is ...
Initially, there were several quality encodings that used to follow different ranges of ASCII characters to denote the quality of read. The range that you mention is a union of all those encoding formats. Nowadays, the most common encoding is Phred+33 (used by Illumina, Sanger, Ion Torrent and other popular sequencers) which uses these characters:
By now I got some interesting answers in this question on Biostars
Basically what I did is the following:
First of all, I checked if Sequence Id contains paired end notation. As described in this wikipedia page, for Illumina reads there are two possible notation for single/paired end reads:
If the last number is /2 in ...
Do you know about BioPython?
Here, on another website, someone already asked this question and a pretty nice answer was provided by Brad Chapman. He gives already written functions to perform this kind of analysis (I personally haven't tried the codes).
In Perl there is Bio::Align::DNAStatistics. You might adapt it to Python.
This might be useful as well.
It seems that no good map of this plasmid is around. Life technology uses it in some of its bacterial strains, the quote:
E. coli also contain the helper plasmid, pMON7124 (13.2 kb), which
encodes the transposase and confers resistance to tetracycline. The
helper plasmid provides the Tn7 transposition function in trans.
They link to the original ...
So, the problem is that you are probably using wrong class for your record. Compare Bio.SeqRecord class with Bio.GenBank.Record class. Reason why in your GenBank format you have this reference date (01-JAN-1980) is because your record has intrinsically no date attribute, so SeqRecord.format set undefined fields to default (look through the source code to see ...
If you want to synthesize a specific DNA sequence chemically, you start by attaching your first nucleotide (one letter in your DNA) to a solid phase. You can then continue adding individual letters to it. Because it is bound to a solid support material, you can wash the whole thing without washing your growing DNA away.
So if you want to synthesize AGC, you ...
The applications for counting the k-mer occurences in a sequence are: building de Bruijn graphs  for de novo assembly from very large number of short reads, produced by next generation sequencing, fast multiple sequence alignment , and  repeat detection.
Compeau PE, Pevzner PA, Tesler G: How to apply de Bruijn graphs to genome assembly. ...
The FTP download files are documented on the UCSC site (from which they also may be downloaded from a web browser). The page for the human genome is http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/. I don't know which files you downloaded, but I quote three of the descriptions:
hg38.2bit - contains the complete human/hg38 genome sequence
What can I infer if I get a high percentage of C from a protein
A highly stable structure that is likely found in the extra-cellular space.
Cysteine can form a disulphide bond with another cysteine. Cysteine can be found as a lone cysteine, but is often paired with another cysteine in the tertiary structure to form these bonds.
If you are not trying to assemble but just to align each read to the genome, you can use exonerate. On a Unix/Linux platform, once you have installed it run something like:
exonerate -m genome2genome WGS.fasta genome.fasta > out.txt
From the exonerate manual:
This model is similar to the cod‐
Download the BLOSUM data and source-code from here. Unzip the archive which has several files.
The file called blosum'XX'.qij will have the co-occurence probabilities, and the subsitution probabilities can be calculated from them.
Also have a look at this article.
Good question, a lot of this is still being figured out. Here's what's known so far:
Fragmentation methods based on restriction enzymes aren't random.
Reverse-transcription performed with poly dT-oligomers, which bind to the 3' poly-A tails, is strongly biased towards 3’ end of transcripts.
Reverse-transcription with random hexamers results in an under-...
Where the reads found in R2 are the reverse complement of those found
This statement seems incorrect.
Paired-end reads comes from opposite ends of a fragment (you could learn the reason it happens from Illumina's video). If insert size is 150bp, read length usually is ~60bp as quality score after 60th bp is unacceptably low. In this case, R1 ...
It is done for checking sequence similarity between two or more different sequences. This will give information about how two sequences are different, what is their evolutionary relationship, which residues are conserved etc. Take a look at following sequence alignment between different sequences. (Image courtesy: Wikimedia Commons)
Ar means aromatic and + means positively charged residues. However, this is not a standard code (as of now).
From the same paper:
Sequences closely matching these optimal binding motifs, R-X-[Ar/S]-[+]-pS-[LEAM]-P and R-[S/Ar]-[+]-pS-[LEAM]-P, denoted as mode 1 and mode 2 consensus sequences, were found to be present in many known 14-3-3 ligands ( Yaffe ...
You can find the data you need in the Protein Data Bank.
Since 1971, the Protein Data Bank archive (PDB) has served as the
single repository of information about the 3D structures of proteins,
nucleic acids, and complex assemblies.
The Worldwide PDB (wwPDB) organization manages the PDB archive and
ensures that the PDB is freely and publicly ...
16S and ITS techniques try to identify organisms in your samples by amplifying short, 'barcode' sequences from each organism's DNA, and using those short sequences to try to identify the organism they came from.
16S can be useful for distinguishing between prokaryotes (bacteria), at least to genus level. It will not always give enough information to ...
The case refers to the degree of conservation of a particular nucleotide in the consensus sequence. Highly conserved nucleotides are written in uppercase. At positions where the sequence varies, the most common nucleotide is given in lower case. That said, I'm not sure what the cut-off would be between "highly conserved" and "most common" and, in my opinion, ...
I would suggest you PAcAlCI or Prediction of Accuracy in Alignments based on Computational Intelligence, though the acronym in wierd the tool is good for testing new Sequence Alignments. They
But before you start testing your algorithm, I suggest take a look at these papers:
 Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence
Bioinformatics journals can deal perfectly with this type of papers. If you target a journal like Bioinformatics, then you can be as technical as you want (and you probably should). Biologists that read those journals will most likely understand the terminology.
Even traditional experimental biology journals, like Nucleic Acids Research, now include a ...
The basic process would be (in pseudocode, I don't know python well enough, I'm a Perl geek):
At this point you will have two arrays or hashes or tuples or dicts or whatever holding the ...
It does sound like you have a lot of data.
I would first try Robert Edgar's other newer tool UPARSE which is faster and can handle more data using the free 32-bit version. I think you'll mainly be limited by machine memory though, right?
Did you try CD-Hit?