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 ...
For directly outputting a sorted bam file you can use the following:
bwa mem genome.fa reads.fastq | samtools sort -o output.bam -
Optionally using multiple threads:
bwa mem -t 8 genome.fa reads.fastq | samtools sort -@8 -o output.bam -
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 ...
There is no perfect cut-off. It always depends on what you're doing. The e-value is basically a measure of how many such alignments you would expect to find in a database this size by chance. Therefore, e-values greater than 1 mean that you'd expect at least one alignment similar to what you've found by chance alone.
As others have stated, the e-value is ...
Since I apparently don't have enough reputation to comment, I'll post this as an answer and let someone move it.
Firstly, try to ensure that the order of the arguments are correct. You should be typing bowtie2 -f -x $TARGET -U $TARGET -S $TARGET.sam, since bowtie2 (as with many other programs) can be a bit picky about the argument order.
Secondly, it's ...
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 the ...
The e-value is supposed to be a metric for the chance that an alignment could occur at random, but it is a crude estimate. As pointed out in other answers, this significantly does not include the length of the query sequence.
It also does not include the conservation of the gene or the frequency of amino acids (in protein blasts). It does take into ...
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)
According to BLAST documentation statistics of local sequence alignment, when doing database searches, is done
by treating the database as a single long sequence of length N.
N is therefore a sum of all sequences with varying sizes from a given database.
Underlying assumption is that
query is a priori more likely to be related to a long than to a ...
No, the search won't be less reliable. It will only be more sensitive, more capable of finding matches. To understand this, you need to understand how the BLAST algorithm works, what the word size means.
When you use a word size of $N$, BLAST starts by looking for a match of length $N$ between your query and target sequences. If such a match is found, ...
You can find the definition here:
the number of hits one can "expect" to see by chance when searching a
database of a particular size.
Also read some of the background information given to understand the meaning:
It decreases exponentially as the Score
(S) of the match increases. Essentially, the E value describes the
random background noise. ...
E-value refers to the expected number of random hits for a given alignment score. Smaller it is more reliable is your match. There is no hard and fast rule for e-value cutoff. You can keep whatever you want depending on the level of stringency that you require. But you should note that for smaller sequences (< 30nt) there is always a higher likelihood of ...
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.
Okay, I emailed Dr. Mark Styczynsk (one of the authors) to ask about a link to the matrix. Here are the links he gave me:
http://web.mit.edu/bamel/blosum/RBLOSUM64 (isentropic with BLOSUM62)
http://web.mit.edu/bamel/blosum/RBLOSUM62 (not isentropic, but it'...
Found a tool: MUMmer is a system for rapidly aligning entire genomes. The current
version (release 3.0) can find all 20 base pair maximal exact matches between
two bacterial genomes of ~5 million base pairs each in 20 seconds, using 90 MB
of memory, on a typical 1.8 GHz Linux desktop computer.
If you know a better programmatic approach, let me know, ...
First of all, don't reinvent the wheel, search for annotated homologs first. Assuming you don't find them, the next step is:
Collect the sequences of your query proteins (not genes) from one species in a multifasta file.
Run a tBLASTn with those sequences against the genomes of all the other species of interest.
Analyze. Look for HSPs with a certain level ...
Yes there is a relationship between them but you may not be able to observe correlation between some of them.
Number of matches and score are definitely proportional, however higher similarity would translate to higher score only if the lengths of the scoring pairs are the same. Gap would have a negative effect on the score but it totally depends on what ...
First of all, do not, I repeat not, use ClustalW. That is horribly out of date and was never very good to begin with1. It was just the first to become common. At least use ClustalΩ. Anyway, while I haven't done much aligning in the past few years, back when I did, I got the best speed/quality ratio from MAFFT.
MAFFT has been used in various sequencing ...
... I was reading the article about Genome Sequencing of a Genetically Tractable Pyrococcus furiosus Strain Reveals a Highly Dynamic Genome in which they compare the Pyrococcus Furiosus reference genome sequence ... with the sequence of a variant in a lab strain population, designated COM1 and that is the genetically tractable strain which has undergone ...
Expanding on the comment by @Chris:
Overlapping sequences imply evolutionarily conserved regions, i.e. preserved by evolution through time due to theirs having some important function.
Assuming the sequences are homologous, overlapping regions of similarity reveal "evolutionarily conserved regions".
These are regions in the ...
There are couple of them. First if you want to sequence analysis basic packages are :
Also, Maqweb seems promising.
My Vote goes to Mafft(insi) as it have ~86% accuracy and results in ~1.2 hour. Though fastest will be kalign takes only ~3 minutes to finish with an accuracy of 74.3%.
For each of the 218 reference alignments in the benchmark, we applied
eight alignment programs, resulting in a total of 1744 automatically
constructed MSAs. The overall ...
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
Addition to previous answers:
PSI-BLAST is a sort of machine learning algorithm which uses the results of the first alignment (PSSM) to score the next iteration of alignment. I would recommend you to refer to the NCBI bookshelf page on PSI-BLAST.
PSI-BLAST adopts a scoring scheme (PSSM) that is built based on a given set of data (the aligned sequences), ...
From Russ Altman (Stanford University) lecture:
Blast takes as an input a sequence, it searches the database, and outputs a set of alignments (a top ranked alignment, followed by a second-top ranked, a third, and so on). Then you take the top scoring sequences (you might use the E-value as a cutoff) and you can create a position-specific scoring matrix (...
PSI-BLAST is Position-Specific Iterated BLAST.
I'm probably out of my depth here, but my understanding is that the results of the first, essentially standard, BLAST search are used to create a multiple sequence alignment. This alignment is then converted to a Position Specific Scoring Matrix (PSSM). (The linked Wikipedia entry shows this for DNA sequences, ...