Hot answers tagged

22

2019-nCoV is a virus that originated from the bat (at least this is the current hypothesis). It shows 96% squence similarity to the BatCoV RaTG13 sequence (see reference 1), showing its origin. It still is 87,99% identical to the "Bat SARS-like coronavirus", which explains the hit you found and is not unexpected, as these viruses are very closely related (...


12

b.nota is correct - Just further adding to his answer. The International Nucleotide Sequence Database Collaboration (INSDC) is a consortium between DNA Data-bank of Japan (DDBJ), EMBL-EBI and NCBI. Contributions to each of the 3 databases are shared on daily basis. (Thanks to @KonradRudolph) Since EMBL-EBI is not directly accountable to any single ...


8

The two most common families of scoring matrices are BLOSUM and PAM. Each of them has a score for every possible alignment combination between the 20 standard amino acids1. They both do more or less the same job but have been derived using different approaches. BLOSUM matrices (image taken from Wikipedia) The BLOSUM matrices are built from actual alignments ...


6

Directionality It is indeed the convention to represent nucleic acid sequences in the 5ʹ to 3ʹ direction. This is implied in the IUPAC/IUB document on Abbreviations and Symbols for Nucleic Acids, Polynucleotides and their Constituents, although not stated explicitly — presumably because this was written in 1974, before the large nucleic acid databases ...


5

Because it has a poor statistical model. It's basically a local alignment tool aided by a fixed-length k-mer index. It estimates homology in terms of sequence identity and while this is often enough to find homology in relatively conservative sequences, the heuristic scoring model doesn't really allow to search motifs. A motif in terms of bioinformatics can ...


5

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, ...


5

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. ...


4

There is no direct option like that but you can set percentage identity filter with -perc_identity However, this is only for reporting. BLAST will still perform all the alignments. That is why I suggested you to use bowtie. EDIT-1 (getting long for comment) If you want a word type of search then use the -n option. But it always starts from the 5'end ...


4

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 ...


4

I'm not sure this is what you need since the sequences you posted are not actually complementary as far as I can tell. However, exonerate is one of the most powerful tools out there and can do this as well. Using these sequences as examples: >query TAGCTTATTGATGGGAGGAGAGTCCGTGCACATGACAGACCTTGGCTGTCCCAGACTGCAGGAAGCCCAGG >target ...


4

A scoring matrix is used to compute a score for finding identical 3-letter words or similar 3-letter words that takes into account the liklihood of one being related to another. Although these matrices are empirical, they encompass factors like the number of mutations required (which is why there are different matrices for different extents of divergence) ...


4

BLAST can be localized to your machine. There are tutorials, such as Run-Blast-Local provided by NCBI. Note: you will need to download any/all databases you want to BLAST against locally. Other options for identifying ORFs and trying to identify potential genes include: USEARCH, VSEARCH (both claim to be as fast, or faster). If you download entire ...


3

You might be confused about what "heuristic" means. "Heuristic" doesn't mean random or arbitrary, instead, an algorithm is termed "heuristic" if it employs a shortcut which means that it does not necessarily yield the theoretically best result. For BLAST, this shortcut is the assumption of first matching the fixed-length words prior to extending the match. ...


3

The "best" is the one with the highest ID score, lowest e-value and greatest length. There is no magic bullet, all of these are important. You should always look for the most similar sequence that matches as much as your query as possible with the lowest e-value possible. Furthermore, it will depend on what you are looking at. If, for example, you are ...


3

First of all, if you want 90% identity, you can discard this hit. None of the HSPs pass that threshold. What's more, since you're working with proteins, there are no splicing issues involved and you should be able to get a single HSP spanning most of the query and subject sequences. Assuming, of course, you have a true homolog. In your output, I see many ...


3

If you have a non-coding gene sequence (e.g. regulatory sequence) this answer should hold your solution: Background theory Firstly you must realize that PSI-BLAST is built for detecting "romote homologues", (i.e. those that have a very "distant evolutionary relationship" to your query) - from a database of sequences. It is therefore known to be a "...


3

They use very different algorithms so they should not have the same E-value. BLAST uses a position-independent substitution matrix (e.g. BLOSUM) while HMMER uses a position-dependent substitution matrix that is different for every profile. Check out the "Background and Brief History" section in the HMMER's user guide. It explains the conceptual differences ...


3

If you had a 100% BLAST hit for 3 species from a single amplicon sequence then it only means that region of DNA is conserved in the three species so you have at least one of those species present not necessarily all three. If you had three different amplicon sequences and each one matched one species to a total of three, then you have all three species ...


3

The convention is to provide the sense strand from 5’ to 3’.


2

This website will allow you to blast all sequenced microbial genomes: http://www.ncbi.nlm.nih.gov/sutils/genom_table.cgi Recovering the full gene sequence might be a bit of a hassle, since BLAST will only allow you to recover the region that matched your query. You may need to set up your own pipeline for this. You may also be able to identify a "protein ...


2

Just run blast with only the Plus-Minus alignments. See this post in biostar; it is similar to what you are interested in. This is for the standalone version. I am not sure about how to do it in the online blast. If you have just two sequences then you can use UNAfold for checking complementarity.


2

You could try using a tool to estimate the binding affinities of the two sequences, i.e. OligoCalc http://www.basic.northwestern.edu/biotools/OligoCalc.html


2

Assuming you are using PSI-BLAST to recruit coding homologous nucleotide sequences to your query nucleotide sequence. Here's a work-around using PSI-BLAST itself: Translate your nucleotide sequence into amino acid sequence Run psi-blast to recruit matching homologous protein sequences Store the names or database IDs (e.g. genbank accession numbers) of ...


2

You can manually restrict the design ranges in Primer-BLAST screen. First of all you should enter the targeted sequence. In this case, it will be NC_000012.12 which corresponds to chromosome 12 that has KRAS gene. You should determine the position of variant that you interested in that chromosome. The KRAS G12D -or rs121913529- variant is in 25245350. ...


2

You can try this: BLAST each sequence to every other sequence (pairwise). Every alignment (with some defined cutoff) denotes a connection. Map all connections. If a sequence is connected to some other directly or indirectly, it falls in a group. Put all the sequences that seq1 aligns to, in Group-1, then go to the alignments of these sequences; put all the ...


2

blast2go is on the verge of getting commercialized (as they have started selling PRO versions) and my previous experience was not so good with it. I used IntrProScan to associate GO terms with the transcripts, it ran long but it reported all the possible sequence features. To use these custom annotation was tricky for visualization, but thanks to BiNGO, I ...


2

I've done a functional annotation of a non-model, and used Blast2Go with fine results. Specifically, I took all of my proteins and queried again the NCBI non redundant (nr) protein database using mpiBLAST, then I analyzed this output using Blast2GO (B2G4PIPE) and a local B2G Database.


2

First check if your RNA sequences are described by existing covariance models (CMs) available in Rfam. You can do this using the Infernal package to search the Rfam database of CMs. For those RNA sequences which match an Rfam CM, you can then use that CM to search the sequence databases for additional matches. For those that do not match an Rfam CM, you ...


2

Define a "Hit" (based on some cutoff- evalue, score etc) Get output in the tabular format Count number of hits per query — it is usually given in the header; if you want to look for some selected hits (based on some cutoff, then you can parse the file and find out) Example file (header): # BLASTN 2.2.27+ # Query: TCONS_00036712 gene=XLOC_017996 # Database: ...


Only top voted, non community-wiki answers of a minimum length are eligible