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7

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 ...


6

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. ...


6

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 ...


4

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: genome2genome This model is similar to the cod‐ ...


4

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 testing: 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 ...


4

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.


4

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 ...


3

The basic process would be (in pseudocode, I don't know python well enough, I'm a Perl geek): $seq1=ATGCCAGGCTGA $seq2=ATGGGACCATAA; for ($i=0;$i<length($seq1);$i++){ codons1[$i]=amino_acid } for ($i=0;$i<length($seq2);$i++){ codons2[$i]=amino_acid } At this point you will have two arrays or hashes or tuples or dicts or whatever holding ...


3

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: [1] Who Watches the Watchmen? An Appraisal of Benchmarks for Multiple Sequence ...


3

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 ...


3

Expanding on the comment by @Chris: Short Answer Overlapping sequences imply evolutionarily conserved regions, i.e. preserved by evolution through time due to theirs having some important function. Long Answer Assuming the sequences are homologous, overlapping regions of similarity reveal "evolutionarily conserved regions". These are regions in the ...


3

I would strongly recommend benchling https://benchling.com This is an awesome web based tool for cloning, primer design, multiple sequence alignments and everything else you are used to doing with the other tools. It is very user friendly, and most importantly you can share designs with your collaborators. Also, the graphics are very beautiful. I recently ...


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

TransDecoder is a commonly used program for extracting likely coding regions from transcriptome assemblies, which does the following to make a call: TransDecoder identifies likely coding sequences based on the following criteria: a minimum length open reading frame (ORF) is found in a transcript sequence a log-likelihood score similar to ...


2

If you're looking for an exact match, you don't really need a complex aligner. Perl regular expressions are pretty powerful at string transformations or conditional matching of substrings. For example, to find all matches of AASYWSRA in a nucleotide sequence $seq, you can do: @matches = $seq =~ m/AA[CG][CT][AT][CG][AG]A/g; The [] brackets are known as ...


2

The first step after sequencing is finding probable genes. After that, genes and their proteins can be classified to belong to protein classes. This is the most what you can do with completely unknown genes. It's possible nowadays to predict the final structure using contact maps (if there is no homologous structure known) but this will still leave you ...


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

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: Lets say you have a large reference, maybe 2000 bp. And you have a sequence, which is about 100 bp. Lets say that the reference contains the ...


2

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?


2

You can find 46-way multiz alignment from UCSC genome browser, it is down on comparative genomics part and labelled as "cons 46-way", which is a genome alignment of 46 vertebrate species. You can use data on their genome browser on the site, or get download information here. If you are interested in pair-wise alignments, I don't know of any pair-wise ...


2

Go to Google Scholar. Enter "Genbank" into the search field and press return. Limit the results to 2013 and later (left side of the results). Skip the few entries that say only "GenBank." Browse through the pages and pages of search results to get a feel for the many ways that GenBank is used in scientific research. It's used from everything for ...


2

messenger RNA has a poli A tail at its 5´ end. thus when the poly-dT hybridize the mRNA in the reverse transcription the cDNA will carry this poly-dT at its 3`end. Thus, as reverse transcription will always start at the 3´ and of the mRNA its is more likely that this region is better rev. transcribed. Bigger the mRNA is bigger will be this concern. About ...


2

Every amino acid has a different isoelectric point: a pH where they do not carry electric charge. This isoelectric point depends on the side chains: By glycine the side chain $-H$ is neutral (while the amino and carbonic acid groups are not) so the IEP is 5.97. By lysine the side chain $-(CH_2)_4\mbox{-}NH_2$ is alkaline $[R\mbox-NH_2 + H^+ ...


2

There are couple of them. First if you want to sequence analysis basic packages are : http://www.bioconductor.org/help/workflows/high-throughput-sequencing/ Also, Maqweb seems promising. http://maqweb.sourceforge.net


2

For birds you can start by searching 18s in http://birdgenenames.org/cgnc/ But generally NCBI Nucleotide database, should provide you with all information and accession numbers. See this broad search: http://www.ncbi.nlm.nih.gov/nuccore/?term=18S+ribosomal+RNA+gene Adding species specification and other parameters, you might find what you are looking for. ...


2

I may be jumping the gun here, but assuming that you haven't searched NCBI or ensembl, I'd do that first. Essentially all genomic information is maintained by a collaboration between US NIH, EU EBI and Japan. All databases are synced to each other, and hence the data should (theoretically) be the same (in practice it never is). I'd suggest you pick one ...


1

The PRANK and PAGAN algorithms have both come out of the Loytynoja lab in Finnland, and are stirring up the pot a bit. They use inferred phylogenetic relationships as a parameter, and tend to yield a much more 'gappy' alignment, supposedly due to more accurate handling of indels. For easy alignments the method doesn't matter so much, but if the sequences are ...


1

I've written a little script to remove identical sequences from fasta to get what I need. To print the list of removed sequences, uncomment line 22 #! /usr/bin/python3 # Removes identical sequences from fasta file import sys from Bio import SeqIO sequences={} #This is where sequences will be stored #likely calling str(seq.seq) on every test will be slower ...


1

I'm not sure if there's a way on GenBank, but UniProt offers UniRef where you can cluster redundant sequences or specify a lower cutoff (like 90% identity).



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