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17

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 ...


7

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: ...


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 ...


6

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. Here is an exhaustive (or almost exhaustive) list of such platforms. Some are more known than others. Personally, I already ...


6

Lowercase letters indicate repeat-masked regions. N's represent gaps. See: https://groups.google.com/a/soe.ucsc.edu/d/msg/genome/S4Sx8UdJAwM/tLTpVVzdhFMJ


5

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 ...


5

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 ...


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

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 ...


4

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 ...


4

N is the IUPAC code for any nucleotide, so in DNA sequence an N signifies any one of the four bases could be in that position. The {4} means 4 of the previous character in the pattern, or NNNN. In Perl regular expressions \d{4} means match 4 digits in a row, so the notation is quite similar.


4

Fold coverage is often derived with respect to a genomic locus, not a read. In sequencing experiments, fold coverage of a genomic loci (coordinate along a reference assembly) will be the number of aligned reads that overlap the position.


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

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?


3

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 ...


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

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: @HWUSI-EAS100R:6:73:941:1973#0/1 If the last number is /2 in ...


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

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


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 ...


3

This is the best review I have seen with a comprehensive discussion on gene duplication and amplification mechanisms (and it is also quite recent). It seems like there may be no definitive answers to your question in the literature--perhaps because of a paucity of viable experimental models that would allow one to test various hypotheses. Also @canadianer ...


3

Where the reads found in R2 are the reverse complement of those found in R1. 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 ...


3

Can't ORFS be of any size? I think the point being made is that in a random sequence of 64 amino acids, on average 3 out of 64 should be stop codons, because there are three possible nucleotide sequences for stop codons (UAA, UAG, UGA). ORFs could theoretically be any size but the longer the sequence the more likely you are to come across a stop codon ...


3

Sequence alignment 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) ...


3

The standard source for open-access nucleotide sequences is GenBank. Searching for Fusarium in GenBank gives 440421 hits, but these are shorter reads, not full genomes. Depending on your purpose, they may be useful to you. You may also be interested in assemblies, which are collections of aligned reads stitched together, often at the scale of whole ...



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