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This an initial solution, but can be improved: out = system2("f:/stride/stride.exe", "f:/stride/1a11.pdb -h", stdout=T) nh = strsplit(out[grepl("HBT", out)], " ")[[1]][2] print(nh) # Output: 26 (hydrogen bonds) I compiled an Windows version of Stride and uploaded here: http://lcrserver.icmc.usp.br/~daniel/bin/stride.exe


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I'm not sure if they will be useful for your application, but you should look into software used to visualize ecological networks (and maybe also software used for drawing electrical charts). The type of data used there is very similar to what you want to plot, with nodes, along with metadata for nodes on e.g. rates or connection strenghts. I can point you ...


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The number of hydrogen bonds cannot actually be said from software only inferred. But you could try using STRIDE (http://structure.usc.edu/stride/) which takes the file name with a -h flag to output the number of hydrogen bonds. You could then write a small shell script which would pass in each file and store the data you wanted in whatever format you liked. ...


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The Coursera Bioinformatic Methods courses include classes on metagenomics and, more importantly, tools to use when applying these methods. I have not taken this particular course, but Coursera is generally very good with getting hands-on experience using computational tools. A course-like setting may also be conducive to group learning.


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If you are using BLAST for short nucleotide searches then you should reduce your word size to <=7. It is better to align with mature sequences. E-value depends on the database size. If your target database is huge then you should increase the e-value cutoff. If miRBase (for a few organisms) is your database then there is no need to run BLAST, also. You ...


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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 ...


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Check these out. They provide the functionality that you want: ApE SnapGene viewer


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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 # ...


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You were looking in the wrong spot. The PTM section you clicked on is for post-translational modification databases such as PhosphoSite. To get the actual modified residues, click on "PTM/Processsing" (sic) further up the page and then select "Modified Residue", and in your results table you'll get a list of all phosphorylations, glycosylations, ...


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As far as I know there are ongoing efforts to find genes that affect forensically relevant traits e.g. facial characteristics and fingerprint pattern type/ridge count, and it's definitely a topic of interest to some law enforcement agencies. However as previously mentioned, there are many factors that could influence complex traits, including in utero ...


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SBML follows in the same vein as XML, in that it provides a standardized and flexible method for structuring information. XML and its ilk aren't just for making web pages, they're for sending structured datasets between systems and programs (in fact, XML is a fairly common configuration format). Why? Because the structure makes parsing simple (well, ...


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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^+ ...


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Your logic on the problem seems correct. The wikipedia article about the Hamming distance describes both a python and a C implementations. The python version assumes two strings of equal length: def hamming_distance(s1, s2): #Return the Hamming distance between equal-length sequences if len(s1) != len(s2): raise ValueError("Undefined for ...


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There are some tools for predicting the binding: TargetScan (based on seed match [primary], extra pairing, sequence context 1 — nucleotide composition around the site etc [secondary]) miRanda (based on hybridization stability and seed match[primary] and sequence context [secondary]) PicTar (adds a layer of evolutionary conservation criteria) 1 Context ...


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Yes, it is possible to sequence a specific region of the genome. The method, as you mentioned, is called targeted sequencing. Resequencing is basically sequencing something that has already been sequenced. This means that instead of assembling all of your sequence reads from scratch, you can just align them to the reference sequence (in your case, the entire ...


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I would thoroughly check the literature on the UTR you are interested in, a lot of this has already been done for many genomic regions since nextgen seq began. You will want to first off use computational prediction algorithms to help guide you in getting a good Candidate list of miRNAs The interaction between a miRNA and its target mRNAs is usually ...



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