Download ##TOP## Duplex Txt
Duplex Tools contains a set of utilities for dealing with Duplex sequencingdata. Tools are provided to identify and prepare duplex pairs for basecallingby Dorado (recommended) and Guppy, and for recovering simplex basecalls from incorrectly concatenatedpairs.
Download duplex txt
To prepare reads for duplex calling Duplex Tools provides two programs. Thefirst parses the sequencing summary output by the Guppy basecaller (or the metadata in a .bam or .sam from dorado) in orderto generate candidate pairs from examining simple read metrics. The secondprogram analyses the basecalls of candidate reads, checking for similarity.
The primary output of the above will be a text file named pair_ids.txt in theuser specified output directory. Although this file can be given to Guppy to performduplex calling we recommend running the second basecall-to-basecall alignmentfiltering provided by the filter_pairs command:
Even though these are two sides of the same printed sheet, Publisher displays them as two separate pages. If your printer supports duplex printing, and if you choose the duplex option when you print, the main message will be printed on one side of a single sheet and the addresses will be printed on the other side.
Many printers support duplex printing, but they don't all make the duplex option available in the same way. With some desktop printers, choosing duplex means that the printer prints all of the copies of the first side of a page, then pauses and asks you to flip the sheets that it just printed and return them to the printer. Then it prints all of the copies of the second side.
The Two-Sided Printing Setup Wizard is a six-step wizard that helps you to correctly print two-sided publications. The steps include identifying how your printer accepts paper and running a test to make sure that your publication is printed the way that you expect. After you run the wizard, it will automatically use the printer's settings for all your two-sided (duplex) printing tasks.
Calculation of metrics may be restricted to a set of regions using the --intervals parameter. Thiscan significantly affect results as off-target reads in duplex sequencing experiments often have verydifferent properties than on-target reads due to the lack of enrichment.
I want to make client download a pdf file generated from my back-end. I am using the pdfkit-lib to generate my pdf file. From the NestJs documentation, I decided to use StreamableFile from a DuplexStream (pdfkit-lib works with a WritableStream and StreamableFile with a ReadableStream) :
An option is available to most parties of interest of Workers' Compensation cases (insurance carriers, TPAs, self-insured employers, and attorneys) to download or retrieve a compressed file (zipped file) containing Adobe PDFs of almost all WC claim correspondence (notices, letters, etc.) from the Board instead of having the correspondence mailed.
The means to retrieve the documents is either via a download through a web browser (https), or via secure FTP. After registering, your representative is given a user ID and password (one per organization) to access your organization's account. That account can be accessed by either a browser or FTP script.
Multiple PDF files for a day are compressed into a single "zipped" file to reduce file storage space and download transfer time. It is this "zipped" file that is available for downloading. The filename structure will be "yyyymmdd.zip", corresponding to a date of "mm/dd/yyyy". This file can be "unzipped" using a commercial product like WinZip (version 5.5 or greater), or shareware software like PKUNZIP (version 2.0 or greater). Before unzipping, your representative may need to determine if there is enough storage space available on the drive they are unzipping to. WinZip will list the space needed, and running "pkunzip -v *.zip" will inform him/her of the space needed. (A sample zip file with fake notices in PDF format is available for downloading at the end on this section.)
When the downloaded file is unzipped into all the individual notice files (extension of '.pdf'), there will also be one text file (with extension '.txt'), which can be used to reconcile what is received. The text file contains three lines providing information on the number of notices contained in the zip file, and if all notices in the file were printed: the number of pages that would print on a duplex printer, and the number of pages that would print on a simplex printer. This can be used to check that the number of notices and pages printed is correct.
A single printed notice might encompass multiple sheets of paper. The documents print on blank 8 x 11 paper; no pre-printed form is needed for any of them. Some of the forms are two-sided, so a printer that supports duplex printing is best; however, if your printer does not support duplex printing it will still print the forms with the back side of a form printing on a second piece of paper. Because of the possible situations described in this paragraph, the number of pages printed out is likely to be greater than the number of notices received (i.e., the number of PDF files). The text file mentioned above that is included in the zip file will provide the number of notices and number of printed pages for duplex and simplex printers.
Files you have downloaded should be deleted off the Board's server after a time in order to avoid filling up your account and thus prevent new files from being loaded to your account. You should keep the copy of the file you downloaded for a few months in case you have any question as to when you received a notice.
You can set your own schedule for retrieving notices - daily, every other day, weekly, etc. Files may be downloaded multiple times; for instance, two different office locations can each download the same file.
We address the problem of predicting the position of a miRNA duplex on a microRNA hairpin via the development and application of a novel SVM-based methodology. Our method combines a unique problem representation and an unbiased optimization protocol to learn from mirBase19.0 an accurate predictive model, termed MiRduplexSVM. This is the first model that provides precise information about all four ends of the miRNA duplex. We show that (a) our method outperforms four state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well as a Simple Geometric Locator when applied on the same training datasets employed for each tool and evaluated on a common blind test set. (b) In all comparisons, MiRduplexSVM shows superior performance, achieving up to a 60% increase in prediction accuracy for mammalian hairpins and can generalize very well on plant hairpins, without any special optimization. (c) The tool has a number of important applications such as the ability to accurately predict the miRNA or the miRNA*, given the opposite strand of a duplex. Its performance on this task is superior to the 2nts overhang rule commonly used in computational studies and similar to that of a comparative genomic approach, without the need for prior knowledge or the complexity of performing multiple alignments. Finally, it is able to evaluate novel, potential miRNAs found either computationally or experimentally. In relation with recent confidence evaluation methods used in miRBase, MiRduplexSVM was successful in identifying high confidence potential miRNAs.
The key idea of the proposed methodology is to train and employ a full polynomial SVM model to score each possible duplex position on a hairpin sequence and select the highest scoring one as the final predicted location. The various steps of our methodology are presented in the following paragraphs.
As described in [15], the production of all possible duplexes on a hairpin structure is employed to generate training examples for the SVM during the training phase but also to produce all duplexes to be scored at prediction time; the highest scoring one is the final prediction.
Not all possible substrings on the two strands define a possible duplex. Several constraints that are obeyed by Nature (as far as we know) need to be satisfied: (1) Two strands that share no matching bases do not form a possible miRNA:miRNA* duplex. (2) The length of each duplex strand should lie within a certain range, which can be deduced from known miRNAs. (3) The duplex overhangs (see S1 Text for details) should also lie within specific ranges, which can be calculated using the training examples. (4) k55
This methodology results in the generation of 10,000 candidate duplexes per hairpin, only one of which is the true duplex. During training, true duplexes are labeled positive and the rest form the negative examples. During testing, the true duplex is occasionally not produced due to the restrictions on the possible ranges of the overhangs described above. In the experiments reported here, loss of true duplexes due to this filtering never exceeded 4%.
Extensive experimentation was first performed in order to find the minimum set of features like sequence, structure or thermodynamics needed to obtain maximum accuracy (see Figures F2 and F3 in S1 Text for a comparison of models using various features). Use of sequence information alone was found to be sufficient. Thus, similarly to a preliminary version of the algorithm [15], miRNA:miRNA* duplexes used as input to the SVM are represented by a fixed-length numerical vector that contains only nucleotide sequence information. Briefly, nucleotide bases A, T, G and U are represented by four binary variables as 1000, 0100, 0010 and 0001, respectively. This specific encoding is known as distributed encoding in Machine Learning [18] and was selected for theoretical reasons in an effort to facilitate detection of patterns by the classifier (for details see the Feature Encoding section in S1 Text). Furthermore, since strand sequences are of variable size, the fixed-length numerical vector representation becomes problematic. In order to overcome this difficulty the maximum possible strand length was identified and we padded with zeros at the end for the missing nucleotides. Zero padding was performed in the middle of a sequence, so that the first and the last variables always represent the first and the last nucleotide, respectively. We used this approach because it was previously shown that the end structure and sequence is the primary determinant of Dicer specificity and efficiency [5]. In signal processing, zero padding is common and even though there may be more efficient ways to treat missing information, it does not affect the estimation of model performance or invalidates any results. 041b061a72