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Ion Torrent Personal Genome Machine (PGM) technology is a mid-length read, low-cost and high-speed next-generation sequencing platform with a relatively high insertion and deletion (indel) error rate. A full systematic assessment of the effectiveness of various error correction algorithms in PGM viral datasets (e.g., hepatitis B virus (HBV)) has not been performed. We examined 19 quality-trimmed PGM datasets for the HBV reverse transcriptase (RT) region and found a total error rate of 0.48% ± 0.12%. Deletion errors were clearly present at the ends of homopolymer runs. Tests using both real and simulated data showed that the algorithms differed in their abilities to detect and correct errors and that the error rate and sequencing depth significantly affected the performance. Of the algorithms tested, Pollux showed a better overall performance but tended to over-correct ‘genuine’ substitution variants, whereas Fiona proved to be better at distinguishing these variants from sequencing errors. We found that the combined use of Pollux and Fiona gave the best results when error-correcting Ion Torrent PGM viral data.
Next-generation sequencing (NGS) has been widely used in the study of viruses and has opened new avenues for research and diagnostic applications (e.g., viral mutant spectra, virus quasispecies theory and dynamics, fitness landscape, and discovery of novel viruses ). Ion Torrent Personal Genome Machine (PGM) technology is a mid-length read, low-cost and high-speed NGS platform with special applications in microbial sequencing. However, PGM has a relatively high insertion and deletion (indel) error rate of 1.5% (range from 0.46% to 2.4%). Several algorithms have been proposed to correct sequencing errors for PGM data (Table ). These algorithms differ with respect to error models, statistical techniques, data features, the determined parameters, and performances. These methods are classified into the following three categories: (1) suffix array/tree-based methods that use a suffix tree to detect and correct substitution and indel errors (e.g., Fiona ); (2) k-spectrum-based methods that divide reads into k-mer lengths and generate a k-mer depth profile to correct the k-mer profile (e.g., Blue and Pollux ); and (3) multiple sequence alignment (MSA)-based methods that use k-mers as seeds and construct a consensus sequence from the multiple alignments to correct errors (e.g., Coral and Karect ).
Two review articles, have systematically surveyed these methods for PGM data and provided guidance concerning which tools to consider for benchmarking based on the data properties. Sequencing data generated in NGS platforms were analyzed in four microbial genomes to assess the coverage distribution, bias, GC distribution, variant detection and accuracy. However, these algorithms have not been fully assessed and applied to viral sequencing data (e.g., hepatitis B virus, HBV). HBV has a partially double-stranded DNA genome, and its replication depends on reverse transcription of an RNA intermediate by reverse transcriptase (RT). Since the RT lacks proofreading, errors in HBV DNA replication occur at a relatively higher rate than other DNA viruses, with an estimated nucleotide substitution rate of 1.4–3.2 × 10 −5 substitutions per site per year. Nucleos(t)ide analogs (NAs) have been widely used in anti-HBV therapy by directly inhibiting the HBV RT enzyme and effectively suppressing viral replication. However, long-term use of NAs leads to drug resistance.
Characterizing the mutation spectrum and reconstructing the viral quasispecies in the HBV RT region has implications for understanding drug resistance due to NA therapy. For example, various HBV quasispecies associated with drug resistance exist prior to treatment and increase in abundance following anti-viral therapy. Therefore, distinguishing true variants, especially low-frequency mutations, from sequencing errors is crucial for viral mutation-related studies, including quasispecies reconstruction, which is feasible only with the longer 454/Roche reads. In the present study, we investigated the performance of error correction algorithms in empirical and simulated PGM data for the HBV RT region. We have the following aims: 1) to characterize the error profiles of 19 quality-trimming PGM datasets for the HBV RT region; 2) to assess the error-correction performance of algorithms in empirical and simulated data under different models; and 3) to provide a benchmark for generating an analysis-ready alignment of PGM data for studies of viral sample sequencing.
(1) where r i was the number of errors in each read ( i), nbase i was the total number of sequenced bases, and n was the total number of reads. For example, the deletion error rate in the homopolymers was calculated by dividing the total number of deletion errors by the total number of sequenced bases in the homopolymer region. A homopolymer region was defined as a homopolymer repeat with a length ≥ hl, where 2 ≤ hl ≤5.
This definition was established to ensure that indel errors, which were common on this platform, were truly reflected by the error rate. To estimate the substitution error rate, we excluded the defined ‘genuine’ mutations (i.e., a variant with a frequency ≥0.5% based on the TEF file from the pre-corrected alignments), because Ion Torrent PGM could detect substitutions occurring at frequencies ≥0.1%. The cumulative distribution of errors in the sequencing reads after quality trimming indicated that 99.48% of the sequencing reads had ≤9 errors (Fig. ). We did not find any ‘true’ indels using Sanger sequencing; therefore, all indels can be considered errors. The distribution of homopolymers with different lengths (2 ≤ hl ≤5) in the HBV RT region (AB033556) is shown in Fig.
We counted the numbers of each type of error in the homopolymer and non-homopolymer regions. Insertion and deletion errors occurred more frequently than substitution errors (Fig. ).
Notably, deletion errors were more likely for homopolymers and were correlated with hl. When hl ≥ 4, the mean deletion error rate in the homopolymers was 0.59%, although the insertion error rate (0.27%) was more likely to be greater than the deletion error rate (0.13%) in the total sequenced regions. As noted previously, PGM data were sensitive to homopolymers, and the indel error rate increased as hl increased,. We manually investigated the behaviors of these algorithms in correcting for insertion (blue arrow), deletion (red arrow) and substitution (green arrow) errors (Fig. ). We found that Pollux and Blue had a greater power for indel error correction but were unable to distinguish ‘genuine’ substitutions from errors. For example, at position 651 (a G → A Sanger-confirmed mutation), most of the mutated ‘A’ alleles (959 out of 7427) were falsely corrected by Pollux (956/959) and Blue (788/959) but not by Fiona, Coral and Karect.
For the insertion errors between positions 762 and 763 (1,070 out of 7,208 sequencing reads), Pollux and Blue corrected 98.2% and 100% of the erroneous insertions, followed by Coral (25.3%), Fiona (1.3%) and Karect (0%). We noted similar behaviors of these algorithms for deletion error (e.g., at position 525) corrections. The ECE toolkit takes all bases differing from the reference as errors and counts all corrections changed to the reference as a TP, resulting in a bias in the calculation of these measures. We set different frequency thresholds (0.1%, 0.5% and 1%) to distinguish ‘genuine’ substitutions and errors, because Ion Torrent PGM can detect substitutions occurring at frequencies ≥0.1% (i.e., a variant was considered to be ‘true’ if its frequency was greater than the cutoff).
Based on the pre- and post-corrected TEF files, we counted the proportion of the identified ‘genuine’ mutations and the corrected errors under different algorithms (Fig. ). We calculated the proportion of the identified ‘genuine’ mutations by dividing the number of mutated alleles in the corrected reads by the number in the original reads.
We found that Pollux and Blue over-corrected for ‘genuine’ substitutions with a higher frequency, whereas Karect and Coral had a lower power for error correction. Fiona corrected most of the substitution errors with frequencies. We also changed the k-mer parameter to optimize the k-spectrum-based algorithms (Blue and Pollux) and the MSA-based method using k-mer (Coral) for error correction. The measure of gain did not differ significantly under different k-mer values (ANOVA, p = 0.45 (Pollux) and 0.20 (Coral)) but was marginal in Blue ( p = 0.04) (Fig. ). The average time costs for Pollux, Blue, Fiona, Coral, and Karect were 5.2, 2.2, 36.1, 18.6 and 1.2 minutes, respectively, showing that Fiona was the most time-consuming algorithm. Performance of error correction algorithms using simulated data We studied the performance of the different algorithms under different simulation scenarios.
First, a model of indel errors (Fig. ) showed that the measures of gain differed significantly among these algorithms (ANOVA, p. Error correction performance in the simulated PGM data. ( a) A model of indel errors. We assumed a fixed substitution rate (0.17%) and read number (60,000) with varied indel error rates, and the deletion error rate was 1.5 times the insertion error rate. ( b) A model of substitution errors. We assumed a fixed insertion (0.04%) and deletion (0.06%) error rate and read number (60,000) with varied substitution error rates; and ( c).
A model of sequencing depth. We assumed fixed insertion (0.04%) and deletion (0.06%) error rates and substitution error rates (0.17%) with different sequencing depths. Second, we investigated the effects of the substitution errors for the performance (i.e., a model of substitution errors) (Fig. ). Similarly, Pollux out-performed the remaining algorithms under different rates. However, Karect obtained a higher measure of gain when the substitution rate was 0.1% partly due to its effects in correcting for low-frequency substitution errors.
Obviously, Blue, Fiona and Coral had better performances at higher substitution rates, but the performances of Blue and Fiona decreased as the errors continued to accumulate. Blue had an especially good performance when the substitution error rate was ≤0.4%, but its power for error correction decreased significantly when the rate was ≥0.4%. We speculated that the enrichment of errors in reads might have a significant effect on the k-mer count profile and error inference. We also simulated a set of data by randomly introducing known variants into the reads, including five rare mutations (with frequencies of 0.1–0.5%) and three low-frequency variants (approximately 5%). The proportion of the remaining mutated alleles and sequencing errors after error correction (Table ) indicated that Pollux and Blue could not distinguish rare and low-frequency variants from sequencing errors, whereas Fiona could identify low-frequency variants but not rare mutations.
Although Coral and Karect could identify the rare and low-frequency variants, these algorithms had little power for sequencing error correction. These results were consistent with our analyses of the empirical data (Fig. ).
Finally, we explored how the sequencing depth affected the performance (i.e., a model of the sequencing depth). The sequencing depth had little effect on Blue and Pollux (Fig. ), whereas Fiona and Karect exhibited a better performance with a lower depth.
However, Coral obtained a negative measure of gain under a lower depth (e.g., 6,000 reads), probably resulting from a higher FP introduced by insertion errors. The combined use of Pollux and Fiona had a similar performance as Pollux. Relatively higher mutation and replication rates in viruses lead to an increased number of mutations, including a large number of rare variants. Ultra-deep sequencing has been widely used for analyses of viral populations, and enables the examination of the diversity of the whole viral population and the identification of important variants present within the viral population at low frequencies (i.e., mutations that increase pathogenicity or convey drug resistance ).
Therefore, the characteristics of viral sequencing data include a higher sequencing depth and a broad frequency spectrum of mutations compared with sequencing data for macro-organisms. Therefore, effectively distinguishing low-frequency variants from sequencing errors remains a great challenge. Described the biases and errors introduced by PGM across a combination of factors in two bacterial species.
The average GC content of Bacillus amyloliquefaciens (46%) is similar to the empirical (49.9%) and simulated data (46.4%) in our study. The authors found indel errors at a rate of 1.38% after quality clipping, which accounted for most of the errors due to inaccurate flow calls. In our PGM data, the deletion errors in the homopolymers (i.e., a polymer consisting of ≥4 identical nucleotides) were significantly greater than those in the non-homopolymers, but the insertion error rate was not increased in the homopolymers (Fig. ).
The adaptor may increase the error rate of Ion Torrent PGM data,; however, the final error rate of Ion Torrent PGM sequencing of all chips was approximately 1% (range from 0.46% to 2.4%). The total error rate in our original reads was 0.61% ± 0.16%, but this rate decreased after quality trimming (0.48% ± 0.12%). The difference in the estimated error rate may be partly due to differences in template preparation, the use of a different sequencing kit, and different species. Of these correction algorithms, we noted different performances in both the empirical and simulated PGM data (Figs and ). Generally, Pollux and Blue had similar performances, and their measures of gain were significantly greater compared to the remaining algorithms, which was consistent with previous studies,. There are several explanations for their ‘outperformances’. First, Pollux and Blue filter and discard reads that appear to still be faulty after correction (averages of 0.48% and 8.21%, respectively, in our 19 PGM data sets).
Second, Pollux performs homopolymer corrections independently after exhausting all other correction possibilities. Third, both algorithms over-corrected for the ‘genuine’ substitutions (Figs, and Table ) (e.g., more than 97% of the mutated alleles of the variants with an approximate frequency of 5% were falsely corrected).
Of the remaining algorithms, Fiona had a greater measure of gain than Coral, which was consistent with Schulz et al., where Fiona showed a higher correction accuracy over a broad range of datasets from 454 and Ion Torrent sequencers and outperformed Coral. Fiona seemed to have a greater power for distinguishing ‘genuine’ substitutions with a relatively higher frequency from errors but a limited power for indel correction (Figs and and Table ). Showed that Karect was more accurate than the other methods (e.g., Fiona, Blue and Coral) in terms of correcting single base errors (up to a 10% increase in gain). Our results indicated that Karect had little power for indel error correction (Fig. ) with the exception of a low substitution error rate and sequencing depth (Fig. ). In summary, sequencing for different species (i.e., eukaryotes, prokaryotes or viruses), the sequencing depth, and error profiles in different platforms may influence the error-correction performance. Since Pollux has a greater performance for indel error correction and Fiona has a greater power for distinguishing ‘genuine’ substitutions from sequencing errors, we suggest the combined use of Pollux and Fiona for Ion Torrent PGM data (PolluxFiona).
The present study has several limitations. First, simulating sequencing reads of substitutions with different frequencies and introducing sequencing errors will clarify whether error correction can be used to reduce ‘genuine’ errors and leave low-frequency variants alone. The substitution errors simulated by ‘CuReSim’ followed an exponential distribution with an increased probability of occurring at the end of the reads, and the error direction was random. Second, we acknowledge a potential lack of robustness in distinguishing ‘genuine’ mutations and errors based only on the defined frequency thresholds (Fig. ), and ‘genuine’ rare mutations may have been present with frequencies less than the given threshold. The simulated data with known variants indicated that these algorithms could not distinguish rare variants from substitution errors (Table ).
Finally, we did not identify ‘genuine’ Sanger-confirmed indels in our HBV sequencing data. We suggest that the use of Pollux may remove the predominant indel errors introduced by poor handling of short homopolymer runs in Ion Torrent. However, as shown in Fig., Pollux and Blue over-corrected the defined ‘genuine’ indels even with a frequency greater than 50%. Therefore, the potential for over-correction of indels cannot be ignored if ‘genuine’ indels exist, which is a common phenomenon in viruses.
In conclusion, we provided a benchmark for error correction algorithms that can be used in PGM data applications for viral genome sequencing. We suggested the combined use of Pollux and Fiona as a better choice for its performance in both the real HBV Ion Torrent PGM and simulated data.
However, vigorous algorithms need to be developed for PGM data in the setting of distinguishing low-frequency variants and sequencing errors.
It’s ideal to have a dedicated machine for your BitTorrent client, so you can seed 24/7. But it’s energy intensive to leave a full rig powered up and online that often. Enter the Raspberry Pi. RELATED: Most desktop PCs draw a fair amount of energy—our modest home office server, for example, consumes nearly $200 worth of electricity per year. The Raspberry Pi, on the other hand, is built around a mobile processor and sips energy like a hummingbird.
The core Raspberry Pi board uses less than $3 of energy per year and even adding in a few external hard drives, you’ll still keep your yearly operating costs at less than a burger and fries. Plus, when it comes to downloading torrents, an always-on machine is king. With torrents, the more you monitor the cloud and seed into it the better your ratio on your tracker (even if you’re leeching from public trackers, an always-on machine ensures you’ll be there when those rare files make an appearance). If that sounds good, read on as we show you how to turn your Pi into a totally remote controlled downloading machine.
What You Need For this tutorial, we assume that you have a Raspberry Pi unit with Raspbian installed, are able to access the device either directly via an attached monitor and keyboard or remotely via SSH and VNC, and that you have an external USB drive (or drives) attached to it. If you need to get up to speed in these areas, we strongly suggest reading the following guides in the order we have them listed here:. Everything in the first tutorial is necessary. The second tutorial is optional (but remote access is incredibly handy to have for this project, as a download box is a perfect candidate for a headless build), and the most important part of the third tutorial is simply setting up the hard drive and configuring it to auto-mount on boot (as described in the third guide). RELATED: In addition, if you’re not overly familiar with the ins and outs of setting up a BitTorrent client for anonymous downloading, you should read up on it. In order to use BitTorrent safely. The proxy mentioned in that guide is cheap and easy, but a good VPN is usually faster and more versatile, so.
Once you’ve reviewed all the material and have the Pi configured, it’s time to get down to the business of turning your Pi into a silent and ultra-low-power downloading beast. Step One: Install Deluge on Raspbian There are several BitTorrent clients for Linux worth considering, but we recommend. It’s just the right balance of features and footprint so that you won’t find yourself wishing a month from now that you had installed something more powerful. You can go about configuring Deluge multiple ways, but not all configurations are suitable for this headless Pi download box. While most people use their torrent client on the desktop like any other app, this doesn’t work very well for our purposes, because it means every time you wanted to interact with your torrents, you would have to log in to the box over remote desktop and mess around with the desktop client. It wastes your time and it wastes resources on the Pi.
You could run the Deluge WebUI, which allows you to access the Deluge client from a browser on another machine. This still isn’t our preferred option, though it does open you up the potential of using a smartphone app to view and control Deluge (more on this later). We recommend configuring Deluge on the remote machine to accept ThinClient connections.
In this manner, we can use the actual Deluge desktop client on another computer (be it a Windows, Linux, or OS X box) to control the Raspberry Pi Deluge installation. You get all the benefits of the desktop client on your actual desktop, while the all the action happens on the remote box. If you can’t decide between those two options, you can actually use both in tandem, though it will take a little longer to set up. Just follow the instructions in both sections below to do so.
Option One: Set up Deluge for ThinClient Access Before you do anything, take a moment to update and upgrade your repositories. Open a Terminal and run the following two commands, one after the other: sudo apt-get update sudo apt-get upgrade Once that’s done, it’s time to begin installing the necessary components for the ThinClient setup. Enter the following commands: sudo apt-get install deluged sudo apt-get install deluge-console This will download the Deluge daemon and console installation packages and run them. When prompted to continue, type Y. After Deluge has finished installing, you need to run the Deluge daemon. Enter the following commands: deluged sudo pkill deluged This starts the Deluge daemon (which creates a configuration file) and then shuts down the daemon. We’re going to edit that configuration file and then start it back up.
Type in the following commands to first make a backup of the original configuration file and then open it for editing: cp /.config/deluge/auth /.config/deluge/auth.old nano /.config/deluge/auth Once inside the nano text editor, you’ll need to add a line to the bottom of the configuration file with the following convention: user:password:level Where user is the username you want for Deluge, password is the password you want, and the level is 10 (the full-access/administrative level for the daemon). So for our purposes, we used pi:raspberry:10. When you’re done editing, hit Ctrl+X on your keyboard and save your changes when prompted. Then, start up the daemon and console again: deluged deluge-console If starting the console gives you an error code instead of nice cleanly formatted console interface, type “exit” and then make sure you’ve started up the daemon. Once inside the console, you’ll need to make a quick configuration change.
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Enter the following: config -s allowremote True config allowremote exit The commands and corresponding output will look like the screenshot below. This enables remote connections to your Deluge daemon and double checks that the config variable has been set. Now it’s time to kill the daemon and restart it one more time so that the config changes take effect: sudo pkill deluged deluged At this point, your Deluge daemon is ready for remote access. Head to your normal PC (not the Raspberry Pi) and install the Deluge desktop program.
You’ll find the installer for your operating system on the page. Once you’ve installed Deluge on your PC, run it for the first time; we need to make some quick changes. Once launched, navigate to Preferences Interface. Within the interface submenu, you’ll see a checkbox for “Classic Mode”. By default it is checked. Click OK and then restart the Deluge desktop client.
This time, when Deluge starts, it will present you with the Connection Manager. Click the “Add” button and then input the IP address of the Raspberry Pi on your network, as well as the username and password you set during the earlier configuration. Leave the port at the default 58846. Back in the Connection Manager, you’ll see the entry for the Raspberry Pi; if all goes well, the indicator light will turn green like so: Click Connect, and you’ll be kicked into the interface, connected to the remote machine: It’s a fresh install, nary a.torrent in site, but our connection between the remote machine and the desktop client is a success! Go ahead and configure the WebUI now (if you wish to do so), or skip down to the next step of this tutorial.
Option Two: Set Up Deluge for WebUI Access Configuring the WebUI is significantly faster, and allows for using some mobile apps to access Deluge. But as we mentioned before, you’ll have access to fewer features than with the full ThinClient experience. For example, ThinClient can associate.torrent files with the Deluge ThinClient for automatic transfer to the Pi, but you can’t do this with the WebUI. First, take a moment to update and upgrade your repositories. Open a Terminal and run the following two commands, one after the other: sudo apt-get update sudo apt-get upgrade Then, to install the WebUI, run the following commands.
Note: If you already installed the Deluge daemon in the ThinClient section of the tutorial, skip the first command here. Sudo apt-get install deluged sudo apt-get install python-mako sudo apt-get install deluge-web deluge-web This sequence installs the Deluge daemon (if you didn’t already install it in the last section), Mako (a template gallery for Python that the WebUI needs), the WebUI itself, and then starts the WebUI program. The default port for the WebUI is 8112. If you wish to change it, run the following commands: sudo pkill deluge-web nano /.config/deluge/web.conf This stops the WebUI and opens up the configuration file for it. Use nano to edit the line: “port”: 8112, and replace the 8112 with any port number above 1000 (as 1-1000 are reserved by the system).
Once you have the WebUI up and running, it’s time to connect to it using a web browser. You can use a browser on the Pi if you ever need to, but it’s not the most pleasant user experience and best left for emergencies. Open up a browser on your regular desktop machine and point it at the IP address of your Pi with the port you just chose (e.g. You’ll be greeted with a password prompt (the default password is “deluge”) and be immediately encouraged to change it after you enter it for the first time.
After that, you’ll be able to interact with Deluge via the lightweight interface. It’s not quite the same as the ThinClient, but it’s robust enough for light use and has the added benefit of serving as the point of connection for lots of torrent-control mobile apps. Step Two: Configure Your Proxy or VPN You might be tempted to start downloading torrents now,but wait! Don’t do that yet.
It’s absolutely reckless to use a BitTorrent Client without first shuttling your connection through a proxy server or VPN. RELATED: If you didn’t read over yet, now is the time to do so.
Read over the first section (for a better understanding of why it is important to protect your BitTorrent connection), and then sign up for a proxy service or, better yet, before continuing on. If you’re using a VPN, it’s pretty simple: Just choose a VPN that offers a Linux client.
Then, download and install the Linux client on your Pi, start it up, and connect to your desired server. (You may even want to set it to launch when the Raspberry Pi boots, so it’s always connected to the VPN.) If you’re using a proxy, you can plug its information into Deluge under Preferences Proxy.
You need to fill out the Peer, Web Seed, Tracker, and DHT sections like so, placing your proxy username and password in the appropriate slots. Your proxy service’s Type, Host, and Port may differ, so be sure to check its documentation. In order for the proxy settings to take effect, you need to restart the Deluge daemon. From the terminal enter the following commands: sudo pkill deluged deluged After that, you should be all set.
The best way to test that you’re actively using the proxy or VPN is to download a torrent file designed expressly to report back its IP address. You can find many of these torrents online, including this one from and this one from. Load either or both torrents into Deluge and wait a moment. After the torrents have had a chance to connect to their respective trackers, select the torrents in the Deluge client and check the “Tracker Status” entry as seen above. Both will report the IP address they detect from your client.
If that IP address matches, then the proxy or VPN is not configured properly and you should return to the previous section to check your configuration. If it is configured properly, you’ll see the proxy or VPN’s IP address and not your own. Step Three: Configure Your Download Location Next, you’ll need to configure Deluge to use your external hard drive. If you followed along with the hard drive mounting instructions in, you’re ready with a hard drive set to auto-mount on boot. From there, all you need to do is change the default locations in Deluge. Navigate to Deluge’s Preferences and head to the Downloads tab. By default, Deluge directs everything to /home/pi.
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That little SD card is going to fill up real fast, however, so we need to change it. First, we’re going to create some new folders in /media/USBHDD1/shares, which is the share folder we already set up in the Low-Power Network Storage tutorial. That way, we can easily access our downloaded torrents over the network and have a network accessible watch folder for auto-loading torrent files. Use the following commands to create the folder set (adjusting the pathnames accordingly for your location if you’re not using the same Pi setup from the previous tutorial like we are): sudo mkdir /media/USBHDD1/shares/torrents/downloading sudo mkdir /media/USBHDD1/shares/torrents/completed sudo mkdir /media/USBHDD1/shares/torrents/watch sudo mkdir /media/USBHDD1/shares/torrents/torrent-backups Then, turn right around and plug those four new directories into Deluge.
Click OK to set the directories. There’s no need to restart as you did with the proxy setup. Step Four: Test Your Connection Now it’s time to download a large enough torrent that we can really see if the system is running smoothly. For our test we grabbed the.torrent file for the –it weighs in at solid 1.7GB, perfect for monitoring the connection speeds.
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Once you’ve confirmed that your connection is stable and the Linux torrent is humming along nicely, it’s time to move onto the next step: automating the client startup. Step Five: Configure Deluge to Run on Startup Before we leave the Deluge setup, there is one final detail to attend to. We need to set up the Deluge daemon and WebUI to run automatically when our Raspberry Pi boots up. To do so simply and without the fuss of editing more complicated init files and settings, we’ll simple annotate the rc.local file. Run the following command in a Terminal to do so. Sudo nano /etc/rc.local With the rc.local file loaded, add the following lines to the end of the file. Note: you do not need to add the the second command ending in “deluge-web” if you are not using the WebGUI.
This may also be a good place to add your VPN program, if you’re using one. # Start Deluge on boot: sudo -u pi /usr/bin/python /usr/bin/deluged sudo -u pi /usr/bin/python /usr/bin/deluge-web Your rc.local file should look something like this when you’re done (possibly with the addition of that VPN): Press Ctrl+X to exit and save your work.
At this point, we would recommend restarting your Raspberry Pi, so fire off a “sudo reboot” at the command line. Once the Pi has finished rebooting, head to your other PC and try to connect to the Deluge ThinClient and/or WebUI to make sure they both work. There are two major errors you may encounter here.
First, a failure to connect at all means that the initialization scripts didn’t work. Open up the terminal on your Pi and manually start the daemon and WebUI using the commands we learned earlier in the tutorial. Check to see that it works now. If it does, go back up and fix your rc.local script. Second, if you can open up the client, but it shows permission errors for your existing torrents (like the Linux torrent we used to test things earlier), that indicates that your external hard drive was not mounted, or mounted incorrectly. Review the sections on installing an external drive and setting it to auto-mount on boot in our tutorial. Enhancing Your Torrenting Experience Now that you have your torrent box configured and ready to rock, there are a few additional tools and modifications you can look into to really enhance your user experience.
None of these tips and tricks are necessary, but they do make your Raspberry Pi turned Torrent Box easier to use. Add Mobile Access: Consider downloading a mobile control app like and for Android. Unfortunately we don’t have any solid suggestions for iOS users, as Apple has taken a really aggressive stance towards torrent-related apps in the App Store (and has banned any apps that slipped through the submission process). Deluge doesn’t currently have a mobile-optimized template for the WebUI, but it’s more than functional on tablets like the iPad and Kindle Fire.
Set Up a Shared Drop Folder: Although we mentioned it briefly earlier in the tutorial, ensure that the /torrents/watch/ folder you created is accessible on your network. It’s really convenient to be able to dump a pile of.torrent files into the folder and have Deluge load them up automatically. Install Browser Plugins: There are several Deluge-centered plugins for Chrome and Firefox that improve the user experience, including:.
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Chrome:.: Enables.torrent adding from the WebUI.: Simple view of current torrents and their progress. Firefox:.: Enables.torrent adding from the WebUI.: Greasemonkey Script that adds clickable icon on webpages for easy torrent adding Activate Deluge Plugins: There are a host of great plugins already included in Deluge, and even more third-party plugins. Some of the included plugins you may want to take advantage of include:.
Notification: You receive email alerts from Deluge on torrent completion and other events. Scheduler: Limit bandwidth based on time of day You can find these in Preferences Plugins. Check the ones you want and a new entry will appear in the preferences menu (e.g. Preferences Notifications).
For more information about third party plugins and how to install them, check out the. After configuring, testing, and tweaking enhancements and plugins, you have a more than capable torrent box that costs mere pennies a day to operate. Find a quiet and out of the way spot to plug it in, load it up with torrents, and leave it to do the heavy lifting of downloading and seeding for you.
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