From a6667dfeb9d02306707761a379b0b69357042f01 Mon Sep 17 00:00:00 2001 From: "C. Titus Brown" Date: Mon, 26 Aug 2013 21:58:54 -0400 Subject: [PATCH] updated stamps to point at protocols --- doc/2013-stamps/index.txt | 3 + doc/mrnaseq/0-download-and-save.txt | 90 ----- doc/mrnaseq/1-quality.txt | 360 ------------------ doc/mrnaseq/2-diginorm.txt | 120 ------ doc/mrnaseq/3-big-assembly.txt | 75 ---- doc/mrnaseq/4-diff-expr.txt | 4 - .../5-building-transcript-families.txt | 123 ------ doc/mrnaseq/coral.txt | 7 - doc/mrnaseq/de-novo-assembly.txt | 144 ------- doc/mrnaseq/images/AttachVolume.png | Bin 51196 -> 0 bytes doc/mrnaseq/images/AttachVolumeWindow.png | Bin 45750 -> 0 bytes doc/mrnaseq/images/ClickVolumes.png | Bin 83673 -> 0 bytes doc/mrnaseq/images/CreateVolume.png | Bin 84340 -> 0 bytes doc/mrnaseq/images/CreateVolumeWindow.png | Bin 32117 -> 0 bytes doc/mrnaseq/index.txt | 18 +- doc/mrnaseq/installing-blastkit.txt | 98 ----- doc/mrnaseq/notes | 14 - doc/mrnaseq/rsem.txt | 96 ----- doc/mrnaseq/saving-data-persistently.txt | 114 ------ doc/mrnaseq/using-screen.txt | 58 --- 20 files changed, 4 insertions(+), 1320 deletions(-) delete mode 100644 doc/mrnaseq/0-download-and-save.txt delete mode 100644 doc/mrnaseq/1-quality.txt delete mode 100644 doc/mrnaseq/2-diginorm.txt delete mode 100644 doc/mrnaseq/3-big-assembly.txt delete mode 100644 doc/mrnaseq/4-diff-expr.txt delete mode 100644 doc/mrnaseq/5-building-transcript-families.txt delete mode 100644 doc/mrnaseq/coral.txt delete mode 100644 doc/mrnaseq/de-novo-assembly.txt delete mode 100644 doc/mrnaseq/images/AttachVolume.png delete mode 100644 doc/mrnaseq/images/AttachVolumeWindow.png delete mode 100644 doc/mrnaseq/images/ClickVolumes.png delete mode 100644 doc/mrnaseq/images/CreateVolume.png delete mode 100644 doc/mrnaseq/images/CreateVolumeWindow.png delete mode 100644 doc/mrnaseq/installing-blastkit.txt delete mode 100644 doc/mrnaseq/notes delete mode 100644 doc/mrnaseq/rsem.txt delete mode 100644 doc/mrnaseq/saving-data-persistently.txt delete mode 100644 doc/mrnaseq/using-screen.txt diff --git a/doc/2013-stamps/index.txt b/doc/2013-stamps/index.txt index 85c6118..f47aceb 100644 --- a/doc/2013-stamps/index.txt +++ b/doc/2013-stamps/index.txt @@ -1,6 +1,9 @@ The STAMPS Metagenome Assembly Tutorial ======================================= +**Note:** This tutorial is being updated and maintained over at +`the khmer protocols `__. + .. toctree:: :maxdepth: 1 diff --git a/doc/mrnaseq/0-download-and-save.txt b/doc/mrnaseq/0-download-and-save.txt deleted file mode 100644 index 1a407bf..0000000 --- a/doc/mrnaseq/0-download-and-save.txt +++ /dev/null @@ -1,90 +0,0 @@ -=========================================== -0. Downloading and Saving Your Initial Data -=========================================== - -We're going to run transcriptome assembly completely in the cloud, -because that way (a) you don't need to buy a big computer, and (b) -I don't have to figure out all the special details of your own -computer system. - -This does mean that the first thing you need to do is get your data -over to the cloud. I tend to just store it there in the first place, -because... - -The basics ----------- - -...Amazon is happy to rent disk space to you, in addition to compute time. -They'll rent you disk space in a few different ways, but the way that's -most useful for us is through what's called Elastic Block Store. This -is essentially a hard-disk rental service. - -There are two basic concepts -- "volume" and "snapshot". A "volume" can -be thought of as a pluggable-in hard drive: you create an empty volume of -a given size, attach it to a running instance, and voila! You have extra -hard disk space. Volume-based hard disks have two problems, however: -first, they cannot be used outside of the "availability zone" they've -been created in, which means that you need to be careful to put them -in the same zone that your instance is running in; and they can't be shared -amongst people. - -Snapshots, the second concept, are the solution to transporting and -sharing the data on volumes. A "snapshot" is essentially a frozen -copy of your volume; you can copy a volume into a snapshot, and a -snapshot into a volume. - -Getting started ---------------- - -Run through :doc:`saving-data-persistently` once, to get the hang of -the mechanics. Essentially you create a disk; attach it; format it; -and then copy things to and from it. - -Downloading and saving your data to a volume --------------------------------------------- - -There are *many* different ways of getting big sequence files to and -from Amazon. The two that I mostly use are 'curl', which downloads -files from a Web site URL; and 'ncftp', which is a robust FTP client -that let's you get files from an FTP site. Sequencing centers almost -always make their data available in one of these two ways. - -.. note:: - - To use ncftp on your Amazon instance, you may need to install it:: - - apt-get -y install ncftp - -For example, to retrieve a file from an FTP site, you would do something -like:: - - cd /mnt - ncftp -u ftp://path/to/FTP/site - -use 'cd' to find the right directory, and then:: - - >> mget * - -to download the files. Then type 'quit'. You can also use 'curl' to -download files one at a time from Web or FTP sites. - -Once you have the files, figure out their size using 'du -sk' (e.g. after the -above, 'du -sk /mnt' will tell you how much data you have saved under /mnt), -and go create and attach a volume (see :doc:`saving-data-persistently`). - -This data is now something that will *stick around* when you shut down -your instance. It's a good rule of thumb to do "savepoints" -- whenever -you complete a big chunk of work, think about saving the data at that -point. I've broken the mRNAseq tutorial down into chunks of work where -you can do this -- after each Web page, basically. - -Some test data --------------- - -To get started with multfile analysis and assembly, I've provided some -test mRNAseq data from embryonic stages of *Nematostella vectensis*; -the source is `this excellent paper -`__ by Tulin et al., "A -quantitative reference transcriptome for Nematostella vectensis". The -data is on snapshot 'snap-f5a9dea7', so go create a volume from that -and mount it as '/data' to get started. diff --git a/doc/mrnaseq/1-quality.txt b/doc/mrnaseq/1-quality.txt deleted file mode 100644 index 9b52b96..0000000 --- a/doc/mrnaseq/1-quality.txt +++ /dev/null @@ -1,360 +0,0 @@ -================================================ -1. Quality Trimming and Filtering Your Sequences -================================================ - -Boot up an m1.xlarge machine from Amazon Web Services; this has about -15 GB of RAM, and 2 CPUs, and will be enough to complete the assembly -of the Nematostella data set. - -.. note:: - - This follows the NGS 2013 tutorial, - :doc:`../tutorials-2013/short-read-quality-evaluation`, but for multiple - files. - -.. note:: - - The end results of this tutorial are available as public snapshot - snap-8b155fd9 on EC2/EBS. - -Install software ----------------- - -Install screed:: - - cd /usr/local/share - git clone https://github.com/ged-lab/screed.git - cd screed - python setup.py install - -Install the bleeding-edge version of khmer:: - - cd /usr/local/share - git clone https://github.com/ged-lab/khmer.git -b bleeding-edge - cd khmer - make - - echo 'export PYTHONPATH=/usr/local/share/khmer/python' >> ~/.bashrc - source ~/.bashrc - -Install Trimmomatic:: - - cd /root - curl -O http://www.usadellab.org/cms/uploads/supplementary/Trimmomatic/Trimmomatic-0.27.zip - unzip Trimmomatic-0.27.zip - cp Trimmomatic-0.27/trimmomatic-0.27.jar /usr/local/bin - -Install libgtextutils and fastx:: - - cd /root - curl -O http://hannonlab.cshl.edu/fastx_toolkit/libgtextutils-0.6.1.tar.bz2 - tar xjf libgtextutils-0.6.1.tar.bz2 - cd libgtextutils-0.6.1/ - ./configure && make && make install - - cd /root - curl -O http://hannonlab.cshl.edu/fastx_toolkit/fastx_toolkit-0.0.13.2.tar.bz2 - tar xjf fastx_toolkit-0.0.13.2.tar.bz2 - cd fastx_toolkit-0.0.13.2/ - ./configure && make && make install - -In each of these cases, we're downloading the software -- you can use -google to figure out what each package is and does if we don't discuss -it below. We're then unpacking it, sometimes compiling it (which we -can discuss later), and then installing it for general use. - -Find your data --------------- - -Either load in your own data (as in :doc:`0-download-and-save`) or -create a volume from snapshot snap-f5a9dea7 and mount it as /data -(again, this is the data from `Tulin et al., 2013 -`__). - -Check:: - - ls /data - -If you see all the files you think you should, good! Otherwise, debug. - -If you're using the Tulin et al. data provided in the snapshot above, -you should see:: - - /data/0Hour_ATCACG_L002_R1_001.fastq.gz - -Link your data into a working directory ---------------------------------------- - -Rather than *copying* the files into the working directory, let's just -*link* them in -- this creates a reference so that UNIX knows where to -find them but doesn't need to actually move them around. :: - - cd /mnt - mkdir work - cd work - - ln -fs /data/*.fastq.gz . - -(The 'ln' command is what does the linking.) - -Download the Illumina adapters ------------------------------- - -In the working directory, :: - - curl -O https://s3.amazonaws.com/public.ged.msu.edu/illuminaClipping.fa - -.. note:: - - You'll need to make sure these are the right adapters for your data. - If they are, you should see that some of them are trimmed off, below; - if they're not, you shouldn't see anything get trimmed. - -Adapter trim each pair of files -------------------------------- - -(From this point on, you may want to be running things inside of -screen, so that you detach and log out while it's running; see -:doc:`using-screen` for more information.) - -If you're following along using the Nematostella data, you should have a -bunch of files that look like this (use 'ls' to show them):: - - 24HourB_GCCAAT_L002_R1_001.fastq.gz - ^^ - -Each file with an R1 in its name should have a matching file with an R2 -- -these are the paired ends. - -.. note:: - - You'll need to replace and , below, with the - names of your actual R1 and R2 files. You'll also need to replace - with something that's unique to each pair of files. - It doesn't really matter what, but you need to make sure it's different - for each pair of files. - -For *each* of these pairs, run the following:: - - # make a temp directory - mkdir trim - cd trim - - # run trimmomatic - java -jar /usr/local/bin/trimmomatic-0.27.jar PE s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10 - - # interleave the remaining paired-end files - python /usr/local/share/khmer/sandbox/interleave.py s1_pe s2_pe | gzip -9c > ../.pe.fq.gz - - # combine the single-ended files - cat s1_se s2_se | gzip -9c > ../.se.fq.gz - - # go back up to the working directory and remove the temp directory - cd .. - rm -r trim - - # make it hard to delete the files you just created - chmod u-w *.pe.fq.gz *.se.fq.gz - -You can read the '#' comments above, but, briefly, this set of commands: - - * creates a temporary directory, 'trim/' - - * runs 'Trimmomatic' in that directory to trim off the adapters, and then - puts remaining pairs (most of them!) in s1_pe and s2_pe, and any orphaned - singletons in s1_se and s2_se. - - * interleaves the paired ends and puts them back in the working directory - - * combines the orphaned reads and puts them back in the working directory - -Automating things a bit -~~~~~~~~~~~~~~~~~~~~~~~ - -OK, once you've done this once or twice, it gets kind of tedious, doesn't it? -I've written a script to write these commands out automatically. Run it -like so:: - - cd /mnt/work - python /usr/local/share/khmer/sandbox/write-trimmomatic.py > trim.sh - -Run this, and then look at 'trim.sh' using the 'more' command -- :: - - more trim.sh - -If it looks like it contains the right commands, you can run it by doing:: - - bash trim.sh - -.. note:: - - This is a prime example of scripting to make your life much easier - and less error prone. Take a look at this file sometime -- - 'more /usr/local/share/khmer/sandbox/write-trimmomatic.py' to get - some idea of how this works. - -Quality trim each pair of files -------------------------------- - -After you run this, you should have a bunch of '*.pe.fq.gz' files and -a bunch of '*.se.fq.gz' files. The former are files that contain paired, -interleaved sequences; the latter contain single-ended, non-interleaved -sequences. - -Next, for each of these files, run:: - - gunzip -c | fastq_quality_filter -Q33 -q 30 -p 50 | gzip -9c > .qc.fq.gz - -This uncompresses each file, removes poor-quality sequences, and then -recompresses it. Note that (following :doc:`../tutorials-2013/short-read-quality-evaluation`) you can also trim things by putting in a 'fastx_trimmer -Q33 -l 70 |' into the mix. - -Automating this step -~~~~~~~~~~~~~~~~~~~~ - -This step can be automated with a 'for' loop at the shell prompt. Try:: - - for i in *.pe.fq.gz *.se.fq.gz - do - echo working with $i - newfile="$(basename $i .fq.gz)" - gunzip -c $i | fastq_quality_filter -Q33 -q 30 -p 50 | gzip -9c > "${newfile}.qc.fq.gz" - done - -What this loop does is: - - - for every file ending in pe.fq.gz and se.fq.gz, - - - print out a message with the filename, - - - construct a name 'newfile' that omits the trailing .fq.gz - - - uncompresses the original file, passes it through fastq, recompresses it, - and saves it as "newfile".qc.fq.gz - -Extracting paired ends from the interleaved files -------------------------------------------------- - -The fastx utilities that we're using to do quality trimming aren't -paired-end aware; they're removing individual sequences. Because the -pe files are interleaved, this means that there may now be some orphaned -sequences in there. Downstream, we will want to pay special attention -to the remaining paired sequences, so we want to separate out the pe -and se files. How do we go about that? Another script, of course! - -The khmer script 'strip-and-split-for-assembly.py' does exactly that. -You run it on an interleaved file that may have some orphans, and it -produces .pe and .se files afterwards, containing pairs and orphans -respectively. - -To run it on all of the pe qc files, do:: - - for i in *.pe*.qc.fq.gz - do - python /usr/local/share/khmer/sandbox/strip-and-split-for-assembly.py $i - done - -Finishing up ------------- - -You should now have a whole mess of files. For example, in the Nematostella -data, for *each* of the original input files, you'll have:: - - 24HourB_GCCAAT_L002_R1_001.fastq.gz - the original data - 24HourB_GCCAAT_L002_R2_001.fastq.gz - 24HourB_GCCAAT_L002_R1_001.pe.fq.gz - adapter trimmed pe - 24HourB_GCCAAT_L002_R1_001.pe.qc.fq.gz - FASTX filtered - 24HourB_GCCAAT_L002_R1_001.pe.qc.fq.gz.pe - FASTX filtered PE - 24HourB_GCCAAT_L002_R1_001.pe.qc.fq.gz.se - FASTX filtered SE - 24HourB_GCCAAT_L002_R1_001.se.fq.gz - adapter trimmed orphans - 24HourB_GCCAAT_L002_R1_001.se.qc.fq.gz - FASTX filtered orphans - -Yikes! What to do? - -Well, first, you can get rid of the original data. You already have it on a -disk somewhere, right? :: - - rm *.fastq.gz - -Next, you can get rid of the 'pe.fq.gz' and 'se.fq.gz' files, since you -only want the QC files. So:: - - rm *.pe.fq.gz *.se.fq.gz - -And, finally, you can toss the pe.fq.gz files, because you've turned *those* -into .pe and .se files. - - rm *.pe.qc.fq.gz - -So now you should be left with only three files for each sample:: - - 24HourB_GCCAAT_L002_R1_001.pe.qc.fq.gz.pe - FASTX filtered PE - 24HourB_GCCAAT_L002_R1_001.pe.qc.fq.gz.se - FASTX filtered SE - 24HourB_GCCAAT_L002_R1_001.se.qc.fq.gz - FASTX filtered orphans - -Things to think about -~~~~~~~~~~~~~~~~~~~~~ - -Note that the filenames, while ugly, are conveniently structured with the -history of what you've done. This is a good idea. - -Also note that we've conveniently named the files so that we can remove -the unwanted ones en masse. This is a good idea, too. - -Renaming files --------------- - -I'm a fan of keeping the files named somewhat sensibly, and keeping them -compressed. Let's do some mass renaming:: - - for i in *.pe.qc.fq.gz.pe - do - newfile="$(basename $i .pe.qc.fq.gz.pe).pe.qc.fq" - mv $i $newfile - gzip $newfile - done - -and also some mass combining:: - - for i in *.pe.qc.fq.gz.se - do - otherfile="$(basename $i .pe.qc.fq.gz.se).se.qc.fq.gz" - gunzip -c $otherfile > combine - cat $i >> combine - gzip -c combine > $otherfile - rm $i - done - -and finally:: - - chmod u-w *.qc.fq.gz - -to make sure you don't accidentally delete something. - -Saving the files ----------------- - -At this point, you should save these files, which will be used in two -ways: first, for assembly; and second, for mapping, to do quantitation -and ultimately comparative expression analysis. You can save them by -doing this:: - - mkdir save - mv *.qc.fq.gz save - du -sk save - -This puts the data you want to save into a subdirectory named 'save', and -calculates the size. - -Now, create a volume of the given size -- divide by a thousand to get -gigabytes, multiply by 1.1 to make sure you have enough room, and then -follow the instructions in :doc:`saving-data-persistently`. Once -you've mounted it properly (I would suggest mounting it on /save -instead of /data!), then do :: - - rsync -av save /save - -which will copy all of the files over from the ./save directory onto the -'/save' disk. Then 'umount /save' and voila, you've got a copy of the files! - -Next stop: :doc:`2-diginorm`. - diff --git a/doc/mrnaseq/2-diginorm.txt b/doc/mrnaseq/2-diginorm.txt deleted file mode 100644 index ab4154c..0000000 --- a/doc/mrnaseq/2-diginorm.txt +++ /dev/null @@ -1,120 +0,0 @@ -================================= -2. Applying Digital Normalization -================================= - -.. note:: - - You can start this tutorial with the contents of EC2/EBS snapshot - snap-126cc847. - -.. note:: - - You'll need ~15 GB of RAM for this, or more if you have a LOT of data. - -Link in your data ------------------ - -Make sure your data is in /mnt/work/. If you've loaded it onto /data, -you can do:: - - cd /mnt - mkdir work - cd /mnt/work - ln -fs /data/*.qc.fq.gz . - -Run digital normalization -------------------------- - -Apply digital normalization to the paired-end reads:: - - python /usr/local/share/khmer/scripts/normalize-by-median.py -p -k 20 -C 20 -N 4 -x 3e9 --savehash normC20k20.kh *.pe.qc.fq.gz - -and then to the single-end reads:: - - python /usr/local/share/khmer/scripts/normalize-by-median.py -k 20 -C 20 -N 4 -x 3e9 --loadhash normC20k20.kh --savehash normC20k20.kh *.se.qc.fq.gz - -Note the '-p' in the first normalize-by-median command -- when run on -PE data, that ensures that no paired ends are orphaned. However, it -will complain on single-ended data, so you have to give the data to it -separately. - -Also note the '-N' and '-x' parameters. These specify how much memory -diginorm should use. The product of these should be less than the -memory size of the machine you selected. The maximum needed for *any* -transcriptome should be in the ~60 GB range, e.g. -N 4 -x 15e9; for -only a few hundred million reads, 16 GB should be plenty. (See -`choosing hash sizes for khmer -`__ -for more information.) - -Trim off likely erroneous k-mers -------------------------------- - -Now, run through all the reads and trim off low-abundance parts of -high-coverage reads:: - - /usr/local/share/khmer/scripts/filter-abund.py -V normC20k20.kh *.keep - -This will turn some reads into orphans, but that's ok -- their partner -read was bad. - -Rename files -~~~~~~~~~~~~ - -You'll have a bunch of 'keep.abundfilt' files -- let's make things prettier. - -First, let's break out the orphaned and still-paired reads:: - - for i in *.pe.*.abundfilt; - do - python /usr/local/share/khmer/sandbox/strip-and-split-for-assembly.py $i - done - -We can combine the orphaned reads into a single file:: - - for i in *.se.qc.fq.gz.keep.abundfilt - do - pe_orphans=$(basename $i .se.qc.fq.gz.keep.abundfilt).pe.qc.fq.gz.keep.abundfilt.se - newfile=$(basename $i .se.qc.fq.gz.keep.abundfilt).se.qc.keep.abundfilt.fq.gz - cat $i $pe_orphans | gzip -c > $newfile - done - -We can also rename the remaining PE reads & compress those files:: - - for i in *.abundfilt.pe - do - newfile=$(basename $i .fq.gz.keep.abundfilt.pe).keep.abundfilt.fq - mv $i $newfile - gzip $newfile - done - -This leaves you with a whole passel o' files, most of which you want to go -away! - - 6Hour_CGATGT_L002_R1_005.pe.qc.fq.gz - 6Hour_CGATGT_L002_R1_005.pe.qc.fq.gz.keep - 6Hour_CGATGT_L002_R1_005.pe.qc.fq.gz.keep.abundfilt - 6Hour_CGATGT_L002_R1_005.pe.qc.fq.gz.keep.abundfilt.se - 6Hour_CGATGT_L002_R1_005.se.qc.fq.gz - 6Hour_CGATGT_L002_R1_005.se.qc.fq.gz.keep - 6Hour_CGATGT_L002_R1_005.se.qc.fq.gz.keep.abundfilt - -So, finally, let's get rid of a lot of the old files :: - - rm *.se.qc.fq.gz.keep.abundfilt - rm *.pe.qc.fq.gz.keep.abundfilt.se - rm *.keep - rm *.abundfilt - rm *.qc.fq.gz - -Gut check -~~~~~~~~~ - -You should now have:: - - 6Hour_CGATGT_L002_R1_005.pe.qc.keep.abundfilt.fq.gz - 6Hour_CGATGT_L002_R1_005.se.qc.keep.abundfilt.fq.gz - -@CTB what are these? - -Save all these files to a new volume, and get ready to assemble! diff --git a/doc/mrnaseq/3-big-assembly.txt b/doc/mrnaseq/3-big-assembly.txt deleted file mode 100644 index e535ff5..0000000 --- a/doc/mrnaseq/3-big-assembly.txt +++ /dev/null @@ -1,75 +0,0 @@ -============================== -3. Running the Actual Assembly -============================== - -All of the below should be run in screen, probably... You will want -at least 15 GB of RAM, maybe more. - -(If you start up a new machine, you'll need to go to -:doc:`1-quality` and install khmer and screed.) - -.. note:: - - You can start this tutorial with the contents of EC2/EBS snapshot - snap-7b0b872e. - -Installing Trinity ------------------- - -To install Trinity:: - - cd /root - - curl -L http://sourceforge.net/projects/trinityrnaseq/files/latest/download?source=files > trinity.tar.gz - - tar xzf trinity.tar.gz - cd trinityrnaseq_r2013-02-25/ - export FORCE_UNSAFE_CONFIGURE=1 - make - -Install bowtie --------------- - -Download and install bowtie:: - - cd /root - curl -O -L http://sourceforge.net/projects/bowtie-bio/files/bowtie/0.12.7/bowtie-0.12.7-linux-x86_64.zip - unzip bowtie-0.12.7-linux-x86_64.zip - cd bowtie-0.12.7 - cp bowtie bowtie-build bowtie-inspect /usr/local/bin - -Build the files to assemble ---------------------------- - -For paired-end data, Trinity expects two files, 'left' and 'right'; -there can be orphan sequences present, however. So, below, we split -all of our interleaved pair files in two, and then add the single-ended -seqs to one of 'em. :: - - cd /mnt/work - for i in *.pe.qc.keep.abundfilt.fq.gz - do - python /usr/local/share/khmer/sandbox/split-pe.py $i - done - - cat *.1 > left.fq - cat *.2 > right.fq - - gunzip -c *.se.qc.keep.abundfilt.fq.gz >> left.fq - -Assembling with Trinity ------------------------ - -Run the assembler! :: - - /root/trinityrnaseq_r2013-02-25/Trinity.pl --left left.fq --right right.fq --seqType fq -JM 15G - -Note that this last bit (15G) is the maximum amount of memory to use. You -can increase (or decrease) it based on what machine you rented. This size -works for the m1.xlarge machines. - -Once this completes (on the Nematostella data it might take about 12 hours), -you'll have an assembled transcriptome in trinity_out_dir/Trinity.fasta. - -You can now copy it over via Dropbox, or set it up for BLAST (see -:doc:`installing-blastkit`). diff --git a/doc/mrnaseq/4-diff-expr.txt b/doc/mrnaseq/4-diff-expr.txt deleted file mode 100644 index f173b6a..0000000 --- a/doc/mrnaseq/4-diff-expr.txt +++ /dev/null @@ -1,4 +0,0 @@ -curl -O http://athyra.idyll.org/~t/petMar_lamp3.longest.fasta.gz -gunzip petMar_lamp3.longest.fasta.gz - -rsem-prepare-reference petMar_lamp3.longest.fasta lamp3 diff --git a/doc/mrnaseq/5-building-transcript-families.txt b/doc/mrnaseq/5-building-transcript-families.txt deleted file mode 100644 index 793b3e8..0000000 --- a/doc/mrnaseq/5-building-transcript-families.txt +++ /dev/null @@ -1,123 +0,0 @@ -============================================================ -5. Building transcript families and annotating the sequences -============================================================ - -Install khmer, screed, and BLAST. (See :doc:`1-quality` and -:doc:`installing-blastkit`). I would suggest using an m1.large or -m1.xlarge machine. - -You'll also need to install some eel-pond scripts:: - - git clone https://github.com/ctb/eel-pond.git /usr/local/share/eel-pond - -Copy in your data -================= - -You need to get ahold of your assembled transcriptome (from -e.g. :doc:`3-big-assembly`). Put it in /mnt. - -For the purposes of your first run through, I suggest just grabbing my copy -of the Nematostella assembly:: - - cd /mnt - curl -O https://s3.amazonaws.com/public.ged.msu.edu/trinity-nematostella-raw.fa.gz - -Run khmer partitioning -====================== - -Partitioning runs a de Bruijn graph-based clustering algorithm that will -cluster your transcripts by transitive sequence overlap. That is, it will -build transcript families :). :: - - /usr/local/share/khmer/scripts/do-partition.py -x 1e9 -N 4 --threads 4 nema trinity-nematostella-raw.fa.gz - -This should take about 15 minutes, and outputs a file ending in '.part' -that contains the partition assignments. Now, group and rename the -sequences:: - - python /usr/local/share/eel-pond/rename-with-partitions.py nema trinity-nematostella-raw.fa.gz.part - mv trinity-nematostella-raw.fa.gz.part.renamed.fasta.gz trinity-nematostella.renamed.fa.gz - -.. tail n.dist -.. (warning) - -.. - -.. (explain sequence names) - -Looking at the renamed sequences -================================ - -Let's look at the renamed sequences:: - - gunzip -c trinity-nematostella.renamed.fa.gz | head - -You'll see that each sequence name looks like this:: - - >nema.id1.tr16001 1_of_1_in_tr16001 len=261 id=1 tr=16001 - -Some explanation: - - - 'nema' is the prefix that you gave the rename script, above; - - - 'idN' is the unique ID for this sequence; it will never be repeated in this - file. - - - 'trN' is the transcript family, which may contain one or more transcripts. - - - '1_of_1_in_tr16001' tells you that this transcript family has only - one transcript in it (this one!) Other transcript families may - (will) have more. - - - 'len' is the sequence length. - -Doing a preliminary annotation against mouse -============================================ - -Now let's assign putative homology & orthology to these transcripts, by -doing BLASTs & reciprocal best hit analysis. First, uncompress your -transcripts file:: - - gunzip trinity-nematostella.renamed.fa.gz - -Now, grab the latest mouse RefSeq:: - - curl -O ftp://ftp.ncbi.nih.gov/refseq/M_musculus/mRNA_Prot/mouse.protein.faa.gz - gunzip mouse.protein.faa.gz - -Format both as BLAST databases:: - - formatdb -i mouse.protein.faa -o T -p T - formatdb -i trinity-nematostella.renamed.fa -o T -p F - -And, now, run BLAST in both directions. Note, this may take ~24 hours or -longer; you probably want to run it in screen:: - - blastall -i trinity-nematostella.renamed.fa -d mouse.protein.faa -e 1e-3 -p blastx -o nema.x.mouse -a 8 -v 4 -b 4 - blastall -i mouse.protein.faa -d trinity-nematostella.renamed.fa -e 1e-3 -p tblastn -o mouse.x.nema -a 8 -v 4 -b 4 - -Assigning names to sequences -============================ - -Now, calculate putative homology (best BLAST hit) and orthology -(reciprocal best hits):: - - python /usr/local/share/eel-pond/make-uni-best-hits.py nema.x.mouse nema.x.mouse.homol - python /usr/local/share/eel-pond/make-reciprocal-best-hits.py nema.x.mouse mouse.x.nema nema.x.mouse.ortho - -Prepare some of the mouse info:: - - python /usr/local/share/eel-pond/make-namedb.py mouse.protein.faa mouse.namedb - python -m screed.fadbm mouse.protein.faa - -And, finally, annotate the sequences:: - - python /usr/local/share/eel-pond/annotate-seqs.py trinity-nematostella.renamed.fa nema.x.mouse.ortho nema.x.mouse.homol - -This will produce a file 'trinity-nematostella.renamed.fa.annot', which -will have sequences that look like this:: - - >nematostella.id1.tr115222 h=43% => suppressor of tumorigenicity 7 protein isoform 2 [Mus musculus] 1_of_7_in_tr115222 len=1635 id=1 tr=115222 1_of_7_in_tr115222 len=1635 id=1 tr=115222 - -I suggest renaming this file to 'nematostella.fa' and using it for -BLASTs (see :doc:`installing-blastkit`). diff --git a/doc/mrnaseq/coral.txt b/doc/mrnaseq/coral.txt deleted file mode 100644 index 673378e..0000000 --- a/doc/mrnaseq/coral.txt +++ /dev/null @@ -1,7 +0,0 @@ - -java -jar /usr/local/bin/trimmomatic-0.27.jar PE P_astreoides_larva_GCCAAT_L005_R?_001.fastq.gz s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10 - - -mkdir larva -root@ip-10-40-10-213:/mnt/two# mv s?_?e larva/ -root@ip-10-40-10-213:/mnt/two# java -jar /usr/local/bin/trimmomatic-0.27.jar PE P_astreoides_settled*_L005_R?_001.fastq.gz s1_pe s1_se s2_pe s2_se ILLUMINACLIP:../illuminaClipping.fa:2:30:10 diff --git a/doc/mrnaseq/de-novo-assembly.txt b/doc/mrnaseq/de-novo-assembly.txt deleted file mode 100644 index 31e3382..0000000 --- a/doc/mrnaseq/de-novo-assembly.txt +++ /dev/null @@ -1,144 +0,0 @@ -=================================================== -Doing a small de novo mRNAseq assembly with Trinity -=================================================== - -Installing Trinity ------------------- - -To install Trinity:: - - cd /root - - curl -L http://sourceforge.net/projects/trinityrnaseq/files/latest/download?source=files > trinity.tar.gz - - tar xzf trinity.tar.gz - cd trinityrnaseq_r2013-02-25/ - export FORCE_UNSAFE_CONFIGURE=1 - make - -Install ctb Python packages ---------------------------- - -Install screed and khmer:: - - cd /usr/local/share - git clone https://github.com/ged-lab/screed.git - cd screed - python setup.py install - - cd /usr/local/share - git clone https://github.com/ged-lab/khmer.git - cd khmer - make - - echo export PYTHONPATH=/usr/local/share/khmer/python >> ~/.bashrc - source ~/.bashrc - -Installing blastkit -------------------- - -Installing some prerequisites:: - - pip install pygr - apt-get -y install lighttpd - -and configure them:: - - cd /etc/lighttpd/conf-enabled - ln -fs ../conf-available/10-cgi.conf ./ - echo 'cgi.assign = ( ".cgi" => "" )' >> 10-cgi.conf - echo 'index-file.names += ( "index.cgi" ) ' >> 10-cgi.conf - - /etc/init.d/lighttpd restart - -Next, install BLAST:: - - cd /root - - curl -O ftp://ftp.ncbi.nih.gov/blast/executables/release/2.2.24/blast-2.2.24-x64-linux.tar.gz - tar xzf blast-2.2.24-x64-linux.tar.gz - cp blast-2.2.24/bin/* /usr/local/bin - cp -r blast-2.2.24/data /usr/local/blast-data - -And put in blastkit:: - - cd /root - git clone https://github.com/ctb/blastkit.git -b ec2 - cd blastkit/www - ln -fs $PWD /var/www/blastkit - - mkdir files - chmod a+rxwt files - chmod +x /root - -Install bowtie --------------- - -Download and install bowtie:: - - cd /root - curl -O -L http://sourceforge.net/projects/bowtie-bio/files/bowtie/0.12.7/bowtie-0.12.7-linux-x86_64.zip - unzip bowtie-0.12.7-linux-x86_64.zip - cd bowtie-0.12.7 - cp bowtie bowtie-build bowtie-inspect /usr/local/bin - -Download and preparing the test data ------------------------------------- - -Grab the coral data:: - - cd /mnt - curl -O https://s3.amazonaws.com/public.ged.msu.edu/coral-settled-400k-pe.fq.gz - -Break the interleaved FASTQ data into left and right files for Trinity:: - - python /usr/local/share/khmer/sandbox/split-pe.py coral-settled-400k-pe.fq.gz - -Assemble! :: - - /root/trinityrnaseq_r2013-02-25/Trinity.pl --left coral-settled-400k-pe.fq.gz.1 --right coral-settled-400k-pe.fq.gz.2 --seqType fq -JM 5G - -Note that this last bit (5G) is the maximum amount of memory to use. You -can increase (or decrease) it based on what machine you rented. - -Are you impatient?? -~~~~~~~~~~~~~~~~~~~ - -Note: I have a little test database that you can install below INSTEAD of -doing the assembly:: - - mkdir /root/blastkit/db - curl https://s3.amazonaws.com/public.ged.msu.edu/coral-mini-assembly.fa.gz | gunzip > /root/blastkit/db/db.fa - -Set up a BLAST database ------------------------ - -Assuming everything is successful, let's make this BLASTable. - -Copy the assembly:: - - mkdir /root/blastkit/db - cp trinity_out_dir/Trinity.fasta /root/blastkit/db/db.fa - -Format the database for BLASTing:: - - cd /root/blastkit/db - formatdb -i db.fa -o T -p F - -Format the database for sequence retrieval:: - - python ../index-db.py db.fa - -Now, go to 'http:///blastkit/' and you should have a simple -BLAST interface. If you're using the coral dataset, above, try the following -query:: - - MSRADPGKNSEPSESKMSLELRPTAPSDLGRSNEAFQDEDLERQNTPGNSTVRNRVVQSGEQGHAKQDDRQITIEQEPLG - NKEDPEDDSEDEHQKGFLERKYDTICEFCRKHRVVLRSTIWAVLLTGFLALVIAACAINFHRALPLFVITLVTIFFVIWD - HLMAKYEQRIDDFLSPGRRLLDRHWFWLKWVVWSSLILAIILWLSLDTAKLGQQNLVSFGGLIMYLILLFLFSKHPTRVY - WRPVFWGIGLQFLLGLLILRTRPGFVAFDWMGRQVQTFLGYTDTGARFVFGEKYTDHFFAFKILPIVVFFSTVMSMLYYL - GLMQWIIRKVGWLMLVTMGSSPIESVVAAGNIFIGQTESPLLVQPYLPHVTKSELHTIMTAGFATIAGSVLGAYISFGVS - STHLLTASVMSAPAALAVAKLFWPETEKPKITLKSAMKMENGDSRNLLEAASQGASSSIPLVANIAANLIAFLALLSFVN - SALSWFGSMFNYPELSFELICSYIFMPFSFMMGVDWQDSFMVAKLIGYKTFFNEFVAYDHLSKLINLRKAAGPKFVNGVQ - QYMSIRSETIATYALCGFANFGSLGIVIGGLTSIAPSRKRDIASGAMRALIAGTIACFMTACIAGILSDTPVDINCHHVL - ENGRVLSNTTEVVSCCQNLFNSTVAKGPNDVVPGGNFSLYALKSCCNLLKPPTLNCNWIPNKL diff --git a/doc/mrnaseq/images/AttachVolume.png b/doc/mrnaseq/images/AttachVolume.png deleted file mode 100644 index 922a1ea545c1c7f06470ff699cecbfff9f553d7d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 51196 zcmY(qV_;@Wvo@S$V%xT@$;7sG$F?!CZQHhO+qP{^jBoaS&UwD~{9N6&s;hc+)hb*a zCNC=n2aOF41Ox;pAug;41O#FQ1O)s868!H-=#DxE5D+@OxsZ^&gpd%Syo0TYxs@>x zkSp+#y0N;7TuY8uJlA4gM@I)v;V+RuA(xQGe1Bg;5M*R%ahm)<-|$3`@I*++P~ZGO zKalwTt(Tse_ilpg&)+?pZrNVPEi02crZ)f<%g<#HU+)w|+{>6;P(ngR?#25=^pLIr zbNEYGeqeHbU|k2$mRda-M@Pfkp5~ndATBIV68HQIpR6Z71o<|Pz&2o@w1EMEYkeDF z#%uq&XiYmaP9R9sNU?(c0J&SxNOQqGjKI5bsHZmwf^5B&9!w}7DDIJUMI0bf1R!A} zP{C3RphD!@Op^I2m}Md8Z;W8B*)0l0AZi!PiZ(Zt-Rv)TZZT3q!c)+ge)Zlze2=c5 z5aftj-$e=hw{I9VT3}ngz8A1koJKff++fdwW6FvorpxCn1f`(&Or2O&3XR(>$3WXqwIOf6JQIEpG#3-@mqufS6>w(w&F2%Y9=m~p> zpXD)8@rV47MW_lM71${`lp(2soza|8R01qS{^V`vqATE(lCZ*0`J;={05Pk4#fBBUZTCv=l#7OR(#lpGf+7q<{N7jn;T z^lglD@^y-Hiiu2@4wcThgav>C$N_8sjJ03&GPv*~Aq%}(!$Akk_D!uio7^@jxN`KO zHvHGJ+2T6F+mgJpUs?XC{wab9`?MmkBKRUGLy$vik_M8zlGc(S#vsN>#=nkGkJ!<& zCF92Rq&Pwm!R2_(_=JJ*0bJ_oxz+L{)llS&qS>ZckD6j-|Xfbq?j|Zez1bG zcC((bKAQ!bvzwKf8=G01&&;9E@fK<43eDfmVa>iSXcu^hb4awv%}CZr+z8}I?(p-; z_6+n+=JxIO#SYu$?9mPIl0X#D7Z4mc4~s?Kq{&MQN~23FNEb=Zq;)edFw(GeFqJUz z8D;AW)I!#&*ALb!*TU9WHPqK-8^D>+8C@BFj*E^I_Z?3pPb`l$k4KxT89wXJ82b!& z4+~7>jqS}S^v;dc^^NtH&0zHbhJQ}F^$yJ#4$AUuO3{EC5+ z!`%g{{**^oc~YuY(oxt`yOVD#xK~J$O3KwJZqFUgJI?>DX)TbYl$EPf-CfvMwO6fB zbSI)Dzo7O|ee6O9RV81AUWI2(cIA3Sk5@QNmBpH+uWg}?vz5{Q;I8Ga;mUZcO{2ekIjyylx>wUkt34hl7*P1p1Iw;(TLUv*J4xOpOJ}mj46%fhz*X_ z%LsfDcG6*TJL4fEB$cnZS3Ok|72w&N*cjPx+0w81Q*#OM*}PoeZNg(oqKBftU?`>U zG2FcTdqj4Ge#53uXOnmPE9pl{;Cw)#FPlBh(eAv!e=E5$Z zxZ%q*hf9eQB{M^7mi{-rGkpjsi{MZFjiuhERp$Yj4Z0ocE$soG6|W_sC6bMUh>~*C z=9BQ0a<%A{@|Jd&>}NFs>zoW;KCeGswy)POX|F2pc<&ppI|zi(c2H&rh47CsmoNq} zTo|nAw78kLorw9U4kSh-iPjL$|oQ* za3U}wC89oIa}i~cB17#%PlNWu+M$|}E0GLRzQ|fA?#OK@3}o~a&kE!V?Ov%IN1 z>&mU#t`ecPf}^_CgL8^Zx$x|H-mYbddAG8M7r8SaO{%Hm_go<}5#XoJ}nB*S*}2vc5ZVSmIb98ct4T9nEj3c21@m zCR2P6KkR%2Z1&Fbr}t;$m*y|UT4FA8`g!r_b>AZ2GoOP#rLMvk#}ib#mgd+-nvVYL z{t2iV+bQiv^TBx8{t!ACnGOCNzMq-R8R@CEVYX4V9kcplm0>&c4gca*o6tPbs@YId zyV7IZ@_j^jj_9F&D|Rb45;r%uY2SNi&q$Pbn*u|)ryE#80n}3t-1`&AH{_LAVukPH z{suPHyFU>Y*pN9FNO3RK26{`ZmWVl)(+EDCfXx?vBvh~4up^W@v_YIcL5F-|NF_l| zfmzW_o@0)`5#9*_Qydd7)gra0;l3fQ?%hGyLDZ4Vq2&JX7X2>iK@Cb7S`~T=ogQ@z zSq4c8H6}SUF)}ecftSKjLsbi&?pdc>gh**lOhPVA3@op{aJ@h^&l@nHwxA&;qp2RE zkfQ!9e=}0X@J0Y@0#_169z`KjEK@Q`KDnbFsTr*)qb|46+45=BV$*YGcPQ=|JRWvV zTf|ZXL*}=hG#u5JH54$R)GO7mIW{uLu~{?N!=OhON~}&kNY2VkOYz}7sCTHiD8MQc zDPF0Q>FDVsD7)YFz(DUQzTPG544?$i4DCspS%_+Qz0EG^rqQlMHXv7XnEFf|_3UM) zBz$-3ofPZE3Ed^dixo|@luay7d{Q%CE35uil~MUFA6kN1y4d;Co-!}*@vU+Y6Y?l? 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Here's a -simple way to set up a stylish little BLAST server that lets you search -your newly assembled sequences. - -Installing blastkit -------------------- - -Installing some prerequisites:: - - pip install pygr - pip install whoosh - pip install git+https://github.com/ctb/pygr-draw.git - pip install git+https://github.com/ged-lab/screed.git - apt-get -y install lighttpd - -and configure them:: - - cd /etc/lighttpd/conf-enabled - ln -fs ../conf-available/10-cgi.conf ./ - echo 'cgi.assign = ( ".cgi" => "" )' >> 10-cgi.conf - echo 'index-file.names += ( "index.cgi" ) ' >> 10-cgi.conf - - /etc/init.d/lighttpd restart - -Next, install BLAST:: - - cd /root - - curl -O ftp://ftp.ncbi.nih.gov/blast/executables/release/2.2.24/blast-2.2.24-x64-linux.tar.gz - tar xzf blast-2.2.24-x64-linux.tar.gz - cp blast-2.2.24/bin/* /usr/local/bin - cp -r blast-2.2.24/data /usr/local/blast-data - -And put in blastkit:: - - cd /root - git clone https://github.com/ctb/blastkit.git -b ec2 - cd blastkit/www - ln -fs $PWD /var/www/blastkit - - mkdir files - chmod a+rxwt files - chmod +x /root - -and run check.py:: - - cd /root/blastkit - python ./check.py - -It should say everything is OK. - -Adding the data ---------------- - -If you've just finished a transcriptome assembly (:doc:`3-big-assembly`) then -you can do this to copy your newly generated assembly into the right place:: - - cp trinity_out_dir/Trinity.fasta /root/blastkit/db/db.fa - -Alternatively, you can grab my version of the assembly (from running this -tutorial):: - - cd /root/blastkit - curl -O https://s3.amazonaws.com/public.ged.msu.edu/trinity-nematostella-raw.fa.gz - gunzip trinity-nematostella-raw.fa.gz - mv trinity-nematostella-raw.fa db/db.fa - -Formatting the database -~~~~~~~~~~~~~~~~~~~~~~~ - -After you've done either of the above, format and install the database -for blastkit:: - - cd /root/blastkit - formatdb -i db/db.fa -o T -p F - python index-db.py db/db.fa - -Done! - -.. note:: - - You can install any file of DNA sequences you want this way; just copy - it into /root/blastkit/db/db.fa and run the indexing commands, above. - -Running blastkit ----------------- - -Figure out what your machine name is -(ec2-???-???-???-???.compute-1.amazonaws.com) and go to:: - - http://machine-name/blastkit/ - -Make sure you have enabled port 80 in your security settings on Amazon. - diff --git a/doc/mrnaseq/notes b/doc/mrnaseq/notes deleted file mode 100644 index 7e0dc7a..0000000 --- a/doc/mrnaseq/notes +++ /dev/null @@ -1,14 +0,0 @@ -apt-get -y install r-cran-gplots -cd /root/rsem-1.2.5 - -make ebseq - -cd /mnt - - -time rsem-calculate-expression -p 4 --paired-end 1_SC_1_week_ATCACG_L002_R1_001.pe.qc.fq.gz.1 1_SC_1_week_ATCACG_L002_R1_001.pe.qc.fq.gz.2 lamp3 2_BR_1_week_pe - -rsem-generate-data-matrix 2_BR_1_week_se.genes.results 1_SC_1_week_se.genes.results > 1_week.matrix - 116 more 1_week.matrix - 117 rsem-find-DE 1_week.matrix 1 0.5 xxx - diff --git a/doc/mrnaseq/rsem.txt b/doc/mrnaseq/rsem.txt deleted file mode 100644 index 4d4dbe1..0000000 --- a/doc/mrnaseq/rsem.txt +++ /dev/null @@ -1,96 +0,0 @@ -=================== -Expression analysis -=================== - -Installing rsem ---------------- - -We'll be using the `RSEM package `__ -to do some expression analysis. To install it:: - - cd /root - - curl -O http://deweylab.biostat.wisc.edu/rsem/src/rsem-1.2.5.tar.gz - - tar xzvf rsem-1.2.5.tar.gz - cd rsem-1.2.5 - make - -And now add this directory into your PATH, which is where Unix looks for -things to run:: - - echo 'export PATH=$PATH:/root/rsem-1.2.5' >> /root/.bashrc - source /root/.bashrc - -Installing bowtie ------------------ - -If you didn't install bowtie on this machine already (e.g. as part of -:doc:`de-novo-assembly`), RSEM needs it; do:: - - cd /root - curl -O -L http://sourceforge.net/projects/bowtie-bio/files/bowtie/0.12.7/bowtie-0.12.7-linux-x86_64.zip - unzip bowtie-0.12.7-linux-x86_64.zip - cd bowtie-0.12.7 - cp bowtie bowtie-build bowtie-inspect /usr/local/bin - -Running rsem ------------- - -Go to a working directory on /mnt:: - - cd /mnt - mkdir rsem - cd rsem - -Now, grab the reference transcriptome:: - - curl https://s3.amazonaws.com/public.ged.msu.edu/coral-mini-assembly.fa.gz | gunzip > coral.fa - - -and some reads:: - - curl -O http://athyra.idyll.org/~t/larva-ct-800k.fq.gz - curl -O http://athyra.idyll.org/~t/settled-ct-800k.fq.gz - -You'll need to uncompress the reads:: - - gunzip *.gz - -Now, prepare the reference for RSEM:: - - rsem-prepare-reference coral.fa coral - -Here, 'coral.fa' can be one of several files (comma-separated) in case -you have multiple different reference data sets. In this case it's -the transcriptome you generated, but there are lots of options (see -`the docs for rsem-prepare-reference -`__). - -And, finally, calculate the expression levels for the 'settled' and 'larva' -data sets:: - - rsem-calculate-expression settled-ct-800k.fq coral settled - rsem-calculate-expression larva-ct-800k.fq coral larva - -(See `the rsem-calculate-expression docs `__ for more info.) - -This outputs a couple files; the two most interesting, for now, are -'larva.genes.results' and 'settled.genes.results'. These contain the -following data:: - - head larva.genes.results - -should show you :: - - gene_id transcript_id(s) length effective_length expected_count TPM FPKM - comp0_c0_seq1 comp0_c0_seq1 291.00 291.00 3.00 102.12 164.47 - ... - -If you've installed Dropbox (see -:doc:`../tutorials-2013/installing-dropbox`), you can now copy the -files to Dropbox:: - - cp larva.genes.results settled.genes.results ~/Dropbox/ - -and you can open them directly in Excel as tab-delimited files. diff --git a/doc/mrnaseq/saving-data-persistently.txt b/doc/mrnaseq/saving-data-persistently.txt deleted file mode 100644 index 6fc8ab5..0000000 --- a/doc/mrnaseq/saving-data-persistently.txt +++ /dev/null @@ -1,114 +0,0 @@ -===================================== -Storing data persistently with Amazon -===================================== - -:Author: Rosangela Canino-Koning -:Date: June 7, 2010 - -If you want to save your data across instances -- that is, if you want -to have persistent data -- Amazon can do that for you, too. You need -to use the Amazon Elastic Block Storage service, which creates a virtual -hard drive that you can (virtually) attach to your EC2 instance. - -Note that EBS charges you based partly on how much disk space you have -allocated per month, rather than on connectivity or anything else, so even if -you don't have an EC2 machine running, you'll be paying Amazon for data -storage. - -You can read more about EBS `here `__. - -Prerequisites -------------- - -This tutorial assumes you've already set up an account on Amazon Web -Services, and that you've completed the EC2 tutorial to set up an -Amazon instance. - -Ask Amazon to create a new Elastic Block Storage Volume for you ---------------------------------------------------------------- - -At the AWS Management Console, on the left menu bar, click "Volumes". - -.. image:: images/ClickVolumes.png - :width: 90% - - -Click "Create Volume". - -.. image:: images/CreateVolume.png - :width: 90% - -Enter the desired size, and select the zone in which your instance is -running. The volume and instance must be in the same zone. Otherwise, -the volume cannot be attached to your instance. - -Then click "Create". - -.. image:: images/CreateVolumeWindow.png - :width: 90% - - -Wait for your volume to finish being created, then click "Attach Volume". - -.. image:: images/AttachVolume.png - :width: 90% - -Select the desired running instance. It will ask you for a device name -to attach; this should be '/dev/sdf', '/dev/sdg', etc. You can name it -anything up to at least 'i' or 'j'. Remember this for later -- it's -how the computer will know which disk to store data on! - -Click "Attach". - -.. image:: images/AttachVolumeWindow.png - :width: 90% - -When attachment is complete, connect to your instances via SSH. - -If the volume is newly created, you must format the volume. -**WARNING: ONLY DO THIS ONCE, WHEN YOU FIRST CREATE THE VOLUME. OTHERWISE, YOU WILL LOSE ALL YOUR -DATA**. -:: - - mkfs -t ext2 /dev/xvdf - -(If you used 'sdg' above, make it 'xvdg' etc. I know it's confusing.) - -It will ask you if you want to use the entire device -- say "y" for "yes. - -Then, mount the volume. You'll do this every time you attach the volume to an instance:: - - mkdir /data - mount /dev/xvdf /data - -Your drive is now ready to use -- it will be available under /data. -Files copied into that directory or directories underneath it will -be stored on your EBS volume. - -Shutting down your instance ---------------------------- - -Any volumes you have attached will automatically detach when you shut -down the instance. You can also stop all processes that are using the -volume, change out of the directory, and type :: - - cd - umount /data - -and then detach the volume via the AWS Web site. - -Snapshotting your volume ------------------------- - -Snapshots are backups of your volume that you can share with other people. -Snapshots are much more reliable long-term than volumes are, and you can use -them as a basis for creating a new volume (in which case the new volume will -start out containing all the data in the snapshot). So, if you upload some -raw data and want to work with it over a few weeks, we suggest: - - - create a volume and load the data onto the volume - - snapshot the original volume - - make a new volume from the snapshot, and delete the original volume - -You'll only be charged money for storing the snapshot and any *differences* -from the snapshot on the volumes based on that snapshot. diff --git a/doc/mrnaseq/using-screen.txt b/doc/mrnaseq/using-screen.txt deleted file mode 100644 index aa45c06..0000000 --- a/doc/mrnaseq/using-screen.txt +++ /dev/null @@ -1,58 +0,0 @@ -============== -Using 'screen' -============== - -:Author: Rosangela Canino-Koning -:Date: June 9, 2011 -:Last Updated: July 24, 2013 - -Persistent Sessions -------------------- - -Screen is a window manager for terminal sessions. Screen allows you to -run a terminal session, and then disconnect from the computer, and be -able to return to the session at a later date. - -To start screen, you run the screen command with a few options:: - - screen -S - -Where *sessionname* is any meaningful or descriptive title for your screen -session. This creates an independent terminal session, and connects you to it. - -Most commands within screen are composed of a prefix key-stroke, -followed by a command character. By default, the prefix is Ctrl-A. In -this tutorial Ctrl-A will represented by "C-a". - -Let's try a few screen commands. - -To disconnect from the session (while leaving it running!), type:: - - C-a d - -This session will remain active until you choose to end it, or you -reboot the computer. You can at this point safely disconnect from SSH, -and the screen session will continue to run. - -To reconnect to the session, make sure you're logged into the UNIX machine, -and type:: - - screen -r - -To illustrate managing multiple screen session, disconnect from the current -session, and create a new session with a second name.:: - - C-a d - screen -S - -Disconnect from the second session, and then list the available sessions:: - - C-a d - screen -list - -Note, typing *screen -r* with multiple active screen sessions will display -the same information. - -To reconnect to the first session, include its name after the -r.:: - - screen -r