Pre-Instructions for Proteomics Bioinformatics session Optional things that you can do before the Proteomics session of the Bioinformatics Course:

Transcription

1 Pre-Instructions for Proteomics Bioinformatics session Optional things that you can do before the Proteomics session of the Bioinformatics Course: (doing the things below ahead of time will ensure that you are able to do the exercises during class - several of the datasets are large and will take time to download from the course site, and if you have a Windows-based computer you may want to pre-install a copy of the Python programming language in order to be able to do some of the optional exercises (Python is pre-installed on all modern Mac's). 1). A LARGE set of initial Proteomic results is included in the OptionalFiles.zip file which can be downloaded - the file is called teaching3.group. An accompanying additional Excel analysis sheet is also included in the OptionalFiles.zip to download (PDST3.xlsx) both takes a long time to load AND should be pre-tested with your computer and Excel version - it is very large and may stretch your computer's resources too thin, or not load if you are running an older version of Excel and have not installed the Microsoft converters to be able to use this type of file on your system. IT IS NOT NECESSARY TO LOAD THESE FILES UNLESS YOU WANT TO TRY TO LOOK AT THEM ON YOUR COMPUTER I will show screenshots of everything we need to see. However, these instructions also pertain to using later Excel sheets from your own experiments, so if interested, please read below and as desired: a). Download the files from the html site b). Start Excel, and TURN OFF Automatic Recalculation (set Recalculation to MANUAL) and turn OFF Autosave - the file is very large and has many tabs, and if Automatic Recalculation is not turned off, every time you change data in a single cell, Excel will take many seconds to

2 minutes (depending on your system resources) to recalculate the entire sheet, stopping you in your tracks. c). Load the file into Excel - please have this already loaded before the start of class, as it can take many seconds to several minutes to load, again depending on your system resources. d). If the file is too large for your system or slows it to a crawl, we will be going over certain aspects of the spreadsheet on the screen, so you can follow along on the projector screen as much as possible. 2). (Windows XP or Windows 7 only, will not work on Macs or Windows Vista, sorry) For Bioinformatic visualization of the teaching3.group results, download and install the ProteinPilot 4.0 Trial Version, the full version of which produces the.group file, at Although this is not clearly explained on the ABSciex web site, installing the trial version and simply not entering any activation code will allow you to use the program as a viewer and look at your results in any proteomic results.group file (you can also download the trial and enter the 30-day activation code they send you - at the end of the trial period, the software will still continue to function as a viewer). Using the ProteinPilot program as a.group file viewer, for example, you can see color-coded indications of which proteins have significantly changed between different samples, see individual peptide MS/MS spectra and how good the match for each peptide was, etc.. 3). Go to the GenePattern page at and "Click to register" so that you can log on when we use these pages for a class exercise:

3 4). Several of the exercises we will be looking at use small scripts written in Python, an excellent general purpose programming language for small-tomedium file manipulation, input/output, and other tasks necessary for many aspects of Bioinformatics, particularly data preparation. Note that there are two "series" of Python at the moment - we will use the stable "2. series", as the more recently released "3. series" changes the way some things are done and affects how well some existing scripts run. (a) For Mac users, the Python programming language is pre-installed (if you type 'python<enter>' without the quotation marks into a terminal window, you should see the version number, usually 2.6.1, and see a different prompt appear ">>>" indicating that you are working in Python instead of in the UNIX BASH shell. Type Control-d or quit()<enter> to leave Python and get back to the BASH shell.) (b) For Windows users, you will need to install Python for your system from On the left side of that page, click the Windows Installer link under the Quicklinks (2.7.1) area (see image below):

4 Ideally you should know before class how to start the Python "IDLE" window on your computer if you want to try any of the exercises. To use the built-in Mac Python, you can invoke IDLE from terminal, by typing "python /System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/

5 idlelib/idle.py<enter>" - you may have to change the "2.6" number if you have a different version number of Python (type "python<enter>" alone in Terminal to see version number), but the path should otherwise be very similar. (if you have installed the Python programs, IDLE is inside the installed Python folder, see examples below):

6 4). Get the Python scripts and create files and folders needed (Mac- and Windows-users) (a). For ease of finding all of these scripts later, create a folder at the top level of your directories called "Python_Scripts" using the underscore character rather than a space (for Python and other programming (e.g., in the UNIX BASH shell command line), spaces in pathnames can work, but often cause confusion so are best avoided by substituting w underscore "_"). Note that the actual folder name and location is not crucial, but for class purposes it is easier to have a fixed known name/location.

7 On a Mac, this is done by clicking your home directory (house icon next to your username), see example screenshot below: On a Windows machine, you create the folder at the C: drive level, e.g., C:\Python_Scripts (example below):

8 (b). Inside that folder, create a subfolder called "latest_nr_db" (again, note underscores rather than spaces in folder names. (c). From wnload.html, download Phil Wilmarth's useful 2010 "FASTA Utilities" and associated documents into the just created "Python_Scripts" folder above. Unzip the downloaded zipfile, which should create a subfolder "fasta_utilities" inside the "Python_Scripts" folder. (d). from the course webpage Optional_Files at download the file "absciex_contaminantdb_ fasta" and put it into the "Python_Scripts" folder; download the files "smallfile.fasta.gz" "gi_taxid_prot.dmp.gz" "nr_fasta_analyze.txt" "gi_to_taxon_pickle.txt" and "taxdump.tar.gz" and put them into the "latest_nr_db" subfolder you created above (these are all files that would be created automatically if you ran the optional "nr_get_analyze.py" Python script below in (e).). (e). (OPTIONAL) For times sake, we may be using a smaller protein database subset "smallfile.fasta.gz" that I extracted from the full NCBInr nr_db of April 13, the full dataset contains all known protein sequences from every species (over 13 million protein sequences, and the gzipped file that one downloads takes up 4-5 GB of disk space). Assuming you have sufficient free disk space and minutes to let the program run and download the full current NCBInr protein sequence dataset, as an optional pre-class exercise (and to pre-test your Python installation so that you are sure you can use Python during class), you can (i) start IDLE in Python as described above; (ii) from within the IDLE window, Open the Python script

9 "nr_get_analyze.py" in the "fasta_utilities" folder: The window that opens (containing this or any other script you open in Python) can be used to EDIT the script to make any necessary or desired changes, or to RUN the script (see below) (iii). Once the window opens with the "nr_get_analyze.py" script in it, choose "Run" from the Menu bar, and then select the "latest_nr_db" folder to install the databases to: (iv). Go get several cups of coffee, take a walk, talk to your family, or otherwise plan for a long time away from your computer (at least 30-60

10 minutes) while the script does its magic and downloads all necessary files and analyzes the entire database (how many species are represented and how many protein sequences there are for each species, how many of these sequences are the more highly-curated Reference Sequences (RefSeq - we will discuss this in class). If you have the disk space and the time, this is very useful information to have at hand, and would often take a long time to find manually if you are interested in more than one or two. In class we will use the "nr_extract_taxon_bas.py" script to extract and create smaller individual species RefSeq databases from this large database, for any species of interest (for time's sake in class, we will use the smaller "smallfile.fasta.gz" subset of this database, containing all the protein sequences from Human, Rat, Mouse, Yeast, Cow, Rhesus Monkeys, Drosophila, Arabidopsis, and Rice, the species that we work with most often for proteomic work). (v). The script will place a new subfolder (inside the "latest_nr_db" folder) named "nr_ " containing all the database associated files, with the date of the download in the " " portion. Do NOT unzip any of these files, the Python scripts we will be using can use the gzipped files directly without expanding. Once the downloads and analyses are complete, you SHOULD MOVE all of the files from within this folder up one level to the "latest_nr_db" folder, since that is where the other Python scripts we will be using expects to find them.