here - TCDB
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1. That s it Get a Family abbreviation from a TC Family digit Names tcdb Names abvr Names get_ family _abr 2 A 1 Contains abrv Get potential substrate from TCID This feature is Still in beta mode so there may be errors in Substrate determination Substrates tcdb Substrates molecules Substrates get_tcid_substrates 2 A 1 1 1 molecules contains a list of potential substrates2. Optional Settings min MIN max MAX flank F_LENGTH method METHOD VSrestrict SSET VTrestrict TSET consecutive fasta_only threads THREADS Vamsee Reddy show program s version number and exit show this help message and exit Path to your subject fasta file Repeat Size Output Name Minimum TMS Requirement Maximum TMS Requirement Size of hydrophilic padding around TMSs 10 aa 1 horizontal 2 Vertical 3 Both Vertical Subject Restriction Vertical Target Restriction Includes only consecutive targets r TMSs apart Build ONLY Fasta DB s No TMS Searching Number of threads lt default 16 gt AncientRep AR is a dense program with lots of features An analysis can be very computationally intensive therefore the program will use all processors by default AR includes two programs for extracting results and will be described further into this tutorial Results can be viewed and analyzed before AncientRep is entirely finished running The first part of this tutorial will describe how to begin searching for repeats The next part of this tutorial will explain how to interpret results Before working through this guide please read The Bio V Suite paper Reddy amp Saier 2012 In specific read the section on AR It is important that you understand how AR works and are familiar with some of the jargon used when discussing this tool Step 1 Obtain a FASTA List This can be done using the protocol1 pr
3. When prompted for the original FASTA file enter the file path or just drag amp drop the original FASTA file that was given to AR When this optimization process is complete two sequences will be returned These are your repeat units If despite lowering your min_sd value no good results were found then you move onto the vertical search results Assuming we used an r value of 2 with a 6 TMS protein the following folder should exist 1 2 2 3 3 4 4 5 amp 5 6 Start at the lowest TMSs Type into your shell cd 1 2 Once you have entered that folder repeat the exact same process as you did for analyzing horizontal repeats The only difference is that the file to analyze is called vertical txt not horizontal txt Programmatic Interface with TCDB This section will instruct you on how to use the TCDB module to interact with the TCDB org website by example First we start by importing the module named tcdb py import tcdb How to retrieve a FASTA from TCDB using an ACC fasta tcdb acc2fasta MYACC Returns a FASTA Get a list of accessions or SeqIO Object of FASTAS to define a family accs tcdb define family 2 A 1 list of accs fastas tcdb define family 2 A 1 True SeqIO object Use a local copy of the TCDB BLAST DB Including this Line will automatically update old DB s over 5 days and Will build the DB with BLAST File will always be found In HOME db tcdb tcdb use_local
4. amp o The optional settings are denoted with a double dash The subject amp target settings are used to annotate sequences within the report These are useful incase you forget what you were originally trying to compare The assign setting is equivalent to the one found in the TSS program The shuffle setting is 500 by default and determines how many shuffles GSAT should perform for each alignment The last two settings are stms and ttms Both of these must be set for either of them to work For example if both of these were set to 12 then the report generated will only deal with proteins containing 12 TMSs Example Usage We have two fasta files subject faa target faa that were called MFS amp Gap Junction respectively that we are comparing We are only interested in MFS proteins with 12 TMSs and Gap Junction protiens with 4 TMSs Here is the command to run for this scenario protocol2 py s subject faa t target faa o comparison subject MFS target Gap Junction Programmatic Usage Protocol2 may also be accessed within programs Doing this will allow a user to generate HTML reports from command line import protocol2 compare protocol2 Compare compare subject file mysubjects faa compare target_file mytargets faa compare outdir path to outdir compare shuffle 500 compare assign 3 compare subject_name MFS compare target_name
5. 8 When to use threads This option is for advanced users By default AR uses the maximum number of threads available to the machine This corresponds to the number of processors the machine has There is no benefit to lowering the default value unless you need to reserve x number of processors for another task Example ancient py i myfastas faa o myoutput r 2 min 6 max 6 threads 5 This is all the information you will need to get AR running The rest of this tutorial will explain how to interpret your AR results Interpreting Results Understanding AR is straight forward once you are familiar with all the ropes There will be a lot of results so the trick is to find results with good SD scores 10 SDs with low percent gaps typically less than 15 These standards are not set in stone An acceptable percent gaps can fluctuate greatly It depends on where the gaps are If the gaps are in the hydrophobic region it is likely that the TMSs being compared are not aligning If it is in the hydrophilic portion then there is a chance that the actual TMSs are aligned and you have found a potential repeat The first results to examine are the horizontal results From your terminal type cd OUTPUTFOLDER and replace OUTPUTFOLDER with the directory you originally wrote Type Ils to see the contents of the folder There should be a file called horizontal txt and several folders with numbers in them Use the readlive py t
6. Junctional Family compare srestrict 12 compare trestrict 6 compare Results found in specified outdir TSS Targeted Smith Waterman Search Filename tss py Description TSSearch is a heuristic Smith amp Waterman search tool that will rapidly find close amp distant homologs between two FASTA files Basic Usage Simply type tss py into the shell to retrieve a list of options Usage tss py options TSSearch is a heuristic Smith amp Waterman search tool that will rapidly find close amp distant homologs between two FASTA files Options h help show this help message and exit s SUBJECT Path to your subject fasta file t TARGET Path to your target fasta file o OUTPUT Output file to create m MAX TARGETS Targets per subject Optional Default 3 r RANDOM Number of times to shuffle each sequence Optional Default 300 Typically the default settings are fine for most scenarios The only settings that are mandatory are subject target amp output For example If a user has two fasta files subjects faa targets faa in their current working directory the syntax for executing a TSS comparison would look like tss py s subjects faa t targets faa o compared The output file will be a tab separated text file containing the subject key target key and z score TSS Programmatic access TSS can be executed within other python scripts A benefit of the TSS wrapper is there is
7. are in one of your PATH folders Also remember to define HMMTOP_PSV and HMMTOP_ARCH in your profile to point to both of HAMTOP s dependencies If you did everything correctly you should be able to execute any BioV program from your shell from any working directory TMStats Filename tmstats py Description Tabulate and generate a bar graph of TMSs and their occurrences within a FASTA file or TC hierarchy Basic Usage tmstats py Usage tmstats py lt fasta_file gt lt title gt lt output gt Where fasta_file is the path to your list of FASTAs Title is the text to display on the graph Output is the file path to write to Example usage tmstats py mfs faa MFS TMS distribution myoutput png To analyze a TC hierarchy use the web based version of TMStats TCDB is frequently updated so this is the best solution TMStats Web can be found here http www tcdb org progs tool tmstats To use the web based version simply enter any TC number You many enter multiple TC numbers just separate by commas Use the symbol to denote a wildcard in the TCID For example to get stats on all the MFS proteins 2 A 1 you would enter 2 A 1 Or to get info on the first TCID of each cluster within the MFS family you would enter 2 A 1 3 1 To combine results of MFS amp APC you would enter 2 A 1 3 2 A 3 Programmatic Access You can interface with the TMStats program within python scripts Here is
8. will build a TinySeq XML format file and araw FASTA file when provided with a list of gi numbers In this example we will use protocol to generate a list of gis and then pass this list to our helper function within blast py import protocoll import blast psi protocoll psi psi query Q7VW14 psi expect 0 005 psi results 500 Result numbers must be in this list 10 50 100 250 500 1000 5000 10000 20000 psi init_blast psi iterate psi iterate psi iterate This will perform 3 additional iterations psi fetch_results Done now generate fasta file with blast tools blast_tool blast tools blast_tool gis protocoll gis Link our gi list blast_tool build_xml Build TinySeq file Handle can be found in blast tool xml_file blast_tool build_raw_fasta Generate raw FASTA file FASTA handle found in blast_tool raw_fasta Both files are temporary handles They will be lost when the program exits The next 3 lines demonstrate how to save it from memory to disk import shutil shutil copy blast_tool xml_ file name TinySeq xml shutil copy blast_tool raw_fasta name myfasta faa DefineFamily Filename define_family py Description Generate FASTA files to represent any TC Family Basic Usage type into the shell define_family py Usage define_family py FAMILY lt P PSI gt OUTPUT FAMILY can be any 3 unit TC ID For example 2 A 1 Select P or PSI This correspon
9. BioV Documentation A set of programs designed to aid research in the field of transport protein evolution vreddy ucalgary ca http biov tcdb org Installation The BioV suite has been tested on Ubuntu 10 and OS X Leopard and OS X Lion However this suite should run on virtually any unix based operating system Before we begin please ensure you have the following dependencies installed Python 2 7 BLAST Latest EMBOSS Package Matplotlib BioPython FASTA Package Mechanize HMMTOP All of these packages are available from most unix installation managers except FASTA package amp HMMTOP It is highly recommended that you install them using a repo manager IE apt get or darwin ports Once all of these programs are installed properly and can be accessed from the shell exist in the PATH variable download the BioV suite here Extract the folder contents to any folder on your machine Add this folder path to your PATH directory and to your PYTHONPATH directory To do this you will need to edit your profile file This path varies in operating systems For OS X it is found in bash_profile And in Ubuntu it is bashrc All BioV scripts have a shebang line pointing to usr local bin python If your installation is somewhere else you should make a dynamic link to this path Most errors arise from an incorrectly installed copy of HAMTOP and SSearch36 FASTA Tools Make sure these executable
10. an example import tmstats fasta path to fasta faa label My bargraph output graph png tabs tms tmstats calculate fasta label output output written to graph png tabs contains raw statistics tms contains TMS locations GSAT Global Sequence Alignment Tool Filename gsat py Description Perform a shuffle based alignment on two sequences Basic Usage From the terminal type gsat py Enter the A sequence and the B sequence when prompted Make sure each sequence contains no line breaks By default this approach uses several default values that have been determined as the optimal settings for many transport proteins These settings are Gap Open Penalty 8 Gap Extension Penalty 2 and 500 random shuffles This should finish instantly and return a report with your alignment and the standard score z score at the very bottom Alternatively this program can be run from the official TCDB website using this link http tcdb org progs tool gsat Using GSAT in a programmatic context The best way to showcase all of GSAT s features is by example Here is a sample code with commentary import gsat Import the package gs gsat cmd Initialize our GSAT object gs asequence ABCD First sequence to compare gs bsequence VBSC Second sequence to compare gs gapopen 8 NW Gap open cost default 8 gs gapextend 2 NW Gap extend cost default 2 gs sh
11. ds to BLASTP or PSI BLAST respectively The last option is OUTPUT This is the FASTA file that will be created For this example we will generate a FASTA file for 2 A 1 using PSI blast and write it to Family faa define family py 2 A 1 PSI Family faa Protocol2 Filename protocol2 py Description Find homologs between two FASTA files amp generate graphical reports Basic Usage In the terminal type protocol2 py Usage protocol2 py options Welcome to Protocol2 This tool will allow you to rapidly locate homologs between two fasta files Subject amp Target are used to label items on mandatory options Subject Target Outdir are the only your actual report EX protocol2 s 2 A 1 faa t 2 A 3 faa o mydir subject APC Options version h help s SUBJECT t TARGET o OUTDIR subject SNAME target TNAME assign NUM shuffle RAND stms SRESTRICT ttms TRESTRICT target MFS Developed by Vamsee Reddy show program s version number and exit show this help message and exit Path to subject file Path to target file Path to output Subject Name to appear on report Target Name to appear on report TSS Setting Number of targets to assign each subject Number of times to shuffle each alignment with GSAT Report will contain X TMS subjects TTMS must be set to work Report will contain X TMS targets work STMS must be set to The only mandatory settings are s t
12. esponds to 40 100 identity A value of 1 will remove only duplicate sequences For more information read the cd hit user manual Below are all the settings that can be applied to Protocol1 The lines in BOLD are applied by default unless explicitly specified otherwise Options version show program s version number and exit h help show this help message and exit q QUERY Gi Accession Sequence to BLAST i ITERATE Number of additional iterations to perform 0 n NUMBER Number of results to fetch each round 500 e EXPECT E Value cutoff 0 005 c CUTOFF CD HIT threshold From 0 4 1 0 8 o OUTDIR Output folder plout tms Include this flag to tabulate TMS stats min MIN Minimum sequence lengths to retrieve max MAX Maximum sequence lengths to retrieve Advanced usage If a user wants to generate a FASTA list of Q7VW14 from a total of 3 iterations Has a blast expectation value of 0 0003 and wants tabulated TMS stats In addition the user wants the retrieved sequences to be between 300 350 aas long In addition the user wants 1000 results or less Here is the command for this circumstance protocoll py q Q7VW14 i 3 n 1000 e 0 0003 tms min 300 max 300 When this is done your files will be in the plout folder by default Protocol1 programmatic access Protocol1 can be accessed within other python scripts However it also requires the aid of a helper module called blast py Blast py
13. mbinations Step 6 When to use the consecutive flag The way AR works is as follows the subject frame selects r TMSs from the beginning of a protein For this example we will use an r value of 2 The subject frame selects TMSs 1 2 The target frame will select 3 4 and will continue to slide in increments of 1 until it reaches the end of the protein Ultimately TMSs 1 2 will be compared to 3 4 4 5 5 6 If the consecutive flag is enabled the target frame will slide in increments of r 2 With this flag enabled TMSs 1 2 will be compared to 3 4 5 6 Using the consecutive flag will increase the speed of AR However this should only be used if you are confident in your r size and understand that the family of transporters you are working with is well conserved Many proteins may have experienced some type of insertion or deletion after the repeat event These events will make the consecutive flag unreliable Here is an example command for the consecutive flag ancient py i myfastas faa o myoutput r 2 min 6 max 6 consecutive Step 7 When to use the fasta_only flag If this flag is included at the end of any command AR will just built a FASTA database of all the individual TMSs without actually performing any comparisons This is useful if you need to build a BLAST DB that consists of only TMSs Example ancient py i myfastas faa o myoutput r 2 min 6 max 6 fasta_only Step
14. n you believe that two sets of TMSs are homologous and don t want to conduct an exhaustive search this can save time Such foresight can be derived from interpreting hydropathy plots For the next few examples we will be looking at a hypothetical six TMS protein Scenario 1 We are certain that TMS 1 amp 2 gave rise to 3 amp 4 We want to restrict the subject VSRestrict to 1 amp 2 and restrict the target VTRestrict to 3 amp 4 Here is an example command with this section in bold ancient py i myfastas faa o myoutput r 2 min 6 max 6 vSRestrict 1 2 VTRestrict 3 4 Scenario 2 We want to only compare TMSs 1 2 with 3 4 amp 5 6 ancient py i myfastas faa o myoutput r 2 min 6 max 6 vSRestrict 1 2 VTRestrict 3 4 5 6 Scenario 3 We only care about which TMSs gave rise to 4 5 This will compare ALL TMSs before TMSs 4 5 to these positions ancient py i myfastas faa o myoutput r 2 min 6 max 6 vTRestrict 4 5 Scenario 4 You have identified TMSs 3 4 as a precursor for something else This command will compare TMSs 3 amp 4 to all the TMSs after this pair Pay attention to the difference with Scenario 3 ancient py i myfastas faa o myoutput r 2 min 6 max 6 vSRestrict 3 4 Keep in mind it is not necessary to set any of these vertical restrictions Leaving these options blank will instruct the program to perform all logical TMS co
15. no need to parse the TSV file A list of tuples can be read directly Below is an example usage of the TSS script import tss compare tss compare compare subject mysubjects faa compare target mytargets faa compare max 3 compare shuffle 300 compare results are found in compare results SSearch Smith Waterman Search Filename ssearch py Description SSearch is very much like TSS but will do shuffle based alignments for every combination of subjects amp targets Basic Usage In the terminal type ssearch py Usage ssearch py options SSearch will compare every sequence in a subject file to every sequence ina Another target file by using a Smith Waterman search Results are returned after each pair is shuffled R number of times and a Z Score is returned to determine the quality of the alignment Options h help show this help message and exit s SUBJECT Path to your subject fasta file t TARGET Path to your target fasta file r RANDOM Number of times to shuffle each sequence Optional Default 300 o OUTPUT Filepath To write output Example usage We have the exact same variables as the TSS programs ssearch py s subjects faa t targets faa o compared Programmatic Usage Programmatic access for SSearch is only slightly different than TSS However they return the same output format import ssearch compare ssearch Compare compare subject mysubjects faa com
16. ogram or the DefineFamily program Step 2 Now you must select a repeat size By looking at hydropathy plots of representative proteins perhaps you should have a theory as to what the repeat size is If your prediction is wrong the data is still valuable The results will often reveal the true repeat size In reality all repeat size analyses regardless if they are correct or incorrect should all point to the same true repeat Just make sure this is an integer value greater than 1 Step 3 It is highly recommended that you set the min amp max settings Ideally these should be the SAME number Failure to set these can result in many false positive results and will make it difficult to interpret the results For example if you are working with the MFS family set these both to 12 This setting will only apply to the vertical search method Step 4 For almost all searches the default flank size is sufficient By default AR uses 10aa padding It is best to perform a vertical and horizontal search By default AR does both Horizontal results are more convincing when available Horizontal searches will reveal recent TMS repeats The vertical approach will find very old ancient repeats Unless you are very confident in what you expect to find it is best to leave these settings untouched Step 5 Using VSRestrict amp VTRestrict These two settings allow you to choose exactly which TMSs you want to compare If for whatever reaso
17. ool to display your results The format for executing the readlive py script is as follows readlive py lt filename gt lt all consec gt lt min_sd gt Filename corresponds to the file we are analyzing In this case it is horizontal txt The next parameter is all or consec Choosing consec will make sure that the target TMSs are consecutive integers to the subject TMSs compared For example the script will retrieve comparisons between TMSs 1 2 vs 3 4 and TMSs 3 4 vs 5 6 It would never retrieve TMSs 1 2 vs 4 5 Consecutive results almost always make evolutionary sense However no data should be ignored The final parameter is min_sd This is the minimum SD scores the program will retrieve This is the full command to run readlive py horizontal txt all 10 If no results are returned try lowering the min_sd value by increments of 1 until results are obtained When results are available they will be sorted by SD and gaps with the best scores displayed at the bottom Scroll up until you find a potential repeat To view the actual alignment copy the entire line to the clipboard Then type into the shell show_alignment py When prompted paste the line into the terminal and hit enter The full alignment should be displayed Verify that none of the TMSs are gapped out If you are happy with the alignment then optimize it This will increase decrease the flank size until the alignment returns the highest SD
18. pare target mytargets faa compare shuffle 300 compare results are found in compare results GBlast Filename gblast py Description Identify transport proteins from an entire proteome file Basic usage from the terminal type gblast3 py Usage gblast py options Welcome to GBlast Easily identify transporters in entire Genomes Proteomes By Vamsee Reddy Options version show program s version number and exit h help show this help message and exit i INPUT Path to genome proteome file o OUTPUT Results output name cdd Include CDD analysis Takes a while This program is very straightforward to use All you need is a FASTA file to analyze GBlast can resume incomplete an analysis as well Including the cdd flag will fetch any overlapping conserved domains as identified by NCBI s CDD tool Including this option will take a long time to complete Example usage gblast3 py i ICH fsa o analysis cdd Filename ancient py AncientRep Description Find TMS Repeats within a list of homologues Usage Type into the terminal ancient py Usage ancient options This is the Ancient program This tool will find very old and new TMS repeat units Results can be viewed as they become available by using the readlive py command Alignments can be viewed using the show_alignment py command This tool was created by Options version h help i INPUT r REPEAT o OUTPUT
19. uffles 500 Number of shuffles to perform default gs Begins the actual process Now that GSAT has run lets extract some data Grab just the needle outfile content This file contains alignment stats for just ONE shuffle gs outfile seek 0 outfile contains the handle rewind it first print gs outfile read Grab calculated data print gs zscore Prints the z score rounded to the nearest whole number print gs zscorep Same as above but not rounded Print gs average Average NW bit score from shuffles print gs gaps prints the percentage of gaps in alignment Protocol 1 Filename protocol1 py Description Perform NCBI PSI Blasts with iterations remotely without downloading a local database In addition one may annotate and tabulate statistics about a generated dataset Basic Usage From the terminal type protocol1 py You will be prompted for an accession number or a gi number You may also enter a sequence to BLAST but make sure it is without the header and contains no line breaks Next you are prompted to count the TMSs This will generate an additional bar chart with the number of TMSs and their occurrences The next prompt is the output path This can be anything and protocol will generate this folder containing your data Finally you will be prompted for a cd hit cutoff value This will remove sequences that are too similar to others in the population This can be a value from 0 4 1 This corr
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