text:
SLiMSuite species codes are designed to follow the UniprotKB organism (species) identification codes,
using them wherever possible. They form part of the standard
gene_SPECIES__AccNum naming convention for sequences within
SLiMSuite. Species codes should be upper case, and unique for each species.
SLiMSuite Short Linear Motif discovery and analysis
Open source bioinformatics for the prediction of short linear motifs (SLiMs) and related sequence analysis.
Friday, 27 January 2017
SLiMSuite species codes
SLiMSuite species codes are designed to follow the UniprotKB organism (species) identification codes,
using them wherever possible. They form part of the standard
Tuesday, 11 October 2016
Odd blog behaviour
For some reason, the Download blog page is not working properly. Until rectified, please check the downloads tag if a link to SLiMSuite downloads breaks.
Update: this should be fixed now!
Monday, 12 September 2016
SLiMSuite Downloads
The current SLiMSuite release is
2016-09-12 and can be downloaded by clicking the button (left).
In addition to the tarball available via the links above, SLiMSuite is now available as a GitHub repository (right).
See also: Installation and Setup.
Previous Releases
SLiMSuite release v1.2.0 (2016-09-12) online
The long-overdue September 2016 release of SLiMSuite
2016-09-12 -
v1.2.0 is now on GitHub. Apart from a few bug fixes, the main updates in this release are to the tools for PacBio genomics, notably
SMRTSCAPE and a new SNP Mapping tool,
Snapper. These are still in development and need further documentation but are ready for use with a little help. Please get in touch if you are interested. Proper documentation and example use will hopefully follow soon, as the first PacBio yeast paper is written.
GABLAM has had some minor tweaks for improved function with
PAGSAT and another developmental tool that will be in the next release (
REVERT - available via the REST servers). These have been focused on the
fragfas=T output of fragmented BLAST hits based on local alignments. This includes addition of a new default to reverse complement reverse hits (
fragrevcomp=T) and the separation of parameters for splitting up local hits into multiple fragments (
gablamfrag=X) and merging close/overlapping fragments (
SLiMSuite updates in this release
Updates in extras/:
• rje_pydocs: Updated from Version 2.16.2.
→ Version 2.16.3: Fixed docstring REST parsing to work with _V* modules.
Updates in libraries/:
• rje: Updated from Version 4.15.1.
→ Version 4.16.0: Added list2dict(inlist,inkeys) and dict2list(indict,inkeys) functions.
→ Version 4.16.1: Improved handling of integer parameters when given bad commands.
→ Version 4.17.0: Added extra functions to randomList()
• rje_blast_V2: Updated from Version 2.9.1.
→ Version 2.10.0: Added nocoverage calculation based on local alignment table.
→ Version 2.11.0: Added localFragFas output method.
→ Version 2.11.1: Fixed snp local table revcomp bug. [Check this!]
→ Version 2.11.2: Fixed GABLAM calculation bug when '*' in protein sequences.
• rje_db: Updated from Version 1.8.0.
→ Version 1.8.1: Added sfdict to saveTable output.
• rje_genbank: Updated from Version 1.3.2.
→ Version 1.4.0: Added addtags=T/F : Add locus_tag identifiers if missing - needed for gene/cds/prot fasta output [False]
→ Version 1.4.1: Fixed genetic code warning.
→ Version 1.5.0: Added setupRefGenome() method based on PAGSAT code.
→ Version 1.5.1: Fixed logskip append locus sequence file bug.
→ Version 1.5.2: Fixed addtag(s) bug.
• rje_hprd: Updated from Version 1.2.
→ Version 1.2.1: Fixed "PROTEIN_ARCHITECTURE" bug.
• rje_menu: Updated from Version 0.3.
→ Version 0.4.0: Changed handling of default for exiting menu loop. May affect behaviour of some existing menus.
• rje_mitab: Updated from Version 0.2.0.
→ Version 0.2.1: Fixed redundant evidence/itype bug (primarily dip)
• rje_obj: Updated from Version 2.1.3.
→ Version 2.2.0: Added screenwrap=X.
→ Version 2.2.1: Improved handling of integer parameters when given bad commands.
• rje_samtools: Updated from Version 0.1.0.
→ Version 0.2.0: Added majmut=T/F : Whether to restrict output and stats to positions with non-reference Major Allele [False]
→ Version 1.0.0: Major reworking. Old version frozen as rje_samtools_V0.
→ Version 1.1.0: Added snptabmap=X,Y alternative SNPTable mapping and read_depth statistics []. Added majref=T/F.
→ Version 1.2.0: Added developmental combining of read mapping onto two different genomes.
→ Version 1.3.0: Major debugging and code clean up.
→ Version 1.4.0: Added parsing of read number (to link SNPs) and fixed deletion error at same time. Added rid=T/F and snponly=T/F.
→ Version 1.5.0: Added biallelic=T/F : Whether to restrict SNPs to pure biallelic SNPs (two alleles meeting mincut) [False]
→ Version 1.5.1: Fixed REF/Ref ALT/Alt bug.
→ Version 1.6.0: Added majfocus=T/F : Whether the focus is on Major Alleles (True) or Mutant/Reference Alleles (False) [True]
→ Version 1.7.0: Added parsing of *.sam files for generating RID table.
→ Version 1.8.0: Added read coverage summary/checks.
→ Version 1.8.1: Fixed issue when RID file not generated by pileup parsing. Set RID=True by default to avoid issues.
• rje_samtools_V0: Created/Renamed/moved.
→ Version 0.0: Initial Compilation.
→ Version 0.1.0: Modified version to handle multiple loci per file. (Original was for single bacterial chromosomes.)
→ Version 0.2.0: Added majmut=T/F : Whether to restrict output and stats to positions with non-reference Major Allele [False]
• rje_seq: Updated from Version 3.23.0.
→ Version 3.24.0: Added REST seqout output.
• rje_seqlist: Updated from Version 1.15.3.
→ Version 1.15.4: Fixed REST server output bug.
→ Version 1.15.5: Fixed reformat=fasta default issue introduced from fixing REST output bug.
→ Version 1.16.0: Added edit=T sequence edit mode upon loading (will switch seqmode=list).
→ Version 1.17.0: Added additional summarise=T output for seqmode=db.
→ Version 1.18.0: Added revcomp to reformat options.
→ Version 1.19.0: Added option log description for deleting sequence during edit.
→ Version 1.20.0: Added option to give a file of changes for edit mode.
→ Version 1.20.1: Fixed edit=FILE deletion bug.
• rje_sequence: Updated from Version 2.5.2.
→ Version 2.5.3: Fixed genetic code warning error.
• rje_slimcore: Updated from Version 2.7.5.
→ Version 2.7.6: Added feature masking log info or warning.
→ Version 2.7.7: Switched feature masking OFF by default to give consistent Uniprot versus FASTA behaviour.
• rje_synteny: Created/Renamed/moved.
→ Version 0.0.0: Initial Compilation.
• rje_taxonomy: Updated from Version 1.1.0.
→ Version 1.2.0: Added storage of Parents.
• rje_tree: Updated from Version 2.13.0.
→ Version 2.14.0: Added cladeSpec().
• rje_uniprot: Updated from Version 3.21.4.
→ Version 3.22.0: Tweaked REST table output.
• rje_xref: Updated from Version 1.8.0.
→ Version 1.8.1: Added rest run mode to avoid XRef table output if no gene ID list is given. Added `genes` and `genelist` as `idlist=LIST` synonym.
→ Version 1.8.2: Catching self.dict['Mapping'] error for REST server.
• snp_mapper: Updated from Version 0.4.0.
→ Version 0.5.0: Added CDS rating.
→ Version 0.6.0: Added AltFT mapping mode (map features to AltLocus and AltPos)
→ Version 0.7.0: Added additional fields for processing Snapper output. (Hopefully will still work for SAMTools etc.)
→ Version 0.8.0: Added parsing of GFF file from Prokka.
→ Version 0.8.1: Corrected "intron" classification for first position of features. Updated FTBest defaults.
→ Version 1.0.0: Version that works with Snapper V1.0.0. Not really designed for standalone running any more.
Updates in tools/:
• comparimotif_V3: Updated from Version 3.12.
→ Version 3.13.0: Added REST server function.
• gablam: Updated from Version 2.20.0.
→ Version 2.21.0: Added nocoverage Table output of regions missing from pairwise SNP Table.
→ Version 2.21.1: Added fragrevcomp=T/F : Whether to reverse-complement DNA fragments that are on reverse strand to query [True]
→ Version 2.22.0: Added description to HitSum table.
→ Version 2.22.1: Added localaln=T/F to keep local alignment sequences in the BLAST local Table.
→ Version 2.22.2: Fixed local output error. (Query/Qry issue - need to fix this and make consistent!)
→ Version 2.22.3: Fixed blastv and blastb error: limit also applies to individual pairwise hits!
→ Version 2.23.0: Divided GablamFrag and FragMerge.
• pagsat: Updated from Version 1.6.1.
→ Version 1.7.0: Added tidy=T/F option. (Development)
→ Version 1.7.1: Updated tidy=T/F to include initial assembly.
→ Version 1.7.2: Fixed some bugs introduced by changing gablam fragment output.
→ Version 1.7.3: Added circularise sequence generation.
→ Version 1.8.0: Added orphan processing and non-chr naming of Reference.
→ Version 1.9.0: Modified the join sorting and merging. Added better tracking of positions when trimming.
→ Version 1.9.1: Added joinmargin=X : Number of extra bases allowed to still be considered an end local BLAST hit [10]
→ Version 1.10.0: Added weighted tree output and removed report warning.
→ Version 1.10.1: Fixed issue related to having Description in GABLAM HitSum tables.
→ Version 1.10.2: Tweaked haploid core output.
→ Version 1.10.3: Fixed tidy bug for RevComp contigs and switched joinsort default to Identity. (Needs testing.)
→ Version 1.10.4: Added genetar option to tidy out genesummary and protsummary output. Incorporated rje_synteny.
→ Version 1.10.5: Set gablamfrag=1 for gene/protein hits.
→ Version 1.11.0: Consolidated automated tidy mode and cleaned up some excess code.
→ Version 1.11.1: Added option for running self-PAGSAT of ctidX contigs versus haploid set. Replaced ctid "X" with "N".
→ Version 1.11.2: Fixed Snapper run choice bug.
• pingu_V4: Updated from Version 4.5.3.
→ Version 4.6.0: Added hubonly=T/F : Whether to restrict pairwise PPI to those with both hub and spoke in hublist [False]
→ Version 4.6.1: Fixed some ppifas=T/F bugs and added combineppi=T/F : Whether to combine all spokes into a single fasta file [False]
→ Version 4.6.2: Added check/filter for multiple SpokeUni pointing to same sequence. (Compilation redundancy mapping failure!)
→ Version 4.6.3: Fixed issue with 1:many SpokeUni:Spoke mappings messing up XHub.
→ Version 4.7.0: Added ppidbreport=T/F : Summary output for PPI compilation of evidence/PPIType/DB overlaps [True]
→ Version 4.8.0: Fixed report duplication issue and added additional summary output
• qslimfinder: Updated from Version 2.1.0.
→ Version 2.1.1: Switched feature masking OFF by default to give consistent Uniprot versus FASTA behaviour.
• seqsuite: Updated from Version 1.11.0.
→ Version 1.11.1: Redirected PacBio to call SMRTSCAPE.
→ Version 1.11.2: Fixed batchrun batchlog=False log error.
→ Version 1.12.0: Added Snapper.
• slimfarmer: Updated from Version 1.4.3.
→ Version 1.4.4: Modified default vmem request to 126GB from 127GB.
→ Version 1.4.5: Updated BLAST loading default to 2.2.31
• slimfinder: Updated from Version 5.2.1.
→ Version 5.2.2: Added warnings for ambocc and minocc that exceed the absolute minima. Updated docstring.
→ Version 5.2.3: Switched feature masking OFF by default to give consistent Uniprot versus FASTA behaviour. Fixed FTMask=T/F bug.
• slimparser: Updated from Version 0.3.3.
→ Version 0.3.4: Tweaked error messages.
→ Version 0.4.0: Added simple json format output.
• slimprob: Updated from Version 2.2.4.
→ Version 2.2.5: Fixed FTMask=T/F bug.
• slimsearch: Updated from Version 1.7.
→ Version 1.7.1: Minor modification to docstring. Preparation for update to SLiMSearch 2.0 optimised for proteome searches.
• slimsuite: Updated from Version 1.5.1.
→ Version 1.5.2: Updated XRef REST call.
→ Version 1.6.0: Removed SLiMCore as default. Default will now show help.
• smrtscape: Updated from Version 1.8.0.
→ Version 1.9.0: Updated empirical preassembly mapefficiency calculation.
→ Version 1.10.0: Added batch processing of subread files.
→ Version 1.10.1: Fixed bug in batch processing.
• snapper: Created/Renamed/moved.
→ Version 0.0.0: Initial Compilation.
→ Version 0.1.0: Tidied up with improved run pickup.
→ Version 0.2.0: Added FASTQ and improved CNV output along with all features.
→ Version 0.2.1: Fixed local output error. (Query/Qry issue - need to fix this and make consistent!) Fixed snp local table revcomp bug.
→ Version 0.2.2: Corrected excess CNV table output (accnum AND shortname).
→ Version 0.2.3: Corrected "intron" classification for first position of features. Updated FTBest defaults.
→ Version 1.0.0: Working version with completed draft manual. Added to SeqSuite.
→ Version 1.0.1: Fixed issues when features missing.
Tuesday, 23 August 2016
SMRTSCAPE: SMRT Subread Coverage & Assembly Parameter Estimator
SMRTSCAPE (SMRT Subread Coverage & Assembly Parameter Estimator) is tool in development as part of our PacBio sequencing projects for predicting and/or assessing the quantity and quality of useable data required/produced for HGAP3 de novo whole genome assembly. The current documentation is below. Some tutorials will be developed in the future - in the meantime, please get in touch if you want to use it and anything isn't clear.
The main functions of
SMRTSCAPE are:
Estimate Genome Coverage and required numbers of SMRT cells given predicted read outputs. NOTE: Default settings for SMRT cell output are not reliable and you should speak to your sequencing provider for up-to-date figures in their hands.
Summarise the amount of sequence data obtained from one or more SMRT cells, including unique coverage (one read per ZMW).
Calculate predicted coverage from subread data for difference length and quality cutoffs.
Predict HGAP3 length and quality settings to achieve a given coverage and accuracy.
SMRTSCAPE will be available in the next SLiMSuite download. The
coverage=T mode can be run from the EdwardsLab server at: http://www.slimsuite.unsw.edu.au/servers/pacbio.php. (This is currently running a slightly old implementation but should be updated shortly.)
SMRTSCAPE Documentation
Version: 1.10.1
Last Edit: 26/05/16
### ~ General Options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
genomesize=X : Genome size (bp) [0]
### ~ Genome Coverage Options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
coverage=T/F : Whether to generate coverage report [False]
avread=X : Average read length (bp) [20000]
smrtreads=X : Average assemble output of a SMRT cell [50000]
smrtunits=X : Units for smrtreads=X (reads/Gb/Mb) [reads]
errperbase=X : Error-rate per base [0.14]
maxcov=X : Maximmum X coverage to calculate [100]
bysmrt=T/F : Whether to output estimated coverage by SMRT cell rather than X coverage [False]
xnlist=LIST : Additional columns giving % sites with coverage >= Xn [1+`minanchorx`->`targetxcov`+`minanchorx`]
### ~ SubRead Summary Options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
summarise=T/F : Generate subread summary statistics including ZMW summary data [False]
seqin=FILE : Subread sequence file for analysis [None]
batch=FILELIST : Batch input of multiple subread fasta files (wildcards allowed) if seqin=None []
targetcov=X : Target percentage coverage for final genome [99.999]
targeterr=X : Target errors per base for preassembly [1/genome size]
calculate=T/F : Calculate X coverage and target X coverage for given seed, anchor + RQ combinations [False]
minanchorx=X : Minimum X coverage for anchor subreads [6]
minreadlen=X : Absolute minimum read length for calculations (use minlen=X to affect summary also) [500]
rq=X,Y : Minimum (X) and maximum (Y) values for read quality cutoffs [0.8,0.9]
rqstep=X : Size of RQ jumps for calculation (min 0.001) [0.01]
### ~ Preassembly Fragmentation analysis Options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
preassembly=FILE: Preassembly fasta file to assess/correct over-fragmentation (use seqin=FILE for subreads) [None]
### ~ Assembly Parameter Options ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
parameters=T/F : Whether to output predicted "best" set of parameters [False]
targetxcov=X : Target 100% X Coverage for pre-assembly [3]
xmargin=X : "Safety margin" inflation of X coverage [1]
mapefficiency=X : [Adv.] Efficiency of mapping anchor subreads onto seed reads for correction [1.0]
xsteplen=X : [Adv.] Size (bp) of increasing coverage steps for calculating required depths of coverage [1e6]
parseparam=FILES: Parse parameter settings from 1+ assembly runs []
paramlist=LIST : List of parameters to retain for parseparam output (file or comma separated, blank=all) []
predict=T/F : Whether to add XCoverage prediction and efficiency estimation from parameters and subreads [False]
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ###
SMRTSCAPE has a number of functions for PacBio sequencing projects, concerned with predicting and/or assessing the quantity and quality of useable data produced:
- Summarise subreads (
summarise=T). This function summarises subread data from a given
seqin=FILEfasta file, or a set of subread fasta files given with
- Calculate length cutoffs (
calculate=T). Calculates length cutoffs for different XCoverage combinations from subread summary data.
- Parameters (
parameters=T). This function attempts to generate predicted optimum assembly settings from the
calculate=Ttable data.
- Genome Coverage (
coverage=T). This method tries to predict genome coverage and accuracy for different depths of PacBio sequencing.
- Preassembly fragmentation analysis (
- Parse Parameters (
parseparam=FILES). This method parses assembly parameters from the SmrtPipeSettings Summary file.
- Prediction (
predict=T). This method compares predicted coverage from seed reads with estimated coverage from preassembly data.
These are explored in more detail below.
The setup phase primarily sorts out the
SMRTSCAPE objects. With the exception of pure
ParseParam runs, it will also set up:
- Genome Size. Unless given with
genomesize=X, this will be asked for as it is required X coverage/depth and accuracy calculations.
- Target Error Rate. Unless given with
targeterr=X, a target error rate of 1/
GenomeSizewill be set.
- Basefile. Finally, if no
basefile=Xsetting is given, the basename for output files will be set to the basename of
seqin=FILEif given (stripping
.subreadsif present), else
Summarise subreads (summarise=T)
This function summarises subread data from a given
seqin=FILE fasta file, or a set of subread fasta files given with
batch=FILELIST. This uses the
summarise=T function of
rje_seqlist to first give text output to the log file of some summary statistics:
- Total number of sequences.
- Total length of sequences.
- Min. length of sequences.
- Max. length of sequences.
- Mean length of sequences.
- Median length of sequences.
- N50 length of sequences.
Next, Pacbio-specific subread header information will be parsed to generate three summary tables (described in more detail below):
*.zmw.tdtsummary of all subreads.
*.unique.tdtsummary of the longest subread per ZMW.
*.rq.tdtsummary of subread data for different Read Quality values.
These are generated by parsing both the sequence data and the sequence name data:
>m150625_001530_42272_c100792502550000001823157609091582_s1_p0/9/0_3967 RQ=0.784
>m150625_001530_42272_c100792502550000001823157609091582_s1_p0/11/0_20195 RQ=0.868
These are split into component parts:
m150625_001530_42272_c100792502550000001823157609091582_s1_p0= SMRT cell (
/0_3967= raw read positions used for subread (5' adaptor removed) (
RQ=0.784= read quality (mean per base accuracy) (
The numbers and "best" subreads for each ZMW are summarised in
#RN log file entries.
Subread output (*.zmw.tdt)
The first output file is the
*.zmw.tdt, which stores the subread information:
RQ= as above.
RN= subread number within ZMW (1 to N).
Len= length of subread.
Seq= sequence file position.
The unique key for this file is:
Unique subread output (*.unique.tdt)
This table is made from
*.zmw.tdt by reducing each ZMW's output to a single read. Reads are kept in preference of (a) longest, (b) read quality (if tied), and finally (c) earliest read (if tied for both). It hase the same fields as
*.zmw.tdt with keys determined by:
Read Quality output (*.rq.tdt)
This table outputs the number and percentage of subreads and longest reads at each read quality (
RQ). Fields:
RQ= read quality (per base accuracy)
xerr= the Xdepth required @ that
RQto meet the
targeterr=Xerror per base accuracy.
subread= number of subreads with that
unique= number of "best" unique subread per ZMW with that
f.subreads= proportion of subreads with that
f.unique= proportion of unique subreads with that
cum.subreads= proportion of subreads with quality >=
cum.unique= proportion of unique subreads with quality >=
x.subreads= XCoverage of subreads with quality >=
x.unique= XCoverage of unqiue subreads with quality >=
MeanRQ= mean read quality of bases in subreads with quality >=
Mean.XErr= the Xdepth required at that
MeanRQto meet the
targeterr=Xerror per base accuracy.
Calculate length cutoffs (calculate=T)
This function calculates length cutoffs for different XCoverage combinations from subread summary data. It uses the
*.unique.tdt and
*.rq.tdt files from above, generating if required (and regenerating if
XCovLimit data are calculated. These are the summed read lengths required to generate the desired genome coverage (
targetcov=X) at different depths of X coverage. Note that the square root of the
targetcov=X value is used, as the HGAP assembly process involves two layers of genome coverage: (1) coverage of seed reads by anchor reads to generate the pre-assembly; (2) coverage of the pre-assembly.
XCovLimit data are calculated by incrementing total summed read lengths in 1 Mb increments (adjusted with
xsteplen=X). At each incremment,
genomesize=X is used to calculate the total X coverage. The probability of the target
X coverage (starting at
1X) given the total X coverage is then calculated using a Poisson distribution. If this probability exceeds the target genome coverage, the current summed length is set as the
XCovLimit for
X and the target
X increased by 1. The total summed read length is then incremented by
xsteplen and the process repeated until the summed length reaches the total length of all subreads of at least the size set by
minreadlen=X (default 500 bp).
Next, the read quality steps to calculate are established using
rq=X,Y (minimum (
X) and maximum (
Y) read quality cutoffs, defaults=0.8-0.9) and
rqstep=X (the size of
RQ steps for calculations, default=0.01 (min 0.001).
Finally, the seed and anchor lengths required to achieve certain minimum Xdepths of target genome coverage are calculated and output to
For each
RQ cutoff,
TargetErr is used to establish the min. required anchor read Xdepth (
AnchorMinX). If this exceeds the specified
minanchorx=X value, this will be used as the minimum target instead. Next, the seed read length (
SeedLen) required to get a combined unique seed read length to give a minimum Xdepth (
SeedMinX) of
targetxcov=X is calculated. Where
xmargin > 0, additional seed lengths will also be calculated to give deeper minimum seed Xdepths. e.g.
targetxcov=3 xmargin=2 will calculate
SeedLen for
4X and
5X. For each
SeedLen, anchor read length cutoffs are also calculated such that the summed length of unique reads where
AnchorLen <= length <
SeedLen is sufficent to give
AnchorMinX values from the minimum established above until
XMargin is reached.
If there is insufficient data to meet the minimum seed and anchor read depths, the "optimal" seed XCoverage will be calculated from the unique seed reads, as described for
coverage=T. In essence,
SeedMinX will be reduced to meet
AnchorMinX until it falls below zero and then the two values will be optimised to try and maximise genome coverage at the required target error. Users may prefer instead to relax the
targetxcov=X and/or
minanchorx=X values. This optimisation is based on unique Xcoverage and the precise values subsequently output in
*.cutoffs.tdt may differ. (Relaxed variations could be run with higher
xmargin=X values to output a range from which the chosen values can be selected for
mapefficiency=X is used during this process to reduce the effective seed coverage at a given summed length and thus inflate the required
SeedX needed to achieve a given
This table contains the main output from the
calculate=T function. Unique entries are determined by combinations of
SeedMinX and
RQ= Read Quality Cutoff.
SeedMinX= Min seed XCoverage for TargetCov % of genome (unique reads). If this is <1, it indicates the proportion of the genome covered at 1X.
SeedLen= Seed length Cutoff.
SeedX= Total Seed read XCoverage (all reads).
AnchorMinX= Min anchor XCoverage for TargetCov % of genome (unique reads). If this is <
minanchorx=X, it indicates the proportion of the genome covered at
AnchorLen= Anchor read (i.e. overall subread) length cutoff.
AnchorX= Total Anchor read XCoverage (all reads).
Preassembly fragmentation analysis (preassembly=FILE)
[ ] : Add preassembly details here.
Optimal Assembly Parameters (parameters=T)
parameters=T function attempts to generate predicted optimum assembly settings from the
summary=T and
calculate=T table data.
- The
*.cutoffs.tdttable is read in (or generated if required).
- The
xmargin=Xsettings are used to determine the desired
SeedMinXvalue, i.e. the miniumum depth of coverage of the chosen seed reads. (NB. The
mapefficiency=Xsetting is used for inflating the required XCoverage needed to meet this Min XDepth during the calculate process.)
- Each potential
RQcutoff is now assessed and the
targeterr=Xsetting used to determine the required Xdepth per base for error correction. Note: This uses the minimum RQ value and should thus be conservative. Using the mean RQ was considered but it was deemed that conservative is best in this scenario.
- The desired
AnchorMinXis calculated from the target Xdepth and
xmargin=X. The final value is reduced by one to allow for the fact that the seed read counts as X=1 for error estimation.
- Parameter combinations have now been reduced to those with the desired Seed and Anchor minimum Xdepth. Two parameter combinations are then output:
- MaxLen parameters using the mininum RQ value and thus the longest seed and anchor read length cutoffs.
- MaxRQ parameters using the maximum RQ value.
Note that the predicted parameter settings are only output as
#PARAM log entries: the full set (including these "optimal" ones) are part of the
*.cutoffs.tdt file.
Genome Coverage (coverage=T)
This method tries to predict genome coverage and accuracy for different depths of PacBio sequencing based on predicted usable output statistics and the genome size. Statistics for existing runs can be generated using the
summarise=T option and used to inform
calculate=T if run together.
Setup. If the
summarise=T option is used and/or there is an existing
BASEFILE.unique.tdt file (and
force=F) then the
*.unique.tdt table will be used to generate SMRT cells statistics: mean read count (used to populate
smrtreads=X); mean total read count (used to populate
avread=X); mean RQ (used to populated
errperbase=X). Otherwise, the corresponding commandline options will be used. If
smrtunits is
Gb or
Mb then
smrtreads=X will be recalculated as
XnList. The second stage of setup is to calculate the %coverage at certain X depth coverage to be calculated along with overall depth of coverage etc. These numbers are based on the subread
summarise and
calculate settings:
targetxcov and
minanchorx. Any levels explicitly chosen by
xnlist=LIST will also be calculated.
TargetXDepth. Next, target Xdepth values are calculated in the same fashion as
XCovLimit data (above), except that these are now converted to Xcoverage (based on
GenomeSize) rather than total subread lengths.
Accuracy. The % genome coverage and accuracy for different X coverage of a genome are then calculated assuming a non-biased error distribution. Calculations use binomial/poisson distributions, assuming independence of sites. Accuracy is based on a majority reads covering a particular base with the correct call, assuming random calls at the other positions (i.e. the correct bases have to exceed 33% of the incorrect positions). The
errperbase=X parameter is used for this calculation.
Coverage. Coverage statistics are then calculated for each Xdepth of sequencing (or SMRT cell if
bysmrt=T) First, the optimal seed read Xcoverage is calculated. The target seed Xdepth (
targetxcov=X) and anchor depth (
minanchorx=X) are used to identify the total target Xcoverage. If this is met (inflating required seed coverage to account for
mapefficiency=X), the largest seedX value that meets the
minanchorx=X anchor depth is selected. If this cannot be achieved with seedX >= 1, the optimal balance between seed length and anchor length is achieved by maximising the probability of 1X seed coverage and
MinAnchorX+ anchor coverage. This seed read length is then used to generate the predicted coverage output.
Main output is the
*.coverage.tdt file. All calculations are based on subreads, and therefore using the "raw" polymerase read data for the
smrtreads=X value for SMRT cells will overestimate coverage. Note that
smrtreads=X can be used to input sequence capacity in Gb (or Mb) rather than read counts by changing
XCoverage= Total mean depth of coverage.
SMRT= Total number of SMRT cells.
%Coverage= Estimated %coverage of assembly.
%Accuracy= Estimated %accuracy of assembled genome. This is established by working out the predicted proportion of the genome at each Xcoverage (given the total
XCoverage) and the accuracy at that depth (as described above).
%Complete= Product of %coverage and %accuracy.
SeedX= Estimated optimal depth of coverage for seed reads.
Parsing assembly parameters (ParseParam=FILES)
This method will take a bunch of
*.settings text files (wildcards allowed) and parse out the assembly parameter settings into a delimited text file. The contents of these files should be consistent with the
Assembly_Metrics_*.xlsx file produced by the SMRT Portal.
Output for this method is a
*.settings.tdt file, which has the following field headers:
Setting. The full setting name, e.g.
Prefix. The setting prefix, e.g.
Suffix. The setting suffix, e.g.
minCorCov. (This is used for some of the
Variable. Whether the parameter is variable ("TRUE") or fixed ("FALSE") in the set of
*.settingsfiles being parsed.
Assemblies. Each assembly (the
*.settings) will get its own field containing the actual value used for that parameter in that assembly.
NOTE: The files coming off SMRT Portal have some undesirable non-unicode characters in them. These are hopefully stripped by
SMRTSCAPE but it is possible that some parameters may not be correctly parsed.
It is possible to parse a selected subset of parameters using
paramlist=LIST. (This is easiest where
LIST is a text file with one parameter per line.) This should be a list of the full parameter name, i.e. the content of the
Setting field.
[ ] : Add the recommended list of parameters here.
Predicting assembly coverage (Predict=T)
This function predicts coverage from parsed assembly parameters and compares to pre-assembly subreads if possible. Its primary function is to check that the parameter settings from
calculate=T are working as expected (at least in terms of preassembly generation) and to tweak the
mapefficiency=X option if required. Where a reference is available, it can also be used to test the make
SRMTSCAPE calculations in terms of coverage etc.
Predict uses data from the
summarise=T and
parseparam=FILES functions. (These will be run if required.) As such, it requires the original subread data (
seqin=FILE) and the list of
*.settings files that identifies the assemblies. (See
ParseParam=FILES.) Pre-assembly
*.preassembly.fasta files should match the
*.settings files. (The file looked for will be identified as a
#PREX log entry.)
If it already exists, the
*.predict.tdt will be loaded and updated. Otherwise a new
*.predict.tdt file will be created. (See below.)
Predict first loads in the relevant data and assembly parameters (see output) before calculating expected coverage from subread data and observed coverage from preassembly data.
Predict output
The output of
Predict mode is a
*.predict.tdt output file with the following fields:
Assembly= The assembly base filename for a given file in
minSubReadLength= parsed
readScore= parsed
minLongReadLength= parsed
minCorCov= parsed
ovlErrorRate= parsed
SeedX= mean depth of coverage for seed reads given seed length and min RQ score.
AnchorX= mean depth of coverage for anchor reads given seed length and min RQ score.
SeedMinX= minimum depth of coverage of unique seed reads to achieve
AnchorMinX= minimum depth of coverage of unique anchor reads to achieve
PreCov= predicted base coverage of pre-assembly.
CorPreCov= corrected predicted base coverage of pre-assembly given
PreX= average depth of coverage of
*.preassembly.fastasequences, given
PreMinX= minimum depth of coverage of
*.preassembly.fastasequences to achieve
SeedXas an estimate of the loss of seed sequence during the preassembly mapping phase. Ideally, this should be close to the
mapefficiency=Xsetting. (NOTE: SMRTSCAPE has not undergone extensive testing of this assumption.
Thursday, 10 December 2015
BioInfoSummer2015 SLiMSuite Workshop
Dr Richard Edwards, University of New South Wales
Thursday 10th December 2015
Session outlineClick for slides.
Part I: Theory
- Introduction to workshop
- What are SLiMs?
- What is SLiMSuite
Part II: Practice
- Installing/running SLiMSuite
- Data types and main input formats
- Motif discovery using the SLiMSuite REST Servers
- Motif discovery using the SLiMScape app for Cytoscape
Additional help and documentation
General information about SLiMs and motif discovery can be found in the literature. Some good places to start are the recent ELM 2016 paper and our 2015 Methods in Molecular Biology review as well as the SLiMScape app paper:
- Motif discovery review paper (PDF)
- SLiMScape app paper (PDF)
- SLiMFinder manual (PDF)
- SLiMProb manual (PDF)
- CompariMotif manual (PDF)
For information about SLiMSuite, please visit the EdwardsLab webpage and the SLiMSuite blog. Help and documentation for the REST servers can also be found at the REST homepage. If in doubt, please email:
Several EdwardsLab publications also cover motifs and SLiMSuite tools.
Installing/Running SLiMSuite
NOTE: For this workshop, you do not need to install SLiMSuite. You will need Cytoscape and the SLiMScape app for the later parts.
The current SLiMSuite release is
2015-11-30 and can be downloaded by clicking the button (left).
In addition to the tarball available via the links above, SLiMSuite is now available as a GitHub repository (right).
See also: Installation and Setup.
For this workshop, we will primarily be running the tools (and looking at pre-generated results) via the online servers:
Data types and main input formats
From a computer science perspective, input and output for SLiMSuite is just plain ASCII text. This makes it easy to plug SLiMSuite into existing scripts and pipelines - and manually view/edit any input or output files if required. However, "plain text" is not very informative, and SLiMSuite actually deals with a lot of different formats of plain text (from a "human formatting" rather than "file type" point of view). The documentation is currently in the process of being updated to better reflect these formats but some commandline options will still simply list
FILES or
FILELIST as input parameters: see the accompanying descriptions to see what format these should be. Ask if it's not clear! (File format documentation will also be added to the SLiMSuite blog, so check there.)
Within SLiMSuite, each file type has a distinct "file extension" that denotes the file type. Note that these are not enforced for input, although some programs may not always recognise the right format if a different extension is used. If you get odd input behaviour/errors that you do not understand, see if changing the file extensions helps. If you want a common file extension to be auto-recognised, let me know and I might be able to add it. SLiMSuite file extensions will not necessarily be recognised by other programs. NOTE: Operating systems will sometimes hide file extensions by default. If you are getting very confused, or have problems of extra
*.txt extensions on everything, try changing the system settings. (And/or becoming familiar with command-line file manipulation.)
The main input formats for SLiM discovery are:
- A source of protein sequence data. This could be a protein FASTA file, a Uniprot plain text file, or a list of Uniprot accession numbers to download. For some tools, a single Uniprot accession number will work.
- A source of motif (regular expression) definitions. This is only required if looking for known (or other pre-defined) motifs and/or wanting to compare a set of de novo predictions with known motifs. A number of different formats are accepted for motif input, including SLiMFinder (summary) results and ELM downloads. The simplest/easiest is a plain text file of regular expressions. For more on motif regular expression formats, please see Edwards and Palopoli 2015.
Common motif discovery tasks
Jobs can be run and retrieved at: http://www.slimsuite.unsw.edu.au/servers.php. (This is a bit easier than making the URL directly, although this is also an option as we will see.)
NOTE: Some of the jobs take a while to run and the SLiMSuite servers have limited resources. It would therefore be useful if you could click on the example JobID links rather than trying to run every example REST command yourself. The first output tab (and the log tab) will show you the run times for that job, so you can see which jobs are fast or slow before you experiment.
Task 1: Find known SLiMs in a protein (ELM/SLiMProb)
ELM. Visit http://http://www.elm.eu.org/ and enter your protein of choice as Uniprot identifier or accession number in the box. (Identifiers will auto-complete and fill in some extra details.) For non-Uniprot protein sequences, you can also enter fasta format.
Try this now with P03070 (LT_SV40) or P03254 (E1A_ADE02). Each of them should have a True Positive LIG_Rb_LxCxE_1 motif.
SLiMProb. We can do a similar search using the
SLiMProb REST server (paste the contents of the grey box onto the end of the
http://rest.slimsuite.unsw.edu.au/ URL):
NOTE: The ELM alias currently searches the 2015 ELM classes.
Task 2: Find custom SLiMS in a protein (SLiMProb)
Task 3: Finding proteome-wide occurrence of a motif using Bioware (SLiMSearch)
The SLiMSearch server is accessible at: http://slim.ucd.ie/slimsearch/. This has been recently updated to Version 4 and now brings in a lot of information, so it is recommended that you read the Help pages for the server.
Example (LIG_CtBP_PxDLS_1): http://slim.ucd.ie/rest/#/slimsearch/annotations?jobId=7R8Tvssm9HEdjWW7jQsgEHUfP0VlHdR6
Human protein PRDM16 is particularly interesting: it does not have an annotated ELM but does match a region annotated to interact with CTBP1. (See the Region column - Expand the instance Feature annotations for a clearer look.) This kind of search can be a good way of identifying new instances of known motifs - some of which may be in the literature but may not have yet made it into database annotation.
The ELM definition for this motif
P[LVIPME][DENS][LM][VASTRG] is very degenerate with a lot of hits - over-prediction is a big problem in motif discovery. We can try to make the definition a little tighter as the expense of some instances, using another tool called SLiMMaker:
Repeating the SLiMSearch analysis with the redefined motif (
P[EILMV][DN]L[ARST]) gives a greater density of known ELMs (see the Motif column) in the top ranked motifs: http://slim.ucd.ie/rest/#/slimsearch/annotations?jobId=L41BRpXQ1oTD6ByDuUSqjWbQZ22WBKbw.
Task 4: Predicting novel SLiMs de novo in a set of proteins (SLiMFinder)
SLiMFinder is designed to look for convergently evolved motifs that are shared between unrelated proteins. For example, we can look at the proteins known (in ELM) to contain the
LIG_PCNA_PIPBox_1. As SLiMs are generally in disordered regions, we will switch disorder masking on with
dismask=T, which uses IUPred to predict globular regions, which are masked out:
(We will look at the UPC and motif cloud output among others.)
Task 5: Identifying known motifs from de novo predictions (CompariMotif)
When you have a lot of motif predictions, it can be tiresome and error-prone to manually scan them for things that look familiar. SLiMSuite has a tool called CompariMotif, which compares sets of motifs for similarity.
The comparimotif server can take motif files/lists (like SLiMProb or SLiMFinder output directly. These are given to the
&motifs and/or
&searchdb options: if no
&searchdb is given then the input motifs are searched against themselves. (This can be useful if clouding goes a bit wrong.)
To pass the output of one server to another, use the format:
&cmd=jobid:XXXXXX:OUTFMT, where
XXXXXX is the Job ID and
OUTFMT is the desired output format. E.g.:
The server is currently in development so output is not sorted usefully yet. This is more of a problem if searching against many SLiMs:
The best advice is to save the
compare output table (
retrieve&jobid=15120900005&outfmt=compare), open it up in Excel and sort on
Score. Alternatively, use the CompariMotif server at http://bioware.ucd.ie.
Task 6: SLiM prediction with conservation masking (SLiMFinder)
Masking is important as it reduces the search space. It can also reduce the signal if it incorrectly masks some true positives but for larger datasets the reduction in "noise" can be more important. As well as
dismask=T/F there are several other masking options in SLiMSuite:
- low complexity masking (
ONby default)
- N-terminal methionines (
ONby default)
- conservation-based masking (
OFFby default)
- Uniprot feature masking (
OFFby default)
- Motif masking (
OFFby default)
For custom sequence input, there is also the option for custom masking based on upper/lower case. For now, we will just look at conservation masking, as this has been shown to improve sensitivity in PPI data. For example, a 2013 compilation of CTBP1 interactors does not yield a significant motif:
But if
consmask=T is also switched on:
The importance of correcting for evolutionary relationships
The UPC correction can be switched off with
efilter=F. Many motif prediction tools calculate estimated expectations without such correction. This can result is massive biases due to shared evolutionary history, which swamp any convergent SLiM evolution signal, for example with the
LIG_CtBP_PxDLS_1 ELM proteins:
Task 7: Look for enrichment or depletion of motifs in a set of proteins (SLiMProb)
We can investigate why the PxDLS motif did not come back with just disorder masking by looking at its enrichment using SLiMProb. When given multiple proteins, SLiMProb will use the same UPC correction as SLiMFinder but also return statistics without UPC correction and simply treating all the sequences as one giant sequence. It can, for example, be used to investigate different definitions of a motif:
In this case, we can see that even though the "true" motif has the most support, it is also expected to occur more by chance. It is enriched, but not enough to survive the multiple testing correction of SLiMChance.
Though not of interest here, the
pUnd statistics can be used to look for depletion/avoidance of a particular motif in a dataset.
Task 8: Find novel motifs from a conservation pattern (SLiMPrints)
Patterns of evolutionary conservation can also be used to directly identify regions of proteins that look like motifs. The tool we have developed for this is called SLiMPrints, which can be run at the Bioware SLiMPrints server. For example, we can look for motif-like regions in one of the CtBP PPI partners, FOG1_HUMAN (Q8IX07): http://bioware.ucd.ie/~compass/biowareweb/cgi-bin/PHP_helper_files/slimprintsInfo.php?jobId=e7GZLf
This protein has a bunch of significant motif-like regions, including the PxDLS motif region at rank 7: http://bioware.ucd.ie/~proviz/ProViz/alignmentViewer/drawer.php?uniprotid=Q8IX07&slim=GPIDL&slimpos=793&column=794.5&width=80&collapse=false
(Note how the precise motif is rarely returned by de novo predictors.)
Task 9: Using the SLiMScape app to visualise a server job
We're now going to fire up Cytoscape and have a quick look at the SLiMScape app. This is fairly well described in the paper, so we will just look at the main ways to run the server.
The simplest is to retrieve an existing run:
- In the SLiMFinder tab, enter
15120900003in the Run ID box and hit Retrieve.
- Apply the default layout.
- Explore the results. Connections are UPC relationships in the data.
Task 10: Running QSLiMFinder through SLiMScape
Now let's imagine we had seen the SLiMPrints results from above for FOG1_HUMAN and knew that it interacted with CtBP1. We could ask the specific question if any motifs in FOG1_HUMAN were enriched in the rest of the PPI dataset. We do this by using QSLiMFinder and giving
Q8IX07 as the query. (
&query=Q8IX07 on the server.)
First, add a node to the network and change its name to
Q8IX07. Enter this in the Query Sequence box then highlight all of the nodes before hitting
Run QSLiMFinder:
This is the essence of molecular mimicry and we could use the same approach to see if E1A_ADE02 shares any motifs by adding
P03254 and using it as a query:
Task 11: Building PPI networks for analysis
The most useful thing of having access to SLiMSuite through Cytoscape is to be able to use it to explore PPI networks and select nodes for analysis. There are in-built tools to get PPI data into Cytoscape. For SLiMSuite, the ID must be a Uniprot ID or accession number, or a Node must have "Uniprot" attribute.
The SLiMSuite REST server also provides some methods for getting PPI data into Cytoscape (and/or for use on the server), using the PINGU server. This is still under development and so the documentation of the available PPI data is currently limited, but just get in touch if you want to use it. (Currently human only.)
PPI data is retrieved by entering one or more gene symbols as a
&hublist, optionally along with a
&ppisource (see the ppisource alias):
This can be used directly for
&uniprotid input using the
&rest=uniprot output:
Alternatively, the PPI data can be imported into Cytoscape using the
pairwise table:
- Start a new session. (Later you can workout how to import and merge networks.)
- Import network from URL: http://rest.slimsuite.unsw.edu.au/retrieve&jobid=15120900009&rest=pairwise
- Rename the
SpokeUnifields to
nameand attribute them to
Source Nodeand
Target Nodeattributes. Make
Interaction Typethen import.
- Select the nodes that are shared interactors of both CtBP proteins.
- Modify the masking settings to include disorder, conservation and feature masking.
- Hit Run:
Monday, 7 December 2015
New SLiMSuite REST Servers
Since the move to UNSW in 2013, the Bioware SLiMSuite servers and REST servers have been undergoing some much needed TLC. As part of this process, a new set of UNSW REST servers were introduced and online with the 2015-06-01 SLiMSuite release.
An overview of how the REST servers work is given on the REST Homepage. The available tools are listed at the REST Tools page. The main ones - accessible through the SLiMScape app for Cytoscape are (or support):
- SLiMFinder de novo SLiM discovery.
- QSLiMFinder query-focused de novo SLiM discovery.
- SLiMProb defined/known SLiM prediction.
- SLiMMaker Simple Regex SLiM generation from peptides.
The primary focus has been setting up new servers to be accessed via a RESTful-style interface whereby a URL can be directly given to the server and used to either download results directly (if accessing programmatically) or view in a web browser. As with the main programs, these servers use plain text inputs and outputs wherever. Whilst this probably makes proper computer scientists very unhappy, it should make it very easy to incorporate SLiMSuite REST functions into your own scripts - you only need to learn how to parse text. (It also makes it easy for me to swap input sources.) If you don't want to write your own, SLiMParser is provided in the SLiMSuite download to do this for you.
The other design consideration that has gone into the REST servers is to make them run as much like the commandline versions as possible: (1) they use the same code; (2) they use the same commandline options, parsed from the URL. This means that (a) you should easily be able to reproduce server results on your own system, and (b) new functions (and bug fixes) should become quickly available via the REST servers.
To save the need for constructing complex URLs, there is a simple on-size-fits-all form at the EdwardsLab server page. Over time, tool-specific forms will be established. Currently, this only exists for SLiMMaker.
As ever, if something about the new servers misbehaves or does not make sense - or you really want some new functions - please get in touch.


text:
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Our specialist team have over 20 years experience in data marketing strategy. They are happy to discuss and advise on the most effective combination of services, based on what you are looking to achieve.

subset: commoncrawl


text:
Historic and current failure to acknowledge voter suppression remains a key issueREAD MORE
Trump victory, Brexit, police brutality top editors' list of big storiesREAD MORE
In this Dec. 17 file photo, President-elect Donald Trump speaks during a rally at Ladd-Peebles Stadium in Mobile, Ala.
From Brexit to the Presidential race to the Oregon occupation, many headlines showed deepening distrust and divisionREAD MORE
This June 18, 2015, file photo, provided by the Charleston County Sheriff's Office shows Dylann Roof. Roof was convicted Thursday, Dec. 15, 2016, in the chilling attack on nine black church members who were shot to death last year during a Bible study, affirming the prosecution's portrayal of a young white man who hoped the slayings would start a race war or bring back segregation. (Charleston County Sheriff's Office via AP, File)
U.S. judge warns Roof acting as his own attorney is a bad ideaREAD MORE
Nielsen membership and field representatives went into classrooms across the country for the Great American Teach In. This group of field associates taught business etiquette to students at Friendship Public Charter School in Southeast Washington DC.
Around one in seven of the Millennial generation (aged 16-34) are African AmericanREAD MORE
In this May 3, 2011, file photo, first lady Michelle Obama dances with students at Alice Deal Middle School in northwest Washington during a surprise visit for the school's Let's Move! event. When Michelle Obama considered the daunting prospect of becoming first lady, she purposely avoided turning to books by her predecessors for guidance. Instead, she turned inward (AP Photo/Manuel Balce Ceneta, File)
Children talk about what Michelle Obama's career, initiatives have meant to themREAD MORE
Amazon has added warehouses and package carriers have increased shipping to address last-minute demandREAD MORE
On Monday, the President granted commutation of sentence to 153 individuals and pardons to 78 individualsREAD MORE
Hillary Clinton speaks in Washington, Nov. 16, 2016. Clinton says Russian interference had a bearing on her defeat in the presidential race, citing a long-running effort by Russian President Vladimir Putin to discredit America and its government. (AP Photo/Cliff Owen, File)
Mistrust is part of the landscape in countries around the worldREAD MORE
AP spoke to 330 electors and found little enthusiasm for revoltREAD MORE


text:
Event: Bollywood celebs Salman Khan, Farah Khan, Prabhu Deva, Sajid Nadiadwala, Boney Kapoor and others were snapped at the special screening of the Pulkit Samrat and Amita Pathak starrer film BITTOO BOSS.
Guest List: Salman Khan, Pulkit Samrat, Amita Pathak, Kumar Mangat Pathak, Raghav Sachar, Sohail Khan, Atul Agnihotri, Alvira Khan, Prabhu Deva, Boney Kapoor, Farah Khan, Ramesh S. Taurani, Kabir Khan
Bollywood star couple Akshay Kumar and Twinkle Khanna were spotted at a cafe in Bandra walking hand-in-hand.
Bollywood's alleged couple Salman Khan and Iulia Vantur together came to launch Mahesh Manjrekar's Marathi film RUBIK'S CUBE music. Salman and Iulia looked lovely together.
Bollywood babes like Sushmita Sen, Ayesha Takia, Anushka Manchanda, Sagarika Ghatge, Shriya Saran, Amyra Dastur, Mandira Bedi, Kim Sharma, Manjari Fadnis at the launch of 145 days caf� and bar in Mumb...
Bollywood celebs like Ayan Mukerji, Sidhanth Kapoor, Sammir Dattani, Adhyayan Suman, Harshwardhan Kapoor, Zaheer Khan, Ayesha takia, Mandira Bedi, Ashish Choudhary, Shriya Sharan, Pooja Bedi, Sushmita...
Bollywood hottie Malaika Arora Khan spotted at local Bandra market leaving everyone by surprise.


text:
Basotho Oral Poetry
At the Beginning of the Twenty-first Century
By William Moruti Tšiu
This book contains a major research into, and deep investigation of Sesotho language oral poetry in Lesotho at the beginning of the twenty-first century. The classical form, the dithoko, which was inspired by tribal wars or battles fought by the Basotho, is explored fully, but absence of wars, urbanisation with the economic and social imperatives of modernism, have inspired new forms of poetry. The new forms include dithoko, i.e. 'praise poetry'; the deifela, 'mine workers' chants' and the diboko, the latter which as 'family odes' are still performed in rural areas.
The research work involved the live performances of 33 diroki, i.e. poets, watched and recorded in their natural environments. The investigators were led by Professor Abiola Irele, then of Ohio State University.
A copy of the stored DVD of the live performances of 33 poets is inserted into this book.
Dr William Moruti Tšiu, now teaching at the University of South Africa, has benefited profusely from collaboration of investigators from Ohio State University and others in the University of South Africa and the National University of Lesotho in bringing the work to life.


text:
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Watch the full event replay of both the Men's and Women's Classical races at the 2016 NC Skiing Championship.
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text:
As expected, I am being contacted about some of the most recent revelations.
I'm asked, if there is no overseer, then what else are you not telling us?
Specifically, a few wanted to know about the existence of the Urim and Thummim and the actual plates.
Well, let me answer them in this way:
How in the hell would any 25 year-old, staunch, LDS/Mormon kid be convinced to sacrifice his entire life to something that didn't make sense when applied to his 25 years of experience on Earth as an LDS/Mormon? That was the only purpose for the appearance of "gold plates," that weren't really gold, but very real to the senses.
How in the hell are you going to convince the same 25 year-old of the things that he is supposed to do when he knows that everything that pops into his head has the possibility of being something he made up himself? How's he supposed to communicate with others who are no part of his logical reality? Well, give him a cell phone that is far more advanced than anything yet invented, yet will be mimicked and introduced not too long into his future.
What part of your religious upbringing, that is all part of "the philosophies of men mingled with scripture," do you not yet understand? ALL RELIGIOUS ASPECTS were creations of mortal men (mostly men) meant to gain power over you and control your actions. The invention of a Godhead, the Father, the Son, and the Holy Ghost, became a part of your emotional construct. These false filters were imbedded in your minds, and very few of you would have ever considered questioning the existence of God, IF we threw everything out at once. You were taught falsehoods line upon line, precept upon precept. We had to counter these things with a system of learning that your brains were accustomed to learning new information.
There is still so much that your minds are incapable of grasping. For this reason, I am under constraint of a huge responsibility to write the book, The Game of Mortal Life, consistent with your learning potential, yet disclosing all real truth to you. Over the past few days I have been overwhelmed and greatly burdened by this task.
But finally, after a lot of discussion with the book's authors, we have changed the outline of the book, all of its chapter headings to meet the needs of my unrelenting doubt that I can do this job correctly.
Here is the new outline, along with some of my notes about the Foreword, Authors' Note, and Introduction, as well as the very first rough draft of Chapter 1.
As you read my comments, the rough of Chapter 1, and then consider the succeeding chapters of the book, you will begin to understand the great importance of this work, and hopefully my personal trepidation in being the one who has to put it all into words.
(Note: these are some notes to the editors who will be editing the book.)
The Foreword deals with the creation, idea, purpose, limitations, and scope of the book. Here we introduce the group of individuals responsible for the Marvelous Work and a Wonder® and the concepts and proposals of the Humanity Party®. This group will remain anonymous as long as I am alive and designated as its spokesperson. It's my designation as its spokesperson that creates the most controversy for its work.
I wouldn't be exaggerating if I stated that my past causes one to consider why I, out of all the people in the world, was chosen to be the spokesperson. I've always thought the group could find another, more worldly-acceptable representative. But that's the point: I am far from "worldly-acceptable." The way the world is today is a result of the acceptable types of spokespersons that represent the works of humanity. Just look at the world! Is it any wonder that they would appoint someone who the world would not value? How could a message so opposed to current worldly values and information be delivered by someone who needed the praise of the world for his value?
I have little to no value for the things of this world that humans have invented and incorporated into their mortal experience upon Earth, except for the work that I do. The value that I once held for worldly things was greatly diminished when my brain was finally able to comprehend our eternal and true nature. How could I continue to value something so fleeting, something that begins and ends, something that creates so much misery and opposition to the happiness that is the purpose of our existence?
My autobiography, The Man From Joe's Bar and Grill, will disclose so much about me and how it has been for me since that fateful day in 1987, when I lost all value for the mortal experience outside of it being a game we are all playing as highly advanced life forms—a game that has only one purpose: provide experiences that keep an eternal human focused on the incredible nature of the most advanced compendium of matter that exists, the self.
Regardless of my past, if one would come to know me on a personal level, that one would have a hard time understanding why my critics have such a hard time with me. My critics have a hard time accepting me, not because of me as a person, but because of the work that I represent. They have failed on so many levels to confound the work, which, if true, would completely destroy the self-worth and value that they have gained from the world. Well is it stated in the Foreword,
"The scope of the subject matter deals with viewpoints about our existence that have never been properly explored nor considered beyond their rejection as simple theories based on the philosophical ramblings of crazy thinkers. Yet, years after their deaths—many murdered because they would not deny their 'crazy beliefs'—some of these same viewpoints are respected as opinions of the world's greatest thinkers. The things revealed in this book have been proposed before but rejected by the masses, often led by ministers of education, science, and religion, and others who would lose their worldly value if these proposals turned out to be the real truth. Because the intent and agenda of these unscrupulous ministers was to retain their value and power, the masses have never had a fair opportunity to consider these proposals."
It was important to the individuals who are responsible for the work that I represent to present a plea to the readers of the book to open their minds to the possibility that everything that they think they know is not real truth, and that considering a different point of view is good for humanity.
They write,
"Your common sense is not different than ours—thus it is common. But if you do not give the information that we present a chance of consideration, then it becomes impossible for us to arrive at an agreement that would create peace among us. Isn't the lack of consideration and compassion for another's truth the underlying problem with our current world?
"We can agree that things are getting worse for the human race. Who will be the first to consider another's truth as an alternative to theirs, accepting that maybe their own truth might be a major contributing factor to hate, fear, and division?
"Please consider the information presented in this book. Ask yourself if it is good for the human race. Ask yourself if it is information that can unite us instead of divide us—a disconnect that causes us to hate and fear each other because of our different perspectives.
"If the information is good, then make our truth a part of your own. And together, we can establish real truth for the sake and betterment of humanity. This is our only hope and the inclusive intent of sharing our truths with you."
An introduction deals with the subject of the book and supplements and introduces some text that indicates a point of view that is hopefully adopted by the reader. The point of view that the Game of Mortal Life is trying to present is that mortals are actually highly advanced life forms that have chosen to participate in an experience that enhances the purpose for their individual existence. Our individual existence is the only existence that is important, and for which all other matter, living or inorganic, exists. We want to expand the idea that we have the power to manipulate all matter for our own individual purposes. However, this individual power over matter is limited and restricted so that our choices of how we use matter does not impede the choices that others make in using the same matter. Mortality in the solar system—our solar system being a collective system of organized matter that WE actually organized as advanced humans with the power to do so according to the laws of nature in which the matter exists—comprises the matter that makes up planet Earth. This matter is finite and equally available for all players to utilize in their own pursuit of happiness, according to their own individual choices.
The current state of our mortal experience demonstrates just how selfish and self-centered humans are, which is not bad, but is the very purpose for which we participate in the Game of Mortal Life.
The purpose of the succeeding chapters of the book should completely detail and explain the point of view introduced in the Introduction, leaving no other questions to be asked or any other explanation possible. It is a HUGE task and responsibility to ask of me, the one who has to dumb it all down and put it written form in the English language. Sigh …
Eternal Matter and the Formation of the Known Universe
Everything, in any environment, of any kind, is matter. There is no place, no space, no time, no dimension where some sort of matter does not exist. It is impossible for there to be nothing. If there were nothing, then there would be nothing. Anywhere that some thing exists, it consists of some form of matter. In order for matter to exist, however, it must have the capacity to be experienced by humans, as humans are the only life forms that have an interest and a capacity to define matter. If a human cannot physically sense it (see, hear, smell, taste, or touch it), then it cannot be considered as matter relevant to that human's existence.
Mortals have invented words that represent the basic parts of matter; i.e., protons, neutrons, and electrons. However, these invented things cannot be physically sensed; therefore, they are not true matter relevant to our existence. In our effort to understand matter and how it works, how it came to be, and how it can be controlled, we have invented these words that define the ideas (theorized) that make sense to the emotional part of our makeup (the mind). But they cannot be empirically (physically) sensed. These emotionally based, invented theories cause human division and inequality, because the majority of humans cannot agree upon their conclusive relevance to our existence.
If we can physically experience something, then there should be little argument about its existence. We can feel a rock—an inorganic (non-living) form of matter—therefore there is no argument or question, regardless of what we call it, in whatever language we name it, that it exists. How and why it exists, however, is the emotional response of the human mind. Our ability to have an emotional response to what we physically experience together unites and defines us as human beings in contrast to all other life forms. No other life form of matter (compendium) wonders and questions how and why a rock exists. None, not one!
It is this emotional (not physical) aspect of our human nature that separates us and causes us to argue among ourselves. Little children do not argue about the existence of a rock. If a little child encounters a rock, the child plays with it, even allowing other children to play with it too. Not at any point, while playing with the rock, does a child question how or why it exists, UNTIL the child is deprived of the rock. Little children get along so well because of their mutual determination to experience their environment only according to their physical senses. They do not wonder about the rock that they are playing with, unless the one in possession of the rock no longer wants to share it or allow others to play with it. Deprived of the opportunity to play with the rock causes a negative emotional response from the deprived little children. It's at this point that the other children begin to question the existence of the rock: how and why it exists. If there were an abundance of rocks, and each child had its unlimited choice of any rock to play with and share with others, there wouldn't be an emotional attachment to the rock.
Deprivation of physical interaction with any type of matter found in an environment always causes a response from the life form who could otherwise physically (through its senses) benefit from the matter's existence. This is true of all matter, living or non-living. But no life form is affected quite like the human. Any other life form, if deprived of something in their environment that could benefit its sensory system, as a part of its physical makeup (the compendium of matter), will do anything within its physical power to obtain the matter for itself, which includes killing other life forms, even its own.
Humans are emotionally affected by sadness and sense of inequality, wondering, "Why does that person get to have that and I don't?" The human response to the emotional effect of the deprivation of any type of matter, that would otherwise physically benefit the human, is the primary cause of all human misery. This is the construct and problem of human nature. These things cause us to kill our own species, which, if left unchecked, would end the human race. Therefore, it seems counterproductive to human existence to be thus separated emotionally in a way that would contribute to our own extermination. This is not how matter reacts in its natural state. All matter, or any form, seeks its own safety and continuation. Therefore, our greatest quest should be to unite ourselves for the safety and perpetuation of our own species. If we do not, then the natural course of things will guarantee our non-existence as mortals.
We naturally seek to be happy, which will be explained in another chapter as the stability of matter. When matter, of any form, is stable, it is fulfilling the measure of its existence. Stability is acting and being acted upon consistent with the life form's original purpose for existing in the first place. Our nature propels us instinctually to pursue happiness. But to understand human nature, we must first understand the matter that makes up the physical human life form that is responsible for this nature, which includes a unique brain that allows an emotional response different than that of other life forms.
Our physical makeup allows us to experience our environment through our natural senses. We can see, hear, smell, taste, and touch the matter that forms our universe. Our known universe, that which we can empirically (physically) sense, consists of matter that we have defined as stars, meteors, asteroids and other minor bodies, along with the major bodies of galaxies of solar systems and solar systems of planets. All matter is made up of atoms, which is the name by which we define the smallest thing that we can physically sense. We can see an atom with an electron microscope—one of the many wonderful technologies that we invented in our quest to understand how and why a rock exists.
We once believed that, in its natural state, matter could not be created or destroyed. Accepting this theory as correct, the question remained: how, then, can an atom exist if it was not created? This perplexed the great thinkers of science for many years until they begin to explore the idea that maybe atoms were created by some means. This exploration led to the theories and experiments related to nuclear reactions, or rather how the basic components of atoms (protons, neutrons, and electrons) form atoms. As we thought about it more we were able to experiment with these ideas until humankind developed the ability to create a reaction in the atom that not only destroyed it, but created new atoms. With this understanding, we learned how to end all human life forms, which is profoundly demonstrated in our nuclear capabilities. We now know that humans can—no other life form can, but we can—destroy matter and the atoms that make it up. Because we can create new atoms by removing or adding protons and neutrons, we can theorize that human life must have been created through a similar process. This process has been conceptualized as the Big Bang Theory.
But there's still a negative emotional attachment to the Big Bang theory: IF it actually occurred, then how and why did it occur? This is where science has ended its value in assuaging the human emotions associated with the need to know the answer. But the simple answer can be found in scientific research and conclusion. Throughout our known (empirically sensed) Universe, all matter exists uniformly and consistent with the way that it has been for billions and billions of years. No one can argue the fact that all matter might have remained in its natural state had not humans invented ways to manipulate and change it by their free will, for their own use.
If matter exists eternally and unchanging in the forms that it naturally does, until humans use their intellect to manipulate and control it, isn't it reasonable to assume that the compendium of matter that makes up the human life form has always existed eternally and unchanging? Have rocks always existed? Are they not a compendium of certain matter? Isn't the human life form much more advanced and complex than a rock?
Consider, again, the Big Bang theory. If it happened, then some thing caused it. There must have existed some sort of eternal matter that was responsible for the explosion. Whatever matter it was that caused the explosion, in its natural form, this matter had existed for billions and billions of years without blowing itself up. Something happened that acted upon the matter that caused the Big Bang. Some other form of matter must have acted upon the matter that exploded in order for the Big Bang to occur. More importantly, since it appears that all matter acts for its own safety and existence, then whatever form of matter that was responsible for the Big Bang, did so to continue its life and purpose.
Science has determined that the overwhelming evidence that a big bang occurred—that was responsible for the history of the universe, which is everything that is known—is that the universe is expanding. The universe is getting bigger. Logically, if the universe is getting bigger, then at one time it must have been much smaller. Again, this is where the perception and theories of science fail to fulfill the emotional response of the human life form that wants its answers to make complete sense. Logic screams that some thing was responsible for the Big Bang, IF this is what actually occurred.
What we can all agree upon is that things seldom change from their natural state of existence until humans do something to change things. It will be explained in the next chapter that the human life form is the greatest compendium of matter that exists; that this life form, in its perfect state, has always existed and will always exist as the greatest life form possible. Being the greatest life form, all other forms of matter exist for the sake of humans. IF there was an explosion that created the known universe, then this explosion was initiated and created by a human, for the sake of that human's existence. In considering the expansion of the universe, science discounts the very premise that should be the focus of all scientific observation: human action.
Consider the construct of a solar system. Planets revolve around a centralized energy source called a star, which we call a sun. The force of energy from the sun provides stability and safety for its planets. Think of our solar system as closed system where nothing can enter in or leave beyond the power of the sun's energy responsible for keeping things in its orbit. Imagine that the force of the sun's energy creates an impenetrable force field around its own planets. This force field does not allow any matter to enter into it or leave it. Consider that this solar system was created by the free will of an advanced human, who knew how to create a sun in just the right way to accommodate that person's desire for a certain number of planets to exist, which included the size of the planets that the person desired when laying out the plans of that person's solar system. When the person created a new sun—in the exact same way that we can create nuclear explosions that begin and end in the closed system of the earth's atmosphere—the energy of its creation pushed all the other existing solar systems away from it. Owing to the fact that any other solar system exists with a protective force field around it that doesn't allow any other matter to effect it, the big bang—the explosion or creation of a new star—would push the fixed and existing solar systems away from it, thus expanding the universe.
The Greatest Compendium of Matter
Happiness: The Stability of Matter
Sexual Nature: The Sustainability of Matter
The Human Pursuit of Happiness
The Game of New Experience
The Game of Mortal Life
The Power of Myth: Gods, Devils, and Saviors
The Philosophies of Men Mingled With Scripture
Recognizing the Self as the True God
The End of Time and the Beginning of Life
(All the information and its presentation in this document is COPYRIGHTED by Christopher Nemelka, the Marvelous Work and a Wonder Purpose Trust, and the Humanity Party®, 2016, 2017)


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We're always keen to hear feedback from our clients about their holidays. Here's what people have said about New Zealand:
Thanks for your email and, more importantly, for organising such a fantastic holiday! We absolutely loved New Zealand, especially our time at Millbrook near Queenstown.
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Thank you very much for all your help. The whole process has been an absolute pleasure, and I know that I will have a totally different experience this time than when I was in New Zealand last - so thank you for the fantastic recommendations for beautiful lodges and exciting excursions.
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Italian Opera Since 1945
By (author) Raymond Fearn
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Italian Opera Since 1945 by Raymond Fearn
Book DescriptionFirst Published in 1998. Routledge is an imprint of Taylor & Francis, an informa company.
Buy Italian Opera Since 1945 book by Raymond Fearn from Australia's Online Bookstore, Boomerang Books.
Book DetailsISBN: 9789057550027
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Pages: 278
Imprint: Routledge
Publisher: Taylor & Francis Ltd
Publish Date: 1-Feb-1998
Country of Publication: United Kingdom
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In this book Raymond Fearn surveys this Italian musico-theatrical phenomenon in the period since the Second World War, examining a wide range of works, illustrated by numerous musical examples and production photographs.
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Twitter Trends Traffic: An Explosive Source For Your Website
Twitter trends occur all of the time.
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Identifying trends
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Log on to Twitter now and have a quick look at what's trending.
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The best example to use here would be Justin Bieber.
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If you run a fan site about Bieber it would make sense to monitor trends on Twitter – every time a Bieber related hash tag pops up, you should join the trend whilst posting a link to your site.
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a no filter sunset from yesterday. 🌤
scared of heights , but this was worth it. ☁
finally received my Hogwarts letter. 📖⚡
"peet stop" - 🚙
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breakfast w/ marcos ☀
cali, you're pretty cool.
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Had the best time at Gradnite yesterday. ❤ thanks to the camera guy for the blurriest picture. #missinghalfofthesquad #gradnite #disneyland


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J.D., University of Michigan Law School
M.P.P., University of Michigan Gerald R. Ford School of Public Policy
B.A., with distinction, University of Michigan
Bar and Court Admissions
District of Columbia Bar
Maryland Bar
John T. Lin
Practice Areas
John represents clients in a variety of telecommunications litigation and regulatory matters.
Representative Experience
- Researches and drafts comments and other written submissions for clients in proceedings before the Federal Communications Commission (FCC) and other federal agencies.
- Counsels telecommunications clients on compliance with federal and state regulatory obligations.
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- Maintains an active pro bono practice with experience arguing a habeas corpus appeal before the U.S. Court of Appeals for the Sixth Circuit, litigating a Title VII Civil Rights case in federal district court, and writing an amicus brief in a First Amendment case before the U.S. Supreme Court.
Professional Experience
- Law Clerk, U.S. House of Representatives, Committee on Oversight and Government Reform (2016)
- Student Attorney, University of Michigan Federal Appellate Litigation Clinic (2015)
- Law Clerk, U.S. Senate, Permanent Subcommittee on Investigations (2014)
- Judicial Intern, Chief Judge Gerald E. Rosen, U.S. District Court for the Eastern District of Michigan (2013)
- Assistant Director of Scheduling/Personal Aide, Office of Governor Rick Snyder (2011-2012)
- Administrative Assistant to the Campaign Manager, Rick Snyder for Michigan (2010)
News & Insights
- January 11, 2017 | AlertAnna M. Gomez, Shawn H. Chang, John T. Lin
- December 27, 2016 | AlertFCC Seeks Data and Comment About Ways to Streamline and Expedite Local Approval of Small Cell Wireless Infrastructure DeploymentChristopher S. Huther, Claire J. Evans, Joshua S. Turner, Megan L. Brown, Shawn H. Chang, John T. Lin
- December 16, 2016 | Press ReleaseWiley Rein Files Supreme Court Amicus Brief in Lee v. Tam on Behalf of Rutherford Institute and Consumers' Research
- December 12, 2016 | AlertShawn H. Chang, Anna M. Gomez, John T. Lin
- December 12, 2016 | AlertDaniel P. Brooks, Anna M. Gomez, John T. Lin
- November 4, 2016 | Alert
- October 24, 2016 | ArticleWileyConnectJohn T. Lin, Author


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Mage Mojo was founded in 2009 by Eric Hileman and Martin Pachol in response to the performance issues they saw clients having with Magento online stores.
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text: TRIUMPH TT600 (2000/W) £1,499 600cc18,706 milesMerseyside Great first bike, good condition and low mileage for year, alarm, 2 keys and fobs, supplied with new MOT and service along with 3 months warranty. Available for viewing at either of our Liverpool or Wallasey showrooms (please call ahead). Dealer Seller +5 Photos Theft Checked Warranty included Get a full history checkHPI check Get an insurance quoteInsurance quote View details


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KidCo Convertible Crib Mesh Bed Rail helps children safely transition from cribs to a big bed. Made of steel and mesh the Convertible Crib Rail is compatible with most convertible cribs.
Please note that free shipping does not apply to sale or clearance items and shipping charges for these items will be calculated after the order has been placed. If for some reason your order does not qualify for free shipping or free shipping has been calculated in error we will contact via email or phone.
Sale, Clearance and Promotional items are all final sale items and cannot be returned or exchanged. For safety, health & hygienic reasons, some products are final sale. These products include, but are not limited to: Car Seats, Hygiene items, potties, nursing bras and intimate items, pacifiers and teethers, skin care products and special order items.
Special orders are items that we do not have in our inventory and have an estimated order lead time. All special order and furniture items are final sales and cannot be cancelled. Once the items have been paid and ordered, they cannot be returned and are a final sale.
You will be informed of the estimated order time for your items prior to completing the special order. Delivery times are estimated and not guaranteed. Please note that estimates are given based on information given by the supplier; WestCoast Kids is not responsible for supplier delays or back orders. As soon as the order arrives in store you will be contacted or it will be shipped to you.
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