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# List of Community Software Tools for Next Generation Sequencing
## Commercial tools
1. **[Strand NGS] (http://www.strand-ngs.com/)**
  * offers many different tools including alignment, RNA-Seq, DNA-Seq, ChIP-Seq, Small RNA-Seq, Genome Browser, visualizations, Biological Interpretation, etc. Supports workflows
“one can import the sample data in FASTA, FASTQ or tag-count format. In addition, prealigned data in SAM, BAM or Illumina-specific ELAND format can be directly imported for analysis.”
  * Alignment feature: Supports alignment from Illumina, Ion Torrent, 454 (Roche), and Pac Bio
  * DNA-Seq Feature, can annotate with dbSNP
1. **[CLC Genomics Workbench] (http://www.clcbio.com/desktop-applications/top-features/)**

  * (QIAGEN). Features include: resequencing, workflow, read mapping, de novo assembly, variant detection, RNA-Seq, ChIP-Seq, Genome Browser, etc (entire list on website); Main Workbench offers database search (Genbank, Blast, Pubmed); 2000 organizations have invested in CLC
  * Accepts VCF files from 1000 Genomes Project
  * Accepts downloaded tracks from dbSNP
  * Also accepts: FASTA, GFF/GTF/GVF, BED, Wiggle, Cosmic, UCSC variant database, complete genomics master var file
  * Read mapping: “In addition to Sanger sequence data, reads from these high-throughput sequencing machines are supported: The 454 FLX System and the 454 GS Junior System from Roche, Illumina Genome Analyzer, Illumina HiSeq, Illumina HiScan, and Illumina MiSeq sequencing systems, SOLiD system from Life Technologies, Ion Torrent system from Life Technologies, Helicos from Helicos BioSciences”
  * De novo assembly: “In addition to Sanger sequence data, reads from these high-throughput sequencing machines are supported The 454 FLX System and the 454 GS Junior System from Roche, Illumina Genome Analyzer, Illumina HiSeq, Illumina HiScan, and Illumina MiSeq sequencing systems, SOLiD system from Life Technologies, Ion Torrent system from Life Technologies”
  * Annotation tracks from Ensembl
1. **[DNAnexus] (https://www.dnanexus.com/product-overview)**
  * Private cloud repository -- formerly a redistributor of SRA and other NCBI resources; command-line or via web, can fetch data from a URL,  build custom pipeline/ workflow
has sra.dnanexus.com site: data downloads come directly from NCBI
1. **[Ingenuity Variant Analysis] (http://www.ingenuity.com/products/variant-analysis)**
  * (QIAGEN) allows for variant identification and analysis, uses NCI-60 data set for cancer, Supported third part informatin: Entrez Gene, RefSeq, ClinVar; gives contextual details of results instead of just A to B relationship
  * Has own database-- “knowledge base” based on COSMIC, OMIM, and TCGA databases
1. **[Lasergene Genomics Suite] (http://www.dnastar.com/t-products-dnastar-lasergene-genomics.aspx)**
  * Comprehensive NGS software pipeline for assembly, alignment, variant calling and analysis of NGS data
  * Supported workflows include: reference-guided and de novo genome and transcriptome assembly and analysis, metagenomics sample assembly, targeted resequencing, exome alignment, gene panels with validation control, variant analysis, and RNA-Seq, ChIP-Seq and miRNA alignment and analysis.
  * #1 in accuracy: fewer false negatives and better sensitivity compared to results obtained from other aligners
  * Aligns exome data and performs variant calling an average of 3 times faster than alternative pipelines
  * Annotates genomic data with allele and genotype frequency, functional impact predictions, evolutionary conservation scores and pathogenicity
  * Supports all major NGS technologies (Illumina, Ion Torrent, Pac Bio and Roche 454) and project types
  * Available on Windows, Mac OS X, Linux, and the Amazon Cloud
1. **[NextGENe] (http://www.softgenetics.com/NextGENe.html)**
  * “perfect analytical partner for the analysis of desktop sequencing data produced by the ION PGM™, Roche Junior, Illumina MiSeq as well as high throughput systems as the Ion Torrent Proton, Roche FLX, Applied BioSystems SOLiD™ and Illumina® platforms.” runs on Windows, free-standing multi-application package-- SNP/Indel analysis, CNV prediction and disease discovery, whole genome alignment, etc.
  * Data can be imported from Clinvar, dbSNP, Genbank: http://www.softgenetics.com/PDF/NextGene_UsersManual_web.pdf
1. **[Partek Genomics Suite] (http://www.partek.com/pgs)**
  * Cited in over 3,500 peer-reviewed scientific publications
  * Workflows for microarray and PCR data include: Gene expression including alternative splicing, miRNA expression, Genome Wide Association Studies, Mother-Father-Child Trio analysis, DNA Copy number including allele specific copy number and Loss of Heterozygosity (LOH), and ChIP, and methylation. Next Generation Sequencing (NGS) workflows include: RNA-Seq, miRNA-Seq, ChIP-Seq, DNA-Seq, and Methylation
  * Powerful statistics and interactive, publication ready visualizations
  * Supports all commercial next generation sequencing and microarray file format as well as text files
  * Can input GEO SOFT files
1. **[Partek Flow] (http://www.partek.com/partekflow)**
  * Installation can be cloud-based or on a local cluster or Linux server
  * Easy to use point-and-click interface
  * Takes NGS data (.fastq, BAM, SAM), microarrays (Affymetrix, Illumina) and text files
  * Supports custom genome builds and annotation databases
  * Performs base trimming, alignment, quantification, quality analysis, statistics, and visualization
  * Includes ten fully customizable aligners (Bowtie, Bowtie 2, BWA, GSNAP, Isaac 2, SHRiMP 2, STAR, TMAP, TopHat and TopHat 2)
  * Applications for RNA-Seq, Small RNA-Seq, WGS/WES, Pathway enrichment, Fusion detection and Variant calling
  * Allows users to create, save, share, or download analysis pipelines for automated and repeatable analysis
  * Collaborate with others without transferring data
  * Integrates microarray and next generation sequencing data
1. **[Golden Helix: SNP and Variation Suite] (http://goldenhelix.com/SNP_Variation/)**
  * used for managing, analyzing and visualizing genotypic and phenotypic data; Features: Genome-wide association studies, genomic prediction, copy number analysis, small sample DNA-Seq workflows, large sample DNA-seq analysis, RNA-seq analysis.
Supported files: .txt, excel XLS & XLSX, CEL, CHP, CNT, Illumina, Plink PED, TPED, BED, Agilent files, NimbleGen data summary files, VCF files, Impute2 GWAS files, HapMap format, MACH output, + 50 other formats
consumes NCBI data directly
1. **[Genomatix] (https://www.genomatix.de/)**
  * Applications: ChIP-Seq, DNA-Seq, RNA-Seq, DNA methylation; enable personalized medicine,
  * Mining Stations: Supports all established NGS sequencing platforms- SOLiD, 454 Life Sciences, Genome Analyzer, HiSeq, MiSeq, IonTorrent
  * Software Suite: can upload sequence of BED files
  * Genome browser: BED and BAM files, Public data- 1500 BED files available for every user
1. **[Biodatomics] (http://www.biodatomics.com/)**
  * Open source platform (SaaS), analysis and genome sequencing tools, integrates over 400 genomic analysis open source tools and pipelines, have a private and public cloud version. Features: genomic data visualization, drag and drop interface, accelerated analysis, real-time collaboration
  * They have a couple modules to do so, and have enabled parts of the sra toolkit
1. **[SolveBio] (https://www.solvebio.com/)**
  * Software product, for clinical genomics professionals, manage, curate, report genomic variation
  * Has own data library -- data from NCBI

## Variant Identification
### Germline Callers
1. **[IMPUTE2] (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html)**
  * Description: phasing observed genotypes and imputing missing genotypes uses reference panels to provide all available halotypes, does not use population labels or genome-wide measures; designed to represent variation in one population; Fairly popular
  * Input:
   * Reference Haplotypes: Links to 1000 Genomes and HapMap downloads
  * Output:
1. **[FreeBayes] (https://github.com/ekg/freebayes)**
  * Description: finds SNPs, Indels, MNPs; reports variants based on alignment; haplotype based
  * Input: BAM- uses BAMtools API to parse
   * Reference genome: FASTA
  * Output: VCF
1. **[SOAPindel] (http://soap.genomics.org.cn/soapindel.html)**
  * Description: detects indels from NGS paired-end sequencing
  * Input: files with read alignment can be SOAP or SAM formats, users must also give raw reads in Fasta or Fastq
   * Reference Sequence used to align reads: FASTA
  * Output:

1. **[2Kplus2] (https://github.com/danmaclean/2kplus2)**
  * Description: algorithm searches graphs produced by de novo assembler Cortex; c++ source code for SNP detection
“2kplus2.cpp is a c++ source code for the detection and the classification of single nucleotide polymorphisms in transformed De Bruijn graphs using Cortex assembler.”
  * Input:
  * Output:
1. **[Atlas 2] ( https://www.hgsc.bcm.edu/software/atlas-2)**
  * Description: specializes in separation of true SNPs and indels from sequencing and mapping errors, last update January 2013
  * Input: takes BAM file,
   * Reference Genome: FASTA
  * Output: produces VCF
1. **[CRISP] (https://sites.google.com/site/vibansal/software/crisp)**
  * Description: identifies SNPs and INDELs from pooled high-throughput NGS, not used for analysis of single samples; implemented in C and uses SAMtools API; latest version should work with diploid genomes
  * Input: requires BAM files (aligned with GATK)
   * Reference Genome: indexed FASTA file
  * Output: VCF files
1. **[Dindel] (http://www.sanger.ac.uk/resources/software/dindel/)**
  * Description: (Wellcome Trust Sanger) calls small indels from short-read sequences, only can handle Illumina data; cannot test candidate indels; written in C++, used on Linux based and Mac computers (not tested in windows)
  * Input: BAM files
  * Output: VCF
1. **[discoSnp++] (http://colibread.inria.fr/software/discosnp/)**
  * Description: detects homozygous and heterozygous SNPs and Indels; software composed of 2 modules (kissnp2 and kissreads)
  * Input: raw NGS datasets; fasta, fastq, gzipped or not;
   * no reference genome required; read pairs can be given
  * Output: FASTA
1. **[FamSeq] (http://odin.mdacc.tmc.edu/~wwang7/FamSeqIndex.html)**
  * Description: family-based sequencing studies- provides probability of an individual carrying variant based on family’s raw measurements; accommodates de novo mutations, can perform variant calling at chrX;
  * Input: VCF
  * Output: VCF
1. **[GeneticThesaurus] (http://sourceforge.net/p/ geneticthesaurus/wiki/Example/)**
  * Description: “Annotation of genetic variants in repetitive regions”
  * Input: Initial variant calling from bam → vcf output
   * Reference Genome: need to provide own fasta file for hg19 genome,
  * Output: vcf.gz, vtf.gz, and baf.tsv.gz output
1. **[glfMultiples] (http://genome.sph.umich.edu/wiki/GlfMultiples)**
  * Description: command-line, variant caller
  * Input: GLF
  * Output: VCF
1. **[glfSingle] (http://genome.sph.umich.edu/wiki/GlfSingle)**
  * Description: uses likelihood-based model for variant calling, starts from genotype likelihoods that have been computed from other tools (ex. Samtools BAQ), the likelihoods combine with individual-based prior p(genotype) to generate posterior probabilities
  * Input: GLF
  * Output: VCF
1. **[Halvade] (https://github.com/ddcap/halvade)**
  * Description: command-line; written in Java, “to run halvade a reference is needed for both GATK and BWA and a SNP (dbSNP!) database is required
  * Input: FASTQ
  * Output: VCF
1. **[indelMINER] (https://github.com/aakrosh/indelMINER)**
  * Description: identifies indels from paired-end reads
  * Input: BAM (aligned in SAMtools API)
  * Output: VCF
1. **[Indelocator] (https://www.broadinstitute.org/cancer/cga/indelocator)**
  * Description: (Broad Institute): does not perform realignment, relies on alignments in BAM files (BAM files need aligned before put into indelocator); recommended to use GATK prior;
  * Input: 2 BAM files(tumor & normal), annotated as germline or somatic; also has single sample mode
  * Output: “Output of Indelocator is a high-sensitivity list of putative indel events containing large numbers of false positives. The statistics reported for each event have to be used to custom-filter the list in order to lower false positive rate”
1. **[Isaac Variant Caller] (https://github.com/sequencing/isaac_variant_caller)**
  * Description: detects SNPs and small indels from diploid sample; designed to run on “nux-like platforms”
  * Input: BAM
  * Output: VCF
1. **[KvarQ] (http://www.swisstph.ch/kvarq)**
  * Description: in silico genotyping for selected loci in bacterial genome, written in Python and C
  * Input: FASTQ
   * reference genome or de novo assembly not needed
  * Output:
1. **[LoFreq] (http://sourceforge.net/projects/lofreq/files/)**
  * Description: SNV caller, Python language, standalone program, uncovers cell-population heterogeneity from high-throughput sequencing datasets; calls variants found in <.05% of the population
  * Input: BAM file input→ suggest running through GATK
  * Output:
1. **[Manta] (https://github.com/Illumina/manta)**
  * Description: Calls indels and SVs from paired end reads; standalone, command line program; Written in C++ and Python
  * Input: BAM (can tolerate non-paired-end reads); a matched tumor sample may be provided as well
  * Output: VCF
1. **[MarginAlign] (https://github.com/benedictpaten/marginAlign)**
  * Description: SNV caller, specifically tailored to Oxford Nanopore Reads, written in Python; Package comes with 3 programs, marginAlign, marginCaller (calls SNVs), marginStats (computes qc stats on sam files)
  * Input: SAM
  * Output: SAM
1. **[MendelScan] (http://gmt.genome.wustl.edu/packages/mendelscan/)**
  * Description: Last release March 2014; for analyzing sequencing data in family studies of inherited diseases; variant calls for a family in VCF file; still in alpha-testing
on github, example data uses 1000 genomes dataset
  * Input:
  * Output:
1. **[nanopore] (https://github.com/mitenjain/nanopore)**
  * Description: UCSC Nanopore group (group at UCSC studying using ion channels for analysis of single RNA/DNA structures) software pipeline; tailored to Oxford Nanopore Reads; command line program
  * Input: FASTQ
   * Reference files: FASTA
  * Output: “For each possible pair of read file, reference genome and mapping algorithm an experiment directory will be created in the nanopore/output directory.”
1. **[Platypus] (http://omictools.com/platypus-s1989.html)**
  * Description: Package program, written in C, Python, Cython;  Can identify SNPs, MNPs, short indels, and larger variants; has been tested on very large datasets (1000 genomes)
  * Input: BAM
   * Reference Genome: FASTA (files must be indexed using Samtools or similar program
  * Output: VCF
1. **[QualitySNPng] (http://www.bioinformatics.nl/QualitySNPng/)**
  * Description: detection of SNPs; “can be used as a standalone application with graphical user interface as part of pipeline system”; does not require fully sequenced reference genome; haplotype strategy
  * Input:SAM, ACE
  * Output: GUI
1. **[ReviSTER] (http://revister.sourceforge.net/)**
  * Description: command line program; automated pipeline; utilizes BWA, BLAT, and SAMTools;  utilizes BWA mapping program;
  * Input: FASTQ,
   * Reference sequence file and list file containing STR locations as inputs
  * Output: SAM
1. **[RVD] (http://dna-discovery.stanford.edu/software/rvd/)**
  * Description: command-line program, detection of rare SNVs, relies upon Samtools, can be run in MATLAB
  * Input: BAM
   * Reference Genome: FASTA
  * Output: “The algorithm output is a call table -- a comma-separated file with one line for each base position and each line in the following format:
   * AlginmentReferencePosition, AlignmentBase, Call ,SecondBase, CenteredErrorPrc, ReferenceErrorPrc, SecondBasePrc”
1. **[SNVer] (http://snver.sourceforge.net/)**
  * Description: calls common and rare variants in pool or individual NGS data, reports overall p-value, operating system independent statistical tool, identifies SNPs and INDELs, written in Java, no dependencies, straightforward command-line
   * (SNVerGUI=GUI version) --SNVerGUI: desktop tool for variant detection
  * Input: chrX annotation, sam.zip, bam.zip
   * reference file must be aligned to the data file
  * Output:
1. **[SNVMix] (http://compbio.bccrc.ca/software/snvmix/)**
  * Description: detects SNVs from NGS, post-alignment tool
  * Input: pileupformat (Maq or Samtools)
  * Output:
1. **[SV-M] (http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/structural-variant-machine--sv-m-.html)**
  * Description: Structural Variant Machine - predicts indels, uses split read alignment profiles, validated by Sanger Sequencng
  * Input:paired-end Illumina reads from 1001 genomes project (uses ref plant- 1001genomes.org)
  * Ouptut:
1. **[SNPest] (https://github.com/slindgreen/SNPest)**
  * Description: Standalone program, language C++, Perl
  * Input: mpileup (SAMtools)
  * Output: VCF
1. **[TrioCaller] (http://genome.sph.umich.edu/wiki/TrioCaller)**
  * Description:Command line program, relies on BWA and samtools; genotype calling for unrelated individuals and parent-offspring trios
  * Input: BAM (that has been aligned in BWA and Samtools
  * Output: BCF that can be formatted to VCF using bcftools
1. **[Snippy] (http://www.vicbioinformatics.com/software.snippy.shtml)**
  * Description: finds indels between haploid reference genome and NGS sequence reads
  * Input:read files- FASTQ or FASTA (can be .gz compressed), output- .aln, .tab, .txt
   * Reference genome in FASTA or GENBANK
  * Output:
1. **[VntrSeek] (http://orca.bu.edu/vntrseek/)**
  * Description: pipeline for discovering microsatellite tandem repeats with high-throughput sequencing data
  * Input: gzip-compressed FASTA or FASTQ
  * Output: VCF files; one for TRs and observed alleles, another file contains link to viewer

### Somatic Callers
1. **[Cake] (http://cakesomatic.sourceforge.net/)**
  * Description: standalone program, “pipeline for the integrated analysis of somatic variants in cancer genomes”; integrates four algorithms; written in Perl; required tools: samtools, tabix, vcftools, VarScan2, bambino, cmake, somaticsniper (User guide; workflow page)
  * Input: tumor and normal reads in BAM files, run through variant calling programs to generate intermediate VCF
  * Output: VCF
1. **[MuTect] (http://www.broadinstitute.org/cancer/cga/mutect)**
  * Description: Broad Institute, identification of somatic point mutations in cancer genomes; requires preprocessing of reads (GATK)
  * Input: same as GATK (FASTA reference genome, SAM read files)
  * Output: call-stats, VCF, wiggle files
1. **[Polymutt] (http://genome.sph.umich.edu/wiki/Polymutt)**
  * Description: calls SNVs and detects de novo point mutations in families
  * Input: GLF or BAM or VCF (must have identical chromosome orders)
  * Output: VCF

1. **[Bassovac] (http://tvap.genome.wustl.edu/tools/bassovac/)**
  * Description: Improved Bayesian inversion somatic caller; unlike other software packages, treats effects fully probabilisticallys instead of using ad-hoc modeling; effects are integrated at the atomic level and standard probability theory integrates read tallies to the sample level and to the tumor-normal pair level; "pending public release"
  * Input:
  * Output:
1. **[CLImAT] (http://bioinformatics.ustc.edu.cn/CLImAT/)**
  * Description: standalone program; “accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole genome sequencing data”
  * Input: depth file generated by DFExtract and a config file
  * Output: .results file, .Gtype, LOG.txt, also generates visualization
1. **[DeNovoGear] (http://denovogear.sourceforge.net/)**
  * Description: de-novo variant calling and interpretation; standalone program; dependencies C++ compiler, CMake, HTSlib, Eigen, Boost
  * Input: PED and BCF
  * Output: “The output format is a single row for each putative de novo mutation (DNM), with the following fields”
1. **[EBCall] (https://github.com/friend1ws/EBCall)**
  * Description: Empirical Baysian Mutation Calling; standalone program; uses tumor/normal paired reads and non-paired normal reference samples; dependent on samtools, R and VGAM pack for R
  * Input: BAM
  * Output: not sure what exact type of file- “The format of the result is suitable for adding annotation by annovar.”
1. **[HapMuc] (https://github.com/usuyama/hapmuc)**
  * Description: standalone program; “utilizes the information of heterozygous germline variants near candidate mutations”; Dependent upon- Boost, SAMtools, BEDtools; 3 step workflow
  * Input: BAM
  * Output: BED
1. **[MultiGeMS] (https://github.com/cui-lab/multigems)**
  * Description: Multi-sample Genotype Model Selection
  * Input: .txt, pileup (SAM/BAM converted to pileup format)
  * Output: VCF
1. **[MultiSNV] (https://bitbucket.org/joseph07/multisnv/wiki/Home)**
  * Description: command-line program; calls SNVs from NGS data from multiple samples from the same patient; dependent on R, Git, cmake, Boost and compile libraries
  * Input: BAM or pileup
  * Output: VCF
1. **[MutationSeq] (http://compbio.bccrc.ca/software/mutationseq/)**
  * Description: standalone program, somatic SNV detection in tumor/normal samples; dependent on python, bamtools, boost, and LAPACK
  * Input: BAM
  * Output: VCF4.1 consisting of two parts (meta information & data lines)
1. **[qSNP] (http://www.qcmg.org/bioinformatics/tiki-index.php)**
  * Description: standalone program; SNV caller for somatic variants in “low cellularity cancer samples”
  * Input: BAM, dbSNP data, Illumina data, chrConv
  * Output: “qSNP output files are named using a 4-element pattern: <subject-id>.<snp-type>.<description>.<extension>”
1. **[RADIA] (https://github.com/aradenbaugh/radia/)**
  * Description: RNA and DNA Integrated Analysis for Somatic Mutation Detection; DNA only Method(tumor/normal pair, ignores RNA) or Triple BAM Method (uses all three datasets from same patient); dependent upon python, samtoools, pysam API, BLAT, SnpEff
  * Input: BAM
   * Reference Genome: FASTA indexed with SAMtools faidx
  * Output: VCF
1. **[RVD2] (http://genomics.wpi.edu/rvd2/)**
  * Description: sensitive, variant detection for low-depth targeted NGS data; python module or command- line program;
  * Input: tab- deliminted depth chart format (converted from pileup files)
  * Output: three hdf5 files and a vcf file
1. **[Shimmer] (https://github.com/nhansen/Shimmer)**
  * Description: standalone program; detects somatic SNVs with multiple testing correction, uses Fisher’s exact test; dependent on git, samtools, R, R statmod package; for tumor/normal matched samples
  * Input: BAM
  * Output: VCF
1. **[SNV-PPILP] (http://www.cs.helsinki.fi/en/gsa/snv-ppilp/)**
  * Description: Refines GATK’s Unified Genotyper SNV calls for “multiple samples assumed to form a phylogeny”
  * Input:
  * Output:
1. **[SomaticSniper] (http://gmt.genome.wustl.edu/packages/somatic-sniper/)**
  * Description: command-line application to identify SNPs between tumor/normal pairs- predicts probability of difference between two
  * Input: BAM
   * Reference Genome in FASTA
  * Output: VCF
1. **[Strelka] (https://sites.google.com/site/strelkasomaticvariantcaller/)**
  * Description: somatic variant calling workflow for matched tumor-normal samples; detects indels; runs on *nux-like platform
  * Input: BAM (must be sorted and indexed)- Strelka does own realignment around indels-- don’t need to do this type of pre-processing
  * Output: pair of VCF files
1. **[Triodenovo] (http://www.pitt.edu/~wec47/triodenovo.html)**
  * Description: Bayesian framework for calling de novo mutations in trios
  * Input: VCF file with PL or GL fields (recommend using GATK or samtools to generate)
  * Output: out_vcf
1. **[UNCeqr] (http://lbg.med.unc.edu/~mwilkers/unceqr_dist/)**
  * Description: finds somatic mutations using integration of DNA and RNA seq data-- boosts sensitivity for low purity tumors and rare mutations;
  * Input:”can accept a variety of sequencing inputs and configurations”
  * Output: “table of somatically mutated sites and associated information. These somatic mutations can be annotated with predicted transcript and protein effects using third party tools, such as Annovar”
1. **[Virmid] (http://sourceforge.net/projects/virmid/)**
  * Description: Virtual Microdissection for SNP calling; Java based; for disease-control matched samples; uncovers SNPs with low allele frequency by considering alpha contamination
  * Input: BAM (must be sorted and indexed- samtools sort)
  * Output: VCF and report file

### Germline + Somatic  Callers
1. **[VarScan 2] (http://massgenomics.org/varscan)**
  * Description: identify germline variants, private and shared variants, somatic mutations, and somatic CNVs; detects indels
  * Input: SAMtools pileup
  * Output: VCF

1. **[BAYSIC] (http://genformatic.com/baysic/)**
  * Description: Bayesian method; combines variant calls from different methods (GATK, FreeBayes, Atlas, Samtools, etc)
  * Input: VCF format from one or more variant calling programs
  * Output: VCF file containing integrated set of variant calls
1. **[MSIsensor] (https://github.com/ding-lab/msisensor)**
  * Description: Microsatellite instability detection; C++ program, detects somatic and germline variants in tumor-normal paired data
  * Input: BAM index files (normal and tumor)
  * Output:
1. **[Beagle version 4] (http://faculty.washington.edu/browning/beagle/beagle.html)**
  * Description: software package: genotype calling, phasing, imputation of ungenotyped markers, and identity-by-descent segment detection:unsure if this one is in the right category; genotype calling, phasing, imputation of ungenotyped markers, and identity-by-descent segment detection;
  * Input: VCF
  * Output: VCF
1. **[QuadGT] (http://www.iro.umontreal.ca/~csuros/quadgt/)**
  * Description: software package, SNV calling from normal-tumor pair and two parent genomes; quantifies descent-by-modification relationships; Written in Java
  * Input: BAM files (parsed by Picard/Samtools API)
   * Reference Genome; FASTA
  * Output: VCF
1. **[RAREVATOR] (http://sourceforge.net/projects/rarevator/)**
  * Description: RAre REference VAriant annotaTOR; command line;  “identification and annotation of germline and somatic variants in rare reference allele loci from second generation sequencing data”; Bayesian genotype likelihood model
  * Input: BED or VCF files from GATK
  * Output: two VCF files (one for SNVs, one for Indels)
1. **[Scalpel] (http://scalpel.sourceforge.net/)**
  * Description: Used for detecting indels in a reference genome; performs localized micro-assembly of specific regions of interest; can do single, de novo, somatic reads; requires that raw reads are aligned with BWA
  * Input: BAM
  * Output: either VCF or ANNOVAR
1. **[SOAPsnp] (http://soap.genomics.org.cn/soapsnp.html)**
  * Description: based on Baye’s theorem; calls consensus genotype
  * Input:SOAP short read alignment results
  * Output: GLF, option of flat tabular format
1. **[VariantMaster] (http://sourceforge.net/projects/variantmaster/)**
  * Description: “extract causative variants for monogenic and sporadic genetic diseases”; uses ANNOVAR;
  * Input: BAM or VCF files (from SAMtools, GATK)
  * Output:

## Downstream Analysis of Variants
1. **[PrediXcan] (https://github.com/hakyimlab/PrediXcan https://github.com/hriordan/PrediXcan/)**
  * Description: command-line, standalone package program; available in Perl, Python, and R versions; predicts liklihood of a gene being related to a certain phenotype- “that directly tests the molecular mechanisms through which genetic variation affects phenotype.”; no actual expression data used, only in silico expression; “PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.”
  * Input: genotype and phenotype file (doesn’t specify file type)
  * Output:default values: genelist, dosages (file format:  snpid rsid) , dosage_prefix, weights, output

1. **[ATHENA] (http://ritchielab.psu.edu/software/athena-downloads)**
  * Description: Analysis Tool for Heritable and Environmental Network Associations; software package, combines machine learning model with biology and statistics to predict non-linear interactions
  * Input: Configuration file, Data file, Map file (includes rsID)
  * Output: Summary file, Best model file, dot file, individual score file, cross-validation file
1. **[CCRaVAT and QuTie] (http://www.sanger.ac.uk/resources/software/rarevariant/#t_2)**
  * Description: (Wellcome Trust Sanger) Case-Control Rare Variant Analysis Tool and Quantitative Trait; software packages for large-scale analysis of rare variants
  * Input: PED file and MAP file
  * Output: Five tab-delimited txt files
1. **[GCTA] (http://cnsgenomics.com/software/gcta/)**
  * Description: Genome Wide Complex Trait Analysis; package program, command line interface; estimates variance by all SNPs; 5 main functions: “data management, estimation of the genetic relationships from SNPs, mixed linear model analysis of variance explained by the SNPs, estimation of the linkage disequilibrium structure, and GWAS simulation”
  * Input: PLINK binary PED files, MACH output format
  * Output:
1. **[GenomeComb] (http://genomecomb.sourceforge.net/)**
  * Description: package for analysis of complete genome data; annotation using public data or custom tracks, automated primer desing for Sanger or Sequenom validation; “The cg process_illumina command can be used to generate annotated multisample data starting from fastq files, using tools such as bwa for alignment and GATK and samtools for variant calling. Sequencing data can also be imported from Complete Genomics (cg_process_sample command), Real Time Genomics (cg_process_rtgsample command) and VariantCallFormat (VCF) variant files (vcf2sft command).”
  * Input: Sequencing data from Complete Genomics, Illumina, SOLiD and VCF;
  * Output: standard file format used is a simple tab delimited file (.sft, .tsv)
1. **[Genome Track Analyzer] (http://ancorr.eimb.ru/)**
  * Description: compares genome tracks; allows user to compare DNA expression/binding;
  * Input: multiple: SGR/TXT, BED, BED6, GFF;  if using prealigned sequence data- use MACS peak caller: BAM, BED, SAM, ELAND
  * Output:
1. **[GVCBLUP] (http://animalgene.umn.edu/gvcblub)**
  * Description: animal gene mapping; “genomic prediction and variance component estimation of additive and dominance effects”; standalone program, command line interface, writting in C++ and Java
  * Input:
  * Output:
1. **[HOMOG] (http://www.jurgott.org/linkage/homog.htm)**
  * Description: Analyzes heterogeneity with respect to single marker loci or known maps of markers; Carries out homogeneity test for alternative hypothesis “Two family types, one with linkage betweeen a trait to a marker or map of markers, the other without linkage”
  * Input: HOMOG.DAT - described on website
  * Output: HOMOG.OUT
1. **[INTERSNP] (http://intersnp.meb.uni-bonn.de/)**
  * Description: GWIA for case-control SNP and quantitative traits; selected for joint analysis using priori information; Provides linear regression framework, Pathway Association Analysis, Genome-wide Haplotype Analysis,
  * Input: PLINK input formats (ped/map, tped/tfam, bed/bim/fam) Compatible with SetID files
   * Gene reference file: Ensembl Release 75
  * Output: covariance matrix for regression models
1. **[mtSet] (https://github.com/PMBio/mtSet)**
  * Description: Currently only the standalone version available, but moving to LIMIX software suite; offers set tests- allows for testing between variants and traits; accounts for confounding factors ex. relatedness
  * Input: sample-to-sample genetic covariance matrix needs to be computed; multiple types of input; simulator requires input genotype and relatedness component;
  * Output: resdir (result file of analysis), outfile (test statistics and p-values), manhattan_plot (flag)
1. **[MultiBLUP] (http://dougspeed.com/multiblup/)**
  * Description: Package program, command line interface; constructs linear prediction models; Best Linear Unbiased Prediction; improves upon BLUP involving kinship matrices; options: pre-specified kinships, regional kinships, adaptive multiblups, LD weightings
  * Input: PLINK format
  * Output:.reml, .indi.blp

## Variant Annotation
1. **[ANNOVAR] (http://annovar.openbioinformatics.org/en/latest/)**
  * Description: command-line tool, supports SNPs, INDELs, CNVs and block substitutions, provides wide variety of annotation techniques, depends upon multiple databases (each needing to be downloaded); annotates genetic variants; utilizes RefSeq, UCSC Genes, and the Ensembl gene annotation systems; can compare mutations detected in dpSNP or 1000 Genomes Project; Very popular
   *“The final command run TABLE_ANNOVAR, using dbSNP version 138, 1000 Genomes Project 2014 Oct version, NIH-NHLBI 6500 exome database version 2 (referred to as esp6400siv2), dbNFSP version 2.6 (referred to as ljb26), dbSNP version 138 (referred to as snp138) databases and remove all temporary files, and generates the output file called myanno.hg19_multianno.txt”
  * Input: VCF, ANNOVAR input format (simple text-based format); can convert other formats into ANNOVAR input format
  * Output: VCF (if input VCF), output file with multiple columns, tab-delimited output file
1. **[wANNOVAR] (http://wannovar.usc.edu/)**
  * provides web-based access to ANNOVAR software
1. **[PolyPhen-2] (http://genetics.bwh.harvard.edu/pph2/)**
  * Description: Very popular; Polymorphism Phenotyping; Web application; predicts impact of amino acid substitution on protein; Calculates Bayes posterior probability (Last update July 2015)
  * Input: FASTA
  * Output:
1. **[SIFT] (http://sift.jcvi.org/)**
  * Description: predicts how an amino acid substitution will affect protein function; Based on degree of conservation of amino acid residues- collected though PSI-BLAST; can be applied to nonsynonymous polymorphisms or laboratory-induced missense mutations; links to dbSNP 132, GRCh37; Standalone or web app program; Very popular
  * Input: Uniprot ID or Accession, Go term ID, Function name, Species Name or ID, etc
  * Output:
1. **[snpEff] (http://snpeff.sourceforge.net/)**
  * Description: Genetic variant annotation and effect prediction toolbox; integrated with Galaxy, GATK, and GNKO; can annotate SNPs, INDELs, and multiple-nucleotide polymorphisms; categorizes effects into classes by functionality; Very popular; Standalone or Web app; Claims to calculate all SNPs in 1000 genomes (EMBI) in less than 15 minutes; can annotate SNPs, MNPs, and insertions and deletions; Provides assessment of impact of the variant ( low, medium or high)
  * Input: VCF, BED
  * Output: VCF (with new ANN field, also used in ANNOVAR and VEP), HTML summary files
1. **[SnpSIFT] (http://snpeff.sourceforge.net/SnpSift.html)**
  * Description: Filter and manipulate annotated files; Part of SnpEff main distribution; one variants have been annotated, this can be used to filter your data to find relevant variants
  * Input:
  * Output:
1. **[VAAST 2] (http://www.yandell-lab.org/software/vaast.html)**
  * Description: Variant Annotation, Analysis, and Search Tool; probabilistic search tool for identifying damage genes and the disease causing variants; can score both coding and non-coding variants; Four tools: VAT (Variant annotation tool), VST (Variant Selection Tool), VAAST, pVAAST (for pedigree data); updated April 2015
  * Input: FASTA, GFF3, GVF
  * Output: CDR (condenser file), VAAST file (both unique to VAAST)
1. **[VEP] (http://useast.ensembl.org/info/docs/tools/vep/index.html?redirect=no)**
  * Description: (Ensembl) Variant Effect Predictor; determines effect of variants on genes, transcripts, and protein sequence; uses SIFT and PolyPhen
  * Input: Coordinates of variants and nucleotide changes; whitespace- separated format, VCF, pileup, HGVS
  * Output: VCF, JSON, Statistics

1. **[ABSOLUTE] (http://www.broadinstitute.org/cancer/cga/absolute)**
  * Description: (Broad Institute); can estimate purity and ploidy to compute absolute copy number and mutation multiplicitie; reextracts data from the mixed DNA population
  * Input: HAPSEQ segdat or segmentation file
  * Output: per-sample output directory and subdirectory providing per-sample text files containing standard out being emitted from R
1. **[Alamut Batch] (http://www.interactive-biosoftware.com/alamut-batch/)**
  * Description: high-throughput annotation software for NGS analysis; for “intensive variant analysis workflows”; “enriches raw NGS variants with dozens of attributes”; based on clinically oriented Alamut database; Supports human genes; easy to integrate into pipeline (Latest Release- July 2015)
  * Input:VCF, tab-delimted file
  * Output: tab-separated file of annotations
1. **[AVIA] (http://avia.abcc.ncifcrf.gov/apps/site/index)**
  * Description: Annotation, Visualization, and Impact Analysis; “The tool is based on coupling a comprehensive annotation pipeline with a flexible visualization method. We leveraged the ANNOVAR (Wang et. al, 2010) framework for assigning functional impact to genomic variations by extending its list of reference annotation databases (RefSeq, UCSC, SIFT, Polyphen etc.) with additional in-house developed sources (Non-B DB, PolyBrowse).”
  * Input: BED
  * Output: Table of annotations with gene annotation features
1. **[BioR] (http://bioinformaticstools.mayo.edu/research/bior/)**
  * Description: (Mayo Clinic) (Page last updated June 2015) Biological Reference Repository; “data integration tool that enables coordinate based searches and joins based on strings”; “BioR consists of two parts 1) the BioR toolkit which depends on Java…. 2) the BioR catalogs which are the data files used by the system”
  * Input: VCF
   * BioR-Supported Catalogs (tar-gzip files): dbSNP, 1000 genomes, HapMap, OMIM, NCBIGene
  * Output: VCF + JSON
1. **[CADD] (http://cadd.gs.washington.edu/)**
  * Description: Combined Annotation Dependent Depletion; tool for scoring SNV deletions/insertions; “integrates multiple annotations into one metric”; Score strongly correlates with allelic diversity and pathogenicity; links to 1000 Genome variants; uses Ensembl Variant Effect Predictor
  * Input: VCF
  * Output: CADD score
1. **[CandiSNPer] (http://www2.hu-berlin.de/wikizbnutztier/software/CandiSNPer/)**
  * Description: web application, characterizes SNPs located in vicinity of SNP of interest;
  * Input: enter SNP ID (rsID), choose population, region, measure for LD, threshold plot format, color of SNPs, and chose to show genes
  * Output: Imagefile
1. **[CanvasDB] (https://github.com/UppsalaGenomeCenter/CanvasDB)**
  * Description: “local database infrastructure for analysis of targeted- and whole genome re-sequencing projects”; dependent on MySQL, R, and ANNOVAR
  * Input:
  * Output:
1. **[CAROL] (http://www.sanger.ac.uk/resources/software/carol/)**
  * Description: (Wellcome Trust Sanger); Combined Annotation scoRing toOL; Combined functional annotation score of nonsynonymous coding variants; Combines information from PolyPhen-2 and SIFT
  * Input: tab-delimited with columns obtained from PolyPhen-2 and SIFT output
  * Output: tab-delimited file
1. **[CHASM] (http://wiki.chasmsoftware.org/index.php/Main_Page)**
  * Description: Cancer-specific High-throughput Annotation of Somatic Mutations; Last updated May 2014; uses Random Forest Method to “distinguish between driver and passenger somatic mutations”; Positive driver class curated from COSMIC database; packed together with  SNVBox (database)
  * Input:Passenger mutation rates, Transcript and amino acid change, Genomic coordinates
  * Output: CHASM score, p-value, FDR
1. **[CRAVAT] (http://www.cravat.us/)**
  * Description: Cancer-Related Analysis of Variants Toolkit; Web application; Uses CHASM, VEST, SNVGet; “CRAVAT provides predictive scores for germline variants, somatic mutations and relative gene importance, as well as annotations from published literature and databases” Latest Release May 2015;
  * Input: VCF, CRAVAT format
  * Output: CRAVAT report- MS Excel spreadsheet or tab-separated file (emailed)
1. **[CUPSAT] (http://cupsat.tu-bs.de/)**
  * Description: Cologne University Protein Stability Analysis Tool; “tool to predict changes in protein stability upon point mutations”; web service program; Can predict mutant stability from existing PDB structures or custom protein structures
  * Input:for PDB- provide PDB ID and Amino Acid Residue Number; for custom- PDB file format
  * Output:
1. **[DANN] (https://cbcl.ics.uci.edu/public_data/DANN/)**
  * Description: Deleterious Annotation of genetic variants; standalone program, uses “the same feature set and training data as CADD to train a deep neural network”; can catch nonlinear relationships; “There are four different datasets: training, validation, testing, and ClinVar_ESP...The ClinVar_ESP dataset is also a testing set containing a set of “gold standard” pathogenic and benign variants”
  * Input:
  * Output:
1. **[ESEfinder] (http://rulai.cshl.edu/cgi-bin/tools/ESE3/esefinder.cgi?process=matrices)**
  * Description: Exonic Splicing Enhancer; useful for interpretation of point mutations/polymorphisms that are disease-associated; GUI interface; web app program
  * Input: FASTA
  * Output: html or plain text format, graphical display of results
1. **[Exomiser] (http://www.sanger.ac.uk/resources/software/exomiser/)**
  * Description: Wellcome Trust Sanger; functionally annotates variants from whole-exome sequencing data; Based on Jannovar and uses UCSC KnownGene; Java program; web app program (Page last modified Feb 2015)
  * Input: VCF
  * Output: TSV, VCF
1. **[FamAn] (https://sites.google.com/site/famannotation/home)**
  * Description: Automated variant annotation pipeline for family-based sequencing studies; Annotaties SNVs and INDELs; 4 models- autosomal dominant, autosomal recessive, de novo mutations and a general model; “A variety of annotations are provided for each segregating variant: number of family (and family ID) each variant hits, variant genomic location and coding effect (based on snpEff), loss-of-function mutation annotation, selected ENCODE annotation, allele frequency in the 1000 Genomes Project, allele frequency in the Exome Variant Server (ESP6500), segmental duplication annotation,  SIFT, PolyPhen2, LRT, MutationTaster, GERP++, PhyloP, SiPhy, etc.” (Last updated May 2014)
  * Input: VCF
  * Output: two excel compatible outputs
1. **[GeneTalk] (http://www.gene-talk.de/)**
  * Description: Combines tool for filtering and data analysis with an online network for genetic professionals; Different degrees- basic license, premium license, in-house solution (the last ones are paid for- Commercial tool?)
  * Input: VCF
  * Output: GeneTalk Annotation- includes clinical data, medical relevance, scientific relevance (http://www.gene-talk.de/public/GeneTalk_Whitepaper_Annotations.pdf)
1. **[GeneVetter] (http://genevetter.kidneyomics.org/)**
  * Description: “GeneVetter is a tool designed for investigation of the background prevalence of exonic variation in the Phase 3 1000 Genomes data under user defined filtering criteria”; web app program; GeneVetter uses GRch37p4 (hs37d5.fa.gz), dbSNP build 138, 1000G Phase 3, clinvar_2014072
  * Input: VCF
  * Output: TIMS score, summary table, PCA plot
1. **[GSITIC] (http://www.broadinstitute.org/software/cprg/?q=node/31)**
  * Description: (Broad Institute) Last update- July 2014; Identifies genomic regions that are significantly “amplified or deleted”; Each is given a G score; gives genomic locations and q-values from aberrant regions
  * Input: segmentation file -seg, markers file -mk (required); -array file list -alf, CNV file -cnv
   * Reference genome: -refgene (created in MATLAB, GISITIC provides four reference genomes: hg16.mat, hg17.mat, hg18.mat, hg19.mat
  * Output: All lesions file (text file), amplifications file (text file), deletion genes file (text file), Gistic Scores file, Segmented copy number (pdf file), amplification score GISTIC plot (pdf file), Deletion score/q-vale GISTIC plot (pdf file)
1. **[HOPE] (http://www.cmbi.ru.nl/hope/about)**
  * Description: Have yOur Protein Explained; Web app program; Automatic mutant analysis server that provides structural effects of a mutation; Uses BLAST against UniProt and PDB along with homology modeling
  * Input: FASTA protein sequence, or accession code of protein of interest
  * Output: a report containing information from a “decision tree” and illustrated figures and animations
1. **[Human Splicing Finder] (http://umd.be/HSF/)**
  * Description: Last update: May 2013; aimed to help study pre-mRNA splicing; combines 12 algorithms to identify mutations’ effect on splicing motifs; uses ensembl database 70
  * Input: Gene Name, Ensembl transcript ID, Ensembl Gene ID, Consensus CDS, RefSeq Peptide ID, or own sequence (looks like you can enter FASTA)
  * Output: Chart with columns for predicted signal, predicted algorithm, cDNA position and interpretation
1. **[LARVA] (http://larva.gersteinlab.org/)**
  * Description: Large-scale Analysis of Variants in noncoding Annotations; New version released July 2015; Command-line program; used for studying noncoding variants; integrates comprehensive set of noncoding elements, modeling their mutation count; Dependent on C++ and BEDtools
  * Input: multiple
  * Output:
1. **[LINKAGE] (http://www.jurgott.org/linkage/LinkagePC.html)**
  * Description:three main programs: mlink (calculates lod scores at fixed values for the recombination fraction in one interval of a genetic map), linkmap (calculates location scores for positions of a disease locus along a marker), and ilink (estimates parameters including recombination fractions, allele frequencies, penetrances, etc)
  * Input: pedfile (processed by MAKEPED) and datafile (reflects loci for each individual; set in PREPLINK)
  * Output:
1. **[MAC] (http://sourceforge.net/projects/mnvannotationcorrector/)**
  * Description: MNV Annotation Corrector; Ad hoc software, fixes incorrect amino acid predictions that are caused by multiple nucleotide variations; Uses existing annotators ANNOVAR, SnpEff, VEP (last update April 2015) (only 1 download this week → not popular)
  * Input: List of called SNVs and corresponding BAM
  * Output: Report identifying block of mutation within codon (BMCs)
1. **[mit-o-matic] (http://genome.igib.res.in/mitomatic/)**
  * Description: focuses on mtDNA, provides clinically relevant information from different resources; two component pipeline: command link for alignment of NGS reads and online version that provides genetic report on mitocondrial variants
  * Input:FASTQ, pileup
   * Reference sequence: rCRSm
  * Output: Online version gives comprehensive genetic report
1. **[Mutadelic] (http://krauthammerlab.med.yale.edu/mutadelic/index.html)**
  * Description: Web App program; “This application generates reports on inherited mutations in five genes (ANK1, SLC4A1, SPTA1, SPTB and EPB42) associated with the following rare Mendelian blood disorders: Hereditary Spherocytosis (HS), Hereditary Elliptocytosis (HE) and Hereditary Pyropoikilocytosis”; Newer program- recently validated on omictools
  * Input: Can upload coordinates of DNA variants or VEP
  * Output: Displayed on web or can be downloaded in Excel or RDF format
1. **[MutationTaster] (http://www.mutationtaster.org/)**
  * Description: (Last post on site 2014) Web app program; Rapid evaluation of disease causing alterations; uses NCBI 37 and Ensembl 69
  * Input: HGNC symbol, NCBI GeneID, or Ensembl ID,
  * Output: Report containing prediction, summary, name of alteration, etc
1. **[MutPred] (http://mutpred.mutdb.org/)**
  * Description: web app tool; Classifies amino acids substituation as disease associated or neutral in humans; Last modified Feb. 2014; Based on SIFT, trained using Human Gene Mutation Database
  * Input:
  * Output: “The output of MutPred contains a general score (g), i.e., the probability that the amino acid substitution is deleterious/disease-associated, and top 5 property scores (p), where p is the P-value that certain structural and functional properties are impacted.”
1. **[MutSigCV] (http://www.broadinstitute.org/cancer/cga/mutsig)**
  * Description: (Broad Institute) Mutation Significance (CV= covariates); Analyzes mutations discovered in DNA sequencing to identify genes that were mutated more often than expected
  * Input: mutations.maf, coverage.txt, covariates.txt
  * Output: output.txt
1. **[NGS-SNP] (http://stothard.afns.ualberta.ca/downloads/NGS-SNP/)**
  * Description: Collection of command-line scripts for providing rich SNP annotations; “NCBI, Ensembl, and Uniprot IDs are provided for genes, transcripts and proteins when applicable”;
  * Input: Samtools consensus pileup, Maq, diBayes, Genetic format, VCF
  * Output: File containing annotated SNPs is copied from SNP list and some classes are added
1. **[Oncotator] (http://www.broadinstitute.org/oncotator)**
  * Description: (Broad Institute) “Tool for annotating human genomic point mutations and data relevant to cancer researchers”; Web app; Supports annotation of data from ClinVar, dbSNP, 1000 genomes (plus many other external sites); Only GRCh27 coordinates supported; Last update: April 2015
  * Input: tal-delimited file
  * Output: tab-delimited MAF
1. **[PANTHER] (http://omictools.com/panther-s649.html)**
  * Description: Protein ANalysis THrough Evolutionary Relationships; Web app program, also has its own database; Classification system used to classify proteins and their genes; Also, “Estimates the likelihood of a particular nonsynonymous (amino-acid changing) coding SNP to cause a functional impact on the protein”; Updated in 2015
  * Input: Data from PANTHER, IDs from Ensembl, EntrezGene, NCBI GI numbers, NCBI UniGene IDs HUGO, UniProt; if ID type is not one of the above, can input txt file or excel format
  * Output: Analysis results displayed online
1. **[PESX] (http://cubio.biology.columbia.edu/pesx/pesx/)**
  * Description: Putative Exonic Splicing Enhancers/Silencers; (Can’t tell if this is outdated or not)
  * Input: FASTA or plain text
  * Output: Excel spread sheet
1. **[Phen-Gen] (http://phen-gen.org/index.html)**
  * Description: Combines patient's’ disease symptoms with sequencing data; Standalone or Web app version; Only excepts 1 family per run, in order to evaluate unrelated individuals, each sample needs to be run individually
  * Input: Variant- VCF; Pheotype- HPO; Pedigree- PED
  * Output: Combined scores file, variants for top genes file
1. **[PMUT] (http://mmb.pcb.ub.es/PMut/)**
  * Description: Aimed at annotation and prediction of pathological mutations; based on different kinds of sequence info and neural networks to process information
  * Input: FASTA
  * Output; Simple yes/no and reliability index
1. **[PROVEAN] (http://provean.jcvi.org/index.php)**
  * Description: Protein Variation Effect Analyzer; predicts whether an amino acid substitution or indel has impact on biological function of the protein; “comparable to SIFT or Polyphen-2”; Standalone, Web app, Command line or GUI; Last update May 2014
  * Input: FASTA, list of variants;
  * Output: tab-separated columns including Variant, Provean Score and prediciton
1. **[Rescue-ESE] (http://genes.mit.edu/burgelab/rescue-ese/)**
  * Description: “An online tool for identifying candidate ESEs in vertebrate exons”; Web application; For human, mouse, zebrafish, pufferfish
  * Input: multi-FASTA or plain text
  * Output:
1. **[SCAN] (http://scandb.org/newinterface/index_v1.html)**
  * Description: Web application program, includes a database as well; Database contains physical-based SNP annotations and functional annotations; “Information on physical, functional, and LD annotation served on the SCAN database comes directly from public resources, including the HapMap (release 23a), NCBI (dbSNP 129), or is information created by us using data downloaded from these public resources”; “SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms”
  * Input:
  * Output: HTML, comma-delimited, tab-delimited
1. **[SeattleSeq Annotation] (http://snp.gs.washington.edu/SeattleSeqAnnotation137/)**
  * Description: “SeattleSeqAnnotation137 was most recently updated October 13, 2013. The current version is 8.08. The most recent site, based on dbSNP build 141, and hg38/NCBI 38”; Provides annotations for SNVs and Indels- includes dbSNP rsID, gene names and accession numbers, variation functions, protein positions and amino acid changes, conservation scores, HapMap frequencies, PolyPhen predictions and clinical association.
  * Input: Maq, gff, CASAVA, VCF, GATK bed, custom
  * Output: “default output file format is a header line (starting with "#") followed by tab-separated annotations”; VCF
1. **[seqminer 3.7] (https://cran.r-project.org/web/packages/seqminer/)**
  * Description: “Efficiently Read Sequence Data (VCF Format, BCF Format and METAL Format) into R”; Command line package program; Published August 2015
  * Input: VCF, BCF
  * Output: VCF
1. **[SG Adviser] (https://genomics.scripps.edu/ADVISER/Home.jsp)**
  * Description: Scripps Genome Annotation and Distributed Variant Interpretation Server, web developed applications for variant annotation, “Downstream applications of variant annotation include: Clinical sequencing applications including: carrier testing, or identification of causal variants in molecular diagnosis, tumor sequencing, or diagnostic odyssey. Prioritization of variants prior to statistical analysis of sequence based disease association studies, especially for automated set-generation and enrichment of likely functional variants within sets. Identification of causal variants in post-GWAS/linkage sequencing studies. Identification of causal variants in forward genetic screens (stay tuned for non-human annotation)”
  * Input: SNV- VCF, BED, and a few others; CNV- BED, CNVator, plus others
  * Output: tab-delimited file
1. **[SNAP-2] (https://rostlab.org/services/snap/)**
  * Description: “SNAP2 is a trained classifier that is based on a machine learning device called "neural network". It distinguishes between effect and neutral variants/non-synonymous SNPs by taking a variety of sequence and variant features into account”; predicts impact of amino acid substitution on protein
  * Input:Protein Sequence in FASTA
  * Output: “Each model outputs one score for each output class (neutral/effect). These scores of 10 models are averaged in a jury decision. The final score is calculated as the difference of the average score for effect and the average score for neutral’
1. **[SNiPA] (http://snipa.helmholtz-muenchen.de/snipa/)**
  * Description: “SNiPA offers both functional annotations and linkage disequilibrium information for bi-allelic genomic variants (SNPs and SNVs)”; Based on EMBI 1000 Genomes Project; uses GRCh37 and Ensembl
  * Input: rs identifiers
  * Output: list of SNiPA cards
1. **[SNPAAMapper] (http://www.ccmb.med.umich.edu/ccdu/SNPAAMapper)**
  * Description: “SNPAAMapper is a downstream variant annotation program that can effectively classify variants by region (e.g. exon, intron, etc), predict amino acid change type (e.g. synonymous, non-synonymous mutation, etc), and prioritize mutation effects (e.g. CDS versus 5’UTR, etc).”
  * Input: VCF in tab-delimited format
  * Output: spreadsheet result file
1. **[SNPMeta] (http://www.tc.umn.edu/~konox006/Code/SNPMeta/)**
  * Description: Generates metadata for SNPs, including gene name, whether the SNP is coding or noncoding, and whether the SNP is synonymous or nonsynonymous; last update October 2013
  * Input: FASTA
  * Output: dbSNP submission report format or tab-delimited file
1. **[SNPnexus] (http://www.snp-nexus.org/)**
  * Description: SNPnexus allows single queries using dbSNP identifiers or chromosomal regions for annotating known variants. The users are also allowed to provide novel in-house SNPs/indels using genomic coordinates on clones, contigs and chromosomes. For practical purposes, SNPnexus allows batch queries comprising SNP data using dbSNP identifiers or genomic coordinates; Web app, also has its own database; Last update May 2014;
  * Input: genomic position, chromosomal region, dbSNP id, tab-delimited, VCF
  * Output: Tables for genomic mapping, gene/protein consequences, effect on protein function, hapmap population, regulatory elements, conservation, phenotype and disease association, structural variants
1. **[SNPs&Go] (http://snps-and-go.biocomp.unibo.it/snps-and-go/)**
  * Description: Predicts human disease-related mutations in proteins with functional annotations; web application; collects framework information;
  * Input: UNIPROT accession number, mutation postition, wild-type residue, substituting residue
  * Output:
1. **[TRAMS] (http://figshare.com/articles/Tool_for_rapid_annotation_of_microbial_SNPs_TRAMS_a_simple_program_for_rapid_annotation_of_genomic_variation_in_prokaryotes_/782261)**
  * Description: Tool for Rapid Annotation of Microbial SNPs; functional annotation of genomic SNPs, annotates as synonymous, nonsynonymous or nonsense; standalone program; have developed workflow in Galaxy
  * Input: tab-delimited file containing SNP locations, reference nucleotide and SNPs in different strands
   * Reference genome: Genbank or EMBL format
  * Output:
1. **[Trinotate] (http://trinotate.github.io/)**
  * Description: Transcriptome functional annotation and analysis; “Trinotate makes use of a number of different well referenced methods for functional annotation including homology search to known sequence data (BLAST+/SwissProt/Uniref90), protein domain identification (HMMER/PFAM), protein signal peptide and transmembrane domain prediction (singalP/tmHMM), and comparison to currently curated annotation databases (EMBL Uniprot eggNOG/GO Pathways databases)”
  * Input: trinity.fasta, BLAST homologies (confusing workflow, don’t really understand, has a ton of dependencies)
  * Output: Annotation report in .xls
1. **[VAGrENT] (http://www.sanger.ac.uk/resources/software/vagrent/)**
  * Description: (Wellcome Trust Sanger) Variation Annotation GENeraTor; Suite of perl modules, compares variants with reference genome annotations and generates effects of each variant; Last modified Feb 2014
  * Input:
  * Output:
1. **[VARIANT] (http://variant.bioinfo.cipf.es/)**
  * Description: VARIant ANalysis Tool; reports properties of any variant in human, mouse or rat genomes; Requires log in
  * Input: VCF, GFF, BED
  * Output: Filtered Variants (same as input); Genes with Variants (txt), Consequence type histogram (txt)
1. **[VariantAnnotation] (http://www.bioconductor.org/packages/2.12/bioc/html/VariantAnnotation.html)**
  * Description: (Bioconductor) Annotates and filters genetic variants; data is read in VCF and variants are identified according to region (conding, intron, intergenic, spice site, etc); Uses SIFT and PolyPhen to provide protein function predictions; uses 1000 Genomes Chromosome 22 for sample data
  * Input; VCF
  * Output:
1. **[Variobox] (http://bioinformatics.ua.pt/software/variobox/)**
  * Description: Desktop tool for annotation, analysis and comparison of human genes; Standalone program; Annotation data obtained from wave, PDB, or UniProt, LRG, or RefSeq; Researched genes compared to sequences for LRG and RefSeq;
  * Input: DNA Sequence chromatogram file (.scf, .abi); DNA electropherogram file (.ab1); FASTA
  * Output: “Gene Panel” (looks like variation viewer)
1. **[VarioWatch] (http://genepipe.ncgm.sinica.edu.tw/variowatch/main.do)**
  * Description: annotations of genomic variants; Last update February 2015; uses dbSNP 141, annotation release 105;
  * Input: gene name, SNP ID, position
  * Output: specially designed views
1. **[VAT] (http://vat.gersteinlab.org/)**
  * Description: Variant Annotation Lab; Updated May 2014,
  * Input:VCF, Interval
  * Output: Gene and sample summaries
1. **[WhopGenome] (https://cran.r-project.org/web/packages/WhopGenome/index.html)**
  * Description: fast access to whole genome, population scale variation data, Links to UCSC Bioconductor, and AmiGO (for pedigree formats)
  * Input: VCF, FASTA, Phylip, MAF, plus others
   * Reference sequence: FASTA
  * Output:

## CNV (copy number variations) Identification
1. **[cn.MOPS] (http://bioconductor.org/packages/2.12/bioc/html/cn.mops.html)**
  * Description: Bioconductor; “Mixture of Poissons for CNV detection of NGS data”; models depth of coverage at each genomic position to reduce read count bias; Bayesian approach, written in C++
  * Input: Read count matrices, genomic range objects (has ability to convert BAM files to these)
  * Output:
1. **[CNA-Seq] (http://chipster.csc.fi/manual/cna-define-experiment.html)**
  * Description: Tool in Chipster analytical pipeline, count reads in bins, segment, and call copy number aberrations, plots CNV profiles, identifies common regions, tests DNA copy number induced differential expression, plots combined profiles and copy numbers, adds cytogenic bands, and counts overlapping CNVs by comparing database of genomic variants (http://dgv.tcag.ca/dgv/app/home)
  * Input:
  * Output:
1. **[CNValidator] (https://code.google.com/p/cnvalidator/)**
  * Description: Package program, Command line interface; “Identifies CNVs based on homozygous SNPs and the ratio of heterozygous SNP reads”;
  * Input: text based tab-delimited files
  * Output: txt output
1. **[CNVer] (http://compbio.cs.toronto.edu/cnver/)**
  * Description: Package program, command line interface; “supplements the depth-of-coverage with paired-end mapping”; Donor graph- unified framework- reduces sequence biases; only works with human build hg19 and hg18
  * Input: BAM, tab delimited text
  * Output:
1. **[CNVnator] (https://github.com/abyzovlab/CNVnator)**
  * Description:Tool for CNV discovery and genotyping
  * Input: Sam/Bam
  * Output: CNV calls or genotype results
1. **[CNVrd2] (http://www.bioconductor.org/packages/devel/bioc/html/CNVrd2.html)**
  * Description: Part of Bioconductor; package program with command line interface; “measure human gene copy number for multiple samples”; written in R
  * Input: multiple; imports from DNAcopy, IRange, Rsamtools
  * Output:
1. **[CONTRA] (http://sourceforge.net/projects/contra-cnv/)**
  * Description: detects CNVs in whole-exome data, requires Python and R, dependent on BEDtools and SAMtools
  * Input: BAM/SAM
   * Reference genome: Ensembl or 1000genomes
  * Output: VCF
1. **[CopyCat] (http://tvap.genome.wustl.edu/tools/copycat/)**
  * Description: Currently under “active development” according to website; utilizes multi-core architecture; “detects copy number aberrations by measuring the depth of coverage
  * Input:
  * Output:
1. **[GASV] (https://code.google.com/p/gasv/)**
  * Description: Geometric Analysis of Structural Variants
  * Input: BAM
  * Output:
1. **[GENSENG/AS-GENSENG] (http://sourceforge.net/projects/asgenseng/?source=directory)**
  * Description: uses Markhov model; read-depth-based method; identifies regions of discrete copy-number changes while accounting for confounders
  * Input: BAM
  * Output: two txt files
1. **[GROM-RD] (http://grigoriev.rutgers.edu/software/grom-rd/index.html)**
  * Description: Standalone program, command line interface; analyzes multiple biases to detect CNVs. More sensitive than CNVnator or RDXplorer
  * Input: BAM
  * Output: “union set of predicted CNV’s from two pipelines”
1. **[m-HMM] (https://www.stt.msu.edu/users/hengwang/mHMM.html)**
  * Description: written in R, the “mixture hidden Markov model” to detect CNVs, package program, command-line interface
  * Input:
  * Output: “data frame of the resulting CNV detection”
1. **[Magnolya] (http://sourceforge.net/projects/magnolya/)**
  * Description: “de novo CNV detection by co-assembly”; No reference genome needed, compared two NGS datasets; package program, command-line interface, written in Python
  * Input: FASTA
  * Output:
1. **[modSaRa] (http://c2s2.yale.edu/software/modSaRa/)**
  * Description: package program, command-line interface; written in R; “modified Screening and Ranking algorithm”; performs quantile normalization, searches for change-point canidates, eliminates unlikely change points, outputs potential CNV segments
  * Input:
  * Output:
1. **[PEMer] (http://sv.gersteinlab.org/pemer/)**
  * Description: Package program, command-line interface; restricted to academic users only, written in Perl and Python; simulation-based error models; three modules- PEMer workflow, SV-Simulation,   BreakDB (own database);
  * Input: FASTA
  * Output:
1. **[QDNAseq] (http://www.bioconductor.org/packages/release/bioc/html/QDNAseq.html)**
  * Description: (Bioconductor) package program, written in R; WGS method for CNV analysis; dependent on R; “the genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome”
  * Input: BAM
  * Output: “read counts per bin, which have been corrected, filtered, normalized, and optionally log2-transformed”
1. **[readDepth] (https://github.com/chrisamiller/readDepth)**
  * Description: package program, command-line; written in R; measures depth of coverage obtained by massively parallel sequencing; “niche uses...obsolete on human data”
  * Input: BED
  * Output: segs.dat or alts.dat
1. **[SLOPE] (http://www-genepi.med.utah.edu/suppl/SLOPE/slope_guide.txt)**
  * Description: package program, command-line; written in C++; “detects structural variants from targeted short DNA reads”; quickly detects insertions and deletions
  * Input:SAM/FASTQ/MAQ
   * Reference file: FASTA
  * Output: html file
1. **[TIGRA] (http://bioinformatics.mdanderson.org/main/TIGRA)**
  * Description: package program, command-line, for academic users only; written in C++; “conducts targeted local assembly of SV”; Uses data from 1000 Genomes
  * Input: “SV calls and a set of bam files that contain reads mapped to a reference genome such as NCBI build36.”
  * Output:
1. **[WISECONDOR] (http://omictools.com/wisecondor-s1816.html)**
  * Description: “WIthin-SamplE COpy Number aberration”; package program, command-line; written in Python; “Detect fetal trisomies and smaller CNV's in a maternal plasma sample using whole-genome data.”
  * Input: SAM, BAM
   * Reference file: FASTA
  * Output: STDOUT plot in PDF

##SV Identification
1. **[BreakDancer] (http://gmt.genome.wustl.edu/packages/breakdancer/)**
  * Description: Genome wide detection of structural variants; Includes two complementary programs, BreakDancerMini (small indel detection) and BreakDancerMax (5 types of structural variants)
  * Input: Set of map files produced by a front-end aligner (MAQ, BWA, NovoAlign, and Bfast) and a tab-delimited file containing locations of the map files, detection parameters and sample information
  * Output:File containing 14 columns
1. **[Delly2] (https://github.com/tobiasrausch/delly)**
  * Description: Can discover and genotype deletions, tandem duplications, inversions and translocations; uses paired-ends and split-reads; SVs can be visualized using Delly-maze and Delly-sauve
  * Input: Bam
  * Output: VCF
1. **[Genome STRiP] (http://www.broadinstitute.org/software/genomestrip/)**
  * Description: Set of tools for structural variant identification and genotyping; requires genomes from multiple individuals (20-30 minimum); Uses GATK and consists of multiple modules
  * Input: BAM
  * Output: VCF
1. **[mrFast] (http://mrfast.sourceforge.net/)**
  * Description: micro-read Fast Alignment Search Tool; designed to map short reads generate with Illumina
  * Input:
  * Output: SAM
1. **[mrsFast] (http://sfu-compbio.github.io/mrsfast/)**
  * Description: Micro-read substitution-only Fast Alignment Search Tool; designed to map short reads to reference genome assemblies
  * Input: FastQ
   *Reference Genome: FASTA
  * Output: SAM
1. **[NovelSeq] (http://novelseq.sourceforge.net)**
  * Description: Designed to detect novel sequence insertions using high-throughput paired-end whole genome sequencing data
  * Input: FASTA or FASTQ
  * Output: FASTA file containing paired-end sequences
1. **[PEMer] (http://sv.gersteinlab.org/pemer/)**
  * Description: Command line package composed of three modules (PEMer workflow, SV-Simulation, and BreakDB) for SV analysis
  * Input: FASTA
  * Output:
1. **[Pindel] (http://gmt.genome.wustl.edu/packages/pindel/)**
  * Description: Command line package program for detection of “large deletions, medium sized insertions, inversions, tandem duplications, and other structural variations”
  * Input: SAM/BAM
  * Output: pindel raw output format (can be converted to VCF)
1. **[SVMerge] (http://svmerge.sourceforge.net/)**
  * Description: Pipeline for detecting structural variants by integrating calls from different SV callers
  * Input: BAM
  * Output: multiple (depends on SV callers used)
1. **[TIGRA] (http://bioinformatics.mdanderson.org/main/TIGRA)**
  * Description: Command line package program that “conducts targeted local assembly of structural variants using targeted iterative graph routing assembly algorithm” Uses data from 1000 Genomes project
  * Input: SV file; group of BAM files
  * Output: Fasta

## Tool Repositories, Analytical Pipelines, & Workflow Systems
### ToolKits
1. **[bamtools] (https://github.com/pezmaster31/bamtools)**
  * Description: command line tool kit, also API version; for reading, writing and manipulating BAM (genome alignment) files
  * Input: BAM
  * Output: BAM
1. **[Bioconductor] (http://www.bioconductor.org/)**
  * Description: Open source software project, Popular R module library with over 1000 modules, can analyze diverse high-throughput genomic data; options for multiple different workflows; also available on AMI
  * Input: Multiple
  * Output: Multiple
1. **[Galaxy Tool Shed] (https://toolshed.g2.bx.psu.edu/)**
  * Description: Open, web-based platform; Has 3374 valid tools for many different purposes. Looks like this site has a different tool repository for each different data type, They have agreed to support NCBI genomes, and SRA on a limited basis
  * Input: Different tools for different types of data
  * Output: Multiple
1. **[GATK] (https://www.broadinstitute.org/gatk/)**
  * Description: (Broad Institute) Genome Analysis ToolKit; software library including tools (depth of coverage analyzers, quality score recalibrator, local realigner, SNP/INDEL caller), requires Ant and Java, extensive documentation and wiki system, active community, can also be used to identify somatic mutations
  * Input: Multiple
  * Output: Multiple
1. **[picard] (http://broadinstitute.github.io/picard/)**
  * Description: (Broad Institute) Java based command line tools for manipulating high throughput sequencing data in the BAM format; supported through GATK
  * Input: BAM, SAM, VCF
  * Output: BAM, SAM
1. **[SAMtools] (http://samtools.sourceforge.net/)**
  * Description: provides utilities for manipulating alignments in the SAM format, including sorting, merging, indexing, and generating alignments; can be used for calling SNPs and Indels
  * Input: Conforms to the specifications produced by GA4GH File Formats working group
  * Output:
1. **[VCFtools] (https://vcftools.github.io/specs.html)**
  * Description: set of tools written in Perl and C++ for working with VCF files
  * Input: VCF, GZVCF, BCF
  * Output: Multiple, can be piped into another program


text:
---
title: Animation
layout: usermanual-page.hbs
position: 2
---

The Animation component enables an entity to specify which animations can be applied to the model assigned to its Model component.

The Animation component can be enabled or disabled using the toggle in the top right of the component panel. If enabled, the component will play the assigned animation.

![Animation component][1]

## Properties

| Property | Description |
|----------|-------------|
| Assets   | The animation assets that can be utilized by this entity. Multiple animations can be assigned via the picker control. |
| Speed    | A multiplier for animation playback speed. 0 will freeze animation playback, and 1 represents the normal playback speed of the asset. |
| Activate | If checked, the component will start playing the animation on load. |
| Loop     | If checked, the animation will continue to loop back to the start on completion. Otherwise, the animation will come to a stop on its final frame. |

## Scripting Interface

You can control an Animation component's properties using a [script component][2]. The Animation component's scripting interface is [here][3].

[1]: /images/user-manual/scenes/components/component-animation.png
[2]: /user-manual/packs/components/script
[3]: /api/pc.AnimationComponent.html

license: MIT

branch_name: refs/heads/dev

extension: md

gha_language: JavaScript

gha_license_id: MIT

license_type: permissive


text:
# EasyXT-GUI
A simple Matlab GUI to leverage EasyXT and centralize complex analysis pipelines in Imaris

# Installation

## Using GIT

Clone this project into a folder. This repo needs [EasyXT](https://github.com/BIOP/EasyXT) as a submodile, hence the `recursive` option

```git clone --recursive https://github.com/BIOP/EasyXT-GUI```

## Using the Latest Release
We have released the first version as a zip file, if you do not want to use GIT. Please visit

https://github.com/BIOP/EasyXT-GUI/releases/

repository: BIOP/EasyXT-GUI

branch_name: refs/heads/master

repo_name: BIOP/EasyXT-GUI


text:
# `@minecraft/server`

Contains many types related to manipulating a Minecraft world, including entities, blocks, dimensions, and more.

See full documentation for this module here:

https://learn.microsoft.com/en-us/minecraft/creator/scriptapi/@minecraft/server/@minecraft/server

gha_language: mcfunction


text:
---
title: What is a stake pool?
description: Shelley about
order: 2
parent: 2020-05-04_04-00-00_about
last_updated: "2020-05-01T09:00:00+01:00"
redirects:
  - from: /en/cardano/shelley/about/what-is-a-stake-pool/
    type: "301"
---
## What is a stake pool?

A stake pool is a reliable server node that focuses on maintenance and holds the combined stake of various stakeholders in a single entity. Stake pools are responsible for processing transactions and producing new blocks and are at the core of Ouroboros, the Cardano proof-of-stake protocol.

To be secure, Ouroboros requires a good number of ada holders to be online and maintaining sufficiently good network connectivity at any given time. This is why Ouroboros relies on stake pools, entities committed to run the protocol 24/7, on behalf of the contributing ada holders.

While Ouroboros is cheaper to run than a proof of work protocol, running Ouroboros still incurs some costs. Therefore, stake pool operators are rewarded for running the protocol in the form of incentives that come from the transaction fees and from inflation of the circulating supply of ada.

repository: michaeljfazio/testnets-cardano-org

repo_name: michaeljfazio/testnets-cardano-org


text:
Our services are on GOV.UK which gets over 50 million visits a month. There’s no guarantee that only your intended audience will find your content.

So, it’s important we make our content as easy to understand as possible. Do this, by ensuring content is:
 - specific
 - informative
 - clear and concise
 - serious but not pompous
 - emotionless (adjectives can be subjective and make the text sound more like spin)

(Adapted from the GOV.UK style guide.)

### Find out what they need to know
People using HMCTS digital services are trying to complete a task. So, your content needs to clearly tell them what they need to know or do to complete that task.

In order to fully understand what your users need, you should:

 - carry out research with your service user groups
 - identify what your users need to know (their needs)
 - structure your content to provide the answers to meet those needs
 - find out the language they use and understand

### Use language they understand
Write content that makes sense to your audience:

 - use the language that they use - find this out by listening to research and testing, reading user feedback, finding out common search terms (use Google analytics and Google Trends)
 - use contractions like you’ll and we’ll (avoid negative contractions like can’t if you find your users struggle with them)
 - write in short sentences, avoid sentences with more than 25 words - break them up to make the content more clear
 - avoid words ending in ‘–ion’ and ‘–ment’ as they tend to make longer and more complicated sentences

You can use programmes like Hemingway to test if your content is easy to read.

<!-- ## When to use this component

## When not to use this component

## How it works

## Research on this component -->

repository: hmcts/design-system

gha_language: HTML

repo_name: hmcts/design-system


text:
---
layout: page
title: Saag
category: Recipes
---

Savory spinach curry, american style. Just like the restaurant makes
it but vaguely healthy! I make this with tofu, but it should be easy
to change it back to the original paneer.

It's important to use plain yogurt, not greek yogurt; the greek yogurt
is too tangy and will dominate the dish. Also, [a good food
processor](https://smile.amazon.com//dp/B01AXM4WV2) is _absolutely
necessary_; trying to chop the spinach by hand won't get it fine
enough no matter how much chopping you do.

Note that I prefer my saag tofu very spicy. You will probably want to
reduce this by using a less-spicy pepper or processing it to remove
the spicy parts.

No picture yet because I'm still trying to figure out how to plate the
stuff in a vaguely palatable manner.

Start to Eat time: ~1 hour:
- Prep: 15 minutes
- Cook: 35 minutes

## Ingredients

- 12 oz extra-firm tofu
- 2 tablespoons turmeric
- 1 teaspoon cayenne
- A pinch of salt
- 3 tablespoons oil
- A bit of cornstarch, maybe a teaspoon or two

- 1 pound fresh baby spinach

- 1 large white onion
- 1.5 inch knob of ginger
- 4 cloves garlic
- 1 serrano or other small hot pepper

- 1.5 tablespoons oil
- 1 teaspoon garam masala
- 2 teaspoons ground coriander
- 1 teaspoon ground cumin
- 1/2 cup plain yogurt
- A tiny splash of heavy cream, maybe a tablespoon

## Directions

1. Prepare your mise in place:
   1. Cube the tofu into 3/4-inch cubes. Toss with the cayenne, salt,
	  oil, and cornstarch to coat. Set aside while you prep the other
	  ingredients.
   1. Wash the spinach and use the food processor to turn it into a
	  paste. You will have to run it in batches. Transfer it to a bowl
	  and set aside for later.
   1. Quarter the onion and throw it in the food processor. Peel the
	  ginger (scraping it with a spoon works well) and throw it into
	  the food processor. Peel the garlic and throw it into the food
	  processor. Prep the pepper however you like; I just throw it in
	  whole, but this makes the dish very spicy and you'll probably
	  want to cut it open and [scrape out the
	  pith](https://www.cayennediane.com/anatomy-of-a-chile-pepper/). Pulse
	  this mixture until finely chopped, just before it starts turning
	  into a paste.
1. Transfer tofu to a large, cold frying pan. Do not use non-stick;
   you want as much crust as possible. Place on medium-high heat. Wait
   until toasty. Turn. Get as crispy as desired, possibly extremely
   crispy. Pour out onto a plate and set aside.
1. Add oil to pan. Add onion, garlic, ginger, chile mixture. Saute
   until evenly toffee-colored, about 15 minutes. If it starts to dry
   out and burn, add a bit of water.
1. Add spices to the pan. If necessary, add some water so they don't
   burn. Cook, stirring often, to wake up the spices, between two and
   five minutes.
1. Add spinach. Stir well. Add some salt and 1/2 cup water. Cook about
   5 minutes with the lid off.
1. Remove from heat. Add yogurt, a little at a time so it doesn't
   curdle. Add a splash of cream.
1. Return tofu to the mixture and stir. Return to heat. Cover, cook
   until warmed through. Serve.

gha_language: SCSS

gha_license_id: NOASSERTION


text:
# Porting guide
The following guide will help you to convert an application that uses version 2.1.5 of the library to the new
version 3.0.0. This was a major update and thus introduces code changes that have to be manually adjusted. The
update mostly added features and changed the internal structure, but existing classes had their APIs cleaned up
as well.

> This guide will only handle changes to existing APIs, i.e. the core and widgets modules with the QtIFW plugin. Any other new additions are not referenced here.

## General API changes
The core change from 2 to 3 are two main things. First is, that the library now supports more than just QtIFW. It
now supports 6 different backends, in the form of plugins. This does not really matter for this guide. Only how you
deploy the application has sligthly changed. Second, the module now allows more sofisticated installations. It can
run installations in parallel and track their progress. This too, does not really affect QtIFW, as it only launches
an external installer. However, on Linux and macOs, it is now possible to run the maintenancetool in parallel to the
running application.

Information about the plugin for QtIFW and it specialities can be found on the
@ref qtautoupdater_backends "backends page" in the documentation.

## Porting QtAutoUpdaterCore
The following changes have been made to the core module. They are mostly cosmetic changes, i.e. changing how the
API is presented. The are listed in the following ordered by classes.

### Changes to UpdateInfo
The UpdateInfo class has been moved out of Updater is now called QtAutoUpdater::UpdateInfo. Is has been converted to
a fully fledged gadget. Instead of members, it has now equally named properties with get and set accessors. In
addition, there are comparators and functions to use them as key to hashes.

The `size` member has been completly removed, as part of the generalization that happend to the library. You can
still access it, via the newly added `data` property:
@code{.cpp}
auto size = info.data()["size"].toULongLong();
@endcode
The size is also the secondary info property for QtIFW. See QtAutoUpdater::UpdaterBackend::secondaryInfo.

### Changes to AdminAuthoriser
This class has always been more of a side-class. Instead of a class it is now a namespace and provides two methods.
The `hasAdminRights` method has been removed and replaced with QtAutoUpdater::AdminAuthoriser::needsAdminPermission.
That method instead tries to guess if the execution of the given executable needs an elevation of rights first, or
if it can be executed in the current context. `executeAsAdmin` has not been changed, but now allows you to add the
working directory as parameter (ignored on macOs).

### Changes to Updater
As the primary class of the module, this class has changed the most. While is functionality stayed the same - to
check for updates and launch the installation - the API has been improved and generalized. Also, since now plugins
are used, the configuration is different

#### Creation
Instead of creating it via the constructor, there is now a factory method - QtAutoUpdater::Updater::create. It
takes a backend and a configuration and creates the updater using that backend from it. To create an updater with a
maintenancetool, use the following code:

@code{.cpp}
auto updater = QtAutoUpdater::Updater::create("qtifw", {
	{"path", pathToTheMaintenancetool}
}, parent);
@endcode

To create an updater instance with the default path, simply leave out the configuration. There are a few other
parameters you can use here. There are partially explained in other parts of this guide - but you can also take a
look at @ref qtautoupdater_backends_qtifw. You can also use a configuration file. It would look like this:

@code{.ini}
[General]
backend=qtifw
path=pathToTheMaintenancetool
...
@endcode

#### Status report
The updater now uses the QtAutoUpdater::Updater::state property to track the state. The
QtAutoUpdater::Updater::running property stayed, but is simply a wrapper around that state to simplify it down to
the information, whether the updater is doing something or not. All the accessors directly linked with the
maintenancetool have been removed. This includes the members `maintenanceToolPath`, `exitedNormally`, `errorCode`
and `errorLog`. This information is now logged internally and cannot be accessed anymore.

#### Checking for updates
The method to start an update check stayed the same: QtAutoUpdater::Updater::checkForUpdates - but it now returns
void instead of bool, as the state can be used to track the progress. The QtAutoUpdater::Updater::progressChanged
signal has been added to provide a more detailed progress for supported plugins. QtIFW does not provide those
details, so you can ignore the signal. Once the update check is done, the QtAutoUpdater::Updater::checkUpdatesDone signal is still beeing emitted, but it
now only has the status as parameter instead of two booleans. The update details can still be accessed via the
QtAutoUpdater::Updater::updateInfo property.

It is also possible to add additional arguments to the update check run of the maintenancetool. To do so, use the
`extraCheckArgs` in the configuration to specify those arguments:

@code{.code}
{
	{"extraCheckArgs", "someArg=tree"}
}
@endcode

#### Installing updates
The method to start an installation has been renamed from `runUpdaterOnExit` to QtAutoUpdater::Updater::runUpdater
and now takes two arguments to specify how updates should be installed. There are various options, but only two of
them are viable:

- QtAutoUpdater::Updater::runUpdater(QtAutoUpdater::Updater::InstallModeFlag::OnExit)
- QtAutoUpdater::Updater::runUpdater(QtAutoUpdater::Updater::InstallModeFlag::Parallel)

The first one works for all platforms and exhibits the same behaviour as the old version. It schedules the installer
to be executed on exit of the running application. The second one only works on Linux and macOs and launches the
installer immediatly, in parallel to the running application. For this variant, you can track the installation via
the state and by waiting for the QtAutoUpdater::Updater::installDone signal.

If you want to specify additional arguments to the installation, use the configuration. The following parameters
can be added. `silent` will make the updater run in background, `extraInstallArgs` allows you to pass any additional
arguments you desire to the installer and `runAsAdmin` can be used to specify if the installer should be run as
admin or not. Leaving that one out will make the backend use the QtAutoUpdater::AdminAuthoriser to guess the
correct value. The code below shows how the old constants are mapped to parameters in the configuration:

@code{.code}
// NormalUpdateArguments
{}
// PassiveUpdateArguments
{
	{"extraInstallArgs", "skipPrompt=true"}
}
// HiddenUpdateArguments
{
	{"silent", true}
}
@endcode

#### Other changes
The remaining changes are mostly stuff that has to do with the new plugin architecture and other small additions.
Read the documentation for more details. Scheduling update checks is also still possible and now even supports use
of the std::chrono classes.

### Added classes
The remaining changes to the module are mostly added classes that have to do with the new plugin architecture.
Again, check the documentation for more details about these classes.

## Porting QtAutoUpdaterGui
The first thing to say here is, that the module has been renamed. It is now called *QtAutoUpdaterWidgets*, as it
only creates a widgets GUI. For QML, there now is the `de.skycoder42.QtAutoUpdater.Quick` module you can import and
use. Refer to the documentation of that namespace and the README for more details on how to use it. There haven't
been any big changes functionality wise to this module. It still uses the controller to show a GUI and provides the
UpdateButton as a dynamic button. However, the way these classes are used has changed and adjusted to the changes
in the core module. No classes have been added or removed.

### Changes to UpdateController
The controllers API has been changed quite a bit. A lot of functionality that was duplicated from the internally
used updater has been removed, as it can now be completly controlled via the updater. The GUIs that are shown by
the controller however have not been changed.

#### Construction and window affiliation
Instead of using a maintenancetool, you now have to create the controller by passing it an updater. The controller
does not take ownership of the updater, which is why the parenting has been united. If you create the controller
with a QWidget as parent, all windows will be parented to that widget as well. If any other QObject is used as
parent, the windows will be parentless toplevel windows instead. If the controller gets destroyed early, the updater
stays alive and can still trigger an installer on exit. Thus, it is recommended to parent the controller correctly.

In addition, the controller will now only show the GUIs if the parent window is visible (or minimized). A windows
counts as visible even if hidden behind other windows or on another desktop. This allows you to add multiple
controllers to different GUIs of your application, without the interferring, as long as only one of those windows
is visible at a times.

Finally, the QtAutoUpdater::UpdateController::createUpdateAction has been made static, the the action only requires
a reference to an updater, not to the controller anymore. Otherwise, the action is still the same. It will however
call QtAutoUpdater::Updater::checkForUpdates instead of using a controller, which means it is not possible to have
an action with asklevel anymore. Any other level still works, as explained in the next section.

#### Auto attachment to updaters
Unlike before, the controller now "attaches" to the updater and can show a GUI in any state of the update check. If
you for example create a controller while already checking for updates, there won't be a progress, but as soon as
the check has finished, a result dialog will be shown (if enabled by display level).

Because of this change, the `running` property of the controller has been removed. Use the updater instead to check
if it is running or not.

#### Setting the display level and running the controller
Another change has been made to the display level handling. The display level is now a property,
QtAutoUpdater::UpdateController::displayLevel. This property can be set and changed even during a running update
check and takes effect as soon as the next dialog is shown. It is know global for all checks run via this
controller. It is still possible to pass the level to the QtAutoUpdater::UpdateController::start method, but this
will only set the property and then call `start` without any parameters.

To run the controller, you should still call `start`, as it will show the ask dialog if wished before checking for
updates. However, it is also possible to use QtAutoUpdater::Updater::checkForUpdates instead, as the controller is
attached to the updater. This will work for all levels but the ask level, as the ask dialog will never be shown when
an update check is started via the updater.

Use the properties and signals of the QtAutoUpdater::Updater to track the progress. All such methods have been
removed from the controller, as they are not needed there anymore.

#### Removed members
There are quite a few properties and methods that have been removed. Those have been mostly removed because the
same functionality is already beeing provided by the underlying updater. The removed members are:
`maintenanceToolPath`, `running`, `runAsAdmin`, `updateRunArgs`, `scheduleUpdate` and `cancelScheduledUpdate`.

In the GUI, the run as admin checkbox has been removed. You should get this information programatically, and not
by prompting the user, as the user typically has no clue about this. You can still specify it, by using the
`runAsAdmin` configuration value.

### Changes to UpdateButton
The update button has seen changes similar to the controller. The main difference is, that it now only needs an
updater instead of a controller, and that it supports a more sofisticated UI, including a state that is shown while
installing parallel updates for Linux or macOs. The rest of it's GUI however has stayed the same.

The fact that the button now uses an QtAutoUpdater::Updater only also means, that there is no display level anymore.
Again, this is not problematic as any controller will automatically show required GUIs, even if completely detached
from the button, as long as they use the same updater.

Another thing that changed is, that you can now trigger an installation via the button, if updates are available.
The QtAutoUpdater::UpdateButton::mode property controlls whether this is possible and how the button operates in
general. If allowed and a user triggers the installation, the QtAutoUpdater::UpdateButton::installMode and
QtAutoUpdater::UpdateButton::installScope properties are used as arguments for the
QtAutoUpdater::Updater::runUpdater method call.

## Final remarks
As you can see from this guide, there have been quite a few changes. Most of them however are nothing more then
sligthly more complex refactorings. Using this document you should be able to easily port any application from the
old to the new version. If you however still have some questions, simply use the
[GitHub Issues](https://github.com/Skycoder42/QtAutoUpdater/issues) to ask for help. I am aware that such a change
can be challenging and I am always willing to provide any help I can.


text:
#puppet-augeas [![Build Status](https://travis-ci.org/Spredzy/puppet-augeas.png)](https://travis-ci.org/Spredzy/puppet-augeas)

=============

A Puppet module that installs augeas latest GitHub version, from the YUM repository (yum.augeas.yanisguenane.fr)[http://yum.augeas.yanisguenane.fr]

# Parameters

* devel (true|false)

Indicate if augeas-devel package should be installed (default: false)

# Usage

    Class['augeas']

or

    class {'augeas' :
      devel => true,
    }

# Compatibility

This module work exclusively on Fedora and RedHat (and its derivative) platforms, for both 32 Bits and 64 Bits platforms.

repository: lugosl/control-repo

repo_name: lugosl/control-repo


text:
---
layout: default
---

# 1998-11-18 - ANNOUNCEMENT: RPK SECURITY ADDS NOTED XIONICS EXECUTIVE GARY AMBROSINO TO BOARD OF DIRECTORS

## Header Data

From: "Jack Oswald / CEO / RPK Security Inc."<EMAIL_ADDRESS>To<EMAIL_ADDRESS>Message Hash: 1f5dd97c97d34f696a181d51cff021362a78dc165880f56c9f19007fb3ed0b62

Message ID<EMAIL_ADDRESS>Reply To: _N/A_

UTC Datetime: 1998-11-18 21:23:24 UTC

Raw Date: Wed, 18 Nov 1998 13:23:24 -0800 (PST)

## Raw message

```
{% raw  %}From: "Jack Oswald / CEO / RPK Security Inc."<EMAIL_ADDRESS>Date: Wed, 18 Nov 1998 13:23:24 -0800 (PST)
To<EMAIL_ADDRESS>Subject: ANNOUNCEMENT: RPK SECURITY ADDS NOTED XIONICS EXECUTIVE GARY AMBROSINO TO BOARD OF DIRECTORS
Message-ID<EMAIL_ADDRESS>MIME-Version: 1.0
Content-Type: text/plain

You have received this message because at some time in the past your name was submitted to our e-mail mailing list database.  If you do not wish (or no longer wish) to receive announcements, updates and news concerning the RPK Encryptonite Engine or the RPK InvisiMail e-mail security products, please forward this message to<EMAIL_ADDRESS>
----------------------------------------------------------------------------------------------

CONTACT
Paula Miller							Lyn Oswald
Nadel Phelan, Inc.						RPK Security Inc.
408-439-5570 x277<PHONE_NUMBER>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
FOR IMMEDIATE RELEASE

RPK SECURITY ADDS NOTED XIONICS EXECUTIVE GARY AMBROSINO TO BOARD OF DIRECTORS

San Francisco, California, October 27, 1998 - RPK Security Inc. (www.rpkusa.com), a technology leader in fast public key encryption, today announced that Gary Ambrosino, recent vice president and general manager of Xionics Document Technologies (Nasdaq:XION), will join its Board of Directors. Following a recent round of seed financing, Mr. Ambrosino's addition to the Board is part of RPK's overall strategy to expand its management team.

Prior to joining RPK, Mr. Ambrosino spent five years at Xionics where he led the marketing and early stage sales program for the company's Multifunction Peripherals embedded control technology. Mr. Ambrosino has headed several seed stage technology companies, and spent eight years with Hewlett-Packard.

"We are pleased to welcome Gary to our Board of Directors," said Jack Oswald, president and CEO of RPK Security. "His experience in driving business development and his extensive industry knowledge will be extremely valuable in the next phase of growth and deployment of our Encryptonite software and FastChip encryption technology into applications like secure remote printing."

"RPK Security has now reached, on schedule, several significant milestones," said Mr. Ambrosino. "These milestones include completion of a very low cost, high-speed encryption chip, the release of the Encryptonite development platform, and continued growth in acceptance of RPK InvisiMail, the world's easiest-to-use secure E-mail add-in."

RPK's core technology, the patented RPK Encryptonite Engine, is an exceptionally fast public key algorithm for encryption, authentication and digital signatures. It offers a unique combination of the benefits of public key systems and the high performance characteristics of symmetric or "secret key" systems. RPK Encryptonite is available as a software toolkit or a high speed ASIC capable of large key encryption speeds of up to 100 megabits per second.   Applications include streaming video and audio narrowcasting, secure cellular and satellite telephones, and VPN implementation. Developed outside of the U.S., the RPK Encryptonite Engine is available globally with strong encryption, unlike competing products that are restricted by U.S. export regulations.

ABOUT RPK SECURITY
Founded in 1995, RPK Security, Inc. is a technology leader in fast public key encryption. RPK's cryptographic research and product development is based in New Zealand, Switzerland and the U.K, with worldwide sales and marketing in San Francisco, CA.  Contact RPK at www.rpkusa.com or<PHONE_NUMBER>.

{% endraw %}
```

## Thread

+ Return to [November 1998](/archive/1998/11)

+ Return to "["Jack Oswald / CEO / RPK Security Inc." <Announcements_reply@rpkusa.com>](/authors/jack_oswald__ceo__rpk_security_inc__announcements_reply_at_rpkusa_com_)"

+ 1998-11-18 (Wed, 18 Nov 1998 13:23:24 -0800 (PST)) - ANNOUNCEMENT: RPK SECURITY ADDS NOTED XIONICS EXECUTIVE GARY AMBROSINO TO BOARD OF DIRECTORS - _"Jack Oswald / CEO / RPK Security Inc."<EMAIL_ADDRESS>