April 2018 Tool Shed contributions

Galaxy ToolShed

Tools contributed to the Galaxy Project ToolShed in April 2018.

New Tools

  • From pravs:

  • From genouest:

    • askor_de: AskoR links EdgeR and AskOmics. AskoR perform DE analyses from raw counts and generates AskOmics compliant files.
    • feelnc2asko: Convert FeelNC GTF to GFF3. Convert FeelNC GTF to GFF3.
    • miranda2asko: Converts miRanda output into AskOmics format.
  • From mheinzl:

    • fsd: fsd. Tool that plots a histogram of sizes of read families.
  • From kyost:

  • From kyu:

  • From iuc:

    • meme_dreme: DREME tool from the meme package. The MEME Suite supports motif-based analysis of DNA, RNA and protein sequences. It provides motif discovery algorithms using both probabilistic (MEME) and discrete models (MEME), which have complementary strengths. It also allows discovery of motifs with arbitrary insertions and deletions (GLAM2). In addition to motif discovery, the MEME Suite provides tools for scanning sequences for matches to motifs (FIMO, MAST and GLAM2Scan), scanning for clusters of motifs (MCAST), comparing motifs to known motifs (Tomtom), finding preferred spacings between motifs (SpaMo), predicting the biological roles of motifs (GOMo), measuring the positional enrichment of sequences for known motifs (CentriMo), and analyzing ChIP-seq and other large datasets (MEME-ChIP). The MEME Suite is comprised of a collection of tools that work together.
    • meme_chip: Performs motif discovery, motif enrichment analysis and clustering on large nucleotide datasets. MEME-ChIP performs motif discovery, motif enrichment analysis and clustering on large nucleotide datasets. The MEME Suite supports motif-based analysis of DNA, RNA and protein sequences. It provides motif discovery algorithms using both probabilistic (MEME) and discrete models (MEME), which have complementary strengths. It also allows discovery of motifs with arbitrary insertions and deletions (GLAM2). In addition to motif discovery, the MEME Suite provides tools for scanning sequences for matches to motifs (FIMO, MAST and GLAM2Scan), scanning for clusters of motifs (MCAST), comparing motifs to known motifs (Tomtom), finding preferred spacings between motifs (SpaMo), predicting the biological roles of motifs (GOMo), measuring the positional enrichment of sequences for known motifs (CentriMo), and analyzing ChIP-seq and other large datasets (MEME-ChIP). The MEME Suite is comprised of a collection of tools that work together.
  • From jfb:

    • promap: for use with Open Contact. This tool is used to determine which atom-atom interactions are shared between multiple snapshots or multiple pictures of NMR or Crystalography.
  • From yqiancolumbia:

    • ctk: CLIP data analysis.
  • From padr:

  • From nml:

    • pseudogenome: Create a pseudogenome from a multiple fasta file either with a JCVI linker or custom length and characters.
  • From dereeper:

    • sniplay3_complete_workflow: SNiPlay3 complete workflow: a package for exploration and large scale analyses of SNP polymorphisms (filtering, density, vcftools, diversity, linkagedisequilibrium, GWAS) (all SNiPlay3 components).
    • fastme: A distance based phylogeny reconstruction algorithm.
    • vcftools_filter_stats_diversity: Subset of VCFtools fonctionalities : Filtering, Statistics, Diversity (slidingWindow).
    • readseq: Convert various alignment formats.
  • From abims-sbr:

    • oligator: Oligator: design PCR primers. Oligator was written by Frederic Lechauve frederic_lechauve@yahoo.fr and integrated in Galaxy by ABiMS - Station Biologique de Roscoff - Sorbonne Universit√© / CNRS.
  • From earlhaminst:

    • gblocks: Gblocks. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis.
  • From galaxyp:

    • msi_combine: combines several mass-spectrometry imaging datasets. This tool can combine multiple mass-spectrometry imaging files into one large file (based on Cardinal MSI).
  • From fabio:

    • btman: 20180404. BloomTree Manager. A fast querying tool to identify all publicly available sequenced samples which express a transcript of interest.
  • From daumsoft:

  • From mvdbeek:

    • damidseq_polii_gene_call: Rscript for calculating average PolII occupancy and FDR for RNA Pol II DamID datasets. Processing DamID-seq data involves extending single-end reads, aligning the reads to the genome and determining the coverage, similar to processing regular ChIP-seq datasets. However, as DamID data is represented as a log2 ratio of (Dam-fusion/Dam), normalisation of the sample and Dam-only control is necessary and adding pseudocounts to mitigate the effect of background counts is highly recommended.
    • damidseq_average_scores: Averages the score column of bed or gff files. Averages the score column of bed or gff files.
    • damidseq_findpeaks: A simple FDR peak caller for DamID data. A simple FDR peak caller. The script is designed for processing DamID-seq datasets such as those generated by the damidseq_pipeline software, but will work on any DNA binding track in bedgraph or GFF format (for example, background-subtracted ChIP-seq data). The output is a GFF file of all peaks with an FDR less than an assigned value (by default, FDR < 0.01). The mean binding intensity of the peak is represented by the score column of the GFF (column 6) and the FDR is present in the attributes column (column 9). The peak file can be loaded into genome browser software such as IGV for viewing, and associated genes can be called with the included peaks2genes script.
    • plot_correlation_matrix: Calculates and plots correlations for many datasets. Calculates and plots correlations for many datasets.
  • From bgruening:

    • sklearn_model_validation: Wrapper for scikit learn tool: Model Validation. Scikit-learn is an open source machine learning library written in python. It provides different flavors of machine learning algorithms for classification, regression, and clustering. It has designed to interoperate with python numerical and scientific libraries Numpy and Scipy. The official repository of Scikit-learn is at https://github.com/scikit-learn/scikit-learn.
    • sklearn_regression_metrics: Wrapper for scikit learn tool: Calculate metrics.
    • sklearn_feature_selection: Wrapper for scikit learn tool: Feature Selection.