Plugins collection

class bsPlugins.Annotate.AnnotatePlugin

Searches closest gene to each feature and returns associated distance and inclusion informations

class bsPlugins.Bam2Density.Bam2DensityPlugin

From a BAM file, creates a track file of the read count/density along the whole genome, in the chosen format.

Read counts are divided by 10^-7 times the normalization factor (which is total number of reads by default). Positive and negative strand densities are generated and optionally merged (averaged) if a shift value >=0 is given. The read extension is the number of basepairs a read will cover, starting from its most 5’ position (e.g. with a read extension of 1, only the starting position of each alignment will be considered, default is read length).

class bsPlugins.BedTools.BedToolsPlugin

Bedtools collection.

class bsPlugins.Combine.IntersectPlugin

class bsPlugins.Combine.UnionPlugin

class bsPlugins.Combine.SubtractPlugin

class bsPlugins.Combine.ComplementPlugin

class bsPlugins.DESeq.DESeqPlugin

Gets the score associated to each genomic feature in each sample and runs DESeq for differential analysis within them. It returns a tab-delimited file with the following fields:

Name, MeanA, MeanB, fold change, adjusted p-value.

The input can be of two different types:

• Two sets of ‘signal’ files - i.e. bedGraph-type text files - one for each of the two groups to compare -, and a list of genomic features - either from a pre-defined list such as Ensembl genes, or a custom bed-like file. For every feature, a score is given for each of the signal samples, and DESeq is run on the resulting table. The name of each sample is the one given in the track definition line (“track name=... description=... etc.”), if specified, otherwise the name of the file (without extension).
• A tab-delimited table with feature names in the first column, then one column of respective scores per sample. The first line is a header of the type “id sample1 sample2 ...”. If sample names are in the format ‘group_name.run_id’, all samples with the same group_name will be considered as replicates of the same group/condition. Else they are considered as belonging to different groups. If there are more than 2 groups, all different pairs of comparisons will be performed and output in separate files.

clean_deseq_output(filename, contrast)

Delete all lines of filename with NA’s everywhere, add 0.5 to zero scores before recalculating the fold change, remove row numbers, and keep only the following fields: Name, MeanA, MeanB, fold change, adjusted p-value. Return the new file name.

class bsPlugins.Domainograms.DomainogramsPlugin

Run a domainogram analysis (ref article) on a set of fragments scores (e.g., such as the one obtained with the 4C-seq pipeline)

class bsPlugins.FileConvert.FileConvertPlugin

Converts a file to another equivalent format (examples: wig to bedgraph, gff to bed). Recognised input formats are bed, bedGraph, bedgraph, bigWig, bigwig, bw, db, fps, gff, gtf, sam, sga, sql, text, txt, wig.

class bsPlugins.FileStatistics.FileStatisticsPlugin

Calculates diverse statistics from a track file, such as a distribution of scores and feature lengths, and prints them to the output file.

class bsPlugins.Filtering.FilteringPlugin

Select features from a track passing a filter.

class bsPlugins.GenomeGraph.GenomeGraphPlugin

Generates a whole genome overview of several signal and/or feature tracks.

class bsPlugins.Intersections.IntersectionsPlugin

Returns the elements that are common to a set of text files, for instance the list of genes common to several lists of genes or annotation files.

In the case when more that two files are given, all possible combinations of intersections are performed (2-by-2, 3-by-3, etc.), in the manner of a Venn diagram. If the elements to intersect are not in the first column, one can specify the column to consider by its index (first column is 1).

Since the number of comparisons is approximately 2^(number of files), it is unadvised to compare more that a dozen of files (10 input files -> 2^10-11=1013 comparisons).

The output is a compressed folder containing a summary file and a sub-folder with all the possible intersections, i.e. for each intersection one text file with the list of common elements.

class bsPlugins.List2Track.List2TrackPlugin

Create a fully annotated track file from a features type or a subset of Ensembl IDs.

Either upload a raw text file with one Ensembl ID on each line, or choose a feature type to fetch them all.

class bsPlugins.Maplot.MaplotPlugin

Creates an MA-plot to compare levels of expression of genomic features across two samples.

The input can be of two different types:

• Two ‘signal’ files, i.e. bedGraph-type text files, and a list of genomic features - either from a pre-defined list such as Ensembl genes, or a custom bed-like file. The name of each sample is the one given in the track definition line (“track name=... description=... etc.”), if specified, otherwise the name of the file (without extension). </li>
• A tab-delimited table with feature names in the first column, then one column of respective scores per sample. The first line is a header of the type “id sample1 sample2 ...”.

class bsPlugins.MergeTracks.MergeTracksPlugin

Shift and average scores from forward and reverse strand densities.

Typically built to merge ChIP-seq signals from both DNA strands, it can also be used to add (average) several numeric genomic tracks, replicates for instance.

The output is the average of all the input signals, position by position.

class bsPlugins.MotifScan.MotifScanPlugin

Scan motifs PWM on a set of a sequences

class bsPlugins.MotifSearch.MotifSearchPlugin

Search over-represented motifs in a set of a genomic regions using MEME

class bsPlugins.Normalize.NormalizePlugin

Normalize the columns of a tab-delimited file using a specified method and returns a normalized tab-delimited file.

class bsPlugins.NumericOperation.NumericOperationPlugin

Apply a numeric transformation to the track scores - such as logarithm or square root.

class bsPlugins.Overlap.OverlapPlugin

Returns only the regions of the first input file that overlap (or contain) some feature from the second (‘filter’).

class bsPlugins.PairedEnd.PairedEndPlugin

Computes statistics and genome-wide distribution of fragment sizes from mapped paired-end reads.

class bsPlugins.QuantifyTable.QuantifyTablePlugin

Quantify signal tracks on a set of regions.

Given a set of signal tracks, and a bed-like track containing intervals (e.g. genes), builds a table of the score of each signal in each of the intervals. That is, each cell of the output table is the score given by one of the tracks to a specific interval.

Scores can be the sum/mean/median/min/max of the tag count in the interval.

class bsPlugins.Ratios.RatiosPlugin

Divides the scores of the first track by the scores of the second, and returns a single track with the ratios as new scores.

class bsPlugins.Smoothing.SmoothingPlugin

Applies a moving average transformation to smooth the signal of a quantitative track. <br /><br />

class bsPlugins.Table2Tracks.Table2TracksPlugin

Generates signal tracks from a tab-delimited table.

class bsPlugins.TopGo.TopGoPlugin

Makes a GO analysis on a list of Ensembl IDs.

Given a file with one Ensembl ID on each line, it returns a summary table (.txt) and GO networks in a pdf.

The first regroups the most significant terms concerning Biological Processes (BP), Cellular Components (CC) and Molecular Function (MF). One can choose the maximum number of each of these terms to include in the output, with a threshold on the p-value.

class bsPlugins.VennDiagram.VennDiagramPlugin

For input as a set of tracks, creates a Venn diagram of the proportions of total coverage/total score attributed to each track.

If the parameter ‘type’ has the value ‘intervals’, the diagram will show the percent of the genome covered by each possible combination of the input tracks. For instance, If tracks A and B are given, it will show the portions covered by A only, B only, or A and B.

If it has the value ‘score’, the diagram will show the percent of the total score due to each combination of the input tracks, as above.

The output includes the figure of the Venn diagram and a text summary of the different statistics. If more than 4 samples are given, no graph is produced, but the text summary still contains all the information.

For input as a table of numeric value, logical rules will be applied to selected columns, and the Venn diagram will be based on the number of rows passing the rulein each combination of columns. Rules (possibly empty) must be specified in the same order as column numbers. ‘