Package: bioinfokit
The bioinfokit toolkit aimed to provide various easy-to-use functionalities to analyze,visualize, and interpret the biological data generated from genome-scale omics experiments.How to install:bioinfokit requiresPython 3NumPyscikit-learnseabornpandasmatplotlibSciPymatplotlib_vennbioinfokit can be installed using pip, easy_install and git.latest bioinfokit version:Install usingpipfor Python 3 (easiest way)# installpipinstallbioinfokit# upgrade to latest versionpipinstallbioinfokit--upgrade# uninstallpipuninstallbioinfokitInstall usingeasy_installfor Python 3 (easiest way)# install latest versioneasy_installbioinfokit# specific versioneasy_installbioinfokit==0.3# uninstallpipuninstallbioinfokitInstall usingcondacondainstall-cbiocondabioinfokitInstall usinggit# download and install bioinfokit (Tested on Linux, Mac, Windows)gitclonehttps://github.com/reneshbedre/bioinfokit.gitcdbioinfokitpythonsetup.pyinstallCheck the version of bioinfokit>>>importbioinfokit>>>bioinfokit.__version__'0.4'How to cite bioinfokit?Renesh Bedre. (2020, March 5). reneshbedre/bioinfokit: Bioinformatics data analysis and visualization toolkit. Zenodo.http://doi.org/10.5281/zenodo.3698145.Additionally checkZenodoto cite specific version of bioinfokitSupportIf you enjoy bioinfokit, consider supporting me,Getting StartedGene expression analysisVolcano plotlatest update v2.0.8bioinfokit.visuz.GeneExpression.volcano(df, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, dim, r, ar,  dotsize, markerdot, sign_line, gstyle, show, figtype, axtickfontsize, axtickfontname, axlabelfontsize,  axlabelfontname, axxlabel, axylabel, xlm, ylm, plotlegend, legendpos, figname, legendanchor, legendlabels, theme)ParametersDescriptiondfPandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-values columnslfcName of a column having log or absolute fold change values [string][default:logFC]pvName of a column having P-values or adjusted P-values [string][default:p_values]lfc_thrLog fold change cutoff for up and downregulated genes [Tupleor list][default:(1.0, 1.0)]pv_thrp value or adjusted p value cutoff for up and downregulated genes [Tupleor list][default:(0.05, 0.05)]colorTupleof three colors [Tupleor list][default: color=("green", "grey", "red")]valphaTransparency of points on volcano plot [float (between 0 and 1)][default: 1.0]geneidName of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]genenamesTupleof gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string, tuple, dict][default: None]gfontFont size for genenames [float][default: 10.0]. gfont not compatible with gstyle=2.dimFigure size [Tupleof two floats (width, height) in inches][default: (5, 5)]rFigure resolution in dpi [int][default: 300]. Not compatible withshow= TruearRotation of X and Y-axis ticks labels [float][default: 90]dotsizeThe size of the dots in the plot [float][default: 8]markerdotShape of the dot marker. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]sign_lineShow grid lines on plot with defined log fold change (lfc_thr) and P-value (pv_thr) threshold value [True or False][default:False]gstyleStyle of the text for genenames. 1 for default text and 2 for box text [int][default: 1]showShow the figure on console instead of saving in current folder [True or False][default:False]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]xlmRange of ticks to plot on X-axis [float (left, right, interval)][default: None]ylmRange of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]plotlegendplot legend on volcano plot  [True or False][default:False]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:"best"]fignamename of figure [string ][default:"volcano"]legendanchorposition of the legend outside of the plot. For more options see bbox_to_anchor parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[list][default:None]legendlabelslegend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Volcano plot image in same directory (volcano.png)Working exampleInverted Volcano plotlatest update v2.0.8bioinfokit.visuz.GeneExpression.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont, gstyle, dotsize, markerdot, r, dim, show, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize,  axtickfontname, plotlegend, legendpos, legendanchor, figname, legendlabels, ar, theme)ParametersDescriptiontablePandas dataframe table having atleast gene IDs, log fold change, P-values or adjusted P-valueslfcName of a column having log fold change values [default:logFC]pvName of a column having P-values or adjusted P-values [default:p_values]lfc_thrLog fold change cutoff for up and downregulated genes [Tupleor list] [default:(1.0, 1.0)]pv_thrp value or adjusted p value cutoff for up and downregulated genes [Tupleor list] [default:(0.05, 0.05)]colorTupleof three colors [Tupleor list][default: color=("green", "grey", "red")]valphaTransparency of points on volcano plot [float (between 0 and 1)][default: 1.0]geneidName of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]genenamesTupleof gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string,Tuple, dict][default: None]gfontFont size for genenames [float][default: 10.0]gstyleStyle of the text for genenames. 1 for default text and 2 for box text [int][default: 1]dotsizeThe size of the dots in the plot [float][default: 8]markerdotShape of the dot marker. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]dimFigure size [Tupleof two floats (width, height) in inches][default: (5, 5)]rFigure resolution in dpi [int][default: 300]. Not compatible withshow= TruefigtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']showShow the figure on console instead of saving in current folder [True or False][default:False]axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]axlabelfontsizeFont size for axis labels [float][default: 9]axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']plotlegendplot legend on inverted volcano plot  [True or False][default:False]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:"best"]legendanchorposition of the legend outside of the plot. For more options see bbox_to_anchor parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[list][default:None]fignamename of figure [string ][default:"involcano"]legendlabelslegend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]arRotation of X and Y-axis ticks labels [float][default: 90]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Inverted volcano plot image in same directory (involcano.png)Working exampleMA plotlatest update v2.0.7bioinfokit.visuz.GeneExpression.ma(df, lfc, ct_count, st_count, pv, basemean, lfc_thr, color, dim, dotsize, show, r, valpha, figtype, axxlabel, axylabel, axlabelfontsize, axtickfontsize, axtickfontname, xlm, ylm, fclines, fclinescolor, legendpos, legendanchor, figname, legendlabels, plotlegend, ar, theme, geneid, genenames, gfont, gstyle, title)ParametersDescriptiondfPandas dataframe  table having atleast gene IDs, log fold change, and normalized counts (control and treatment) columnslfcName of a column having log fold change values [default:"logFC"]ct_countName of a column having count values for control sample.Ignored if basemean provided [default:"value1"]st_countName of a column having count values for treatment sample. Ignored if basemean provided [default:"value2"]pvName of a column havingpvalues or adjustedpvaluesbasemeanBasemean (mean of normalized counts) from DESeq2 resultslfc_thrLog fold change cutoff for up and downregulated genes [Tupleor list][default:(1.0, 1.0)]colorTupleof three colors [Tupleor list][default: ("green", "grey", "red")]dotsizeThe size of the dots in the plot [float][default: 8]markerdotShape of the dot marker. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]valphaTransparency of points on plot [float (between 0 and 1)][default: 1.0]dimFigure size [Tupleof two floats (width, height) in inches][default: (5, 5)]rFigure resolution in dpi [int][default: 300]. Not compatible withshow= TruefigtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']showShow the figure on console instead of saving in current folder [True or False][default:False]axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]axlabelfontsizeFont size for axis labels [float][default: 9]axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']xlmRange of ticks to plot on X-axis [float (left, right, interval)][default: None]ylmRange of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]fclinesdraw log fold change threshold lines as defines bylfc[True or False][default:False]fclinescolorcolor of fclines  [string][default: '#2660a4']plotlegendplot legend on MA plot  [True or False][default:False]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:"best"]legendanchorposition of the legend outside of the plot. For more options see bbox_to_anchor parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[list][default:None]fignamename of figure [string ][default:"ma"]legendlabelslegend label names. If you provide custom label names keep the same order of label names as default [list][default:['significant up', 'not significant', 'significant down']]arRotation of X and Y-axis ticks labels [float][default: 90]themeChange background theme. If theme set todark_background, the dark background will be produced instead of default white. See more themeshere[string][default:'None']geneidName of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]genenamesTupleof gene Ids to label the points. The gene Ids must be present in the geneid column. If this option set to "deg" it will label all genes defined by lfc_thr and pv_thr [string,Tuple, dict][default: None]gfontFont size for genenames [float][default: 10.0]gstyleStyle of the text for genenames. 1 for default text and 2 for box text [int][default: 1]titleAdd main title to the plot [string][default: None]Returns:MA plot image in same directory (ma.png)Working exampleHeatmaplatest update v2.0.1bioinfokit.visuz.gene_exp.hmap(table, cmap='seismic', scale=True, dim=(6, 8), rowclus=True, colclus=True, zscore=None,  xlabel=True, ylabel=True, tickfont=(12, 12), show, r, figtype, figname, theme)ParametersDescriptionfileCSV delimited data file. It should not have NA or missing valuescmapColor Palette for heatmap [string][default: 'seismic']scaleDraw a color key with heatmap [boolean (True or False)][default: True]dimheatmap figure size [Tupleof two floats (width, height) in inches][default: (6, 8)]rowclusDraw hierarchical clustering for rows  [boolean (True or False)][default: True]colclusDraw hierarchical clustering for columns [boolean (True or False)][default: True]zscoreZ-score standardization of row (0) or column (1). It works when clus is True. [None, 0, 1][default: None]xlabelPlot X-label [boolean (True or False)][default: True]ylabelPlot Y-label [boolean (True or False)][default: True]tickfontFontsize for X and Y-axis tick labels [Tupleof two floats][default: (14, 14)]showShow the figure on console instead of saving in current folder [True or False][default:False]rFigure resolution in dpi [int][default: 300]. Not compatible withshow= TruefigtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']fignamename of figure [string ][default:"heatmap"]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:heatmap plot (heatmap.png, heatmap_clus.png)Working exampleClustering analysisScree plotlatest update v2.0.1bioinfokit.visuz.cluster.screeplot(obj, axlabelfontsize, axlabelfontname, axxlabel, axylabel, figtype, r, show, dim, theme)ParametersDescriptionobjlist of component name and component varianceaxlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']rFigure resolution in dpi [int][default: 300]showShow the figure on console instead of saving in current folder [True or False][default:False]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Scree plot image (screeplot.png will be saved in same directory)Working ExamplePrincipal component analysis (PCA) loadings plotslatest update v2.0.1bioinfokit.visuz.cluster.pcaplot(x, y, z, labels, var1, var2, var3, axlabelfontsize, axlabelfontname, figtype, r, show, plotlabels, dim, theme)ParametersDescriptionxloadings (correlation coefficient) for principal component 1 (PC1)yloadings (correlation coefficient) for principal component 2 (PC2)zloadings (correlation coefficient) for principal component 3 (PC2)labelsoriginal variables labels from dataframe used for PCAvar1Proportion of PC1 variance [float (0 to 1)]var2Proportion of PC2 variance [float (0 to 1)]var3Proportion of PC3 variance [float (0 to 1)]axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']rFigure resolution in dpi [int][default: 300]showShow the figure on console instead of saving in current folder [True or False][default:False]plotlabelsPlot labels as defined by labels parameter [True or False][default:True]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:PCA loadings plot 2D and 3D image (pcaplot_2d.png and pcaplot_3d.png will be saved in same directory)Working ExamplePrincipal component analysis (PCA)  biplotslatest update v2.0.2bioinfokit.visuz.cluster.biplot(cscore, loadings, labels, var1, var2, var3, axlabelfontsize, axlabelfontname, figtype, r, show, markerdot, dotsize, valphadot, colordot, arrowcolor, valphaarrow, arrowlinestyle, arrowlinewidth, centerlines, colorlist, legendpos, datapoints, dim, theme)ParametersDescriptioncscoreprincipal component scores (obtained from PCA().fit_transfrom() function in sklearn.decomposition)loadingsloadings (correlation coefficient) for principal componentslabelsoriginal variables labels from dataframe used for PCAvar1Proportion of PC1 variance [float (0 to 1)]var2Proportion of PC2 variance [float (0 to 1)]var3Proportion of PC3 variance [float (0 to 1)]axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']rFigure resolution in dpi [int][default: 300]showShow the figure on console instead of saving in current folder [True or False][default:False]markerdotShape of the dot on plot. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]dotsizeThe size of the dots in the plot [float][default: 6]valphadotTransparency of dots on plot [float (between 0 and 1)][default: 1]colordotColor of dots on plot [string or list ][default:"#4a4e4d"]arrowcolorColor of the arrow [string ][default:"#fe8a71"]valphaarrowTransparency of the arrow [float (between 0 and 1)][default: 1]arrowlinestyleline style of the arrow. check more styles athttps://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html[string][default: '-']arrowlinewidthline width of the arrow [float][default: 1.0]centerlinesdraw center lines at x=0 and y=0 for 2D plot [bool (True or False)][default: True]colorlistlist of the categories to assign the color [list][default:None]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:"best"]datapointsplot data points on graph [bool (True or False)][default: True]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:PCA biplot 2D and 3D image (biplot_2d.png and biplot_3d.png will be saved in same directory)Working Examplet-SNE plotlatest update v2.0.1bioinfokit.visuz.cluster.tsneplot(score, colorlist, axlabelfontsize, axlabelfontname, figtype, r, show, markerdot, dotsize, valphadot, colordot, dim, figname, legendpos, legendanchor, theme)ParametersDescriptionscoret-SNE component embeddings (obtained from TSNE().fit_transfrom() function in sklearn.manifold)colorlistlist of the categories to assign the color [list][default:None]axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']rFigure resolution in dpi [int][default: 300]showShow the figure on console instead of saving in current folder [True or False][default:False]markerdotShape of the dot on plot. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]dotsizeThe size of the dots in the plot [float][default: 6]valphadotTransparency of dots on plot [float (between 0 and 1)][default: 1]colordotColor of dots on plot [string or list ][default:"#4a4e4d"]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:"best"]legendanchorposition of the legend outside of the plot. For more options see bbox_to_anchor parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[list][default:None]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]fignamename of figure [string ][default:"tsne_2d"]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:t-SNE 2D image (tsne_2d.png will be saved in same directory)Working ExampleNormalizationRPM or CPM normalizationlatest update v0.8.9Normalize raw gene expression counts into Reads per million mapped reads (RPM) or Counts per million mapped reads (CPM)bioinfokit.analys.norm.cpm(df)ParametersDescriptiondfPandas dataframe containing raw gene expression values. Genes with missing expression values (NA) will be dropped.Returns:RPM or CPM normalized Pandas dataframe as class attributes (cpm_norm)Working ExampleRPKM or FPKM normalizationlatest update v0.9Normalize raw gene expression counts into Reads per kilo base per million mapped reads (RPKM) or
Fragments per kilo base per million mapped reads (FPKM)bioinfokit.analys.norm.rpkm(df, gl)ParametersDescriptiondfPandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.glName of a column having gene length in bp [string][default: None]Returns:RPKM or FPKM normalized Pandas dataframe as class attributes (rpkm_norm)Working ExampleTPM normalizationlatest update v0.9.1Normalize raw gene expression counts into Transcript per million (TPM)bioinfokit.analys.norm.tpm(df, gl)ParametersDescriptiondfPandas dataframe containing raw gene expression values. Genes with missing expression or gene length values (NA) will be dropped.glName of a column having gene length in bp [string][default: None]Returns:TPM normalized Pandas dataframe as class attributes (tpm_norm)Working ExampleVariant analysisManhattan plotlatest update v2.0.1bioinfokit.visuz.marker.mhat(df, chr, pv, log_scale, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol,  markernames, gfont, valpha, show, figtype, axxlabel, axylabel, axlabelfontsize, ylm, gstyle, figname, theme)ParametersDescriptiondfPandas dataframe object with atleast SNP, chromosome, and P-values columnschrName of a column having chromosome numbers [string][default:None]pvName of a column having P-values. Must be numeric column [string][default:None]log_scaleChange the values provided inpvcolumn to minus log10 scale. If set toFalse, the original values inpvwill be used. This is useful in case of Fst values.  [Boolean (True or False)][default:True]colorList the name of the colors to be plotted. It can accept two alternate colors or the number colors equal to chromosome number. If nothing (None) provided, it will randomly assign the color to each chromosome [list][default:None]gwas_sign_linePlot statistical significant threshold line defined by optiongwasp[Boolean (True or False)][default: False]gwaspStatistical significant threshold to identify significant SNPs [float][default: 5E-08]dotsizeThe size of the dots in the plot [float][default: 8]markeridcolName of a column having SNPs. This is necessary for plotting SNP names on the plot [string][default: None]markernamesThe list of the SNPs to display on the plot. These SNP should be present in SNP column. Additionally, it also accepts the dict of SNPs and its associated gene name. If this option set to True, it will label all SNPs with P-value significant score defined bygwasp[string, list,Tuple, dict][default: True]gfontFont size for SNP names to display on the plot [float][default: 8]. gfont not compatible with gstyle=2.valphaTransparency of points on plot [float (between 0 and 1)][default: 1.0]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]rFigure resolution in dpi [int][default: 300]arRotation of X-axis labels [float][default: 90]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']showShow the figure on console instead of saving in current folder [Boolean (True or False)][default:False]axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]axlabelfontsizeFont size for axis labels [float][default: 9]ylmRange of ticks to plot on Y-axis [floatTuple(bottom, top, interval)][default: None]gstyleStyle of the text for markernames. 1 for default text and 2 for box text [int][default: 1]fignamename of figure [string][default:"manhattan"]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Manhattan plot image in same directory (Manhattan.png)Working exampleVariant annotationlatest update v0.9.3Assign genetic features and function to the variants in VCF filebioinfokit.analys.marker.vcf_anot(file, id, gff_file, anot_attr)ParametersDescriptionfileVCF fileidchromosome id column in VCF file [string][default='#CHROM']gff_fileGFF3 genome annotation fileanot_attrGene function tag in attributes field of GFF3 fileReturns:Tab-delimited text file with annotation (annotated text file will be saved in same directory)Working ExampleConcatenate VCF fileslatest update v0.9.4Concatenate multiple VCF files into single VCF file (for example, VCF files for each chromosome)bioinfokit.analys.marker.concatvcf(file)ParametersDescriptionfileMultiple vcf files separated by commaReturns:Concatenated VCF file (concat_vcf.vcf)Working exampleSplit VCF filebioinfokit.analys.marker.splitvcf(file)Split single VCF file containing variants for all chromosomes into individual file containing variants for each chromosomeParametersDescriptionfileVCF file to splitidchromosome id column in VCF file [string][default='#CHROM']Returns:VCF files for each chromosomeWorking exampleHigh-throughput sequence analysisFASTQ batch downloads from SRA databaselatest update v0.9.7bioinfokit.analys.fastq.sra_bd(file, t, other_opts)FASTQ files will be downloaded usingfasterq-dump. Make sure you have the latest version of the NCBI SRA toolkit
(version 2.10.8) is installed and binaries are added to the system pathParametersDescriptionfileList of SRA accessions for batch download. All accession must be separated by a newline in the file.tNumber of threads for parallel run [int][default=4]other_optsProvide other relevant options forfasterq-dump[str][default=None]Provide the options as a space-separated string. You can get a detailed option forfasterq-dumpusing the-helpoption.Returns:FASTQ files for each SRA accession in the current directory unless specified byother_optsDescription and working exampleFASTQ quality format detectionbioinfokit.analys.format.fq_qual_var(file)ParametersDescriptionfileFASTQ file to detect quality format [deafult: None]Returns:Quality format encoding name for FASTQ file (Supports only Sanger, Illumina 1.8+ and Illumina  1.3/1.4)Working ExampleSequencing coveragelatest update v0.9.7bioinfokit.analys.fastq.seqcov(file, gs)ParametersDescriptionfileFASTQ filegsGenome size in MbpReturns:Sequencing coverage of the given FASTQ fileDescription and Working exampleSplit the sequence into smaller subsequenceslatest update v2.0.6bioinfokit.analys.Fasta.split_seq(seq, seq_size, seq_overlap, any_cond, outfmt)ParametersDescriptionseqInput sequence [string]seq_sizesubsequence size [int][default: 3]seq_overlapSplit the sequence in overlap mode [bool][default: True]any_condSplit sequence based on a condition. Note yet defined.outfmtOutput format for the subsequences. If parameter set to 'fasta', the file will be saved in same folder with nameoutput_chunks.fasta['list' or 'fasta'][default: 'list']Returns:Subsequences in list or fasta file (output_chunks.fasta) formatDescription and Working exampleReverse complement of DNA sequencelatest update v2.1.1bioinfokit.analys.Fasta.rev_com(sequence)ParametersDescriptionseqDNA sequence to perform reverse complementfileDNA sequence in a fasta fileReturns:Reverse complement of original DNA sequenceWorking exampleFile format conversionsbioinfokit.analys.formatFunctionParametersDescriptionbioinfokit.analys.format.fqtofa(file)FASTQ fileConvert FASTQ file into FASTA formatbioinfokit.analys.format.hmmtocsv(file)HMM fileConvert HMM text output (from HMMER tool) to CSV formatbioinfokit.analys.format.tabtocsv(file)TAB fileConvert TAB file to CSV formatbioinfokit.analys.format.csvtotab(file)CSV fileConvert CSV file to TAB formatReturns:Output will be saved in same directoryWorking exampleGFF3 to GTF file format conversionlatest update v1.0.1bioinfokit.analys.gff.gff_to_gtf(file, trn_feature_name)ParametersDescriptionfileGFF3 genome annotation filetrn_feature_nameName of the feature (column 3 of GFF3 file) of RNA transcripts if other than 'mRNA' or 'transcript'Returns:GTF format genome annotation file (file.gtf will be saved in same directory)Working ExampleBioinformatics file readers and processing (FASTA, FASTQ, and VCF)latest update v2.0.4FunctionParametersDescriptionbioinfokit.analys.Fasta.fasta_reader(file)FASTA fileFASTA file readerbioinfokit.analys.fastq.fastq_reader(file)FASTQ fileFASTQ file readerbioinfokit.analys.marker.vcfreader(file)VCF fileVCF file readerReturns:File generator object (can be iterated only once) that can be parsed for the recordDescription and working exampleExtract subsequence from FASTA fileslatest update v2.0.4bioinfokit.analys.Fasta.ext_subseq(file, id, st, end, strand)Extract the subsequence of specified region from FASTA file. If the target subsequence region is on minus strand. the
reverse complementary of subsequence will be printed.ParametersDescriptionfileFASTA file [file]idThe ID of sequence from FASTA file to extract the subsequence [string]stStart integer coordinate of subsequnece [int]endEnd integer coordinate of subsequnece [int]strandStrand of the subsequence ['plus' or 'minus'][default: 'plus']Returns:Subsequence to stdoutExtract sequences from FASTA filelatest update v2.1.3bioinfokit.analys.Fasta.extract_seq(file, id)Extract the sequences from FASTA file based on the list of sequence IDs provided from other fileParametersDescriptionfileFASTA file [file]idList of sequence IDs separated by new line. This file can also contain the ID, start and end coordinates separated by TAB [file]Returns:Sequences extracted from FASTA file based on the given IDs provided in id file. Output FASTA file will be saved as
output.fasta in current working directory.Description and working exampleSplit FASTA file into multiple FASTA fileslatest update v2.0.4bioinfokit.analys.Fasta.split_fasta(file, n, bases_per_line)Split one big FASTA file into multiple smaller FASTA filesParametersDescriptionfileFASTA file [file]nNumber of FASTA files to split the big FASTA file [int][default: 2]bases_per_lineNumber of bases per line for ouput FASTA files [int][default: 60]Returns:Number of smaller FASTA files with prefix output (output_0.fasta, output_1.fasta and so on)Convert multi-line FASTA into single-line FASTAlatest update v2.1.2bioinfokit.analys.Fasta.multi_to_single_line(file)Convert multi-line FASTA (where sequences are on multi lines) into single-line FASTA (where sequences are in single line)ParametersDescriptionfileFASTA file [file]Returns:Single line FASTA (output.fasta). Output FASTA file will be saved as output.fasta in current working directory.Description and working exampleMerge counts files from featureCountslatest update v2.0.5bioinfokit.analys.HtsAna.merge_featureCount(pattern, gene_column_name)Merge counts files generated from featureCounts when it runs individually on large samples. The count files must be in
same folder and should end with .txt file extension.ParametersDescriptionpatternfile name pattern for each count file [default: '*.txt']gene_column_namegene id column name for feature and meta-features [default: 'Geneid']Returns:Merge count file (gene_matrix_count.csv) in same folderSplit BED file by chromosomelatest update v2.0.9bioinfokit.analys.HtsAna.split_bed(bed)Split the BED file by chromosome namesParametersDescriptionbedInput BED file [default: None]Returns:BED file for each chromosome (files will be saved in same directory)Working exampleFunctional enrichment analysisGene family enrichment analysis (GenFam)latest update v1.0.0bioinfokit.analys.genfam.fam_enrich(id_file, species, id_type, stat_sign_test, multi_test_corr, min_map_ids, alpha)GenFam is a comprehensive classification and enrichment analysis tool for plant genomes. It provides a unique way to
characterize the large-scale gene datasets such as those from transcriptome analysis (readGenFampaper for more details)ParametersDescriptionid_fileText file containing the list of gene IDs to analyze using GenFam. IDs must be separated by newline.speciesPlant species ID for GenFam analysis. All plant species ID providedhereid_typePlant species ID type1: Phytozome locus ID2: Phytozome transcript ID3: Phytozome PAC IDstat_sign_testStatistical significance test for enrichment analysis [default=1].1: Fisher exact test2: Hypergeometric distribution3: Binomial distribution4: Chi-squared distributionmulti_test_corrMultiple testing correction test [default=3].1: Bonferroni2: Bonferroni-Holm3: Benjamini-Hochbergmin_map_idsMinimum number of gene IDs from the user list (id_file) must be mapped to the background database for performing GenFam analysis [default=5]alphaSignificance level [float][default: 0.05]Returns:AttributeDescriptiondf_enrichEnriched gene families with p < 0.05genfam_infoGenFam run informationOutput filesOutput figures and files from GenFam analysisgenfam_enrich.png: GenFam figure for enriched gene familiesfam_enrich_out.txt: List of enriched gene families with mapped gene IDs, GO annotation, and detailed statisticsfam_all_out.txt: List of all gene families with mapped gene IDs, GO annotation, and detailed statisticsDescription and working exampleCheck allowed ID types for plant species for GenFamlatest update v1.0.0bioinfokit.analys.genfam.check_allowed_ids(species)ParametersDescriptionspeciesPlant species ID to check for allowed ID type. All plant species ID providedhereReturns:Allowed ID types for GenFamDescription and working exampleBiostatistical analysisCorrelation matrix plotlatest update v2.0.1bioinfokit.visuz.stat.corr_mat(table, corm, cmap, r, dim, show, figtype, axtickfontsize, axtickfontname, theme)ParametersDescriptiontableDataframe object with numerical variables (columns) to find correlation. Ideally, you should have three or more variables. Dataframe should not have identifier column.cormCorrelation method [pearson,kendall,spearman] [default:pearson]cmapColor Palette for heatmap [string][default: 'seismic']. More colormaps are available athttps://matplotlib.org/3.1.0/tutorials/colors/colormaps.htmlrFigure resolution in dpi [int][default: 300]. Not compatible withshow= TruedimFigure size [Tupleof two floats (width, height) in inches][default: (6, 5)]showShow the figure on console instead of saving in current folder [True or False][default:False]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']axtickfontsizeFont size for axis ticks [float][default: 7]axtickfontnameFont name for axis ticks [string][default: 'Arial']themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Correlation matrix plot image in same directory (corr_mat.png)Working exampleBar-dot plotlatest update v0.8.5bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar,  valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, ylm, axtickfontsize, axtickfontname, yerrlw, yerrcw)ParametersDescriptiondfPandas dataframe objectcolorbarColor of bar graph [string or list][default:"#bbcfff"]colordotColor of dots on bar [string or list][default:"#ee8972"]bwWidth of bar [float][default: 0.4]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]rFigure resolution in dpi [int][default: 300]arRotation of X-axis labels [float][default: 0]hbsizeHorizontal bar size for standard error bars [float][default: 4]errorbarDraw standard error bars [bool (True or False)][default: True]dotsizeThe size of the dots in the plot [float][default: 6]markerdotShape of the dot marker. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]valphabarTransparency of bars on plot [float (between 0 and 1)][default: 1]valphadotTransparency of dots on plot [float (between 0 and 1)][default: 1]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']showShow the figure on console instead of saving in current folder [True or False][default:False]axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']ylmRange of ticks to plot on Y-axis [floatTuple(bottom, top, interval)][default: None]axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']yerrlwError bar line width [float][default: None]yerrcwError bar cap width [float][default: None]Returns:Bar-dot plot image in same directory (bardot.png)Working ExampleOne sample and two sample Z-testslatest update v2.1.0bioinfokit.analys.stat.ztest(df, x, y, mu, x_std, y_std, alpha, test_type)ParametersDescriptiondfPandas dataframe for appropriate Z-test.One sample: It should have atleast one variableTwo sample independent: It should have atleast two variablesxcolumn name for x group [string][default: None]ycolumn name for x group [string][default: None]muPopulation or known mean for the one sample Z-test [float][default: None]x_stdPopulation standard deviation for x group [float][default: None]y_stdPopulation standard deviation for y group [float][default: None]alphaSignificance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated  [float][default: 0.05]test_typeType of Z-test [int (1,2)][default: None].1: One sample Z-test2: Two sample Z-testReturns:Summary output as class attribute (summary and result)Description and Working exampleOne sample and two sample (independent and paired) t-testslatest update v2.1.0bioinfokit.analys.stat.ttest(df, xfac, res, evar, alpha, test_type, mu)ParametersDescriptiondfPandas dataframe for appropriate t-test.One sample: It should have atleast dependent (res) variableTwo sample independent: It should have independent (xfac) and dependent (res) variablesTwo sample paired: It should have two dependent (res) variablesxfacIndependent group column name with two levels [string][default: None]resDependent variable column name [string or list orTuple][default: None]evart-test with equal variance [bool (True or False)][default: True]alphaSignificance level for confidence interval (CI). If alpha=0.05, then 95% CI will be calculated  [float][default: 0.05]test_typeType of t-test [int (1,2,3)][default: None].1: One sample t-test2: Two sample independent t-test3: Two sample paired t-testmuPopulation or known mean for the one sample t-test [float][default: None]Returns:Summary output as class attribute (summary and result)Description and Working exampleChi-square testlatest update v0.9.5bioinfokit.analys.stat.chisq(df, p)ParametersDescriptiondfPandas dataframe. It should be one or two-dimensional contingency table.pTheoretical expected probabilities for each group. It must be non-negative and sum to 1. If p is provide Goodness of Fit test will be performed [list orTuple][default: None]Returns:Summary and expected counts as class attributes (summary and expected_df)Working exampleLinear regression analysisbioinfokit.visuz.stat.lin_reg(df, x, y)ParametersDescriptiondfPandas dataframe objectxName of column having independent X variables [list][default:None]yName of column having dependent Y variables [list][default:None]Returns:Regression analysis summaryWorking ExampleRegression plotlatest update v2.0.1bioinfokit.visuz.stat.regplot(df, x, y, yhat, dim, colordot, colorline, r, ar, dotsize, markerdot, linewidth,  valphaline, valphadot, show, figtype, axxlabel, axylabel, axlabelfontsize, axlabelfontname, xlm, ylm, axtickfontsize, axtickfontname, theme)ParametersDescriptiondfPandas dataframe objectxName of column having independent X variables [string][default:None]yName of column having dependent Y variables [string][default:None]yhatName of column having predicted response of Y variable (y_hat) from regression [string][default:None]dimFigure size [Tupleof two floats (width, height) in inches][default: (6, 4)]rFigure resolution in dpi [int][default: 300]arRotation of X-axis labels [float][default: 0]dotsizeThe size of the dots in the plot [float][default: 6]markerdotShape of the dot marker. See more options athttps://matplotlib.org/3.1.1/api/markers_api.html[string][default: "o"]valphalineTransparency of regression line on plot [float (between 0 and 1)][default: 1]valphadotTransparency of dots on plot [float (between 0 and 1)][default: 1]linewidthWidth of regression line [float][default: 1]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']showShow the figure on console instead of saving in current folder [True or False][default:False]axxlabelLabel for X-axis. If you provide this option, default label will be replaced [string][default: None]axylabelLabel for Y-axis. If you provide this option, default label will be replaced [string][default: None]axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']xlmRange of ticks to plot on X-axis [floatTuple(bottom, top, interval)][default: None]ylmRange of ticks to plot on Y-axis [floatTuple(bottom, top, interval)][default: None]axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:Regression plot image in same directory (reg_plot.png)Working ExampleTukey HSD testlatest update v1.0.3bioinfokit.analys.stat.tukey_hsd(df, res_var, xfac_var, anova_model, phalpha, ss_typ)It performs multiple pairwise comparisons of treatment groups using Tukey's HSD (Honestly Significant Difference) test
to check if group means are significantly different from each other. It uses the Tukey-Kramer approach if the sample sizes
are unequal among the groups.ParametersDescriptiondfPandas dataframe with the variables mentioned in theres_var,xfac_varandanova_modeloptions. It should not have missing data. The missing data will be omitted.res_varName of a column having response variable [string][default: None]xfac_varName of a column having factor or group for pairwise comparison [string][default: None]anova_modelANOVA model (calculated using statsmodelsolsfunction) [string][default: None]phalphaSignificance level [float][default: 0.05]ss_typType of sum of square to perform ANOVA [int][default: 2]Returns:AttributeDescriptiontukey_summaryPairwise comparisons for main and interaction effects by Tukey HSD testDescription and Working exampleBartlett's testlatest update v1.0.3bioinfokit.analys.stat.bartlett(df, xfac_var, res_var)It performs Bartlett's test to check the homogeneity of variances among the treatment groups. It accepts the input
table in a stacked format. More detailshttps://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.bartlett.htmlParametersDescriptiondfPandas dataframe containing response (res_var) and independent variables (xfac_var) in a stacked format. It should not have missing data. The missing data will be omitted.res_varName of a column having response variable [string][default:None]xfac_varName of a column having treatment groups (independent variables) [string or list][default:None]Returns:AttributeDescriptionbartlett_summaryPandas dataframe containing Bartlett's test statistics, degree of freedom, andpvalueDescription and Working exampleLevene's testlatest update v1.0.3bioinfokit.analys.stat.levene(df, xfac_var, res_var)It performs Levene's test to check the homogeneity of variances among the treatment groups. It accepts the input
table in a stacked format. More detailshttps://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.levene.htmlParametersDescriptiondfPandas dataframe containing response (res_var) and independent variables (xfac_var) in a stacked format. It should not have missing data. The missing data will be omitted.res_varName of a column having response variable [string][default:None]xfac_varName of a column having treatment groups (independent variables) [string or list][default:None]centerChoice for the Levene's test [string (median,mean,trimmed)] [default:median]median: Brown-Forsythe Levene-type testmean: original Levene's testtrimmed: Brown-Forsythe Levene-type testReturns:AttributeDescriptionlevene_summaryPandas dataframe containing Levene's test statistics, degree of freedom, andpvalueDescription and Working exampleROC plotlatest update v2.0.1bioinfokit.visuz.stat.roc(fpr, tpr, c_line_style, c_line_color, c_line_width, diag_line, diag_line_style,  diag_line_width, diag_line_color, auc, shade_auc, shade_auc_color, axxlabel, axylabel, axtickfontsize,  axtickfontname, axlabelfontsize, axlabelfontname, plotlegend, legendpos, legendanchor, legendcols, legendfontsize, legendlabelframe, legend_columnspacing, dim, show, figtype, figname, r, ylm, theme)Receiver operating characteristic (ROC) curve for visualizing classification performanceParametersDescriptionfprIncreasing false positive rates obtained fromsklearn.metrics.roc_curve[list][default:None]tprIncreasing true positive rates obtained fromsklearn.metrics.roc_curve[list][default:None]c_line_styleLine style for ROC curve [string][default:'-']c_line_colorLine color for ROC curve [string][default:'#f05f21']c_line_widthLine width for ROC curve [float][default:1]diag_linePlot reference line [True or False][default: True]diag_line_styleLine style for  reference line [string][default:'--']diag_line_widthLine width for  reference line [float][default:1]diag_line_colorLine color for reference line [string][default:'b']aucArea under ROC. It can be obtained fromsklearn.metrics.roc_auc_score[float][default: None]shade_aucShade are for AUC [True or False][default: False]shade_auc_colorShade color for AUC [string][default: '#f48d60']axxlabelLabel for X-axis [string][default: 'False Positive Rate (1 - Specificity)']axylabelLabel for Y-axis [string][default: 'True Positive Rate (Sensitivity)']axtickfontsizeFont size for axis ticks [float][default: 9]axtickfontnameFont name for axis ticks [string][default: 'Arial']axlabelfontsizeFont size for axis labels [float][default: 9]axlabelfontnameFont name for axis labels [string][default: 'Arial']plotlegendplot legend   [True or False][default:True]legendposposition of the legend on plot. For more options see loc parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[string ][default:'lower right']legendanchorposition of the legend outside of the plot. For more options see bbox_to_anchor parameter athttps://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.legend.html[list][default:None]legendcolsNumber of columns for legends [int][default: 1]legendfontsizeFont size for the legends [float][default:8]legendlabelframeBox frame for the legend  [True or False][default: False]legend_columnspacingSpacing between the legends  [float][default: None]dimFigure size [Tupleof two floats (width, height) in inches][default: (5, 4)]showShow the figure on console instead of saving in current folder [True or False][default:False]figtypeFormat of figure to save. Supported format are eps, pdf, pgf, png, ps, raw, rgba, svg, svgz [string][default:'png']fignamename of figure [string ][default:'roc']rFigure resolution in dpi [int][default: 300]. Not compatible withshow= TrueylmRange of ticks to plot on Y-axis [float (bottom, top, interval)][default: None]themeChange background theme. If theme set todark, the dark background will be produced instead of white [string][default:'None']Returns:ROC plot image in same directory (roc.png)Working exampleRegression metricsCalculate Root Mean Square Error (RMSE), Mean squared error (MSE), Mean absolute error (MAE), and Mean absolute percent
error (MAPE) from regression fitlatest update v1.0.8bioinfokit.analys.stat.reg_metric(y, yhat, resid)ParametersDescriptionyOriginal values for dependent variable [numpy array] [default: None]yhatPredicted values from regression [numpy array] [default: None]residRegression residuals  [numpy array][default: None]Returns:Pandas dataframe with values for RMSE, MSE, MAE, and MAPEWorking exampleVenn Diagrambioinfokit.visuz.venn(vennset, venncolor, vennalpha, vennlabel)ParametersDescriptionvennsetVenn dataset for 3 and 2-way venn. Data should be in the format of (100,010,110,001,101,011,111) for 3-way venn and 2-way venn (10, 01, 11) [default: (1,1,1,1,1,1,1)]venncolorColor Palette for Venn [color code][default: ('#00909e', '#f67280', '#ff971d')]vennalphaTransparency of Venn  [float (0 to 1)][default: 0.5]vennlabelLabels to Venn [string][default: ('A', 'B', 'C')]Returns:Venn plot (venn3.png, venn2.png)Working exampleReferences:Travis E. Oliphant. A guide to NumPy, USA: Trelgol Publishing, (2006).John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007),
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261-272.David C. Howell. Multiple Comparisons With Unequal Sample Sizes.https://www.uvm.edu/~statdhtx/StatPages/MultipleComparisons/unequal_ns_and_mult_comp.htmlLast updated: November 20, 2021