bioinfokit 0.4

Bioinformatics data analysis and visualization toolkit

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 requires

• Python 3
• NumPy
• scikit-learn
• seaborn
• pandas
• matplotlib
• SciPy

git clone https://github.com/reneshbedre/bioinfokit.git
cd bioinfokit
python setup.py install

Volcano plot

bioinfokit.visuz.volcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont)

Parameters	Description
table	Comma separated (csv) gene expression table having atleast gene IDs, log fold change, P-values or adjusted P-values columns
lfc	Name of a column having log fold change values [string][default:logFC]
pv	Name of a column having P-values or adjusted P-values [string][default:p_values]
lfc_thr	Log fold change cutoff for up and downregulated genes [float][default:1.0]
pv_thr	P-values or adjusted P-values cutoff for up and downregulated genes [float][default:0.05]
color	Tuple of two colors [tuple][default: ("green", "red")]
valpha	Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
geneid	Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
genenames	Tuple of 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]
gfont	Font size for genenames [float][default: 10.0]

Returns:

Volcano plot image in same directory (volcano.png)

Working example

MA plot

bioinfokit.visuz.ma(table, lfc, ct_count, st_count, pv_thr)

Parameters	Description
table	Comma separated (csv) gene expression table having atleast gene IDs, log fold change, and counts (control and treatment) columns
lfc	Name of a column having log fold change values [default:logFC]
ct_count	Name of a column having count values for control sample [default:value1]
st_count	Name of a column having count values for treatment sample [default:value2]
lfc_thr	Log fold change cutoff for up and downregulated genes [default:1]

Returns:

MA plot image in same directory (ma.png)

Working example

Inverted Volcano plot

bioinfokit.visuz.involcano(table, lfc, pv, lfc_thr, pv_thr, color, valpha, geneid, genenames, gfont)

Parameters	Description
table	Comma separated (csv) gene expression table having atleast gene IDs, log fold change, P-values or adjusted P-values
lfc	Name of a column having log fold change values [default:logFC]
pv	Name of a column having P-values or adjusted P-values [default:p_values]
lfc_thr	Log fold change cutoff for up and downregulated genes [default:1]
pv_thr	P-values or adjusted P-values cutoff for up and downregulated genes [default:0.05]
color	Tuple of two colors [tuple][default: ("green", "red")]
valpha	Transparency of points on volcano plot [float (between 0 and 1)][default: 1.0]
geneid	Name of a column having gene Ids. This is necessary for plotting gene label on the points [string][default: None]
genenames	Tuple of 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]
gfont	Font size for genenames [float][default: 10.0]

Returns:

Inverted volcano plot image in same directory (involcano.png)

Working example

Correlation matrix plot

bioinfokit.visuz.corr_mat(table, corm)

Parameters	Description
table	Dataframe object with numerical variables (columns) to find correlation. Ideally, you should have three or more variables. Dataframe should not have identifier column.
corm	Correlation method [pearson,kendall,spearman] [default:pearson]

Returns:

Correlation matrix plot image in same directory (corr_mat.png)

Working example

Merge VCF files

bioinfokit.analys.mergevcf(file)

Parameters	Description
file	Multiple vcf files and separate them by comma

Returns:

Merged VCF file (merge_vcf.vcf)

Working example

Merge VCF files

bioinfokit.analys.mergevcf(file)

Parameters	Description
file	Multiple vcf files and separate them by comma

Returns:

Merged VCF file (merge_vcf.vcf)

Working example

PCA

bioinfokit.analys.pca(table)

Parameters	Description
table	Dataframe object with numerical variables (columns). Dataframe should not have identifier column.

Returns:

PCA summary, scree plot (screepca.png), and 2D/3D pca plots (pcaplot_2d.png and pcaplot_3d.png)

Working example

Reverse complement of DNA sequence

bioinfokit.analys.rev_com(sequence)

Parameters	Description
seq	DNA sequence to perform reverse complement
file	DNA sequence in a fasta file

Returns:

Reverse complement of original DNA sequence

Working example

Sequencing coverage

bioinfokit.analys.seqcov(file, gs)

Parameters	Description
file	FASTQ file
gs	Genome size in Mbp

Returns:

Sequencing coverage of the given FASTQ file

Working example

Convert TAB to CSV file

bioinfokit.analys.tcsv(file)

Parameters	Description
file	TAB delimited text file

Returns:

CSV delimited file (out.csv)

Heatmap

bioinfokit.visuz.hmap(table, cmap='seismic', scale=True, dim=(6, 8), clus=True, zscore=None, xlabel=True, ylabel=True, tickfont=(12, 12))

Parameters	Description
file	CSV delimited data file. It should not have NA or missing values
cmap	Color Palette for heatmap [string][default: 'seismic']
scale	Draw a color key with heatmap [boolean (True or False)][default: True]
dim	heatmap figure size [tuple of two floats (width, height) in inches][default: (6, 8)]
clus	Draw hierarchical clustering with heatmap [boolean (True or False)][default: True]
zscore	Z-score standardization of row (0) or column (1). It works when clus is True. [None, 0, 1][default: None]
xlable	Plot X-label [boolean (True or False)][default: True]
ylable	Plot Y-label [boolean (True or False)][default: True]
tickfont	Fontsize for X and Y-axis tick labels [tuple of two floats][default: (14, 14)]

Returns:

heatmap plot (heatmap.png, heatmap_clus.png)

Working example

Venn Diagram

bioinfokit.visuz.venn(vennset, venncolor, vennalpha, vennlabel)

Parameters	Description
vennset	Venn 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)]
venncolor	Color Palette for Venn [color code][default: ('#00909e', '#f67280', '#ff971d')]
vennalpha	Transparency of Venn [float (0 to 1)][default: 0.5]
vennlabel	Labels to Venn [string][default: ('A', 'B', 'C')]

Returns:

Venn plot (venn3.png, venn2.png)

Working example

Two sample t-test with equal and unequal variance

bioinfokit.analys.ttsam(table, xfac, res, evar)

Parameters	Description
table	CSV delimited data file. It should be stacked table with independent (xfac) and dependent (res) variable columns.
xfac	Independent group column name with two levels [string][default: None]
res	Response variable column name [string][default: None]
evar	t-test with equal variance [bool (True or False)][default: True]

Returns:

summary output and group boxplot (ttsam_boxplot.png)

Working example

Chi-square test for independence

bioinfokit.analys.chisq(table)

Parameters	Description
table	CSV delimited data file. It should be contingency table.

Returns:

summary output and variable mosaic plot (mosaic.png)

Working example

File format conversions

bioinfokit.analys.format

Function	Parameters	Description
bioinfokit.analys.format.fqtofa(file)	FASTQ file	Convert FASTQ file into FASTA format
bioinfokit.analys.format.hmmtocsv(file)	HMM file	Convert HMM text output (from HMMER tool) to CSV format
bioinfokit.analys.format.tabtocsv(file)	TAB file	Convert TAB file to CSV format
bioinfokit.analys.format.csvtotab(file)	CSV file	Convert CSV file to TAB format

Returns:

Output will be saved in same directory

Working example

One-way ANOVA

bioinfokit.stat.oanova(table, res, xfac, ph, phalpha)

Parameters	Description
table	Pandas dataframe in stacked table format
res	Response variable (dependent variable) [string][default: None]
xfac	Treatments or groups or factors (independent variable) [string][default: None]
ph	perform pairwise comparisons with Tukey HSD test [bool (True or False)] [default: False]
phalpha	significance level Tukey HSD test [float (0 to 1)][default: 0.05]

Returns:

ANOVA summary, multiple pairwise comparisons, and assumption tests statistics

Working example

Manhatten plot

bioinfokit.visuz.marker.mhat(df, chr, pv, color, dim, r, ar, gwas_sign_line, gwasp, dotsize, markeridcol, markernames, gfont, valpha)

Parameters	Description
df	Pandas dataframe object with atleast SNP, chromosome, and P-values columns
chr	Name of a column having chromosome numbers [string][default:None]
pv	Name of a column having P-values. Must be numeric column [string][default:None]
color	List 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]
dim	Figure size [tuple of two floats (width, height) in inches][default: (6, 4)]
r	Figure resolution in dpi [int][default: 300]
ar	Rotation of X-axis labels [float][default: 90]
gwas_sign_line	Plot statistical significant threshold line defined by option gwasp [bool (True or False)][default: False]
gwasp	Statistical significant threshold to identify significant SNPs [float][default: 5E-08]
dotsize	The size of the dots in the plot [float][default: 8]
markeridcol	Name of a column having SNPs. This is necessary for plotting SNP names on the plot [string][default: None]
markernames	The 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 by gwasp [string, list, dict][default: True]
gfont	Font size for SNP names to display on the plot [float][default: 8]
valpha	Transparency of points on plot [float (between 0 and 1)][default: 1.0]

Returns:

Manhatten plot image in same directory (manhatten.png)

Working example

Extract the sequences from the FASTA file

bioinfokit.analys.extract_seq(file, id)

Parameters	Description
file	input FASTA file from which sequneces to be extracted
id	sequence ID file

Returns: Extracted sequences in FASTA format file in same directory (out.fasta)

Bar-dot plot

bioinfokit.visuz.stat.bardot(df, colorbar, colordot, bw, dim, r, ar, hbsize, errorbar, dotsize, markerdot, valphabar, valphadot)

Parameters	Description
df	Pandas dataframe object
colorbar	Color of bar graph [string or list][default:"#bbcfff"]
colordot	Color of dots on bar [string or list][default:"#ee8972"]
bw	Width of bar [float][default: 0.4]
dim	Figure size [tuple of two floats (width, height) in inches][default: (6, 4)]
r	Figure resolution in dpi [int][default: 300]
ar	Rotation of X-axis labels [float][default: 0]
hbsize	Horizontal bar size for standard error bars [float][default: 4]
errorbar	Draw standard error bars [bool (True or False)][default: True]
dotsize	The size of the dots in the plot [float][default: 6]
markerdot	Shape of the dot marker. See more options at https://matplotlib.org/3.1.1/api/markers_api.html [string][default: "o"]
valphabar	Transparency of bars on plot [float (between 0 and 1)][default: 1]
valphadot	Transparency of dots on plot [float (between 0 and 1)][default: 1]

Returns:

Bra-dot plot image in same directory (bardot.png)

Working Example

FASTQ quality format detection

bioinfokit.analys.format.fq_qual_var(file)

Parameters	Description
file	FASTQ 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 Example

References:

• 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), DOI:10.1109/MCSE.2007.55 (publisher link)
• Fernando Pérez and Brian E. Granger. IPython: A System for Interactive Scientific Computing, Computing in Science & Engineering, 9, 21-29 (2007), DOI:10.1109/MCSE.2007.53 (publisher link)
• Michael Waskom, Olga Botvinnik, Joel Ostblom, Saulius Lukauskas, Paul Hobson, MaozGelbart, … Constantine Evans. (2020, January 24). mwaskom/seaborn: v0.10.0 (January 2020) (Version v0.10.0). Zenodo. http://doi.org/10.5281/zenodo.3629446
• Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay. Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830 (2011)
• Wes McKinney. Data Structures for Statistical Computing in Python, Proceedings of the 9th Python in Science Conference, 51-56 (2010)