BOL: Related items

GenomeTools: The versatile open source genome analysis software

Neel — Wed, 02 Feb 2022 04:00:21 -0600

The GenomeTools genome analysis system is a free collection of bioinformatics tools (in the realm of genome informatics) combined into a single binary named gt. It is based on a C library named “libgenometools” which consists of several modules.

If you are interested in gene prediction, have a look at GenomeThreader.

http://genometools.org/pub/

Address of the bookmark: http://genometools.org/

Calculate the significance of the difference between two trends

BioStar — Tue, 14 Mar 2023 05:41:53 -0500

To calculate the significance of the difference between two trends, you can use a statistical test such as a t-test or ANOVA (analysis of variance). Here are the general steps to follow:

Define your null hypothesis (H0) and alternative hypothesis (H1). For example, H0 might be that there is no significant difference between the two trends, while H1 might be that there is a significant difference.
Collect data on the two trends. Make sure that the data is independent, normally distributed, and has equal variances.
Calculate the means and standard deviations of each trend.
Calculate the test statistic using a t-test or ANOVA. The test statistic will depend on the specific test you choose, but it will generally compare the difference in means between the two trends to the variability within each trend.
Determine the p-value associated with the test statistic. The p-value represents the probability of obtaining a test statistic as extreme as the one you calculated, assuming that the null hypothesis is true.
Compare the p-value to your chosen significance level (usually 0.05 or 0.01). If the p-value is less than or equal to the significance level, reject the null hypothesis and conclude that there is a significant difference between the two trends. If the p-value is greater than the significance level, fail to reject the null hypothesis and conclude that there is not enough evidence to support a significant difference.

It's important to note that the specific details of each step will depend on the type of test you choose and the software you use to perform the analysis.

The most common methods for comparing means include:

Methods	R function	Description
T-test	t.test()	Compare two groups (parametric)
Wilcoxon test	wilcox.test()	Compare two groups (non-parametric)
ANOVA	aov() or anova()	Compare multiple groups (parametric)
Kruskal-Wallis	kruskal.test()	Compare multiple groups (non-parametric)

Data Visualization in Bioinformatics: Useful and Eye-Catching Plots for Data Analysis

LEGE — Sat, 14 Dec 2024 12:41:53 -0600

Data visualization is a cornerstone of bioinformatics, enabling researchers to interpret complex datasets effectively. With a plethora of data types—genomic sequences, expression profiles, protein interactions, and more—the right visualizations can make or break an analysis. This blog highlights some of the most useful and visually compelling plots for bioinformatics data analysis, along with tools to create them.

1. Heatmaps: Exploring Patterns in High-Dimensional Data

Heatmaps are a go-to visualization for representing high-dimensional datasets, such as gene expression or metabolomics data. They use color gradients to display data intensity, making patterns and clusters easily detectable.

Applications: Gene expression analysis, pathway enrichment, methylation studies.
Tools: Seaborn (Python), ComplexHeatmap (R), Morpheus (web-based).

Tip: Add dendrograms to visualize clustering of rows and columns for hierarchical relationships.

2. Volcano Plots: Highlighting Differential Features

Volcano plots are indispensable for identifying significantly differentially expressed genes or proteins. They plot the log2 fold change against –log10(p-value), making it easy to spot statistically significant changes.

Applications: RNA-seq, proteomics, and metabolomics.
Tools: ggplot2 (R), EnhancedVolcano (R), Plotly (Python).

Tip: Use color to highlight significant features and label key genes or proteins.

3. PCA Plots: Reducing Complexity with Principal Component Analysis

Principal Component Analysis (PCA) plots are used to reduce dimensionality and uncover trends or clusters in data. They provide insights into sample variability and grouping.

Applications: Transcriptomics, metabolomics, microbiome studies.
Tools: scikit-learn + Matplotlib (Python), prcomp (R), ClustVis (web-based).

Tip: Annotate clusters with metadata to enhance interpretability.

4. Manhattan Plots: Genome-Wide Association Studies

Manhattan plots visualize p-values across the genome, making it easy to identify significant associations in genome-wide studies. They resemble city skylines, with the highest peaks indicating loci of interest.

Applications: GWAS, QTL mapping.
Tools: qqman (R), Matplotlib (Python).

Tip: Use alternating colors for chromosomes and highlight significant SNPs for clarity.

5. Circular Plots (Circos): Visualizing Genomic Relationships

Circular plots are ideal for visualizing relationships across the genome, such as structural variations, gene duplications, or synteny.

Applications: Comparative genomics, structural variation studies.
Tools: Circos (standalone), Rcircos (R), pyCircos (Python).

Tip: Keep the plot clean and avoid overcrowding to maintain readability.

6. Sankey Diagrams: Tracking Data Flows

Sankey diagrams visualize flows or relationships between categories, often used to track changes in gene expression or pathway enrichment across conditions.

Applications: Pathway analysis, gene set enrichment analysis.
Tools: Plotly (Python), networkD3 (R).

Tip: Use gradients or distinct colors to highlight key transitions.

7. Network Graphs: Mapping Interactions

Network graphs represent relationships between entities, such as protein-protein interactions or gene regulatory networks. Nodes represent entities, and edges represent relationships.

Applications: Systems biology, interactomics.
Tools: Cytoscape (standalone), igraph (R), NetworkX (Python).

Tip: Use edge thickness or node size to represent interaction strength or centrality.

8. Violin Plots: Visualizing Data Distribution

Violin plots combine a boxplot with a density plot, showing the distribution and variability of data.

Applications: Single-cell RNA-seq, quantitative trait analysis.
Tools: Seaborn (Python), ggplot2 (R).

Tip: Split violins by groups for side-by-side comparisons.

9. Time-Series Plots: Monitoring Changes Over Time

Time-series plots display changes in variables across time points, useful for tracking gene expression dynamics or metabolic fluxes.

Applications: Time-course experiments, cell cycle studies.
Tools: Matplotlib (Python), ggplot2 (R).

Tip: Smooth the data to highlight trends while avoiding overfitting.

10. Genome Tracks: Visualizing Genomic Features

Genome tracks display multiple layers of genomic data, such as gene annotations, sequencing coverage, and epigenetic marks.

Applications: ChIP-seq, ATAC-seq, whole-genome sequencing.
Tools: IGV (standalone), pyGenomeTracks (Python).

Tip: Stack related tracks for direct comparisons.

11. UpSet Plots: Visualizing Set Intersections

UpSet plots are a powerful alternative to Venn diagrams for visualizing intersections between multiple datasets.

Applications: Overlap analysis for gene sets, pathways, or variants.
Tools: UpSetR (R), ComplexUpset (Python).

Tip: Use bar plots to represent the size of each intersection for added clarity.

12. Ridge Plots: Comparing Distributions

Ridge plots visualize the distributions of multiple datasets, stacked for easy comparison.

Applications: Transcriptomics, single-cell RNA-seq.
Tools: ggridges (R), Matplotlib (Python).

Tip: Use transparency and consistent scaling for better readability.

13. Chord Diagrams: Visualizing Connections Between Groups

Chord diagrams illustrate relationships between categories, such as shared genes between pathways or overlaps in regulatory elements.

Applications: Pathway overlap, synteny, co-expression networks.
Tools: Circlize (R), Holoviews (Python).

Tip: Use distinct colors for each group to emphasize relationships.

14. Treemaps: Hierarchical Data Representation

Treemaps visualize hierarchical data as nested rectangles, with area proportional to data size.

Applications: Ontology enrichment, pathway analysis.
Tools: Treemapify (R), Plotly (Python).

Tip: Use colors to represent additional variables, like significance or enrichment scores.

15. T-SNE/UMAP Plots: Dimensionality Reduction for Clustering

T-SNE and UMAP plots are great for visualizing high-dimensional data in two dimensions while preserving local or global structure.

Applications: Single-cell transcriptomics, clustering analyses.
Tools: scikit-learn (Python), Seurat (R).

Tip: Combine with metadata annotations for better cluster interpretation.

Bringing It All Together

The choice of visualization can significantly impact the insights gained from bioinformatics data. By selecting plots tailored to your data type and analysis goals, you can effectively communicate your findings and make your research more impactful. Whether you’re a seasoned bioinformatician or a beginner, mastering these visualizations will elevate your analyses and presentations.

PureCN: copy number calling and SNV classification using targeted short read sequencing

Jit — Thu, 09 Aug 2018 04:09:37 -0500

This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples.

Author: Markus Riester [aut, cre], Angad P. Singh [aut]

Maintainer: Markus Riester

Citation (from within R, enter citation("PureCN")):

Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D, Morrissey M (2016). “PureCN: Copy number calling and SNV classification using targeted short read sequencing.” Source Code for Biology and Medicine, 11, 13. doi: 10.1186/s13029-016-0060-z.

Address of the bookmark: http://bioconductor.org/packages/release/bioc/html/PureCN.html

Type of SSR

BioStar — Thu, 09 Mar 2023 04:35:41 -0600

Types of SSRs (simple sequence repeats), SSRs are short DNA sequences consisting of a tandem repeat of a few nucleotides, typically 2-6 nucleotides in length. There are different types of SSRs based on the length and pattern of the repeated sequence, as well as the presence or absence of interruptions of non-repeated nucleotides within the repeat array. The four types of SSRs are:

Perfect SSR: This is the simplest type of SSR, where the same repeat motif is present adjacent to each other without any interruption of any other nucleotide. For example, a perfect SSR with the repeat motif "CAT" would be "CATCATCATCAT", where the "CAT" sequence is repeated four times.
Imperfect SSR: This type of SSR contains repeat motifs that are interrupted by one or a few non-repeat nucleotides. For example, an imperfect SSR with the repeat motif "CAT" would be "CATCATGGCATCATCAT", where the "CAT" sequence is repeated twice, but interrupted by "GG".
Compound perfect SSR: This type of SSR contains two or more repeat motifs lying adjacent to each other, separated by no or very few intervening nucleotides. For example, a compound perfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATGTCGTC", where the "CAT" sequence is repeated three times, followed by the "GTC" sequence repeated twice.
Compound imperfect SSR: This type of SSR contains two or more repeat motifs interrupted by several non-repeat nucleotides. For example, a compound imperfect SSR with the repeat motifs "CAT" and "GTC" would be "CATCATCATNNNNNNNGTCGTCGTC", where the "CAT" sequence is repeated three times, interrupted by several non-repeat nucleotides, followed by the "GTC" sequence repeated three times.

UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

BioStar — Mon, 05 Aug 2024 23:01:29 -0500

UnCoVar: Workflow for Transparent and Robust Virus Variant Calling, Genome Reconstruction and Lineage Assignment

Using state of the art tools, easily extended for other viruses
Tool and database updates for critical components via Conda
Built using modern design patterns with Conda and Snakemake
Extensible and easy to customize
Submission Ready Genomes
Customizable reporting with comprehensive visualization

https://ikim-essen.github.io/uncovar/

Github https://github.com/IKIM-Essen/uncovar

Address of the bookmark: https://ikim-essen.github.io/uncovar/

Understanding BLASTn output format 6 !

Rahul Nayak — Wed, 27 Jun 2018 18:38:21 -0500

BLASTn output format 6

BLASTn maps DNA against DNA, for example gene sequences against a reference genome

blastn -query genes.ffn -subject genome.fna -outfmt 6

BLASTn tabular output format 6

Column headers:
qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore

1.	qseqid	query (e.g., gene) sequence id
2.	sseqid	subject (e.g., reference genome) sequence id
3.	pident	percentage of identical matches
4.	length	alignment length
5.	mismatch	number of mismatches
6.	gapopen	number of gap openings
7.	qstart	start of alignment in query
8.	qend	end of alignment in query
9.	sstart	start of alignment in subject
10.	send	end of alignment in subject
11.	evalue	expect value
12.	bitscore	bit score

Define your own output format

by adding the option -outfmt, as for example:

-outfmt "6 qseqid sseqid pident qlen length mismatch gapope evalue bitscore"

supported format specifiers are:
qseqid    Query Seq-id
qgi   Query GI
qacc    Query accesion
qaccver   Query accesion.version
qlen    Query sequence length
sseqid    Subject Seq-id
sallseqid All subject Seq-id(s), separated by a ';'
sgi       Subject GI
sallgi    All subject GIs
sacc      Subject accession
saccver   Subject accession.version
sallacc   All subject accessions
slen      Subject sequence length
qstart    Start of alignment in query
qend    End of alignment in query
sstart    Start of alignment in subject
send      End of alignment in subject
qseq      Aligned part of query sequence
sseq      Aligned part of subject sequence
evalue    Expect value
bitscore  Bit score
score   Raw score
length    Alignment length
pident    Percentage of identical matches
nident    Number of identical matches
mismatch  Number of mismatches
positive  Number of positive-scoring matches
gapopen   Number of gap openings
gaps      Total number of gaps
ppos      Percentage of positive-scoring matches
frames    Query and subject frames separated by a '/'
qframe    Query frame
sframe    Subject frame
btop      Blast traceback operations (BTOP)
staxids   Subject Taxonomy ID(s), separated by a ';'
sscinames Subject Scientific Name(s), separated by a ';'
scomnames Subject Common Name(s), separated by a ';'
sblastnames Subject Blast Name(s), separated by a ';'   (in alphabetical order)
sskingdoms  Subject Super Kingdom(s), separated by a ';'     (in alphabetical order)
stitle    Subject Title
salltitles  All Subject Title(s), separated by a '<>'
sstrand   Subject Strand
qcovs   Query Coverage Per Subject
qcovhsp   Query Coverage Per HSP

default values are:
-outfmt "6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore"

Arvados

Martin Jones — Sat, 20 Sep 2014 16:54:21 -0500

Arvados is a free and open source bioinformatics platform for genomic and biomedical data. User can Store | Organize | Compute | Share the data for free.

Address of the bookmark: https://arvados.org/

methylKit

Jit — Fri, 03 Jun 2016 10:09:29 -0500

methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods such as Agilent SureSelect methyl-seq. In addition, methylKit can deal with base-pair resolution data for 5hmC obtained from Tab-seq or oxBS-seq. It can also handle whole-genome bisulfite sequencing data if proper input format is provided.

Address of the bookmark: https://github.com/al2na/methylKit

QuIN’s web server

Jit — Mon, 27 Jun 2016 10:44:16 -0500

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions.

AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub:https://github.com/UcarLab/QuIN/.

Address of the bookmark: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004809