BOL: Related items

SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis

BioStar — Mon, 17 Aug 2020 05:21:09 -0500

SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [Rognes 2016] which showed to be both accurate and fast [Wescott 2015].The statistical analysis is based on DESeq2 R package [Anders and Huber 2010] which robustly identifies the differential abundant features as suggested in [McMurdie and Holmes 2014] and [Jonsson2016]. SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [Love 2014].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. A presentation about SHAMAN is available here and a poster here.

More at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03666-4

Address of the bookmark: https://github.com/aghozlane/shaman

Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome

BioStar — Wed, 22 Jul 2020 10:11:13 -0500

We have developed the Sequence Ontology Bioinformatics Analysis (SOBA) tool to provide a simple statistical and graphical summary of an annotated genome. We envisage its use during annotation jamborees, genome comparison and for use by developers for rapid feedback during annotation software development and testing. SOBA also provides annotation consistency feedback to ensure correct use of terminology within annotations, and guides users to add new terms to the Sequence Ontology when required. SOBA is available at http://www.sequenceontology.org/cgi-bin/soba.cgi.

More at https://pubmed.ncbi.nlm.nih.gov/20494974/

Address of the bookmark: http://www.sequenceontology.org/cgi-bin/soba.cgi

Troyanskaya Lab

Tue, 04 Feb 2020 06:40:36 -0600

The goal of our research is to interpret and distill this complexity through accurate analysis and modeling of molecular pathways, particularly those in which malfunctions lead to the manifestation of disease. We are inventing integrative methods for systems-level pathway modeling through integrative analysis of genome-scale datasets. We apply these approaches in studying challenging biological problems, such as how pathways function in diverse cell types and how they change dynamically.

https://function.princeton.edu/

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)

Trelliscope: flexibly visualize large, complex data in great detail from within the R statistical programming environment.

Jit — Tue, 21 Jan 2020 04:22:49 -0600

Trelliscope provides a way to flexibly visualize large, complex data in great detail from within the R statistical programming environment. Trelliscope is a component in the DeltaRho environment.

For those familiar with Trellis Display, faceting in ggplot, or the notion of small multiples, Trelliscope provides a scalable way to break a set of data into pieces, apply a plot method to each piece, and then arrange those plots in a grid and interactively sort, filter, and query panels of the display based on metrics of interest. With Trelliscope, we are able to create multipanel displays on data with a very large number of subsets and view them in an interactive and meaningful way.

Address of the bookmark: http://deltarho.org/docs-trelliscope/#introduction

GenomeScope: open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads

Jit — Fri, 21 Oct 2016 05:46:43 -0500

Summary: GenomeScope is an open-source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate, and repeat content from unprocessed short reads. These features are essential for studying genome evolution, and help to choose parameters for downstream analysis. We demonstrate its accuracy on 324 simulated and 16 real datasets with a wide range in genome sizes, heterozygosity levels, and error rates. Availability and Implementation: http://qb.cshl.edu/genomescope/, https://github.com/schatzlab/genomescope.git

Address of the bookmark: http://qb.cshl.edu/genomescope/

GIGGLE: a search engine for large-scale integrated genome analysis

Jit — Wed, 10 Jan 2018 03:10:45 -0600

GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (https://github.com/ryanlayer/giggle) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation.

https://www.nature.com/articles/nmeth.4556

Address of the bookmark: https://github.com/ryanlayer/giggle

netGO: R-Shiny package for network-integrated pathway enrichment analysis

Jit — Wed, 12 Feb 2020 12:40:54 -0600

netGO is an R/Shiny package for network-integrated pathway enrichment analysis.
netGO provides user-interactive visualization of enrichment analysis results and related networks.

Currently, netGO supports analysis for four species (Human, Mouse, Arabidopsis thaliana,and Yeast)
These data are available from netGO-Data repository.

https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa077/5728635

Address of the bookmark: https://github.com/unistbig/netGO

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

It is designed to work with patterned data. Famous examples of problems related to patterned data are:

recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.

Address of the bookmark: https://github.com/fbertran/Patterns

S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences

Abhimanyu Singh — Tue, 02 Oct 2018 17:57:27 -0500

S-plot2 creates an interactive, two-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). In S-plot2, whole eukaryotic chromosomes and smaller prokaryotic genomes can be efficiently compared. The tool includes functionality to extract, analyze, and automate BLAST queries of regions of interest within the heatmap. This facilitates the investigation of quickly evolving coding regions, novel coding regions, and laterally transferred elements.

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot