<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/40583?offset=240 https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization Mon, 18 Dec 2017 04:16:31 -0600 https://bioinformaticsonline.com/bookmarks/view/34704/nanosim-nanopore-sequence-read-simulator-based-on-statistical-characterization <![CDATA[NanoSim: nanopore sequence read simulator based on statistical characterization.]]> NanoSim, a fast and scalable read simulator that captures the technology-specific features of ONT data and allows for adjustments upon improvement of nanopore sequencing technology. The first step of NanoSim is read characterization, which provides a comprehensive alignment-based analysis and generates a set of read profiles serving as the input to the next step, the simulation stage. The simulation stage uses the model built in the previous step to produce in silico reads for a given reference genome. NanoSim is written in Python and R. The source files and manual are available at the Genome Sciences Centre website: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

https://github.com/bcgsc/NanoSim

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/nanosim

]]> Jit https://bioinformaticsonline.com/blog/view/44257/calculate-the-significance-of-the-difference-between-two-trends Tue, 14 Mar 2023 05:41:53 -0500 https://bioinformaticsonline.com/blog/view/44257/calculate-the-significance-of-the-difference-between-two-trends <![CDATA[Calculate the significance of the difference between two trends]]>

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:

  1. 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.

  2. Collect data on the two trends. Make sure that the data is independent, normally distributed, and has equal variances.

  3. Calculate the means and standard deviations of each trend.

  4. 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.

  5. 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.

  6. 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:

MethodsR functionDescription
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)
]]> BioStar https://bioinformaticsonline.com/bookmarks/view/31014/sockeye Fri, 17 Feb 2017 08:51:16 -0600 https://bioinformaticsonline.com/bookmarks/view/31014/sockeye <![CDATA[sockeye]]> This sockeye software uses the Ensembl database project to import sequence and annotation information from several eukaryotic species. A user can additionally import their own custom sequence and annotation data. Individual annotation objects are displayed in Sockeye by using custom 3D models. Ensembl-derived and imported sequences can be analyzed by using a suite of multiple and pair-wise alignment algorithms. The results of these comparative analyses are also displayed in the 3D environment of Sockeye. By using the Java3D API to visualize genomic data in a 3D environment, we are able to compactly display cross-sequence comparisons. This provides the user with a novel platform for visualizing and comparing genomic feature organization.

Address of the bookmark: http://www.bcgsc.ca/platform/bioinfo/software/sockeye/releases/1.3

]]> Jit https://bioinformaticsonline.com/bookmarks/view/35550/circoletto-visualizing-sequence-similarity-with-circos Fri, 09 Feb 2018 10:23:40 -0600 https://bioinformaticsonline.com/bookmarks/view/35550/circoletto-visualizing-sequence-similarity-with-circos <![CDATA[Circoletto: visualizing sequence similarity with Circos]]> Circoletto, an online visualization tool based on Circos, which provides a fast, aesthetically pleasing and informative overview of sequence similarity search results.

Online version and downloadable software package for offline use (source code in PERL) freely available at http://bat.ina.certh.gr/tools/circoletto/

Contact:ndarz@certh.gr

Address of the bookmark: http://tools.bat.infspire.org/circoletto/

]]> Jit https://bioinformaticsonline.com/bookmarks/view/44307/genomenotebook Thu, 20 Apr 2023 13:19:01 -0500 https://bioinformaticsonline.com/bookmarks/view/44307/genomenotebook <![CDATA[genomenotebook]]> https://dbikard.github.io/genomenotebook/

Install

pip install genomenotebook

How to use

Create a simple genome browser with a search bar. The sequence appears when zooming in.

import genomenotebook as gn

g=gn.GenomeBrowser(genome_path, gff_path, init_pos=10000)
g.show()

Tracks can be added to visualize your favorite genomics data. See Examples for more !!!!

Address of the bookmark: https://dbikard.github.io/genomenotebook/

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