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)

MinION_GC: An R script to do some QC on MinION data

Radha Agarkar — Sun, 03 Dec 2017 15:19:18 -0600

Other tools focus on getting data out of the fastq or fast5 files, which is slow and computationally intensive. The benefit of this approach is that it works on a single, small, .txt summary file. So it's a lot quicker than most other things out there: it takes about a minute to analyse a 4GB flowcell on my laptop.

https://github.com/roblanf/minion_qc

Address of the bookmark: https://github.com/roblanf/minion_qc

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

NextSV: a meta-caller for structural variants from low-coverage long-read sequencing data

Jit — Mon, 06 Aug 2018 17:24:53 -0500

NextSV, a meta SV caller and a computational pipeline to perform SV calling from low coverage long-read sequencing data. NextSV integrates three aligners and three SV callers and generates two integrated call sets (sensitive/stringent) for different analysis purpose. The output of NextSV is in ANNOVAR-compatible bed format. Users can easily perform downstream annotation using ANNOVAR and disease gene discovery using Phenolyzer.

Address of the bookmark: https://github.com/Nextomics/NextSV

BamView: a free interactive display of read alignments in BAM data files

Neel — Fri, 09 Nov 2018 13:43:22 -0600

To run the application on UNIX from the downloaded jar file run the UNIX:

java -mx512m -jar BamView.jar

and extra command line options are given when '-h' is used:

java -jar BamView.jar -h

BAM files can be specified on the command line with the '-a' option:

java -mx512m -jar BamView.jar -a pathToFile/sorted.bam

If a BAM filename is not given on the command line BamView will prompt for a file to be entered. The BAM index file should have the same name as the BAM file but with a '.bai' suffix. Multiple BAM files can be loaded and overlaid in the viewer. To make this easier BamView will read in files that contain a list of filenames.

Address of the bookmark: http://bamview.sourceforge.net/

OMTools: a software package for visualizing and processing optical mapping data

BioJoker — Fri, 29 Mar 2019 01:21:54 -0500

OMTools, an efficient and intuitive data processing and visualization suite to handle and explore large-scale optical mapping profiles. OMTools includes modules for visualization (OMView), data processing and simulation. These modules together form an accessible and convenient pipeline for optical mapping analyses.

https://github.com/TF-Chan-Lab/OMTools

Address of the bookmark: https://github.com/TF-Chan-Lab/OMTools

vt: a variant tool set that discovers short variants from Next Generation Sequencing data.

Jit — Tue, 28 Jan 2020 03:44:43 -0600

vt is a variant tool set that discovers short variants from Next Generation Sequencing data.

https://genome.sph.umich.edu/wiki/Vt

https://github.com/atks/vt

Address of the bookmark: https://genome.sph.umich.edu/wiki/Vt

Phytozome v12.1: plant science community hub for accessing palnts genomic data

Surabhi Chaudhary — Tue, 17 Mar 2020 07:30:17 -0500

Phytozome, the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute, provides JGI users and the broader plant science community a hub for accessing, visualizing and analyzing JGI-sequenced plant genomes, as well as selected genomes and datasets that have been sequenced elsewhere. As of release v12.1.6, Phytozome hosts 93 assembled and annotated genomes, from 82 Viridiplantae species. More than half of these genomes have been sequenced, assembled and/or annotated with JGI Plant Science program resources. By integrating this large collection of plant genomes into a single resource and performing comprehensive and uniform annotation and analyses, Phytozome facilitates accurate and insightful comparative genomics studies.

Address of the bookmark: https://phytozome.jgi.doe.gov/pz/portal.html

proActiv: Estimation of Promoter Activity from RNA-Seq data

BioStar — Thu, 13 Aug 2020 10:21:44 -0500

proActiv is an R package that estimates promoter activity from RNA-Seq data. proActiv uses aligned reads and genome annotations as input, and provides absolute and relative promoter activity as output. The package can be used to identify active promoters and alternative promoters, the details of the method are described in Demircioglu et al.

Additional data on differential promoters in tissues and cancers from TCGA, ICGC, GTEx, and PCAWG can be downloaded here: https://jglab.org/data-and-software/

Address of the bookmark: https://github.com/GoekeLab/proActiv