BOL: Related items

ENCODE3: A collection of research articles and related content describing the Encyclopedia of DNA Elements, its datasets and tools.

Shruti Paniwala — Sat, 08 Aug 2020 08:25:21 -0500

How cells, tissues and organisms interpret the information encoded in the genome has vital implications for our understanding of development, health and disease. Launched in 2003, the ENCyclopedia Of DNA Elements (ENCODE) project has the aim of mapping the functional elements in the human genome (later expanded to include model organisms).

During the first phase of ENCODE, published in 2007, microarray-based technologies were used to detect regions associated with transcription factors, certain histone modifications and open chromatin within a pre-specified 1% of the human genome.

ENCODE’s second phase saw a switch to sequencing-based technologies, the addition of new assay types and the analysis of functional elements genome-wide, described in a collection of research articles in 2012.

The Encyclopedia paper of ENCODE 3, published in Nature, gives an overview of the various assays that were performed in human and mouse cell lines and tissues and describes a Registry of human and mouse candidate cis-regulatory elements (cCREs).

More at https://www.nature.com/immersive/d42859-020-00027-2/index.html

MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly

Abhi — Sat, 08 Jun 2024 16:15:25 -0500

The MetaGraph framework is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as sequence search or differential assembly.

Searcg link https://metagraph.ethz.ch/search

Pre-print https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4

Address of the bookmark: https://metagraph.ethz.ch/

Biological databases !

BioStar — Wed, 12 Feb 2020 01:16:29 -0600

Now a days there are a lots of genomics databases available around the world. This bookmark is created to provide all links in one place ...

ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

Pacman

Rahul Nayak — Mon, 16 Feb 2015 12:15:17 -0600

The pacman package is an R package management tool that combines the functionality of base library related functions into intuitively named functions. This package is ideally added to .Rprofile to increase workflow by reducing time recalling obscurely named functions, reducing code and integrating functionality of base functions to simultaneously perform multiple actions.

Function names in the pacman package follow the format of p_xxx where ‘xxx’ is the task the function performs. For instance the p_load function allows the user to load one or more packages as a more generic substitute for the library or require functions and if the package isn’t available locally it will install it for you.

Installation

To download the development version of pacman:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the devtools package to install the development version:

## Make sure your current packages are up to date
update.packages()
## devtools is required
devtools::install_github("trinker/pacman")

Note: Windows users need Rtools and devtools to install this way.

More at https://github.com/trinker/pacman

clusterProfiler

Jit — Thu, 16 Jun 2016 18:57:03 -0500

statistical analysis and visulization of functional profiles for genes and gene clusters

Bioconductor version: Release (3.3)

This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.

Author: Guangchuang Yu with contributions from Li-Gen Wang and Giovanni Dall'Olio.

Maintainer: Guangchuang Yu

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

Yu G, Wang L, Han Y and He Q (2012). “clusterProfiler: an R package for comparing biological themes among gene clusters.” OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287.
Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("clusterProfiler")

https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html

Address of the bookmark: https://www.bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html

Instructions for Creating Your Own R Package

Jit — Wed, 19 Feb 2020 01:22:48 -0600

The following is a step-by-step guide to creating your own R package. Even beyond this course, youmay find this useful for storing functions you create for your own research or for editing existingR packages to suit your needs.

This guide contains three different sets of instructions. If you use RStudio, you can follow the “Ba-sic Instructions” in Section 2 which involve using RStudio’s interface.

Address of the bookmark: http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf

scikit-bio™ is an open-source, BSD-licensed, python package providing data structures, algorithms, and educational resources for bioinformatics.

Jit — Fri, 02 Mar 2018 04:29:47 -0600

scikit-bio is currently in beta. We are very actively developing it, and backward-incompatible interface changes can and will arise. To avoid these types of changes being a surprise to our users, our public APIs are decorated to make it clear to users when an API can be relied upon (stable) and when it may be subject to change (experimental). See the API stability docs for more details, including what we mean by stable and experimental in this context.

Address of the bookmark: http://scikit-bio.org/

regioneR: an R package for the management and comparison of genomic regions

Jit — Tue, 11 Dec 2018 08:43:51 -0600

Regioner is an R package for the management and comparison of genomic regions. It offers a set of function for basic manipulation of region sets extending the functionality of GenomicRanges and a powerful and customizable permutation test framework. With it, it's possible to study the association of a set of regions with other sets of regions, functions defined over the genome or essentially any user defined function.

http://gattaca.imppc.org/regioner/

Address of the bookmark: http://gattaca.imppc.org/regioner/

chromoMap-An R package for Interactive Visualization and Annotation of Chromosomes

Neel — Fri, 12 Apr 2019 05:30:41 -0500

Provides interactive, configurable and elegant graphics visualization of the chromosomes or chromosome regions of any living organism allowing users to map chromosome elements (like genes, SNPs etc.) on the chromosome plot. It introduces a special plot viz. the "chromosome heatmap" that, in addition to mapping elements, can visualize the data associated with chromosome elements (like gene expression) in the form of heat colors which can be highly advantageous in the scientific interpretations and research work. The package provide multiple features like visualizing multiple sets, chromosome heat-maps, group annotations, adding hyperlinks, and labelling. The plots can be saved as HTML documents that can be customized and shared easily. In addition, you can include them in R Markdown or in R 'Shiny' applications.

Address of the bookmark: https://cran.r-project.org/web/packages/chromoMap/vignettes/chromoMap.html

GeneMates: an R package for Detecting Horizontal Gene Co-transfer between Bacteria Using Gene-gene Associations Controlled for Population Structure

Rahul Nayak — Sat, 07 Mar 2020 05:52:20 -0600

GeneMates is an R package implementing a network approach to identify horizontal gene co-transfer (HGcoT) between bacteria using whole-genome sequencing (WGS) data. It is particularly useful for investigating intra-species HGcoT, where presence-absence status of acquired genes is usually confounded by bacterial population structure due to clonal reproduction.

https://www.biorxiv.org/content/10.1101/2020.02.29.970970v1

Address of the bookmark: https://github.com/wanyuac/GeneMates