BOL: Related items

dupRadar package

Jit — Sun, 04 Feb 2018 14:28:57 -0600

The dupRadar package gives an insight into the duplication problem by graphically relating the gene expression level and the duplication rate present on it. Thus, failed experiments can be easily identified at a glance

Address of the bookmark: https://bioconductor.org/packages/3.7/bioc/vignettes/dupRadar/inst/doc/dupRadar.html

PANEV: an R package for a pathway-based network visualization

Jit — Sun, 09 Feb 2020 12:41:52 -0600

PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3371-7

Address of the bookmark: https://github.com/vpalombo/PANEV

DeCoSTAR - Detection of Co-evolution

Jit — Fri, 14 Apr 2017 06:27:25 -0500

DeCoSTAR is a software which aims at reconstructing ancestral gene or genome organizations, in the form of sets of neighborhood relations -adjacencies- between pairs of ancestral genes or gene domains.
Ancestral genes or domains are deduced from reconciled gene trees in a context of birth, speciation, duplication, loss, transfer, which are either given as input or computed with the ecceTERA package, to which DeCoSTAR is integrated. DeCoSTAR constructs parsimonious scenarios of gains and breakages of adjacencies, and contains in particular all the features of previous software DeCo, DeCoLT, ArtDeCo and DeClone. It provides statistical supports on ancestral adjacencies, or the possibility to handle badly assembled genomes.
DeCoSTAR is able to reconstruct the histories of domains inside genes, including gene fusion and fission events, as well as ancestral genome structures for dozens of whole genomes from all kingdoms of life in a few minutes.

Address of the bookmark: http://pbil.univ-lyon1.fr/software/DeCoSTAR/

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants

Jit — Tue, 28 Jan 2020 03:39:22 -0600

MALVA is able to genotype multi-allelic SNPs and indels without mapping reads

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

Computational resources for TE discovery and TE detection

Abhimanyu Singh — Mon, 12 Feb 2018 10:29:18 -0600

Transposable Elements (TEs) to genome structure and evolution as well as their impact on genome sequencing, assembly, annotation and alignment has generated increasing interest in developing new methods for their computational analysis.

Following are the list of resource and location for TE discovery and TE detection:

BLASTER suite http://urgi.versailles.inra.fr/development/blaster/

Censor http://www.girinst.org/censor/download.php

find_ltr http://darwin.informatics.indiana.edu/cgi-bin/evolution/ltr.pl

FINDMITE http://jaketu.biochem.vt.edu/dl_software.htm

HMMER http://hmmer.janelia.org/

LTR_FINDER http://tlife.fudan.edu.cn/ltr_finder/

LTR_STRUC http://www.genetics.uga.edu/retrolab/data/LTR_Struc.html

LTR_MINER http://genomebiology.com/2004/5/10/R79/suppl/s7

LTR_par http://www.eecs.wsu.edu/~ananth/software.htm

MAK http://wesslercluster.plantbio.uga.edu/mak06.html

MaskerAid http://blast.wustl.edu/maskeraid/

mer-engine http://mer-engine.cshl.edu/mer-home.php

mreps http://bioinfo.lifl.fr/mreps/

PILER http://www.drive5.com/piler/

PLOTREP http://repeats.abc.hu/cgi-bin/plotrep.pl

RepBase http://www.girinst.org/

RepeatFinder http://cbcb.umd.edu/software/RepeatFinder/

RepeatGluer http://nbcr.sdsc.edu/euler/intro_tmp.htm

RepeatMasker http://www.repeatmasker.org/

RepeatRunner http://www.yandell-lab.org/repeat_runner/index.html

RepeatScout http://repeatscout.bioprojects.org/

repeat-match http://mummer.sourceforge.net/

REPuter http://www.genomes.de/

RetroMap http://www.burchsite.com/bioi/RetroMapHome.html

SMaRTFinder http://bioinf.dimi.uniud.it/software/software/smartfinder

Tandem Repeats Finder http://tandem.bu.edu/trf/trf.html

Transposon Cluster Finder http://www.mssm.edu/labs/warbup01/paper/files.html

TE nest http://www.plantgdb.org/prj/TE_nest/TE_nest.html

TRANSPO http://alggen.lsi.upc.es/recerca/search/transpo/transpo.html

TSDfinder http://www.ncbi.nlm.nih.gov/CBBresearch/Landsman/TSDfinder/

Tu Lab TE tools http://jaketu.biochem.vt.edu/dl_software.htm

WU-BLAST http://blast.wustl.edu

ReMILO: reference assisted misassembly detection algorithm using short and long reads.

Jit — Fri, 06 Jul 2018 04:27:49 -0500

ReMILO, a reference assisted misassembly detection algorithm that uses both short reads and PacBio SMRT long reads. ReMILO aligns the initial short reads to both the contigs and reference genome, and then constructs a novel data structure called red-black multipositional de Bruijn graph to detect misassemblies. In addition, ReMILO also aligns the contigs to long reads and find their differences from the long reads to detect more misassemblies.

Address of the bookmark: https://github.com/songc001/remilo

ShadowCaster: a hybrid approach for the detection of horizontal gene transfer events in prokaryotes

Jit — Tue, 14 Jul 2020 06:42:10 -0500

ShadowCaster implements an evolutionary model to calculate Bayesian likelihoods for each ‘alien genes’ with an unusual sequence composition according to the host genome background to detect HGT events in prokaryotes.

https://www.mdpi.com/2073-4425/11/7/756/htm

https://shadowcaster.readthedocs.io/en/latest/

https://github.com/dani2s/ShadowCaster_testData

Address of the bookmark: https://github.com/dani2s/ShadowCaster

The Hidden Pages of Our DNA

LEGE — Sat, 05 Sep 2026 01:04:22 -0500

Imagine opening the instruction manual for life.

It is enormous, billions of letters long. Some of those letters are easy to understand because they contain instructions for building proteins, the tiny machines that keep our cells alive.

But what if some of the most important instructions aren't in those obvious chapters?

Scientists have long suspected that the mysterious stretches of DNA between protein-coding genes may contain some of life's most interesting secrets. These regions can act like switches, telling genes when, where, and how strongly to turn on. Among them are conserved non-coding elements, or CNEs DNA sequences that have remained remarkably similar across different species.

Think of a CNE as a sentence that nature has copied into several editions of the same book.

A mouse has it.

A bird has it.

A human has it.

And despite millions of years of evolution, much of the sentence remains almost unchanged.

That conservation is a clue: this piece of DNA probably matters.

But there was a problem. Finding these hidden sentences across enormous genomes is difficult. Researchers needed to compare many genomes, identify conserved regions, and then determine which of those regions might have evolved unusually quickly in particular branches of the evolutionary tree.

Enter CNEwrap. https://github.com/YanCCscu/CNEwrap

The researchers developed CNEwrap as a streamlined toolkit capable of handling large-scale genome comparisons, conserved-element detection, and evolutionary analysis. Inside it is a new algorithm called EvoAcc, designed to identify CNEs that show accelerated evolution in specific species or lineages.

Why does that matter?

Because evolution isn't just about what stays the same.

Sometimes, the most interesting story is hidden in what changed.

Imagine that nearly every species carries the same regulatory sentence, but one lineage suddenly has several unusual edits. Perhaps those changes helped shape a new body structure, a different adaptation, or another distinctive trait.

EvoAcc is designed to help researchers find these evolutionary clues. In the study's tests, it performed particularly well in scenarios involving two or three accelerated lineages and showed improved sensitivity to insertion and deletion mutations compared with several existing approaches.

The researchers also tested CNEwrap using functional genomic regions from mammals and found that EvoAcc could recover human-specific accelerated regions while identifying some signals that other methods missed.

So the story of CNEwrap is really a story about looking beyond the obvious.

For decades, scientists have often focused on the DNA that directly encodes proteins. But the genome is more like a vast library: the protein-coding genes are the main text, while non-coding regions may contain punctuation, instructions, switches, annotations, and editing notes that tell the cell how to read the book.

CNEwrap gives researchers a new way to search that hidden material.

And perhaps somewhere inside those billions of DNA letters is an evolutionary story we haven't read yet.

The exciting part is that we now have better tools to find it.

Read more about it https://academic.oup.com/nar/article/54/14/gkag709/8738333?

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/

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