BOL: Related items

Published a dataset of 363 genomes from approximately 92 percent of bird families

Jit — Thu, 19 Nov 2020 07:04:41 -0600

A research team published a dataset of 363 genomes from approximately 92 percent of bird families and showed the significance of sampling dense organisms for biodiversity research. The study was jointly conducted by Chinese and international institutions and museums and was led by researchers from the Kunming Institute of Zoology (KIZ) of the Chinese Academy of Sciences (CAS). Total of 267 were newly published among the 363 sequenced genomes. They were mainly taken from samples of avian tissue kept in museums around the world, enabling researchers to sequence rare and endangered birds' genomes.

Its descendants have adapted to a wide variety of ecological niches since the first bird formed more than 150 million years ago, giving rise to small, hovering hummingbirds, plunge-diving pelicans and showy paradise birds. More than 10,000 bird species live on the planet today - and now scientists are well on their way to capturing a full genetic image of that diversity.

B10K is expanding its efforts to encompass the next stage of avian classification with 363 genomes complete. The team will sequence thousands of extra genomes in this process, attempting to represent each of the approximately 2,300 bird genera.

The genomic resource is expected to provide new insights on evolutionary processes in cross-species comparative studies and assist in efforts to protect species, according to the research findings reported as a cover story in the journal Nature.

Ref at Dense sampling of bird diversity increases power of comparative genomics https://www.nature.com/articles/s41586-020-2873-9

Proksee: in-depth characterization and visualization of bacterial genomes

LEGE — Tue, 09 May 2023 19:38:52 -0500

Proksee is an expert system for genome assembly, annotation and visualization. To begin using Proksee, provide a complete genome sequence, sequencing reads or a CGView/Proksee map JSON file.

Address of the bookmark: https://proksee.ca/

GeneProf: analysis of high-throughput sequencing experiment

Jit — Wed, 05 Jul 2017 16:47:05 -0500

GeneProf is a web-based, graphical software suite that allows users to analyse data produced using high-throughput sequencing platforms (RNA-seq and ChIP-seq; "Next-Generation Sequencing" or NGS): Next-gen analysis for next-gen data!

Some of GeneProf's highlights include:

Easy-to-use web-based interface:Access your data at any time from any computer with a working internet connection -- no need to install software! (cp. Section 'System Requirements').
Analysis wizards make your life easy:Step-by-step workflows make it easy to analyse high-throughput data within a minimum of hands-on time. (cp. SubConcept 'Analysis Wizards').
Versatile modules:Advanced users and data analysis experts benefit from GeneProf's broad range of analysis modules, which can be combined freely into sophisticated workflows (cp. Concept 'Workflows').
Integrated Analysis:Analysis of ChIP-seq and RNA-seq data in one place, plus support for the integration of other external data (e.g. from microarrays).
Comprehensive Resource:GeneProf provides a comprehensive resource of fully analyzed next-generation sequencing data. Experimental results can be easily accessed and compared and the analysis procedures employed to produce the data are fully transparent (cp. Tutorial 'Examining Public Next-Gen Data..').
Extensibility:Algorithm developers and computer programmers can develop their own modules and extend GeneProf. Existing software can be easily wrapped in the workflow framework (cp. Section 'Module Development: Adding new..') and data from GeneProf may be used externally (cp. Section 'Web API: Retrieving Data from ..').

GeneProf is academic software developed at the Centre for Regenerative Medicine / Institute for Stem Cell Research, University of Edinburgh and has benefited from funding by the Medical Research Council and the EU Framework 7 Project "EuroSyStem".

Address of the bookmark: https://www.geneprof.org/GeneProf/index.jsp

MetaEuk - sensitive, high-throughput gene discovery and annotation for large-scale eukaryotic metagenomics

Jit — Wed, 13 Jan 2021 19:29:32 -0600

MetaEuk is a modular toolkit designed for large-scale gene discovery and annotation in eukaryotic metagenomic contigs. Metaeuk combines the fast and sensitive homology search capabilities of MMseqs2 with a dynamic programming procedure to recover optimal exons sets. It reduces redundancies in multiple discoveries of the same gene and resolves conflicting gene predictions on the same strand. MetaEuk is GPL-licensed open source software that is implemented in C++ and available for Linux and macOS. The software is designed to run on multiple cores.

Address of the bookmark: https://github.com/soedinglab/metaeuk

GKNO

Neel — Fri, 20 May 2016 18:56:37 -0500

gkno opens the world of complex bioinformatic analysis to people of all level of computational expertise. This site contains documentation, tutorials and information on all the tools that comprise gkno.

http://gkno.me/how-to/install.html

http://gkno.me/software.html

Address of the bookmark: http://gkno.me/

TULIP - The Uncorrected Long read Itegration Pipeline

Jit — Thu, 23 Nov 2017 09:30:01 -0600

#Running TULIP (The Uncorrected Long-read Integration Process), version 0.4 late 2016 (European eel)

TULIP currently consists of to Perl scripts, tulipseed.perl and tulipbulb.perl. These are very much intended as prototypes, and additional components and/or implementations are likely to follow.
Tulipseed takes as input alignments files of long reads to sparse short seeds, and outputs a graph and scaffold structures. Tulipbulb adds long read sequencing data to these.

https://github.com/Generade-nl/TULIP

Address of the bookmark: https://github.com/Generade-nl/TULIP

Roary: the Pan Genome Pipeline

Jit — Tue, 22 Jan 2019 05:52:07 -0600

Roary is a high speed stand alone pan genome pipeline, which takes annotated assemblies in GFF3 format (produced by Prokka (Seemann, 2014)) and calculates the pan genome. Using a standard desktop PC, it can analyse datasets with thousands of samples, something which is computationally infeasible with existing methods, without compromising the quality of the results. 128 samples can be analysed in under 1 hour using 1 GB of RAM and a single processor. To perform this analysis using existing methods would take weeks and hundreds of GB of RAM. Roary is not intended for meta-genomics or for comparing extremely diverse sets of genomes.

Address of the bookmark: https://sanger-pathogens.github.io/Roary/

PhyloHerb: Phylogenomic Analysis Pipeline for Herbarium Specimens

LEGE — Wed, 21 Feb 2024 06:15:13 -0600

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

From DNA Chaos to a Clear Microbial Story

BioStar — Fri, 21 Aug 2026 11:22:54 -0500

Imagine opening a metagenomic dataset and finding millions of DNA fragments, each carrying a tiny piece of information about the microbial world inside a sample. The challenge is not generating the data — it is turning that data into a meaningful story.

This is where MeTAline comes in.

MeTAline is a reproducible workflow designed to simplify shotgun metagenomic analysis. It brings together important steps such as quality control, host-DNA removal, taxonomic profiling and functional analysis, using tools including Kraken2, MetaPhlAn4 and HUMAnN.

What makes it especially useful is its ability to handle large datasets while keeping analyses organized and reproducible. Built with Snakemake and supporting containerized environments, MeTAline can also be used on high-performance computing systems.

In simple terms, MeTAline helps researchers move from millions of confusing DNA fragments to a clearer picture of who is there, what they might be doing, and what the data could mean.

Sometimes, the biggest challenge in science isn't collecting more data — it's finding the story hidden inside it.

Read more at https://academic.oup.com/nargab/article/7/4/lqaf158/8340158?login=false

HiC-Pro: an optimized and flexible pipeline for Hi-C data processing

Jit — Wed, 06 Dec 2017 01:05:21 -0600

HiC-Pro was designed to process Hi-C data, from raw fastq files (paired-end Illumina data) to the normalized contact maps. Since version 2.7.0, HiC-Pro supports the main Hi-C protocols, including digestion protocols as well as protocols that do not require restriction enzyme such as DNase Hi-C. In practice, HiC-Pro can be used to process dilution Hi-C, in situ Hi-C, DNase Hi-C, Micro-C, capture-C, capture Hi-C or HiChip data.

http://nservant.github.io/HiC-Pro/

Address of the bookmark: http://nservant.github.io/HiC-Pro/