BOL: Related items

Bactopia: a Flexible Pipeline for Complete Analysis of Bacterial Genomes

Abhi — Wed, 15 May 2024 14:36:12 -0500

Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is to process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!

Bactopia can be split into two main parts: Bactopia Analysis Pipeline, and Bactopia Tools.

Bactopia Analysis Pipeline is the main per-isolate workflow in Bactopia. Built with Nextflow, input FASTQs (local or available from SRA/ENA) are put through numerous analyses including: quality control, assembly, annotation, minmer sketch queries, sequence typing, and more.

Bactopia Tools are a set a independent workflows fo

Address of the bookmark: https://github.com/bactopia/bactopia

TRITEX sequence assembly pipeline for Triticeae genomes

Jit — Tue, 20 Aug 2019 09:47:14 -0500

The pipeline is open-source and hosted in a public Bitbucket repository.

TRITEX has been run on highly inbred genotypes of barley (Hordeum vulgare), tetraploid wheat (Triticum turgidum) and hexaploid wheat (T. aestivum) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.

A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in Himmelbach et al. 2018. Refer to the technical notes of 10X Genomics on how to generate Chromium data.

Address of the bookmark: https://tritexassembly.bitbucket.io/

CSA: A high-throughput chromosome-scale assembly pipeline for vertebrate genomes

Jit — Wed, 10 Mar 2021 06:13:49 -0600

The pipeline can use information from scaffolded assemblies (for example from HiC or 10X Genomics), or even from diverged (~65-100 Mya) reference genomes for ordering the contigs and thus support the assembly process. This typically results in improved contig N50 when compared to current state of the art methods.

For smaller vertebrate genomes (~1 Gbp) chromosome scale assemblies can be achieved within 12h on high-end Desktop computers (Intel i7, 12 CPU threads, 128 GB RAM). Larger mammalian genomes (~3Gbp) can be processed within 15-18 h on server equipment (Xeon, 96 CPU threads, 1TB RAM).

Address of the bookmark: https://github.com/HMPNK/CSA2.6

TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes

LEGE — Fri, 14 Feb 2025 10:53:48 -0600

This is the documentation of TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes. It was developed in the research group Domestication Genomics at the Leibniz Institute of Plant Genetics and Crop Research (IPK) Gatersleben.

Address of the bookmark: https://tritexassembly.bitbucket.io/

Ancestral Genomes: a resource for reconstructed ancestral genes and genomes across the tree of life

Abhimanyu Singh — Fri, 02 Nov 2018 08:16:27 -0500

Ancestral Genomes (http://ancestralgenomes.org) is a resource for comprehensive reconstructions of these ‘fossil genomes’. Comprehensive sets of protein-coding genes have been reconstructed for 78 genomes of now-extinct species that were the common ancestors of extant species from across the tree of life.

Address of the bookmark: http://ancestralgenomes.org/

Unicycler: Hybrid assembly pipeline for bacterial genomes

Jit — Fri, 10 Nov 2017 03:58:27 -0600

Unicycler is an assembly pipeline for bacterial genomes. It can assemble Illumina-only read sets where it functions as a SPAdes-optimiser. It can also assembly long-read-only sets (PacBio or Nanopore) where it runs a miniasm+Racon pipeline. For the best possible assemblies, give it both Illumina reads and long reads, and it will conduct a hybrid assembly.

Address of the bookmark: https://github.com/rrwick/Unicycler

PyParanoid: a pipeline for rapid identification of homologous gene families in a set of genomes

BioStar — Thu, 13 Aug 2020 10:06:19 -0500

PyParanoid is a pipeline for rapid identification of homologous gene families in a set of genomes - a central task of any comparative genomics analysis. The "gold standard" for identifying homologs is to use reciprocal best hits (RBHs) which depends on performing a all-vs-all sequence comparison, usually using BLAST, to determine homology. However, these methods are computationally expensive, requiring O(n2) resources to identify RBHs. This is problematic, as the modern deluge of sequencing data means that comparative genomics analyses could be performed on datasets of thousands of strains.

Address of the bookmark: https://github.com/ryanmelnyk/PyParanoid

Bactopia: a flexible pipeline for complete analysis of bacterial genomes

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

Bactopia is a flexible pipeline for complete analysis of bacterial genomes. The goal of Bactopia is process your data with a broad set of tools, so that you can get to the fun part of analyses quicker!

Bactopia was inspired by Staphopia, a workflow we (Tim Read and myself) released that is targeted towards Staphylococcus aureus genomes. Using what we learned from Staphopia and user feedback, Bactopia was developed from scratch with usability, portability, and speed in mind from the start.

Bactopia uses Nextflow to manage the workflow, allowing for support of many types of environments (e.g. cluster or cloud). Bactopia allows for the usage of many public datasets as well as your own datasets to further enhance the analysis of your sequencing. Bactopia only uses software packages available from Bioconda and Conda-Forge to make installation as simple as possible for all users.

To highlight the use of Bactopia and Bactopia Tools, we performed an analysis of 1,664 public Lactobacillus genomes, focusing on Lactobacillus crispatus, a species that is a common part of the human vaginal microbiome. The results from this analysis are published in mSystems under the title: Bactopia: a flexible pipeline for complete analysis of bacterial genomes

Address of the bookmark: https://bactopia.github.io/latest/

Translational Bioinformatics: Transforming 300 Billion Points of Data

Tue, 20 Aug 2013 19:03:47 -0500

Translational Bioinformatics: Transforming 300 Billion Points of Data into Diagnostics, Therapeutics, and New Insights into Disease Air date: Wednesday, June 20, 2012, 3:00:00 PM Time displayed is Eastern Time, Washington DC Local Description: There is an urgent need to translate genome-era discoveries into clinical utility, but the difficulties in making bench-to-bedside translations haven't been well described. The nascent field of translational bioinformatics may help. Dr. Butte's lab at Stanford University builds and applies tools that convert more than 300 billion points of molecular, clinical, and epidemiological data (measured by researchers and clinicians over the past decade) into diagnostics, therapeutics, and new insights into disease. Dr. Butte, a bioinformatician and pediatric endocrinologist, will highlight his lab's work on using publicly available molecular measurements to find new uses for drugs, discovering new treatable mechanisms of disease in type 2 diabetes, and evaluating patients presenting with whole genomes sequenced. The NIH Wednesday Afternoon Lecture Series includes weekly scientific talks by some of the top researchers in the biomedical sciences worldwide. For more information, visit: The NIH Director's Wednesday Afternoon Lecture Series Author: Atul Butte, M.D., Ph.D., Stanford University Runtime: 01:07:42 Permanent link: http://videocast.nih.gov/launch.asp?17321

ANGES: reconstructing ANcestral GEnomeS maps

Abhimanyu Singh — Thu, 18 May 2017 05:27:08 -0500

This page contains the software ANGES 1.01, that aims at reconstucting ancestral genome maps from homologous markers in extant related genomes.

Download

Program, version 1.01 (July 10, 2012, documentation updated in August 2014)
Examples with results (featured ancestors: boreoeutherian, amniote, yeasts, Burkholderia, monocots); please refer to the documentation of the distribution above.

Address of the bookmark: http://paleogenomics.irmacs.sfu.ca/ANGES/