BOL: Related items

DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication

Jit — Tue, 14 Nov 2017 10:26:16 -0600

We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7,000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 minutes, with rich information such as pseudogenes, translation exceptions, and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.

Availability and Implementation

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

Scripts for the analysis of HGT in genome sequence data.

Jit — Wed, 29 Nov 2017 16:44:10 -0600

Scripts for the analysis of HGT in genome sequence data

Address of the bookmark: https://github.com/reubwn/hgt

Reference Sequence Resource!

LEGE — Wed, 15 Sep 2021 21:15:22 -0500

The ENCODE project uses Reference Genomes from NCBI or UCSC to provide a consistent framework for mapping high-throughput sequencing data. In general, ENCODE data are mapped consistently to 2 human (GRCH38, hg19) and 2 mouse (mm9/mm10) genomes for historical comparability. Drosophia melanogaster experiments are mapped to either dm3 or dm6 and Caenorhabdilis elegans experiments are mapped to ce10 or ce11. T

Address of the bookmark: https://www.encodeproject.org/data-standards/reference-sequences/

mScaffolder: A comparative genome scaffolding tool

Jit — Fri, 15 Jun 2018 04:48:01 -0500

A comparative genome scaffolding tool based on MUMmer

mScaffolder scaffolds a genome using an existing high quality genome as the reference. It aligns the two genomes using nucmer utility from MUMmer and then orders and orients the contigs of the candidate genome guided by their alignments to the reference genome. Please send your questions and comments to mchakrab@uci.edu.

Citation https://www.nature.com/articles/s41588-017-0010-y

Address of the bookmark: https://github.com/mahulchak/mscaffolder

Purge Haplotigs: Pipeline to help with curating heterozygous diploid genome assemblies

Rahul Nayak — Mon, 17 Dec 2018 03:17:20 -0600

Some parts of a genome may have a very high degree of heterozygosity. This causes contigs for both haplotypes of that part of the genome to be assembled as separate primary contigs, rather than as a contig and an associated haplotig. This can be an issue for downstream analysis whether you're working on the haploid or phased-diploid assembly.

Identify pairs of contigs that are syntenic and move one of them to the haplotig 'pool'. The pipeline uses mapped read coverage and Minimap2 alignments to determine which contigs to keep for the haploid assembly. Dotplots are optionally produced for all flagged contig matches, juxtaposed with read-coverage, to help the user determine the proper assignment of any remaining ambiguous contigs. The pipeline will run on either a haploid assembly (i.e. Canu, FALCON or FALCON-Unzip primary contigs) or on a phased-diploid assembly (i.e. FALCON-Unzip primary contigs + haplotigs). Here are two examples of how Purge Haplotigs can improve a haploid and diploid assembly.

Address of the bookmark: https://bitbucket.org/mroachawri/purge_haplotigs

odgi: optimized dynamic genome/graph implementation

Abhimanyu Singh — Tue, 01 Feb 2022 23:42:21 -0600

odgi provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi

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/

pipesnake: bioinformatics best-practice analysis pipeline for phylogenomic reconstruction

LEGE — Wed, 21 Feb 2024 06:19:41 -0600

ausarg/pipesnake is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

Address of the bookmark: https://github.com/AusARG/pipesnake

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/

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