BOL: Related items

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

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/

3d-dna: 3D de novo assembly (3D DNA) pipeline

Jit — Thu, 28 Dec 2017 10:09:37 -0600

This code is designed to enable anyone to reproduce the Hs2-HiC and the AaegL4 genomes reported in: Dudchenko et al., De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science, 2017.

Unless otherwise noted, all terminology below is consistent with this paper, and all references to figures and tables in this readme refer to this paper. Specifically, some of the terminology used below is outlined in Figure S2. The assembly procedure is described in detail in the Supporting Online Materials, specifically in the section labelled “Pipeline description”.

In addition, the pipeline uses tools and methods from Juicer (Durand & Shamim et al., Cell Systems, 2016) and Juicebox (Durand & Robinson et al., Cell Systems, 2016), as well as additional dependencies noted below.

Feel free to post your questions and comments at: http://www.aidenlab.org/forum.html

http://aidenlab.org/documentation.html

Address of the bookmark: https://github.com/theaidenlab/3d-dna

MCAT: Motif Combining and Association Tool

Neel — Sun, 13 Jan 2019 06:27:28 -0600

This is a pipeline for finding motifs in fasta files.
It can be run from the command line as follows:

usage: orange_pipeline_refine.py [-h] [-w W] [--nmotifs NMOTIFS] [--iter ITER] [-c C]
[-s S] [-d] [-ff] [-v V]
positive_seq negative_seq

positional arguments:
positive_seq the fasta file for the positive sequences
negative_seq the fasta file for the negative sequences

Address of the bookmark: https://github.com/yanshen43/MCAT

dnaPipeTE: a pipeline designed to find, annotate and quantify Transposable Elements

Jit — Mon, 12 Aug 2019 21:56:08 -0500

dnaPipeTE (for de-novo assembly & annotation Pipeline for Transposable Elements), is a pipeline designed to find, annotate and quantify Transposable Elements in small samples of NGS datasets. It is very useful to quantify the proportion of TEs in newly sequenced genomes since it does not require genome assembly and works on small datasets (< 1X).

https://github.com/clemgoub/dnaPipeTE/wiki/dnaPipeTE-WIKI-home

Address of the bookmark: https://github.com/clemgoub/dnaPipeTE

DeepVariant : an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

Jit — Sat, 25 Jan 2020 13:28:09 -0600

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data.

DeepVariant is an analysis pipeline that uses a deep neural network to call genetic variants from next-generation DNA sequencing data. DeepVariant relies on Nucleus, a library of Python and C++ code for reading and writing data in common genomics file formats (like SAM and VCF) designed for painless integration with the TensorFlow machine learning framework.

https://ai.googleblog.com/2017/12/deepvariant-highly-accurate-genomes.html

https://www.biorxiv.org/content/10.1101/092890v6

Address of the bookmark: https://github.com/google/deepvariant

sunbeam: A robust, extensible metagenomics pipeline

Jit — Thu, 18 Jun 2020 06:58:52 -0500

Sunbeam is a pipeline written in snakemake that simplifies and automates many of the steps in metagenomic sequencing analysis. It uses conda to manage dependencies, so it doesn't have pre-existing dependencies or admin privileges, and can be deployed on most Linux workstations and clusters. To read more, check out our paper in Microbiome.

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

Address of the bookmark: https://github.com/sunbeam-labs/sunbeam

Liftoff: An accurate GFF3/GTF lift over pipeline

Neel — Sun, 20 Dec 2020 01:36:37 -0600

Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.

Address of the bookmark: https://github.com/agshumate/Liftoff