BOL: Related items

HaploTypo: a variant-calling pipeline for phased genomes

Jit — Thu, 19 Dec 2019 07:33:40 -0600

An increasing number of phased (i.e. with resolved haplotypes) reference genomes are available. However, most genetic variant calling tools do not explicitly account for haplotype structure. Here, we present HaploTypo, a pipeline tailored to resolve haplotypes in genetic variation analyses. HaploTypo infers the haplotype correspondence for each heterozygous variant called on a phased reference genome.

Availability and Implementation

HaploTypo is implemented in Python 2.7 and Python 3.5, and is freely available at https://github.com/gabaldonlab/haplotypo, and as a Docker image.

Address of the bookmark: https://github.com/gabaldonlab/haplotypo

SLR-superscaffolder: A scaffold assemble pipeline for stLFR reads.

Jit — Fri, 14 Feb 2020 14:23:30 -0600

This is a scaffold assembler designed for stLFR reads[1]. It uses the link-reads information from stLFR reads to assemble contigs to scaffolds.

Here is an illustration of this pipeline:

Address of the bookmark: https://github.com/BGI-Qingdao/SLR-superscaffolder

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

JUDI: Just Do It

Jit — Mon, 06 Sep 2021 02:44:35 -0500

judi comes from the idea of bringing the power and efficiency of doit to execute any kind of task under many combinations of parameter settings.

https://github.com/ncbi/JUDI

Address of the bookmark: https://github.com/ncbi/JUDI

PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome skimming data

Abhi — Wed, 06 Sep 2023 00:14:28 -0500

Phylogenomic Analysis Pipeline for Herbarium Specimens

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.

License: GNU General Public License

Citation:

Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

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

HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies

Jit — Tue, 15 May 2018 07:35:26 -0500

HapCUT2 is a maximum-likelihood-based tool for assembling haplotypes from DNA sequence reads, designed to "just work" with excellent speed and accuracy. We found that previously described haplotype assembly methods are specialized for specific read technologies or protocols, with slow or inaccurate performance on others. With this in mind, HapCUT2 is designed for speed and accuracy across diverse sequencing technologies, including but not limited to: NGS short reads (Illumina HiSeq) clone-based sequencing (Fosmid or BAC clones) SMRT reads (PacBio) Oxford Nanopore reads 10X Genomics Linked-Reads proximity-ligation (Hi-C) reads high-coverage sequencing (>40x coverage-per-SNP) using above technologies combinations of the above technologies (e.g. scaffold long reads with Hi-C reads) See below for specific examples of command line options and best practices for some of these technologies. NOTE: At this time HapCUT2 is for diploid organisms only. VCF input should contain diploid variants. If you use HapCUT2 in your research, please cite: Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. gr.213462.116 (2016). doi:10.1101/gr.213462.116

Address of the bookmark: https://github.com/vibansal/HapCUT2

SocBiN Bioinformatics 2014

Tue, 03 Sep 2013 18:50:20 -0500

14th annual conference in Bioinformatics

Date : June 10-13

Organizers: The Society for Bioinformatics in Northern European countries (SocBiN) and the Norwegian Bioinformatics Platform / ELIXIR.NO

Venue: Department of Informatics, University of Oslo, Norway

Topics:
Tools and technologies for integrative bioinformatics
Metagenomics
Comparative genomics and phylogeny
Post-ENCODE bioinformatics
Gene regulation
Cancer genomes
Marine genomics

MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph

Jit — Wed, 14 Nov 2018 04:50:27 -0600

MEGAHIT is a single node assembler for large and complex metagenomics NGS reads, such as soil. It makes use of succinct de Bruijn graph (SdBG) to achieve low memory assembly. MEGAHIT can optionally utilize a CUDA-enabled GPU to accelerate its SdBG contstruction. The GPU-accelerated version of MEGAHIT has been tested on NVIDIA GTX680 (4G memory) and Tesla K40c (12G memory) with CUDA 5.5, 6.0 and 6.5. MEGAHIT v1.0 or greater also supports IBM Power PC and has been tested on IBM POWER8.

https://academic.oup.com/bioinformatics/article/31/10/1674/177884

Address of the bookmark: https://github.com/voutcn/megahit

CoverM: Read coverage calculator for metagenomics

Neel — Thu, 29 Apr 2021 23:39:14 -0500

CoverM aims to be a configurable, easy to use and fast DNA read coverage and relative abundance calculator focused on metagenomics applications.

CoverM calculates coverage of genomes/MAGs coverm genome (help) or individual contigs coverm contig (help). Calculating coverage by read mapping, its input can either be BAM files sorted by reference, or raw reads and reference genomes in various formats.

Address of the bookmark: https://github.com/wwood/CoverM

nf-core/mag v5: Advancing Genome-Resolved Metagenomics

LEGE — Sat, 12 Sep 2026 20:27:51 -0500

Metagenomics is rapidly moving beyond short-read sequencing. With the increasing adoption of long-read technologies, researchers can generate more contiguous assemblies—but converting these reads into reliable metagenome-assembled genomes (MAGs) remains computationally challenging.

nf-core/mag v5 (https://github.com/nf-core/mag) addresses this challenge by extending its reproducible Nextflow-based workflow for modern genome-resolved metagenomics.

A key addition is support for long-read-only metagenomic assembly and bin refinement, enabling long-read data to be processed through an integrated workflow. The release also introduces five additional binning tools, providing complementary strategies for recovering genomes from complex microbial communities.

The workflow has also expanded beyond conventional bacterial and archaeal MAGs, with improved classification of viruses and eukaryotes, together with enhanced genome-quality assessment.

Conceptually, the workflow brings together:

Reads → QC → Assembly → Binning → Bin refinement → Taxonomic classification → MAG quality assessment

The real strength of nf-core/mag is not any single algorithm, but the integration and reproducibility of multiple tools within a standardized workflow. This is particularly important for large-scale metagenomic studies where software versions, parameters, databases and computational environments can strongly influence results.

After seven years of development involving multiple curator teams and the wider nf-core community, v5 demonstrates how community-driven workflow development can keep metagenomic analysis aligned with rapidly evolving sequencing technologies.

The future of genome-resolved metagenomics is not simply longer reads—it is better integration of assembly, binning, refinement and quality control within reproducible computational workflows.

Read the full paper in Bioinformatics https://academic.oup.com/bioinformatics/article/42/9/btag628/8770536?