BOL: Related items

Hapsembler: An Assembler for Highly Polymorphic Genomes

Jit — Tue, 22 May 2018 04:09:53 -0500

Hapsembler is a haplotype-specific genome assembly toolkit that is designed for genomes that are rich in SNPs and other types of polymorphism. Hapsembler can be used to assemble reads from a variety of platforms including Illumina and Roche/454. http://compbio.cs.toronto.edu/hapsembler/

Address of the bookmark: http://compbio.cs.toronto.edu/hapsembler/

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

Omega2: metagenome assembly pipeline

Jit — Mon, 10 Jul 2017 05:56:07 -0500

Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets.

Address of the bookmark: http://omega.omicsbio.org/

3D de novo assembly (3D DNA) pipeline

Jit — Sun, 02 Feb 2020 13:41:55 -0600

For a detailed description of the pipeline and how it integrates with other tools designed by the Aiden Lab see Genome Assembly Cookbook on http://aidenlab.org/assembly.

For the original version of the pipeline and to reproduce the Hs2-HiC and the AaegL4 genomes reported in (Dudchenko et al., Science, 2017) see the original commit.

For the detailed description of the merge section see https://github.com/theaidenlab/AGWG-merge.

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

Meraculous: Haplotype-sensitive Assembly of Highly Heterozygous genomes.

Jit — Wed, 20 Dec 2017 18:59:42 -0600

Meraculous is a whole genome assembler for Next Generation Sequencing data geared for large genomes. It is a hybrid k-mer/read-based assembler that capitalizes on the high accuracy of Illumina sequence by eschewing an explicit error correction step which we argue to be redundant with the assembly process. Meraculous achieves high performance with large datasets by utilizing lightweight data structures and multi-threaded parallelization, allowing to assemble human-sized genomes on commodity clusters in under a day. The process pipeline implements a highly transparent and portable model of job control and monitoring where different assembly stages can be executed and re-executed separately or in unison on a wide variety of architectures.

https://jgi.doe.gov/data-and-tools/meraculous/

https://arxiv.org/ftp/arxiv/papers/1703/1703.09852.pdf

Address of the bookmark: https://sourceforge.net/projects/meraculous20/

LACHESIS: Genome Assembly with Hi-C-based Contact Probability Maps (LACHESIS)

Jit — Mon, 14 May 2018 04:26:30 -0500

LACHESIS is method that exploits contact probability map data (e.g. from Hi-C) for chromosome-scale de novo genome assembly.

Further information about LACHESIS, including source code, documentation and a user's guide are available at: http://shendurelab.github.io/LACHESIS.

Manuscript describing LACHESIS was published as: Burton JN#, Adey A, Patwardhan RP, Qiu R, Kitzman JO, Shendure J#. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nature Biotechnology 2013 Dec;31(12):1119-25. doi: 10.1038/nbt.272. PubMed PMID: 24185095.

http://shendurelab.github.io/LACHESIS/

Address of the bookmark: http://shendurelab.github.io/LACHESIS/

Ranbow: a haplotype assembler for polyploid genomes

Jit — Fri, 01 Jun 2018 07:21:54 -0500

Ranbow is a haplotype assembler for polyploid genomes. It has been developed for the haplotype assembly of the hexaploid sweet potato genome, which is highly heterozygous. Ranbow can also be applied to other polyploid genomes. After a first phasing, Ranbow utilizes the assembled haplotypes to improve the accuracy of variant calling results and to infer the evolutionary history of the organism´s genome. Ranbow has three main modes of function: ranbow hap: for haplotyping ranbow eval: for evaluating of the assemble haplotypes by gold standard (long) reads ranbow phylo: for the phylogenetic analysis

Address of the bookmark: https://www.molgen.mpg.de/ranbow

Tallymer: method to compute K-mer frequencies and its application to annotate large repetitive plant genomes

Jit — Thu, 15 Feb 2018 10:21:02 -0600

Tallymer is based on enhanced suffix arrays. This gives a much larger flexibility concerning the choice of the k-mer size. Tallymer can process large data sizes of several billion bases. We used it in a variety of applications to study the genomes of maize and other plant species. In particular, Tallymer was used to index a set whole genome shotgun sequences from maize (B73) (total size 10⁹ bp).
Tallymer was effective in a variety of applications to aid genome annotation in maize, despite limitations imposed by the relatively low coverage of sequence available.

A manual can be found here.

Address of the bookmark: https://www.zbh.uni-hamburg.de/forschung/arbeitsgruppe-genominformatik/software/tallymer.html

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/

ALLHiC: Phasing and scaffolding polyploid genomes based on Hi-C data

BioStar — Thu, 20 Dec 2018 12:03:32 -0600

The major problem of scaffolding polyploid genome is that Hi-C signals are frequently detected between allelic haplotypes and any existing stat of art Hi-C scaffolding program links the allelic haplotypes together. To solve the problem, we developed a new Hi-C scaffolding pipeline, called ALLHIC, specifically tailored to the polyploid genomes. ALLHIC pipeline contains a total of 5 steps: prune, partition, rescue, optimize and build.

Address of the bookmark: https://github.com/tangerzhang/ALLHiC/wiki