BOL: Related items

GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads

Jit — Mon, 14 May 2018 05:25:48 -0500

This software is provided ``as is” without warranty of any kind. In no event shall the author be held responsible for any damage resulting from the use of this software. The program package, including source codes, executables, and this documentation, is distributed free of charge. If you use this program in a publication, please cite the following reference:
Chong Chu, Xin Li, and Yufeng Wu. "GAPPadder: A Sensitive Approach for Closing Gaps on Draft Genomes with Short Sequence Reads." bioRxiv (2017): 125534.

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

GMcloser: closing gaps in assemblies accurately with a likelihood-based selection of contig or long-read alignments

Shruti Paniwala — Mon, 11 Jun 2018 05:43:44 -0500

GMcloser uses likelihood-based classifiers calculated from the alignment statistics between scaffolds, contigs and paired-end reads to correctly assign contigs or long reads to gap regions of scaffolds, thereby achieving accurate and efficient gap closure. We demonstrate with sequencing data from various organisms that the gap-closing accuracy of GMcloser is 3–100-fold higher than those of other available tools, with similar efficiency. https://academic.oup.com/bioinformatics/article/31/23/3733/209212

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/23/3733/209212

SIMBA: a Genome Assembly Project Management System

Neel — Thu, 29 Nov 2018 08:52:25 -0600

SIMBA, SImple Manager for Bacterial Assemblies, is a Web interface for managing assembly projects of bacterial genomes. SIMBA was created to assist bioinformaticians to assemble bacterial genomes sequenced with NextGeneration Sequencing (NGS) platforms quickly, easily and effectively. SIMBA also is open source tool, i.e., can be freely downloaded, shared and modified.

Address of the bookmark: http://ufmg-simba.sourceforge.net/

SPADes team announce new version SPADes v3.15

BioStar — Fri, 15 Jan 2021 10:24:27 -0600

New SPAdes 3.15.0.0. announced by the SPADes team This release includes such new features as:
- CoronaSPAdes pipeline for the assembly of transcriptomic and metatranscriptomic data of full-length coronaviridae genomes;
- Meta-Viral and RNA-Viral pipelines for metagenomic and metatranscriptomic data defining viral genomes;
-New trusted contiguous use algorithm;
-Switched to the memory allocator mimalloc;
- PlasmidSPAdes and bgcSPAdes are now provided as an input assembly graph;
- Important improvements and corrections to the metaplasmid pipeline;
- Multiple performance improvements in procedures for simplification and repeat resolving.
Please, consider updating.

Check out more at https://cab.spbu.ru/software/spades/

Genome Assembly Workshop 2020

Jit — Wed, 25 Aug 2021 04:30:32 -0500

Our team offers custom bioinformatics services to academic and private organizations. We have a strong academic background with a focus on cutting edge, open source software. We replicate standard analysis pipelines (best practices) when appropriate, and/or develop novel applications and pipelines when needed, however we always emphasize biological interpretation of the data.

More at https://ucdavis-bioinformatics-training.github.io/

Address of the bookmark: https://ucdavis-bioinformatics-training.github.io/2020-Genome_Assembly_Workshop/snakemake/snakemake_intro

ALE: Assembly Likelihood Estimator

BioStar — Wed, 08 Mar 2023 01:39:33 -0600

Just import the assembly, bam and ALE scores. You can convert the .ale file to a set of .wig files with ale2wiggle.py and IGV can read those directly. Depending on your genome size you may want to convert the .wig files to the BigWig format.

Address of the bookmark: https://github.com/sc932/ALE

MetaGraph: Ultra Scalable Framework for DNA Search, Alignment, Assembly

Abhi — Sat, 08 Jun 2024 16:15:25 -0500

The MetaGraph framework is designed to work with a wide range of input data sets, indexing from a few samples up to the contents of entire archives with hundreds of thousands of records. The indexing workflow always follows the same principle, transforming single input samples into error-removed, refined sample graphs, which are then merged into a joint metagraph index. Each input sample is annotated in the joint index as a subgraph. This graph index enriched with metadata can then be used for downstream applications such as sequence search or differential assembly.

Searcg link https://metagraph.ethz.ch/search

Pre-print https://www.biorxiv.org/content/10.1101/2020.10.01.322164v4

Address of the bookmark: https://metagraph.ethz.ch/

Metassembler: merging and optimizing de novo genome assemblies

Rahul Nayak — Tue, 08 May 2018 04:52:33 -0500

Metassembler combines multiple whole genome de novo assemblies into a combined consensus assembly using the best segments of the individual assemblies.

Genome assembly projects typically run multiple algorithms in an attempt to find the single best assembly, although those assemblies often have complementary, if untapped, strengths and weaknesses. We present our metassembler algorithm that merges multiple assemblies of a genome into a single superior sequence.

Address of the bookmark: https://sourceforge.net/projects/metassembler/?source=directory

RapClust: Accurate, Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes

Rahul Nayak — Thu, 04 Oct 2018 17:57:10 -0500

RapClust is a tool for clustering contigs from de novo transcriptome assemblies. RapClust is designed to be run downstream of the Sailfish or Salmon tools for rapid transcript-level quantification. Specifically, RapClust relies on the fragment equivalence classes computed by these tools in order to determine how seqeunce is shared across the transcriptome, and how reads map to potentially-related contigs across different conditions.

Address of the bookmark: https://github.com/COMBINE-lab/RapClust

NovoGraph: building whole genome graphs from long-read-based de novo assemblies

Jit — Thu, 15 Nov 2018 12:48:30 -0600

NovoGraph: building whole genome graphs from long-read-based de novo assemblies

An algorithmically novel approach to construct a genome graph representation of long-read-based de novo sequence assemblies. We then provide a proof of principle by creating a genome graph of seven ethnically-diverse human genomes.

https://f1000research.com/articles/7-1391/v1

Address of the bookmark: https://github.com/NCBI-Hackathons/NovoGraph