BOL: Related items

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/

Katuali is a flexible consensus pipeline implemented in Snakemake to basecall, assemble, and polish Oxford Nanopore Technologies' sequencing data

Jit — Tue, 22 Jan 2019 06:26:55 -0600

Run a pipeline processing fast5s to a consensus in a single command.
Recommended fixed "standard" and "fast" pipelines.
Interchange basecaller, assembler, and consensus components of the pipelines simply by changing the target filepath.
Seemless distribution of tasks over local or distributed compute.
Highly configurable.
Open source (Mozilla Public License 2.0).

Documentation can be found at https://nanoporetech.github.io/katuali/.

Address of the bookmark: https://github.com/nanoporetech/katuali

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

RNA-Bloom: a fast and memory-efficient de novo transcript sequence assembler

LEGE — Thu, 09 Jul 2020 03:13:06 -0500

RNA-Bloom is a fast and memory-efficient de novo transcript sequence assembler. It is designed for the following sequencing data types:

single-end/paired-end bulk RNA-seq (strand-specific/agnostic)
paired-end single-cell RNA-seq (strand-specific/agnostic)
nanopore RNA-seq (PCR cDNA/direct cDNA/direct RNA)

Written by Ka Ming Nip ✉️

Address of the bookmark: https://github.com/bcgsc/RNA-Bloom

HALC: High throughput algorithm for long read error correction

Jit — Fri, 08 Jun 2018 10:47:41 -0500

HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach) HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.

Address of the bookmark: https://github.com/lanl001/halc

LSC :a long read error correction tool

Jit — Thu, 02 Aug 2018 07:39:46 -0500

Getting Started

These simple steps will help you integrate LSC into your transcriptomics analysis pipeline.

Read the LSC_requirements for running LSC.
Download and set-up the LSC package.
Follow the tutorial to see how LSC works on some example data.
Read the manual if anything is unclear.
You're ready, Happy LSCing!

Latest publication

Kin Fai Au, Jason Underwood, Lawrence Lee and Wing Hung Wong
Improving PacBio Long Read Accuracy by Short Read Alignment [Manuscript]
PLoS ONE 2012. 7(10): e46679. doi:10.1371/journal.pone.0046679

Address of the bookmark: https://www.healthcare.uiowa.edu/labs/au/LSC/

Shasta long read assembler

Jit — Tue, 14 Jan 2020 06:47:07 -0600

The goal of the Shasta long read assembler is to rapidly produce accurate assembled sequence using as input DNA reads generated by Oxford Nanopore flow cells.

Computational methods used by the Shasta assembler include:

Using a run-length representation of the read sequence. This makes the assembly process more resilient to errors in homopolymer repeat counts, which are the most common type of errors in Oxford Nanopore reads.
Using in some phases of the computation a representation of the read sequence based on markers, a fixed subset of short k-mers (k ≈ 10).

More at https://chanzuckerberg.github.io/shasta/index.html

Address of the bookmark: https://github.com/chanzuckerberg/shasta

ARCS: scaffolding genome drafts with linked reads

Rahul Nayak — Tue, 06 Mar 2018 16:35:26 -0600

ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiensgenome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.

Address of the bookmark: https://github.com/bcgsc/ARCS/