BOL: Related items

Krona

Jit — Wed, 22 Mar 2017 04:47:35 -0500

Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. Krona charts can be created using an Excel template or KronaTools, which includes support for several bioinformatics tools and raw data formats. The interactive charts are self-contained and can be viewed with any modern web browser (see Browser support).

Address of the bookmark: https://github.com/marbl/Krona/wiki

BEAR: Better Emulation for Artificial Reads

BioStar — Sat, 06 Jul 2024 04:27:53 -0500

Created by Stephen Johnson, Brett Trost, Dr. Jeffrey R. Long, Dr. Anthony Kusalik University of Saskatchewan, Department of Computer Science

BEAR is intended to be an easy-to-use collection of scripts for generating simulated WGS metagenomic reads with read lengths, quality scores, error profiles, and species abundances derived from real user-supplied WGS data.

Address of the bookmark: https://github.com/sej917/BEAR

Comparison of Short Read De Novo Alignment Algorithms

Rahul Agarwal — Wed, 21 Aug 2013 07:56:01 -0500

Excellent article to introduce different sequencing methods along with tools for de novo assembly of sequencing reads and their relevant references.

Title: Comparison of Short Read De Novo Alignment Algorithms

Author: Nikhil Gopal

Address of the bookmark: http://biochem218.stanford.edu/Projects%202011/Gopal%202011.pdf

coursera genome assembly tutorial

Jit — Sat, 25 Nov 2017 08:57:25 -0600

Solutions to Coursera Genome Sequencing (Bioinformatics II)

Address of the bookmark: https://github.com/iansealy/coursera-assembly

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

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Rahul Nayak — Tue, 05 Jun 2018 09:57:11 -0500

PERGA - Paired End Reads Guided Assembler PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate. The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.

Address of the bookmark: https://github.com/hitbio/PERGA

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/

Nanopolis: polish a genome assembly

Rahul Nayak — Thu, 26 Jul 2018 04:51:28 -0500

Software package for signal-level analysis of Oxford Nanopore sequencing data. Nanopolish can calculate an improved consensus sequence for a draft genome assembly, detect base modifications, call SNPs and indels with respect to a reference genome and more (see Nanopolish modules, below).

Quickstart

http://nanopolish.readthedocs.io/en/latest/quickstart_consensus.html

Algorithms

http://simpsonlab.github.io/2017/06/30/nanopolish-v0.7.0/

Address of the bookmark: https://github.com/jts/nanopolish

QUAST-LG: Versatile genome assembly evaluation

Jit — Thu, 25 Oct 2018 10:46:55 -0500

QUAST-LG-a tool that compares large genomic de novo assemblies against reference sequences and computes relevant quality metrics. Since genomes generally cannot be reconstructed completely due to complex repeat patterns and low coverage regions, we introduce a concept of upper bound assembly for a given genome and set of reads, and compute theoretical limits on assembly correctness and completeness. Using QUAST-LG, we show how close the assemblies are to the theoretical optimum, and how far this optimum is from the finished reference.

AVAILABILITY AND IMPLEMENTATION:

http://cab.spbu.ru/software/quast-lg

Address of the bookmark: http://cab.spbu.ru/software/quast-lg/

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