BOL: Related items

Meraculous: De Novo Genome Assembly with Short Paired-End Reads

Jit — Tue, 07 Nov 2017 04:36:10 -0600

We describe a new algorithm, meraculous, for whole genome assembly of deep paired-end short reads, and apply it to the assembly of a dataset of paired 75-bp Illumina reads derived from the 15.4 megabase genome of the haploid yeast Pichia stipitis. More than 95% of the genome is recovered, with no errors; half the assembled sequence is in contigs longer than 101 kilobases and in scaffolds longer than 269 kilobases. Incorporating fosmid ends recovers entire chromosomes. Meraculous relies on an efficient and conservative traversal of the subgraph of the k-mer (deBruijn) graph of oligonucleotides with unique high quality extensions in the dataset, avoiding an explicit error correction step as used in other short-read assemblers. A novel memory-efficient hashing scheme is introduced. The resulting contigs are ordered and oriented using paired reads separated by ∼280 bp or ∼3.2 kbp, and many gaps between contigs can be closed using paired-end placements. Practical issues with the dataset are described, and prospects for assembling larger genomes are discussed.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3158087/

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/

De novo Genome Assembly for Illumina Data

Rahul Nayak — Mon, 20 Jan 2020 05:13:29 -0600

Written and maintained by Simon Gladman - Melbourne Bioinformatics (formerly VLSCI)

Protocol Overview / Introduction

In this protocol we discuss and outline the process of de novo assembly for small to medium sized genomes.

https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/

Address of the bookmark: https://www.melbournebioinformatics.org.au/tutorials/tutorials/assembly/assembly-protocol/

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

JBrowse: Embeddable genome browser built completely with JavaScript and HTML5

Jit — Fri, 29 Jun 2018 09:19:56 -0500

JBrowse is a fast, embeddable genome browser built completely with JavaScript and HTML5, with optional run-once data formatting tools written in Perl. Headline Features: Fast, smooth scrolling and zooming. Explore your genome with unparalleled speed. Scales easily to multi-gigabase genomes and deep-coverage sequencing. Quickly open and view data files on your computer without uploading them to any server. Supports GFF3, BED, FASTA, Wiggle, BigWig, BAM, VCF (with either .tbi or .idx index), REST, and more. BAM, BigBed, BigWig, and VCF data are displayed directly from chunks of the compressed binary files, no conversion needed. Includes an optional “faceted” track selector (see demo) suitable for large installations with thousands of tracks. Very light server resource requirements. In fact, JBrowse has no back-end server code, just tools for formatting data files to be read directly over HTTP. Serve huge datasets from a single low-cost cloud instance. Can run as a stand-alone app on OSX and Windows using the Electron platform Highly extensible plugin architecture, with a large plugin registry of existing examples here https://gmod.github.io/jbrowse-registry https://jbrowse.org/

Address of the bookmark: https://github.com/GMOD/jbrowse

KAT: a K-mer analysis toolkit to quality control NGS datasets and genome assemblies

Jit — Fri, 06 Jul 2018 03:36:45 -0500

KAT is a suite of tools that analyse jellyfish hashes or sequence files (fasta or fastq) using kmer counts. The following tools are currently available in KAT:

hist: Create an histogram of k-mer occurrences from a sequence file. Adds metadata in output for easy plotting.
gcp: K-mer GC Processor. Creates a matrix of the number of K-mers found given a GC count and a K-mer count.
comp: K-mer comparison tool. Creates a matrix of shared K-mers between two (or three) sequence files or hashes.
sect: SEquence Coverage estimator Tool. Estimates the coverage of each sequence in a file using K-mers from another sequence file.
blob: Given, reads and an assembly, calculates both the read and assembly K-mer coverage along with GC% for each sequence in the assembly.SEquence Coverage estimator Tool.
filter: Filtering tools. Contains tools for filtering k-mer hashes and FastQ/A files:
- kmer: Produces a k-mer hash containing only k-mers within specified coverage and GC tolerances.
- seq: Filters a sequence file based on whether or not the sequences contain k-mers within a provided hash.
plot: Plotting tools. Contains several plotting tools to visualise K-mer and compare distributions. The following plot tools are available:
- density: Creates a density plot from a matrix created with the "comp" tool. Typically this is used to compare two K-mer hashes produced by different NGS reads.
- profile: Creates a K-mer coverage plot for a single sequence. Takes in fasta coverage output coverage from the "sect" tool
- spectra-cn: Creates a stacked histogram using a matrix created with the "comp" tool. Typically this is used to compare a jellyfish hash produced from a read set to a jellyfish hash produced from an assembly. The plot shows the amount of distinct K-mers absent, as well as the copy number variation present within the assembly.
- spectra-hist: Creates a K-mer spectra plot for a set of K-mer histograms produced either by jellyfish-histo or kat-histo.
- spectra-mx: Creates a K-mer spectra plot for a set of K-mer histograms that are derived from selected rows or columns in a matrix produced by the "comp".

In addition, KAT contains a python script for analysing the mathematical distributions present in the K-mer spectra in order to determine how much content is present in each peak.

This README only contains some brief details of how to install and use KAT. For more extensive documentation please visit: https://kat.readthedocs.org/en/latest/

https://academic.oup.com/bioinformatics/article/33/4/574/2664339

Address of the bookmark: https://github.com/TGAC/KAT

P_RNA_scaffolder: a fast and accurate genome scaffolder using paired-end RNA-sequencing reads

BioStar — Fri, 07 Sep 2018 05:19:06 -0500

P_RNA_scaffolder is a novel scaffolding tool using Pair-end RNA-seq to scaffold genome fragments. The method is suitable for most genomes. The program could utilize Illumina Paired-end RNA-sequencing reads from target speciesies. Our method provides another practical alternative to existing mate-pair_based approaches or other Protein-based approaches (for instance, PEP_scaffolder ) for scaffolding genome sequences. The most important feature of this method is to improve the completeness of gene regions and long-coding gene regions (for instance, circRNA).

Address of the bookmark: http://www.fishbrowser.org/software/P_RNA_scaffolder/#