BOL: Related items

NGS Tutorial

Jit — Mon, 05 Sep 2016 09:50:46 -0500

These tutorials are written for hundreds of bioinformaticians trying to cope with large volume of next-generation sequencing (NGS) data. NGS technologies brought a dramatic shift in the world of sequencing. Merely five years back, genome sequencing of higher eukaryotes used to be very expensive endeavor. To get a genome of interest sequenced, hundreds of scientists had to raise funds together by writing a joint white-paper and petitioning to various government agencies. The tasks of sequencing and assembly were handled by dedicated sequencing facilities, of which only a few existed around the globe. Naturally, the capacities at those sequencing facilities were significantly constrained from high volume of requests

Address of the bookmark: http://www.homolog.us/Tutorials/index.php

CABOG: Celera Assembler with Best Overlap Graph

Abhimanyu Singh — Mon, 15 May 2017 05:04:39 -0500

CABOG (Celera Assembler with Best Overlap Graph) is scientific software for DNA research. CABOG has been a critical component of many genome sequencing projects. CABOG operates on small genomes such as bacterial as well as large genomes such as mammalian. CABOG is an extension of the Celera Assembler software that was originally developed at Celera for the 2001 publication of the first draft human genome sequence. The software was released to the public domain in 2004. Its open source repository on Source Forge is an internet resource for scientists around the world.

CABOG is one of many software programs called genome assemblers. These programs exist to overcome the fundamental limitation of all sequencing machines, namely, that they read out very few DNA letters at a time. These programs reconstruct genomes that are billions of letters long from the hundreds of letters per read that modern sequencers provide. What these programs do is often described as a scaled up version of a family solving a jigsaw puzzle.

The CABOG software was the first to accomplish many scientific goals. It was the first to assemble the genome of a multicellular organism (Drosophila melanogaster, 2000). It was the first to assemble both parental haplotypes of one human genome (J. Craig Venter, 2007). It was the first to assemble environmental sequence from the oceans (Sargasso Sea in 2004 and Global Ocean Sampling in 2007). It was first to combine reads from first-generation Sanger sequencing machines and second-generation pyrosequencing machines (Marine microbes, 2006). Today, CABOG is one of the leading assembly programs for data sets that include paired end data from the Roche 454 line of sequencing machines.

Address of the bookmark: http://www.jcvi.org/cms/research/projects/cabog/overview/

The MARVEL assembler

Jit — Fri, 04 May 2018 19:18:41 -0500

MARVEL consists of a set of tools that facilitate the overlapping, patching, correction and assembly of noisy (not so noisy ones as well) long reads.

The assembly process can be summarized as follows:

overlap
patch reads
overlap (again)
scrubbing
assembly graph construction and touring
optional read correction
fasta file creation

Address of the bookmark: https://github.com/schloi/MARVEL

BASE: a practical de novo assembler for large genomes using long NGS reads

Rahul Nayak — Fri, 19 Oct 2018 07:25:21 -0500

new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.

Address of the bookmark: https://github.com/dhlbh/BASE

HASLR: a hybrid assembler which uses both second and third generation sequencing reads

BioStar — Mon, 04 May 2020 02:04:03 -0500

HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples. Availability. HASLR is an open source tool available at https://github.com/vpc-ccg/haslr.

Address of the bookmark: https://github.com/vpc-ccg/haslr

MIX: Combining multiple assemblies from NGS data

Rahul Nayak — Tue, 08 May 2018 04:58:05 -0500

Mix is a tool that combines two or more draft assemblies, without relying on a reference genome and has the goal to reduce contig fragmentation and thus speed-up genome finishing. The proposed algorithm builds an extension graph where vertices represent extremities of contigs and edges represent existing alignments between these extremities. These alignment edges are used for contig extension. The resulting output assembly corresponds to a path in the extension graph that maximizes the cumulative contig length.

The Mix algorithm, approach and results were published in BMC bioinformatics : http://www.biomedcentral.com/1471-2105/14/S15/S16.

Address of the bookmark: https://github.com/cbib/MIX

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

Synima: a Synteny imaging tool for annotated genome assemblies

Abhimanyu Singh — Tue, 30 Oct 2018 10:49:13 -0500

Synima written in Perl, which uses the graphical features of R. Synima takes orthologues computed from reciprocal best BLAST hits or OrthoMCL, and DAGchainer, and outputs an overview of genome-wide synteny in PDF. Each of these programs are included with the Synima package, and a pipeline for their use. Synima has a range of graphical parameters including size, colours, order, and labels, which are specified in a config file generated by the first run of Synima – and can be subsequently edited. Synima runs quickly on a command line to generate informative and publication quality figures. Synima is open source and freely available from https://github.com/rhysf/Synima under the MIT License.

Address of the bookmark: https://github.com/rhysf/Synima

CrossMap: program for genome coordinates conversion between different assemblies

Rahul Nayak — Tue, 25 Jan 2022 17:59:32 -0600

CrossMap is a program for genome coordinates conversion between different assemblies (such as hg18 (NCBI36) <=> hg19 (GRCh37)). It supports commonly used file formats including BAM, CRAM, SAM, Wiggle, BigWig, BED, GFF, GTF, MAF VCF, and gVCF.

Address of the bookmark: http://crossmap.sourceforge.net/

Oxford Nanopore Sequencing, Hybrid Error Correction, and de novo Assembly of a Eukaryotic Genome

Jit — Wed, 29 Nov 2017 05:08:53 -0600

Monitoring the progress of DNA molecules through a membrane pore has been postulated as a method for sequencing DNA for several decades. Recently, a nanopore-based sequencing instrument, the Oxford Nanopore MinION, has become available that we used for sequencing the S. cerevisiae genome. To make use of these data, we developed a novel open-source hybrid error correction algorithm Nanocorr (https://github.com/jgurtowski/nanocorr) specifically for Oxford Nanopore reads, as existing packages were incapable of assembling the long read lengths (5-50kbp) at such high error rate (between ~5 and 40% error). With this new method we were able to perform a hybrid error correction of the nanopore reads using complementary MiSeq data and produce a de novo assembly that is highly contiguous and accurate: the contig N50 length is more than ten-times greater than an Illumina-only assembly (678kb versus 59.9kbp), and has greater than 99.88% consensus identity when compared to the reference. Furthermore, the assembly with the long nanopore reads presents a much more complete representation of the features of the genome and correctly assembles gene cassettes, rRNAs, transposable elements, and other genomic features that were almost entirely absent in the Illumina-only assembly.

Address of the bookmark: http://schatzlab.cshl.edu/data/nanocorr/