BOL: Related items

quickmerge: A simple and fast metassembler and assembly gap filler designed for long molecule based assemblies.

Jit — Mon, 19 Dec 2016 10:23:36 -0600

quickmerge uses a simple concept to improve contiguity of genome assemblies based on long molecule sequences, often with dramatic outcomes. The program uses information from assemblies made with illumina short reads and PacBio long reads to improve contiguities of an assembly generated with PacBio long reads alone. This is counterintuitive because illumina short reads are not typically considered to cover genomic regions which PacBio long reads cannot. Although we have not evaluated this program for assemblies generated with Oxford nanopore sequences, the program should work with ONP-assemblies too.

Address of the bookmark: https://github.com/mahulchak/quickmerge

MetaBAT: An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities

Jit — Mon, 06 Mar 2017 03:44:34 -0600

MetaBAT, An Efficient Tool for Accurately Reconstructing Single Genomes from Complex Microbial Communities

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (>200kb) in 3 hours, where 621 genome bins are >50% complete with <5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.

Address of the bookmark: https://bitbucket.org/berkeleylab/metabat

ABACAS

Surabhi Chaudhary — Thu, 16 Feb 2017 12:15:55 -0600

ABACAS is intended to rapidly contiguate (align, order, orientate) , visualize and design primers to close gaps on shotgun assembled contigs based on a reference sequence. It uses MUMmer to find alignment positions and identify syntenies of assembly contigs against the reference. The output is then processed to generate a pseudomolecule taking overlaping contigs and gaps in to account. MUMmer's alignment generating programs, Nucmer and Promer are used followed by the 'delta-filter' utility function. Users could also run tblastx on contigs that are not used to generate the pseudomolecule.

Address of the bookmark: http://abacas.sourceforge.net/Manual.html#9._Colour_code

Multi-metagenome assembly

Radha Agarkar — Fri, 03 Mar 2017 10:14:18 -0600

This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:

Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, Kåre L. Nielsen and Per .H. Nielsen

Nature Biotechnology 2013, doi: 10.1038/nbt.2579

Address of the bookmark: https://github.com/MadsAlbertsen/multi-metagenome

PhenoGram

Jit — Tue, 07 Mar 2017 08:35:12 -0600

With PhenoGram researchers can create chomosomal ideograms annotated with lines in color at specific base-pair locations, or colored base-pair to base-pair regions, with or without other annotation. PhenoGram allows for annotation of chromosomal locations and/or regions with shapes in different colors, gene identifiers, or other text. PhenoGram also allows for creation of plots showing expanded chromosomal locations, providing a way to show results for specific chromosomal regions in greater detail.

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads

Multigenome assembly

Jit — Tue, 14 Mar 2017 04:41:23 -0500

This project contains scripts and tutorials on how to assemble individual microbial genomes from metagenomes, as described in:

Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes

Mads Albertsen, Philip Hugenholtz, Adam Skarshewski, Gene W. Tyson, Kåre L. Nielsen and Per .H. Nielsen

Nature Biotechnology 2013, doi: 10.1038/nbt.2579

See the associated online guide for detailed information.

https://github.com/MadsAlbertsen/multi-metagenome

Address of the bookmark: https://github.com/MadsAlbertsen/multi-metagenome

MeDuSa: a multi-draft based scaffolder

Abhimanyu Singh — Wed, 14 Feb 2018 02:49:00 -0600

MeDuSa (Multi-Draft based Scaffolder), an algorithm for genome scaffolding. MeDuSa exploits information obtained from a set of (draft or closed) genomes from related organisms to determine the correct order and orientation of the contigs. MeDuSa formalises the scaffolding problem by means of a combinatorial optimisation formulation on graphs and implements an efficient constant factor approximation algorithm to solve it. In contrast to currently used scaffolders, it does not require either prior knowledge on the microrganisms dataset under analysis (e.g. their phylogenetic relationships) or the availability of paired end read libraries.

Address of the bookmark: https://github.com/combogenomics/medusa

Protocol for De novo Genome Assembly using Illumina Reads

BioStar — Sat, 16 Jan 2021 21:42:11 -0600

In this protocol, we address and describe the de novo assembly method for small to medium-sized genomes.

What is de novo genome assembly?
The method of taking a large number of short DNA sequences and placing them back together to create a reflection of the original chromosomes from which the DNA originated relates to genome assembly. No previous knowledge of the source DNA sequence length, structure or composition is inferred by De novo genome assemblies. The DNA of the target organism is split up into millions of tiny parts and read on a sequencing computer in a genome sequencing experiment. Depending on the sequencing system used, these "reads" range from 20 to 1000 nucleotide base pairs (bp) in length. Usually, length reads of 36 - 150 bp are produced for Illumina style short read sequencing. These reads can be either “single ended” as described above or “paired end.”

Why genome assembly?
In basic research into why and how they live, as well as in applied topics, identifying the DNA sequence of an organism is useful. Awareness of a DNA sequence may be useful in virtually any biological research because of the relevance of DNA to living things. For example, it may be used in medicine to classify, diagnose and eventually improve genetic disorder therapies. Similarly, pathogens study can lead to treatments for infectious diseases.

Raw NGS data
Reads can be saved as a Fasta file as text or in a FastQ file with their attributes. FastQ is the most common read file format since this is what the Illumina sequencing pipeline creates. This will henceforth be the subject of our conversation.

In a nutshell the protocol:
Get the sequence file(s) read from the sequencing machine (s).
Look at the readings - have an idea of what you have and what the standard is like.
If required, raw data cleanup/quality trimming.
Choose an adequate parameter set for assembly.
Assemble the data into scaffolds/contigs.
Examine the assembly performance and determine the efficiency of the assembly.

Read Quality Control:
Check the qualiy with fastQC.
Script
https://bioinformaticsonline.com/snippets/view/42540/install-fastqc-using-conda

Quality trimming/cleanup of read files.
This function trims adapters, barcodes and other contaminants from the reads.
Script
https://bioinformaticsonline.com/snippets/view/42542/trimmomatic-command

Genome Assembly:
The object of this portion of the protocol is to explain the method of assembling the reads trimmed by quality into draft contigs.

spades.py -1 illumina_R1.fastq.gz -2 illumina_R2.fastq.gz --careful --cov-cutoff auto -o result_of_spades_assembly_all_illumina

A significant range of short-read assemblers are available. Everyone with strengths and disadvantages of their own.
Some of the assemblers available include:
Velvet
SOAP-denovo
MIRA
ALLPATHS

Next step is to assess the suitability and what to do with a draft package of contiguous details for the remainder of the study now. Few stuff you can note about the contigs you just created: They're the draft Contigs. Any mis-assemblies can occur.

Mis-assembly checking and assembly metric tools:
QUAST - Quality assessment tool for genome assembly http://bioinf.spbau.ru/quast
Mauve assembly metrics - http://code.google.com/p/ngopt/wiki/How_To_Score_Genome_Assemblies_with_Mauve
InGAP-SV - https://sites.google.com/site/nextgengenomics/ingap and http://ingap.sourceforge.net/
inGAP is also useful for finding structural variants between genomes from read mappings.

Genome finishing tools:
Semi-automated gap fillers:
Gap filler - http://www.baseclear.com/landingpages/basetools-a-wide-range-of-bioinformatics-solutions/gapfiller/

IMAGE (V2) - http://sourceforge.net/apps/mediawiki/image2/index.php?title=Main_Page

Genome visualisers and editors:
Artemis - http://www.sanger.ac.uk/resources/software/artemis/
IGV - http://www.broadinstitute.org/igv/

Automated and semi automated annotation tools:
Prokka - https://github.com/tseemann/prokka
RAST - http://www.nmpdr.org/FIG/wiki/view.cgi/FIG/RapidAnnotationServer
JCVI Annotation Service - http://www.jcvi.org/cms/research/projects/annotation-service/

Frequent command use for the analysis are at:

https://bioinformaticsonline.com/blog/view/38765/list-of-tools-frequently-used-while-genome-assembly
https://bioinformaticsonline.com/pages/view/42275/frequent-parameters-for-bioinformatics-tools

Peregrine & SHIMMER Genome Assembly Toolkit

Abhi — Thu, 16 Dec 2021 02:50:19 -0600

Peregrine is a fast genome assembler for accurate long reads (length > 10kb, accuracy > 99%). It can assemble a human genome from 30x reads within 20 cpu hours from reads to polished consensus. It uses Sparse HIereachical MimiMizER (SHIMMER) for fast read-to-read overlaping without quadratic comparisions used in other OLC assemblers.

Address of the bookmark: https://github.com/cschin/Peregrine

Software and Tools to detect structure variation with long reads !!

Archana Malhotra — Wed, 15 Mar 2017 14:31:09 -0500

Uncovering the connection between genetics and heritable diseases requires an approach that looks at all the variant bases and types in a genome. While a PacBio de novo assembly resolves the most novel SV variants. 8-10X PacBio coverage of single genomes or trios reveals triple the SVs detectable by short-read data.

With Single Molecule, Real-Time (SMRT) Sequencing, you can access structural variations having a broad range of sizes, types, and GC content with the ability to:

Uncover missing heritability linked to structural variation
Unambiguously identify genomic context and variant breakpoints at the sequence level to unravel the genetic etiology of disease
Resolve structural variation across the complete size spectrum with basepair resolution

Following are the SV tools, which can assist you to achieve your goal.

Sniffles: Structural variation caller using third generation sequencing

Sniffles is a structural variation caller using third generation sequencing (PacBio or Oxford Nanopore). It detects all types of SVs using evidence from split-read alignments, high-mismatch regions, and coverage analysis. Please note the current version of Sniffles requires sorted output from BWA-MEM (use -M and -x parameter) or NGM-LR with the optional SAM attributes enabled!

More at https://github.com/fritzsedlazeck/Sniffles

MultiBreak-SV: It identifies structural variants from next-generation paired end data, third-generation long read data, or data from a combination of sequencing platforms.

There are two pieces of software in this release: (1) a pre-processor that takes machineformat (.m5) BLASR files, and (2) MultiBreak-SV. For installation and usage instructions, see doc/MultiBreakSV-Manual.txt.

More at https://github.com/raphael-group/multibreak-sv

Parliament: A Structural Variation Tool. Why ask a single sv-detection approach to find every variant when you can have a parliament of tools deciding?

Publication about the algorithm and “…the first long-read characterization of structural variation in a diploid human personal genome…” (HS1011) - “Assessing structural variation in a personal genome—towards a human reference diploid genome”

More at https://sourceforge.net/projects/parliamentsv/

https://www.dnanexus.com/papers/Parliament_Info_Sheet.pdf

PBHoney: the structural variation discovery tool

PBHoney is an implementation of two variant-identification approaches designed to exploit the high mappability of long reads (i.e., greater than 10,000 bp). PBHoney considers both intra-read discordance and soft-clipped tails of long reads to identify structural variants.

Read The Paper http://www.biomedcentral.com/1471-2105/15/180/abstract

More at https://sourceforge.net/projects/pb-jelly/

SMRT-SV: Structural variant and indel caller for PacBio reads

Structural variant (SV) and indel caller for PacBio reads based on methods from Chaisson et al. 2014.

SMRT-SV provides an official software package for tools described in Chaisson et al. 2014 and adds several key features including the following.

Unified variant calling user interface with built-in cluster compute support
Small indel calling (2-49 bp)
Improved inversion calling (screenInversions)
Quality metric for SV calls based on number of local assemblies supporting each call
Higher sensitivity for SV calls using tiled local assemblies across the entire genome instead of "signature" regions
Genotyping of SVs with Illumina paired-end reads from WGS samples

More at https://github.com/EichlerLab/pacbio_variant_caller