BOL: Related items

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

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

OPERA : Optimal Paired-End Read Assembler

Jit — Fri, 09 Sep 2016 05:28:58 -0500

OPERA (Optimal Paired-End Read Assembler) is a sequence assembly program (http://en.wikipedia.org/wiki/Sequence_assembly). It uses information from paired-end/mate-pair/long reads to order and orient the intermediate contigs/scaffolds assembled in a genome assembly project, in a process known as Scaffolding. OPERA is based on an exact algorithm that is guaranteed to minimize the discordance of scaffolds with the information provided by the paired-end/mate-pair/long reads (for further details see Gao et al, 2011).

Note that since the original publication, we have made significant changes to OPERA (v1.0 onwards) including refinements to its basic algorithm (to reduce local errors, improve efficiency etc.) and incorporated features that are important for scaffolding large genomes (multi-library support, better repeat-handling etc.), in addition to other scalability and usability improvements (bam and gzip support, smaller memory footprint). We therefore encourage you to download and use our latest version: OPERA-LG. In our benchmarks, it has significantly improved corrected N50 and reduced the number of scaffolding errors. Furthermore, our latest release contains the wrapper script OPERA-long-read that enables scaffolding with long-reads from third-generation sequencing technologies (PacBio or Oxford Nanopore). The manuscript describing the new features and algorithms is available at Genome Biology. We look forward to getting your feedback to improve it further.

Address of the bookmark: https://sourceforge.net/p/operasf/wiki/The%20OPERA%20wiki/

Long read assembly workshop !

Rahul Nayak — Thu, 04 Oct 2018 17:23:18 -0500

This is a tutorial for a workshop on long-read (PacBio) genome assembly.

It demonstrates how to use long PacBio sequencing reads to assemble a bacterial genome, and includes additional steps for circularising, trimming, finding plasmids, and correcting the assembly with short-read Illumina data.

Please comment if you know any other long read addembly tutorial.

Address of the bookmark: http://sepsis-omics.github.io/tutorials/modules/cmdline_assembly_v2/

PHYMMBL

Jit — Mon, 10 Oct 2016 08:56:34 -0500

Metagenomics sequencing projects collect samples of DNA from uncharacterized environments that may contain hundreds or even thousands of species. One of the main challenges in analyzing a metagenome is phylogenetic classification of raw sequence reads into groups representing the same or similar species. Such classification is a useful prerequisite for genome assembly and for analysis of the biological diversity present in a sample. The newest sequencing technologies have simultaneously made metagenomics easier, by making the sequencing process faster, and more difficult, by producing shorter read lengths than previous technologies. Methods for classifying sequences as short as 100 base pairs (bp) have until now been relatively inaccurate, requiring metagenomics projects to use older, long-read technologies. Phymm, a new classification approach for metagenomics data which uses interpolated Markov models (IMMs) to taxonomically classify DNA sequences, can accurately classify reads as short as 100 bp. Its accuracy for short reads represents a significant leap forward over previous composition-based classification methods. PhymmBL (rhymes with "thimble"), the hybrid classifier included in this distribution which combines analysis from both Phymm and BLAST, produces even higher accuracy.

Address of the bookmark: http://www.cbcb.umd.edu/software/phymm/

BIMA V3: an aligner customized for mate pair library sequencing

Abhimanyu Singh — Wed, 14 Dec 2016 15:20:00 -0600

Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and
time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing.
Availability: http://bioinformaticstools.mayo.edu/research/bima/
Contact: vasmatzis.george@mayo.edu

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf

liftover

Jitendra Narayan — Mon, 08 Feb 2016 15:45:03 -0600

Convenient conversions between genome assemblie. The liftover package makes it easy to remap genomic coordinates to a different genome assembly.

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

DISCOVAR

Abhimanyu Singh — Mon, 18 Apr 2016 11:59:16 -0500

DISCOVAR is a new variant caller and DISCOVAR de novo a new genome assembler, both designed for state-of-the-art data. Their inputs are chosen to optimize quality while keeping costs low. Currently it takes as input Illumina reads of length 250 or longer — produced on MiSeq or HiSeq 2500 — and from a single PCR-free library. These data enable a level of completeness and continuity that was not previously possible.

DISCOVAR can call variants on a region by region basis, potentially tiling an entire large genome. DISCOVAR variant calling is under active development and transitioning to VCF.

DISCOVAR de novo can generate de novo assemblies for both large and small genomes. It currently does not call variants.

More at https://www.broadinstitute.org/software/discovar/blog/?page_id=14

Address of the bookmark: https://www.broadinstitute.org/software/discovar/blog/

RECORD

Bulbul — Fri, 25 Nov 2016 08:23:36 -0600

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used. Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge. Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

More at https://sourceforge.net/projects/record-genome-assembler/files/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pubmed/26558255

SGA: String Graph Assembler

Jit — Thu, 08 Dec 2016 05:08:59 -0600

SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

More at

https://github.com/jts/sga

SGA dependencies:
-google sparse hash library (http://code.google.com/p/google-sparsehash/)
-the bamtools library (https://github.com/pezmaster31/bamtools)
-zlib (http://www.zlib.net/)
-(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)

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