BOL: Related items

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

ACANA: An accurate and consistent alignment tool for DNA sequences

Jit — Wed, 06 Dec 2017 09:45:29 -0600

ACANA is an accurate and consistent alignment tool for DNA sequences. ACANA is specifically designed for aligning sequences that share only some moderately conserved regions and/or have a high frequency of long insertions or deletions. It attempts to combine the best of local and global alignments algorithms in searching for evolutionarily related regions of sequences in order to achieve the best alignment. ACANA is also robust to the small changes of alignment parameters, particularly the gap extension score. As an accurate alignment tool, ACANA is particularly useful in comparative sequence analysis for identifying conserved functional regulatory elements.

Address of the bookmark: https://www.niehs.nih.gov/research/resources/software/biostatistics/acana/index.cfm

WhatsHap: fast and accurate read-based phasing

Jit — Mon, 28 May 2018 09:52:16 -0500

WhatsHap is a software for phasing genomic variants using DNA sequencing reads, also called read-based phasing or haplotype assembly. It is especially suitable for long reads, but works also well with short reads.

Features

Very accurate results (Martin et al., WhatsHap: fast and accurate read-based phasing)

Works well with Illumina, PacBio, Oxford Nanopore and other types of reads

It phases SNVs, indels and even “complex” variants (such as TCG → AGAA)

Pedigree phasing mode uses reads from related individuals (such as trios) to improve results and to reduce coverage requirements (Garg et al., Read-Based Phasing of Related Individuals).

WhatsHap is easy to install

It is easy to use: Pass in a VCF and one or more BAM files, get out a phased VCF. Supports multi-sample VCFs.

It produces standard-compliant VCF output by default

If desired, get output that is compatible with ReadBackedPhasing

Open Source (MIT license)

Address of the bookmark: https://whatshap.readthedocs.io/en/latest/

MSAProbs - Parallel and accurate multiple sequence alignment

Neel — Tue, 09 Jul 2019 23:58:44 -0500

MSAProbs is a well-established state-of-the-art multiple sequence alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. In addition, MSAProbs is optimized for shared-memory CPUs by employing a multi-threaded design, and further parallelized for distributed-memory systems using MPI to overcome high memory overhead barrier and achieve good parallel and data-size scalability.

Address of the bookmark: http://msaprobs.sourceforge.net/homepage.htm#latest

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

http://clark.cs.ucr.edu/Spaced/

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/

Liftoff: An accurate GFF3/GTF lift over pipeline

Neel — Sun, 20 Dec 2020 01:36:37 -0600

Liftoff is a tool that accurately maps annotations in GFF or GTF between assemblies of the same, or closely-related species. Unlike current coordinate lift-over tools which require a pre-generated “chain” file as input, Liftoff is a standalone tool that takes two genome assemblies and a reference annotation as input and outputs an annotation of the target genome.

Address of the bookmark: https://github.com/agshumate/Liftoff

HELIANO: A fast and accurate tool for detection of Helitron-like elements

LEGE — Tue, 13 Aug 2024 07:16:34 -0500

Helitron-like elements (HLE1 and HLE2) are DNA transposons. They have been found in diverse species and seem to play significant roles in the evolution of host genomes. Although known for over twenty years, Helitron sequences are still challenging to identify. Here, we propose HELIANO (Helitron-like elements annotator) as an efficient solution for detecting Helitron-like elements.

https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkae679/7730539?login=true

Address of the bookmark: https://github.com/Zhenlisme/heliano/

OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

Jit — Tue, 14 Nov 2017 09:30:35 -0600

Orthology relations can be used to transfer annotations from one gene (or protein) to another. Hence, detecting orthology relations has become an important task in the post-genomic era. Various genomic events, such as duplication and horizontal gene transfer, can cause erroneous assignment of orthology relations. In closely-related species, gene neighborhood information can be used to resolve many ambiguities in orthology inference. Here we present OrthoGNC, a software for accurately predicting pairwise orthology relations based on gene neighborhood conservation. Analyses on simulated and real data reveal the high accuracy of OrthoGNC. In addition to orthology detection, OrthoGNC can be employed to investigate the conservation of genomic context among potential orthologs detected by other methods. OrthoGNC is freely available online at http://bs.ipm.ir/softwares/orthognc and http://tinyurl.com/orthoGNC.

http://www.comp.nus.edu.sg/~wongls/projects/orthoGNC/

Address of the bookmark: http://www.sciencedirect.com/science/article/pii/S1672022917301663

JASMINE: Jointly Accurate Sv Merging with Intersample Network Edges

Shruti Paniwala — Sat, 02 Jul 2022 11:41:53 -0500

This tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.

Address of the bookmark: https://github.com/mkirsche/Jasmine