BOL: Related items

Genome assembly stats plotting

Jit — Wed, 28 Feb 2018 03:45:39 -0600

A de novo genome assembly can be summarised b

y a number of metrics, including:

Overall assembly length
Number of scaffolds/contigs
Length of longest scaffold/contig
Scaffold/contig N50 and N90Assembly base composition, in particular percentage GC and percentage Ns
CEGMA completeness
Scaffold/contig length/count distribution

assembly-stats supports two widely used presentations of these values, tabular and cumulative length plots, and introduces an additional circular plot that summarises most commonly used assembly metrics in a single visualisation. Each of these presentations is generated using javascript from a common (JSON) data structure, allowing toggling between alternative views, and each can be applied to a single or multiple assemblies to allow direct comparison of alternate assemblies.

Tabular presentation allows direct comparison of exact values between assemblies, the limitations of this approach lie in the necessary omission of distributions and the challenge of interpreting ratios of values that may vary by several orders of magnitude.

Address of the bookmark: https://github.com/rjchallis/assembly-stats

HaploMerger2: rebuilding both haploid sub-assemblies from high-heterozygosity diploid genome assembly

BioStar — Thu, 27 Sep 2018 07:08:47 -0500

HM2 can process any diploid assemblies, but it is especially suitable for diploid assemblies with high heterozygosity (≥3%), which can be difficult for other tools. This pipeline also implements flexible and sensitive assembly error detection, a hierarchical scaffolding procedure and a reliable gap-closing method for haploid sub-assemblies.

Source code, executables and the testing dataset are freely available at https://github.com/mapleforest/HaploMerger2/releases/.

Address of the bookmark: https://github.com/mapleforest/HaploMerger2/releases/

Evaluation of genome assembly software based on long reads

BioStar — Fri, 01 Feb 2019 11:55:54 -0600

TGS technologies have been used to produce highly accurate de novo assemblies of hundreds of microbial genomes and highly contiguous reconstructions of many dozens of plant and animal genomes, enabling new insights into evolution and sequence diversity. They have also been applied to resequencing analyses, to create detailed maps of structural variations in many species. Also, these new technologies have been used to fill in many of the gaps in the human reference genome.

In this report, we compare and evaluate several genome assembly software based on TSG technology. The experimentation has been performed on 4 reference genomes and the results evaluated with the QUAST software. The 11 software that have been evaluated are: Celera Assembler , Falcon , Miniasm, Newbler , SGA Assembler, Smartdenovo, Abruijn, Ra, DBG2OLC, Spades and Cerulean. The first 8 software use only long reads, while the 3 last software can merge long and short reads

GMASS: a novel measure for genomeassembly structural similarity

Abhimanyu Singh — Sun, 14 Apr 2019 20:35:40 -0500

The GMASS score is a novel measure for representing structural similarity between two assemblies. It will contribute to the understanding of assembly output and developing de novo assemblers.

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2710-z

Address of the bookmark: http://bioinfo.konkuk.ac.kr/GMASS/htdocs/syncircos.php

Mapping NGS

Abhimanyu Singh — Tue, 02 May 2017 07:58:07 -0500

NGS data are just a bunch of sequences, you have no idea which region in the genome each sequences comes from, which gene it represents...
To know that you have to align the sequences to the reference sequence. The reference sequence is in most cases the full genome sequence but sometimes, a library of EST sequences is used.
In either way, aligning your sequence reads to the reference sequence is called mapping.

The most used mappers of DNA-seq data are BWA and Bowtie for DNA-Seq data and Tophat, STAR or HISAT for RNA-Seq data. Mappers differ in which options they can take in, how fast and how accurate they are. Bowtie is faster than BWA, but looses some sensitivity (does not map an equal amount of reads to the correct position in the genome).

Address of the bookmark: http://wiki.bits.vib.be/index.php/Mapping_of_NGS_data

RefKA: A fast and efficient long-read genome assembly approach for large and complex genomes

Rahul Nayak — Fri, 01 May 2020 03:00:40 -0500

RefKA, a reference-based approach for long read genome assembly. This approach relies on breaking up a closely related reference genome into bins, aligning k-mers unique to each bin with PacBio reads, and then assembling each bin in parallel followed by a final bin-stitching step.

Address of the bookmark: https://github.com/AppliedBioinformatics/RefKA

Bioinformatics lectures !

Jit — Wed, 29 Nov 2017 05:39:54 -0600

Computational Biology is a huge field of study, that touches upon many distinct algorithmic and biological areas of study. What we are able to cover in this course will depend, in part, on the pace at which we move, which I will attempt to adjust as appropriate. However, here is a tentative list of topics I hope to cover this semester (not necessarily in order).

Optimal sequence alignment (global, local, and glocal alignment &mdash with constant & affine gap penalties
Algorithms and data structures for efficient text indexing and exact search
Heuristics for read alignment and mapping &mdash mapping DNA-seq and RNA-seq reads
Genome assembly &mdash k-mers, De Brujin graph construction and representation, long-read technology and read-overlap graph assembly
Motif finding via Gibbs sampling
Gene finding &mdash statistical models for ab initio and evidence-guided prediction of genes
RNA-seq and transcriptomics &mdash transcript assembly, abundance estimation and differential expression testing
Phylogenetics &mdash The small and large phylogeny problem; parsimony, maximum likelihood and Bayesian methods

Address of the bookmark: https://rob-p.github.io/CSE549F16/lectures/

India and Germany to begin joint research in the area of 'Bioinformatics in Health Research'

Rahul Nayak — Wed, 17 Jan 2018 14:10:36 -0600

To facilitate bilateral cooperation in biotechnology between the scientific communities of India and Germany, the Department of Biotechnology (DBT) will soon begin collaborative research in the identified priority area of 'Bioinformatics in Health Research' under the programme of Indo-German Cooperation in Health Research.

The purpose of the programme is to stimulate new collaborations, e.g. the preparation of joint projects under national funding programmes. The programme facilitates bilateral cooperation in biotechnology between the scientific communities of India and Germany by way of joint research projects which will encompass bilateral workshops/seminar and exchange visits of scientists.

The programme is being implemented within the agreement of Indo-German cooperation in S&T of 1974, under which the Department of Biotechnology, Government of India and Forschungszentrum Julich BMBH (FZJ), Federal Republic of Germany, have agreed for cooperative programme in biotechnology.

DBT of the Ministry of Science & Technology, Government of India and the Project Management Agency at the German Aerospace Center (DLR-PT, European and International Cooperation), Bonn are the nodal implementing agencies from the Indian and German side respectively.

Through this programme, it is expected that the funded cooperation enables the partners to develop applicable scientific results which can be published and/ or could be commercialised and may lead to formation of joint ventures. All publications, patents coming out of these projects, need to be jointly authored by both Indian and German scientists. All necessary approvals like ethical clearance, HMSC approval from Indian point of view as well as EU, if applicable, from German point of view, e.g. before conducting animal experimentation if any needs to be obtained by PIs before undertaking the project.

Now, both the nodal agencies have invited research proposals in identified priority area of 'Bioinformatics in Health Research' from eligible scientists. Joint research projects are required to be submitted to both the nodal agencies by 15 January 2018. Scientists/faculty members working in regular capacity in universities, national R&D laboratories/institutes and private R&D institutes can be part of this joint research programme. For the private sector, partners from all kind of private sectors are eligible, but financing is limited. For Indian scientists from the private sector, only local hospitality in Germany as part of the exchange visit is available from the German side. For German scientists from the private sector, only travel costs are available for small and medium size enterprises (for definition of SME ref. to 2003/361/EC) as well as local hospitality in India will be borne by themselves.

An Introduction to Applied Bioinformatics

Jit — Fri, 02 Mar 2018 04:26:38 -0600

IAB is primarily being developed by Greg Caporaso(GitHub/Twitter: @gregcaporaso) in the Caporaso Lab at Northern Arizona University. You can find information on the courses I teach on my teaching website and information on my research and lab on my lab website.

Address of the bookmark: http://readiab.org/

Merfin: improved variant filtering, assembly evaluation and polishing via k-mer validation

Neel — Sun, 03 Apr 2022 20:35:19 -0500

Merfin, a k-mer based variant-filtering algorithm for improved accuracy in genotyping and genome assembly polishing. Merfin evaluates each variant based on the expected k-mer multiplicity in the reads, independently of the quality of the read alignment and variant caller’s internal score. Merfin increased the precision of genotyped calls in several benchmarks, improved consensus accuracy and reduced frameshift errors when applied to human and nonhuman assemblies built from Pacific Biosciences HiFi and continuous long reads or Oxford Nanopore reads, including the first complete human genome. Moreover, we introduce assembly quality and completeness metrics that account for the expected genomic copy numbers.

More at https://www.nature.com/articles/s41592-022-01445-y

Address of the bookmark: https://github.com/arangrhie/merfin