BOL: Related items

GRAbB: Selective Assembly of Genomic Regions, a New Niche for Genomic Research

Rahul Nayak — Sat, 26 Jan 2019 18:58:16 -0600

GRAbB is shown to be more efficient than MITObim in terms of speed, memory and disk usage. The other functionalities (handling multiple targets simultaneously and extracting homologous regions) of the new program are not matched by other programs. The program is available with explanatory documentation at https://github.com/b-brankovics/grabb. GRAbB has been tested on Ubuntu (12.04 and 14.04), Fedora (23), CentOS (7.1.1503) and Mac OS X (10.7). Furthermore, GRAbB is available as a docker repository: brankovics/grabb (https://hub.docker.com/r/brankovics/grabb/).

Address of the bookmark: https://github.com/b-brankovics/grabb

Senior Bioinformatician (Assembly) Moore Aquatic Symbiosis Project Tree of Life

Sat, 02 Oct 2021 00:28:30 -0500

You will have some previous experience with genome bioinformatics or other large scale scientific data analysis, or a newly qualified graduate student with data science skills interested in DNA sequence data. While desirable, previous experience with DNA sequencing data is not strictly necessary for the position. We have a strong publication record and culture of producing open data resources and open source software development. This role requires an investigative and solution-oriented mindset and excellent communication skills to work effectively within large national and international consortia.

More at https://jobs.sanger.ac.uk/vacancy/senior-bioinformatician-assembly-moore-aquatic-symbiosis-project-tree-of-life-458923.html

NCBI Datasets pages

BioStar — Wed, 12 Jul 2023 06:29:31 -0500

Update! Assembly and Genome record pages now redirect to new NCBI Datasets pages. NCBI Datasets is a new resource that makes it easier to find and download genome data. Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2023/07/11/ncbi-datasets-genome-assembly-pages/ #NCBICGR

Effective July 10, 2023, NCBI’s Assembly and Genome record pages now redirect to new NCBI Datasets pages. As previously announced, these updates are part of our ongoing effort to modernize and improve your user experience. NCBI Datasets is a new resource that makes it easier to find and download genome data.  

The following pages have been updated:

The NCBI Assembly record pages now redirect to the new NCBI Datasets Genome record pages that describe assembled genomes and provide links to related NCBI tools such as Genome Data Viewer and BLAST. 
The NCBI Genome record pages now redirect to the NCBI Datasets Taxonomy record pages that provide a taxonomy-focused portal to genes, genomes, and additional NCBI resources.

During this transition, you will have the option to return to the legacy Genome and Assembly record pages. We will remove the legacy pages in early 2024. 

Step-by-Step Guide to Running Genome Assembly

Abhi — Fri, 13 Dec 2024 11:35:55 -0600

Genome assembly is a critical process in bioinformatics, enabling the reconstruction of an organism's genome from short DNA sequence reads. Whether you’re working on a new microbial genome or a complex eukaryotic organism, this guide will walk you through the steps of genome assembly using state-of-the-art tools and best practices.

What is Genome Assembly?

Genome assembly involves piecing together short DNA sequence reads generated by sequencing platforms (e.g., Illumina, PacBio, Oxford Nanopore) into longer, contiguous sequences called contigs. This can be performed as:

De Novo Assembly: Without a reference genome.
Reference-Guided Assembly: Using a reference genome to guide the assembly process.

Step 1: Preparing Your Data

Before starting the assembly, ensure that your raw sequencing data is high quality.

Input Data
- Short Reads: Illumina sequencing generates short, accurate reads ideal for scaffolding.
- Long Reads: PacBio and Nanopore sequencing provide long reads for resolving repetitive regions.
Quality Control (QC)
Use tools like FastQC or MultiQC to assess the quality of your reads:

fastqc reads.fastq multiqc .

Look for issues like low-quality bases, adapter contamination, or overrepresented sequences.
Read Trimming and Filtering
Trim low-quality bases and adapters using Trimmomatic or Cutadapt:

trimmomatic PE reads_R1.fastq reads_R2.fastq trimmed_R1.fastq trimmed_R2.fastq \ ILLUMINACLIP:adapters.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:36

Step 2: Choosing an Assembly Strategy

Select an assembly strategy based on your data type:

Short-Read Assemblers:
- SPAdes: Popular for microbial genomes.
- Velvet: Fast for smaller genomes.
Long-Read Assemblers:
- Canu: Ideal for long-read datasets.
- Flye: Versatile for small and large genomes.
Hybrid Assemblers:
- MaSuRCA: Combines short and long reads.
- Unicycler: Optimized for bacterial genomes.

Step 3: Running the Assembly

3.1. SPAdes (Short-Read Assembly)

SPAdes is an excellent choice for small genomes, such as bacteria.

spades.py -1 trimmed_R1.fastq -2 trimmed_R2.fastq -o spades_output

The output includes assembled contigs (contigs.fasta) and scaffolds (scaffolds.fasta).

3.2. Canu (Long-Read Assembly)

Canu is designed for high-error long reads from PacBio or Nanopore.

canu -p genome -d canu_output genomeSize=4.7m -nanopore-raw reads.fastq

The output will be in canu_output/genome.contigs.fasta.

3.3. Hybrid Assembly with Unicycler

Unicycler combines short and long reads for improved assemblies.

unicycler -1 trimmed_R1.fastq -2 trimmed_R2.fastq -l long_reads.fastq -o unicycler_output

Step 4: Assessing Assembly Quality

After assembly, evaluate its quality using the following tools:

QUAST
QUAST generates assembly statistics, such as N50, genome size, and GC content:

quast contigs.fasta -o quast_output
BUSCO
BUSCO checks genome completeness by identifying conserved genes:

busco -i contigs.fasta -o busco_output -l fungi_odb10 -m genome
Assembly Graph Visualization
Visualize assembly graphs with Bandage:

Bandage load assembly_graph.gfa

Step 5: Post-Assembly Steps

Polishing
Improve assembly accuracy using tools like Pilon (for short reads) or Racon (for long reads).

racon long_reads.fasta mapped_reads.sam contigs.fasta > polished_contigs.fasta
Scaffolding
Link contigs into scaffolds using tools like SSPACE or Opera-LG if required.
Annotation
Annotate the assembled genome using Prokka for prokaryotes or Maker for eukaryotes.

prokka --outdir annotation_output --prefix genome contigs.fasta

Step 6: Sharing and Archiving

Submit to Public Repositories
Share your assembly in databases like NCBI GenBank, ENA, or DDBJ.
Metadata Preparation
Include detailed metadata for your submission, such as organism name, sequencing platform, and coverage.

Best Practices

Always perform quality checks at each stage to ensure data integrity.
Use multiple tools to cross-validate results when working with complex genomes.
Document parameters and software versions for reproducibility.

Conclusion

Genome assembly is a powerful process that transforms raw sequencing data into a coherent representation of an organism’s genome. By following this step-by-step guide, you can successfully assemble genomes and uncover valuable biological insights. Whether you’re assembling a microbial genome or tackling the complexities of a eukaryotic genome, these tools and strategies will set you on the path to success.

CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation

Shruti Paniwala — Thu, 26 May 2022 00:59:49 -0500

CrowdGO is a protein Gene Ontology predictor using a meta approach, analyzing the predictions of other tools in order to get an improved precision and recall.

Please note that the CrowdGO snakemake workflow is currently only tested on Ubuntu. It should work on OSX, but please report any errors to maarten.reijnders@unil.ch or create an issue.

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010075

Address of the bookmark: https://gitlab.com/mreijnders/crowdgo

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

Rahul Nayak — Tue, 29 May 2018 08:37:53 -0500

The k-mer Weighted Inner Product.

This software implements a de novo, alignment free measure of sample genetic dissimilarity which operates upon raw sequencing reads. It is able to calculate the genetic dissimilarity between samples without any reference genome, and without assembling one.

De novo estimates of genetic relatedness from next-gen sequencing data https://kwip.readthedocs.org

Address of the bookmark: https://github.com/kdmurray91/kwip

SALSA: A tool to scaffold long read assemblies with Hi-C

Jit — Fri, 15 Jun 2018 04:01:15 -0500

This code is used to scaffold your assemblies using Hi-C data. This version implements some improvements in the original SALSA algorithm. If you want to use the old version, it can be found in the old_salsa branch. To use the latest version, first run the following commands: cd SALSA make To run the code, you will need Python 2.7, BOOST libraries and Networkx(version lower than 1.2). If you consider using this tool, please cite our publication which describes the methods used for scaffolding. Ghurye, J., Pop, M., Koren, S., Bickhart, D., & Chin, C. S. (2017). Scaffolding of long read assemblies using long range contact information. BMC genomics, 18(1), 527. Link Ghurye, J., Rhie, A., Walenz, B.P., Schmitt, A., Selvaraj, S., Pop, M., Phillippy, A.M. and Koren, S., 2018. Integrating Hi-C links with assembly graphs for chromosome-scale assembly. bioRxiv, p.261149 Link For any queries, please either ask on github issue page or send an email to Jay Ghurye (jayg@cs.umd.edu).

Address of the bookmark: https://github.com/machinegun/SALSA

ECTOOLS: Long Read Correction and other Correction tools

Jit — Fri, 05 Jan 2018 04:02:22 -0600

Long Read Correction and other Correction tools

This package is a loose collection of scripts. To run the correction
routine see the section below. Descriptions of the other scripts
are at the bottom of this file.

Contact: gurtowsk@cshl.edu

In short, the correction algorithm takes as input the unitigs from a short read assembly and uses them to correct long read data. More background information for the algorithm can be found:
http://schatzlab.cshl.edu/presentations/2013-06-18.PBUserMeeting.pdf

Address of the bookmark: https://github.com/jgurtowski/ectools

shovill: Assemble bacterial isolate genomes from Illumina paired-end reads

BioStar — Sat, 02 Jan 2021 07:05:36 -0600

Shovill is a pipeline which uses SPAdes at its core, but alters the steps before and after the primary assembly step to get similar results in less time. Shovill also supports other assemblers like SKESA, Velvet and Megahit, so you can take advantage of the pre- and post-processing the Shovill provides with those too.

Address of the bookmark: https://github.com/tseemann/shovill

Reference-free prediction of rearrangement breakpoint reads

Neel — Thu, 08 Mar 2018 05:05:25 -0600

lideSort-BPR ( b reak p oint r eads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

https://github.com/ewijaya/slidesort-bpr

Address of the bookmark: https://code.google.com/archive/p/slidesort-bpr/