BOL: Related items

Biological file format tutorial

Jit — Sun, 17 Dec 2017 18:13:03 -0600

This section explains some of the commonly used file formats in bioinformatics. The information provided here is basic and designed to help users to distinguish the difference between different formats. Please refer user manual or other information resources on web for more details.

Address of the bookmark: https://bioinformatics.uconn.edu/resources-and-events/tutorials/file-formats-tutorial/

Patterns: a modeling tool dedicated to biological network modeling

Abhimanyu Singh — Fri, 26 Jul 2019 01:11:59 -0500

It is designed to work with patterned data. Famous examples of problems related to patterned data are:

recovering signals in networks after a stimulation (cascade network reverse engineering),
analysing periodic signals.

Address of the bookmark: https://github.com/fbertran/Patterns

k-mers tutorial - classification and taxonomy

Neel — Thu, 26 Aug 2021 10:28:43 -0500

DNA k-mers underlie much of our assembly work, and we (along with many others!) have spent a lot of time thinking about how to store k-mer graphs efficiently, discard redundant data, and count them efficiently.

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

A guide to machine learning for biologists

Abhi — Tue, 28 Dec 2021 01:43:25 -0600

Because of the increasing size and inherent complexity of biological data, there has been an increase in the application of machine learning in biology to create useful and predictive models of the underlying biological processes. All machine learning techniques fit models to data; nevertheless, the specific methods are highly variable and can appear baffling at first glance. In this Review, we hope to give readers a moderate introduction to a few fundamental machine learning techniques, including the most recently created and frequently used deep neural network techniques. We illustrate how different algorithms may be adapted to specific types of biological data, as well as some best practises and points to consider when embarking on machine learning studies. There is also discussion of several upcoming directions in machine learning methodology.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

A guide to machine learning for biologists

LEGE — Wed, 15 Sep 2021 13:21:08 -0500

We aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.

Address of the bookmark: https://www.nature.com/articles/s41580-021-00407-0

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.

VG: variation graph data structures, interchange formats, alignment, genotyping, and variant calling methods

Jit — Tue, 28 Jan 2020 03:53:24 -0600

Variation graphs provide a succinct encoding of the sequences of many genomes. A variation graph (in particular as implemented in vg) is composed of:

nodes, which are labeled by sequences and ids
edges, which connect two nodes via either of their respective ends
paths, describe genomes, sequence alignments, and annotations (such as gene models and transcripts) as walks through nodes connected by edges

Address of the bookmark: https://github.com/vgteam/vg

dcGOR

Martin Jones — Sat, 08 Nov 2014 14:54:28 -0600

An R package for analysing ontologies and protein domain annotations has been published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1003929). The package is distributed as part of CRAN (http://cran.r-project.org/package=dcGOR), and also at GitHub for version control.

The dedicated website is available in http://supfam.org/dcGOR, from which several demos are also provided:

1. Analysing SCOP domains: http://supfam.org/dcGOR/demo-Fang.html

2. Analysing Pfam domains: http://supfam.org/dcGOR/demo-Basu.html

3. Analysing InterPro domains: http://supfam.org/dcGOR/demo-Customisation.html

RCircos: an R package for Circos 2D track plots

Jit — Fri, 20 May 2016 11:01:13 -0500

RCircos package provides a simple and flexible way to make Circos 2D track plots with R and could be easily integrated into other R data processing and graphic manipulation pipelines for presenting large-scale multi-sample genomic research data. It can also serve as a base tool to generate complex Circos images.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src