This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Microbial metagenomes are like a blueprint of all the functions performed by a microbial community. Some microbes can't be grown in the lab, so metagenomics is important for investigating otherwise-unknown microbial "dark matter". Short-read sequencing provides large amounts of data, but it's hard to assemble into complete genomes. A recent study combined short-read data with nanopore long-read data using Iterative Hybrid Assembly (IHA). The researchers reconstructed 49 metagenome-assembled genomes (MAGs), including some with very low coverage. In total, 34 MAGs did not belong to any known genus, representing unknown microbe groups. The IHA method revealed more of the genes present than a short-read-only approach and showed that the anammox genome of genus Ca. Brocadia contains two identical hydrazine synthase genes. The current method is best for enriched microbial communities and will be extended to high-complexity samples in the future..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"High-quality reference genomes are needed to understand the physiology and function of uncultured microbes in complex ecosystems. Metagenomics has been an incredibly useful tool for studying microbial communities, but assigning sequence assemblies accurately to genomes is difficult in microbial species or strains that lack a reference genome. These 'consensus genomes' have lower resolution than those generated from cultured isolates. Combining single-cell genomics with metagenomics may allow us to overcome these methodological weaknesses. Thus, researchers recently developed a framework called SMAGLinker, which integrates single-cell genomes from microfluidic droplets and uses them as guides for metagenome assembly. Compared to metagenomics alone, SMAGLinker showed more precise contig binning and higher recovery rates of rRNA and plasmids in a mock microbial community. In human gut and skin microbiota samples, SMAGLinker returned more genomes than the conventional metagenomics frameworks..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This genomics education lesson plan was formulated and tested on some year 6 students with the help of their teacher Michelle Pardini at the Hong Kong ICS School. Using the example of the ongoing citizen science Bahinia Genome project from Hong Kong it hopes to serve as a model to inspire and inform other national genome projects, and aid the development of crucial genomic literacy and skills across the globe. Inspiring and training a new generation of scientists to use these tools to tackle the biggest threats to mankind: climate change, disease, and food security. It is released under a CC-BY SA 4.0 license, and utilised the following slide deck and final quiz. Promoting open science, all of the data and resources produced from the project is immediately put into the public domain. Please feel free to utilise, adapt and build upon any of these as you wish. The open licence makes these open education resources usable just with attribution and posting of modified resources under a similar manner. Contact BauhiniaGenome if you have any questions or feedback.Bauhinia Genome overviewFor a slidedeck for the lesson plan laid out here you can use the set in slideshare here.

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Metagenomic techniques can be used to create microbial gene “catalogs” for different environments. First, shotgun sequencing reads are assembled, and genes are then predicted from the assembled reads. Typically, the reads are assembled either for individual samples separately or for all samples together (co-assembly). However, neither method is ideal, so a new study investigated if a third combined method, mix-assembly, could be a better choice. For mix-assembly, the genes from the individual assemblies were clustered according to their encoded proteins. The resulting nonredundant genes were then clustered with the genes from the co-assembly according to their encoded proteins to yield the final gene set. Compared with the other methods, mix-assembly produced a larger nonredundant gene set for metagenomic samples from the Baltic Sea. Mix-assembly also yielded more genes that were complete and more genes whose functions could be annotated..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.