A beginner’s guide to bioinformatics – part I A beginner’s guide to bioinformatics – part II Algorithms for Next-gen Sequence Analysis Large Computer, Distributed Cluster or Amazon Cloud? Must-have Tools for a Bioinformatician
Three Helpful Guides for Those Working on Genome Assembly
Besides the interesting recall of the Intelligent Systems for Molecular Biology (ISMB) annual conferences on computational biology, it offers a nice insight into current state-of-the-art methodologies and upcoming trends in the discipline.
Background MicroRNAs (miRNAs) are important regulators of gene expression encoded by a variety of organisms, including viruses. Although the function of most of the viral miRNAs is currently unknown, there is evidence that both viral and host miRNAs contribute to the interactions between viruses and their hosts. miRNAs constitute a complex combinatorial network, where one miRNA may target many genes and one gene may be targeted by multiple miRNAs. In particular, viral and host miRNAs may also have mutual target genes. Based on published evidence linking viral and host miRNAs there are three modes of mutual regulation: competing, cooperating, and compensating modes.
Results In this paper we explore the compensating mode of mutual regulation upon Human Cytomegalovirus (HCMV) infection, when host miRNAs are down regulated and viral miRNAs compensate by mimicking their function. To achieve this, we develop a new algorithm which finds groups, called quasi-modules, of viral and host miRNAs and their mutual target genes, and use a new host miRNA expression data for HCMV-infected and uninfected cells. For two of the reported quasi-modules, supporting evidence from biological and medical literature is provided.
Conclusions The modules found by our method may advance the understanding of the role of miRNAs in host-viral interactions, and the genes in these modules may serve as candidates for further experimental validation.
Scripts are a way of linking tasks together to process large numbers of data items or to automate a series of tasks. Common utility tasks in bioinformatics include things like sequence formatting or blast report parsing.
Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study.
Roche 454 sequencing is the leading sequencing technology for producing long read high throughput sequence data. Unlike most methods where sequencing errors translate to base uncertainties, 454 sequencing inaccuracies create nucleotide gaps.
Registration is now open for the 2012 Bioinformatics Short Course on Next-Generation Sequencing sponsored by the UC Davis Bioinformatics Core. To register for the 2012 Bioinformatics Short Course and the fall workshops, click here.
Summary: Counting all the k-mers (substrings of length k) in DNA/RNA sequencing reads is the preliminary step of many bioinformatics applications. However, state of the art k-mer counting methods require that a large data structure resides in memory. Such structure typically grows with the number of distinct k-mers to count.
MOTIVATION: DOOSS (Data Overlaid On Secondary Structures) is a tool for visualising annotated secondary structures of large single-stranded nucleotide sequences (such as full-length virus genomes). The purpose of this tool is to assist investigators in evaluating the biological relevance of secondary structures within particular sequences.
AVAILABILITY: DOOSS is written in Java and is available from: http://dooss.computingforbiology.org CONTACT: firstname.lastname@example.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Bioinformatics training: a review of challenges, actions and support requirements. Briefings In Bioinformatics,11(6), 544-551. doi:10.1093/bib/bbq021. Stein, L. (2010). The case for cloud computing in genome informatics.
Bioinformatics is the study of data generated from biological experiments. With the advent of high-throughput sequencing and many other rapidly improving technologies Biologists are often producing far more data than they can properly analyze. With data being so easy to produce we have massive amounts of data, much of it publicly available – which could hold the keys to new medicines, cures, of breakthroughs. This data just needs someone to look through it. Using data mining software bioinformaticians look though data to find interesting patterns or to find answers to questions. But just like DIY Biology, bioinformatics isn’t restricted to professionals. Given that all you need to do bioinformatics is a computer and some spare time, anyone can do it.