Systems Biology: The Next Frontier for Bioinformatics

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from ‘omics platform technologies, in particular “downstream” technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful we…
Advances in Bioinformatics via MedWorm.com

A greedy, graph-based algorithm for the alignment of multiple homologous gene lists

Motivation: Many comparative genomics studies rely on the correct identification of homologous genomic regions using accurate alignment tools. In such case, the alphabet of the input sequences consists of complete genes, rather than nucleotides or amino acids. As optimal multiple sequence alignment is computationally impractical, a progressive alignment strategy is often employed. However, such an approach is susceptible to the propagation of alignment errors in early pairwise alignment steps, especially when dealing with strongly diverged genomic regions. In this article, we present a novel accurate and efficient greedy, graph-based algorithm for the alignment of multiple homologous genomic segments, represented as ordered gene lists.

Results: Based on provable properties of the graph structure, several heuristics are developed to resolve local alignment conflicts that occur due to gene duplication and/or rearrangement events on the different genomic segments. The performance of the algorithm is assessed by comparing the alignment results of homologous genomic segments in Arabidopsis thaliana to those obtained by using both a progressive alignment method and an earlier graph-based implementation. Especially for datasets that contain strongly diverged segments, the proposed method achieves a substantially higher alignment accuracy, and proves to be sufficiently fast for large datasets including a few dozens of eukaryotic genomes.

Availability: http://bioinformatics.psb.ugent.be/software. The algorithm is implemented as a part of the i-ADHoRe 3.0 package.

Contact: yves.vandepeer@psb.vib-ugent.be

Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics – recent issues

Discovering Genomics, Proteomics and Bioinformatics (2nd Edition)

Discovering Genomics, Proteomics and Bioinformatics (2nd Edition)

KEY BENEFIT: Discovering Genomics is the first genomics text that combines web activities and case studies with a problem-solving approach to teach upper-level undergraduates and first-year graduate students the fundamentals of genomic analysis. More of a workbook than a traditional text, Discovering Genomics, Second Edition allows students to work with real genomic data in solving problems and provides the user with an active learning experience. KEY TOPICS: Genomic Medicine Case Study: What’s wrong with my child? Genome Sequence Acquisition and Analysis, Comparative Genomics in Evolution and Medicine, Genome Variations, Genomic Medicine Case Study: Why Can’t I Just Take a Pill to Lose Weight? Basic Research with DNA Microarrays, Applied Research with DNA Microarrays, Proteomics, Genomic Medicine Case Study: Why Can’t We Cure More Diseases? Genomic Circuits in Single Genes, Integrated Genomic Circuits, Modeling Whole-Genome Circuits. MARKET: For all readers interested in genomics.

Rating: (out of 7 reviews)

List Price: $ 115.00

Price: $ 66.97

Comparative genomics motif finding

The recent discovery of the first small modulatory RNA (smRNA) presents the challenge of finding other molecules of similar length and conservation level. Unlike short interfering RNA (siRNA) and micro-RNA (miRNA), effective computational and experimental screening methods are not currently known for this species of RNA molecule, and the discovery of the one known example was partly fortuitous because it happened to be complementary to a well-studied DNA binding motif (the Neuron Restrictive Silencer Element). Existing comparative genomics approaches (e.g., phylogenetic footprinting) rely on alignments of orthologous regions across multiple genomes. This approach, while extremely valuable, is not suitable for finding motifs with highly diverged “non-alignable” flanking regions. In this website, we demonstrate that several unusually long and well conserved motifs can be discovered de novo through a comparative genomics approach that does not require an alignment of orthologous upstream regions

http://bix-nrse.ucsd.edu/

A computational genomics pipeline for prokaryotic sequencing projects.

Publication Date: 2010 Jun 2 PMID: 20519285
Authors: Kislyuk, A. O. – Katz, L. S. – Agrawal, S. – Hagen, M. S. – Conley, A. B. – Jayaraman, P. – Nelakuditi, V. – Humphrey, J. C. – Sammons, S. A. – Govil, D. – Mair, R. D. – Tatti, K. M. – Tondella, M. L. – Harcourt, B. H. – Mayer, L. W. – Jordan, I. K.
Journal: Bioinformatics

MOTIVATION: New sequencing technologies have accelerated research on prokaryotic genomes and have made genome sequencing operations outside major genome sequencing centers routine. However, no off-the-shelf solution exists for the combined assembly, gene prediction, genome annotation, and data presentation necessary to interpret sequencing data. The resulting requirement to invest significant resources into custom informatics support for genome sequencing projects remains a major impediment to the accessibility of high-throughput sequence data. RESULTS: We present a self-contained, automated high-throughput open source genome sequencing and computational genomics pipeline suitable for prokaryotic sequencing projects. The pipeline has been used at the Georgia Institute of Technology and the Centers for Disease Control and Prevention for the analysis of Neisseria meningitidis and Bordetella bronchiseptica genomes. The pipeline is capable of enhanced or manually assisted reference-based assembly using multiple assemblers and modes; gene predictor combining; and functional annotation of genes and gene products. Because every component of the pipeline is executed on a local machine with no need to access resources over the Internet, the pipeline is suitable for projects of a sensitive nature. Annotation of virulence-related features makes the pipeline particularly useful for projects working with pathogenic prokaryotes. Availability and Implementation: The pipeline is licensed under the open-source GNU General Public License and available at the Georgia Tech Neisseria Base (http://nbase.biology.gatech.edu/). The pipeline is implemented with a combination of Perl, Bourne Shell, and MySQL and is compatible with Linux and other Unix systems. CONTACT: king.jordan@biology.gatech.edu SUPPLEMENTARY INFORMATION: See http://nbase.biology.gatech.edu.

post to: CiteULike

View full post on Bioinformatics

Next Page »