Bioinformatics for biomedical science and clinical by Kung-Hao Liang

By Kung-Hao Liang

Dr. King-Hao Liang presents biomedical researchers and scholars with a cutting-edge overview of the appliance of bioinformatics to biomedical technological know-how and medical functions. modern biomedical and medical researches are continually driven ahead via the speedy development of varied excessive throughput applied sciences on the entire DNA, RNA and protein degrees. those applied sciences contain, for instance, the complex genome sequencing know-how, microarray, ChIP-Chip and mass spectrometry. they allow researchers to behavior investigations on human well-being and illness in a scientific, genome-wide scale.

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Thus, sequencing of genomic DNA needs to start by breaking the DNA macromolecule into smaller pieces. Sequencing is then performed on the pieces. The final step is to computationally or manually stitch the pieces together into a complete genome, an inverse problem called assembly. NGS platforms, so-called because of their relative advancement of conventional capillary sequencers in terms of higher speed and lower cost, have been made commercially available. Major vendors (and platforms) include Illumina (Solexa), Roche (454) and Applied Biosystems (SOlid).

2 From sequence fragments to a genome Assembly Genome assembly is an important technique to stitch together the sequence readings from sequencers into chromosome-scale sequences. This is how reference genomes (including human) are built. Reference sequences are of tremendous value in all aspects of biology. Once they are built, all the following sequencing tasks of the same species (called re-sequencing) can make use of the reference sequence. 1). When a reference genome is unavailable, then direct assembly from sequence readings (called de novo assembly) is still required for organizing sequence readings.

The underlying concept for an association study is that genotypes which cause a clinical trait, directly or indirectly, will be more enriched in the case groups, producing a difference in allele frequencies. In the past, association studies were done by the candidate-gene approach that scrutinizes a targeted set of genes, which is hypothesized to exert effects on disease etiologies based on prior knowledge. This approach has gradually been replaced by GWAS, which is done in a holistic perspective and hypothesis free style with all known genes screened in an unbiased fashion.

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