A highly efficient multi-core algorithm for clustering extremely large datasets

<p>Abstract</p> <p>Background</p> <p>In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.</p> <p>Results</p> <p>We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization.</p> <p>Conclusions</p> <p>Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.</p

Amyotrophic lateral sclerosis: Genotypes and phenotypes

Mendelian forms of amyotrophic lateral sclerosis (ALS) account for nearly 10 % of all cases. To date, 19 disease genes, usually but not exclusively inherited with an autosomal dominant pattern, have been reported to be associated with ALS or with atypical motor neuron diseases with or without associated frontotemporal dementia (ALS-FTD). Often, it is possible to draw correlations between distinct ALS-associated mutations and specifi c clinical phenotypes. This information is essential for biologists and clinicians alike, providing at the same time an unparalleled insight into the pathogenesis of the disease and invaluable tools for genetic counseling, diagnosis, and development of preventive strategies and treatments for ALS

Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis

To elucidate the genetic architecture of amyotrophic lateral sclerosis (ALS) and find associated loci, we assembled a custom imputation reference panel from whole-genome-sequenced patients with ALS and matched controls (n = 1,861). Through imputation and mixed-model association analysis in 12,577 cases and 23,475 controls, combined with 2,579 cases and 2,767 controls in an independent replication cohort, we fine-mapped a new risk locus on chromosome 21 and identified C21orf2 as a gene associated with ALS risk. In addition, we identified MOBP and SCFD1 as new associated risk loci. We established evidence of ALS being a complex genetic trait with a polygenic architecture. Furthermore, we estimated the SNP-based heritability at 8.5%, with a distinct and important role for low-frequency variants (frequency 1-10%). This study motivates the interrogation of larger samples with full genome coverage to identify rare causal variants that underpin ALS risk