Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

The evolution of the surface of the mineral schreibersite in prebiotic chemistry

We present a study of the reactions of the meteoritic mineral schreibersite (Fe, Ni)(3)P, focusing primarily on surface chemistry and prebiotic phosphorylation. In this work, a synthetic analogue of the mineral was synthesized by mixing stoichiometric proportions of elemental iron, nickel and phosphorus and heating in a tube furnace at 820 degrees C for approximately 235 hours under argon or under vacuum, a modification of the method of Skala and Drabek (2002). Once synthesized, the schreibersite was characterized to confirm the identity of the product as well as to elucidate the oxidation processes affecting the surface. In addition to characterization of the solid product, this schreibersite was reacted with water or with organic solutes in a choline chloride-urea deep eutectic mixture, to constrain potential prebiotic products. Major inorganic solutes produced by reaction of water include orthophosphate, phosphite, pyrophosphate and hypophosphate consistent with prior work on Fe3P corrosion. Additionally, schreibersite corrodes in water and dries down to form a deep eutectic solution, generating phosphorylated products, in this case phosphocholine, using this synthesized schreibersite.FWN – Publicaties zonder aanstelling Universiteit Leide

Rare/uncommon (MAF<5%) variants analysis with lipid traits in NHWs.

<p>SKAT-O: optimal sequencing Kernel association test, N.RV: number of rare variants, P: p-value.</p><p>Rare/uncommon (MAF<5%) variants analysis with lipid traits in NHWs.</p

Haplotype analysis with lipid traits in African Blacks.

<p>Haplotype windows for LDL-C (<b>a</b>), for ApoB (<b>b</b>), for HDL-C (<b>c</b>), and for TG (<b>d</b>). X-axis has the genotyped markers name and the Y-axis has the –log (global p-value), horizontal lines represent the 4-SNP windows, red-line represents the p-value threshold (p = 0.05) and everything below the threshold is considered non-significant and vice versa.</p

Gene-based association analysis with lipid traits in NHWs.

<p>nSNPs: represents the number of SNPs included in the analysis; two rare variants were excluded from the gene-based association analysis because of the missing phenotype data (ApoB data was available in a subset of the NHW sample); Test: represent the overall test statistic; P-value: the overall p-value; SNP p-value: p-value of the best SNPs contributed to the significance.</p><p>Gene-based association analysis with lipid traits in NHWs.</p

Single-site association analysis with lipid traits in NHWs.

<p>* (NA) unavailable results because of missing phenotype data for subjects who carry the rare allele, Nucleotide position is according to the reference sequence (Accession # AF261279.1); Chr. Position: chromosomal position is according to NCBI dbSNP human Build 141. ^aBox-Cox transformed variables. MAF: minor allele frequency. HWE-P: Hardy Weinberg equilibrium p-value.</p><p>RegDB scores: RegulomeDB scores. LDL-C: low-density lipoprotein cholesterol; ApoB: Apolipoprotein B; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol. <b>Bold</b> values represent significant p-values</p><p>Single-site association analysis with lipid traits in NHWs.</p

Single-site association analysis with lipid traits in African Blacks.

<p>Nucleotide position is according to the reference sequence (Accession # AF261279.1); Chr. Position: chromosomal position is according to NCBI dbSNP human Build 141. ^aBox-Cox transformed variables. MAF: minor allele frequency. HWE-P: Hardy Weinberg equilibrium p-value. RegDB scores: RegulomeDB scores. LDL-C: low-density lipoprotein cholesterol; ApoB: Apolipoprotein B; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol. <b>Bold</b> values represent significant p-values</p><p>Single-site association analysis with lipid traits in African Blacks.</p