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This article focuses on hierarchical abstraction in the protein structure recognition. The system ARIADNE identifies the optimal match between a given complex pattern descriptor and genetic (protein) sequences annotated with various inferred properties, by abstracting intermediate levels of structural organization. Inference is grounded solely in knowledge derivable from the primary sequence, and exploits such weakly inferred properties as secondary structure predictions and hydrophobicity. There are many situations in which a detailed low level description encodes, through a hierarchical organization, a recognizable higher-order pattern. For example, in the micro-world of VLSI integrated circuits, transistors are organized into inverters, inverters into register cells, register cells into register banks, and so on up to microprocessor. Recognition of a hierarchical organization from low level detail proceeds most naturally by hierarchical construction of the intervening patterns.

Building predictive models for iterative drug design in the absence of a known target protein structure is an important challenge. We present a novel technique, Compass, that removes a major obstacle to accurate prediction by automatically selecting conformations and alignments of molecules without the benefit of a characterized active site. The technique combines explicit representation of molecular shape with neural network learning methods to produce highly predictive models, even across chemically distinct classes of molecules. We apply the method to predicting human perception of musk odor and show how the resulting models can provide graphical guidance for chemical modifications.

Motivation: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100 000 SNPs per sample. Results: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100 000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. Availability: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10K and 100K mapping array data are available on the Affymetrix website. Contact: xiaojun_di@affymetrix.com

Motivation: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering. Results: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site. Availability: The algorithms will be available commercially in the Affymetrix software package.

A FORTRAN program PRSTRC has been developed for protein secondary structure prediction, which is a modified Chou and Fasman (1978) analysis. This implementation carries out a running average of amino acid structure occurrence frequencies, utilizes a simple set of nucleation conditions, and allows user control over nucleation threshold and cutoff parameters. The algorithm includes prediction of the newly defined secondary structure elements: omega loops (1986). It also generates a charge distribution and hydropathy profile. Output includes a simple graphic display for a printer, or a CRT using color addition. Correct structures are predicted for T. dyscritum hemerythrin and the variable domain of mouse immunoglobin k-chain.