Robert David

Mapping and sequencing of structural variation from eight human genomes

Genetic variation among individual humans occurs on many different scales, ranging from gross alterations in the human karyotype to single nucleotide changes. Here we explore variation on an intermediate scale—particularly insertions, deletions and inversions affecting from a few thousand to a few million base pairs. We employed a clone-based method to interrogate this intermediate structural variation in eight individuals of diverse geographic ancestry. Our analysis provides a comprehensive overview of the normal pattern of structural variation present in these genomes, refining the location of 1,695 structural variants. We find that 50% were seen in more than one individual and that nearly half lay outside regions of the genome previously described as structurally variant. We discover 525 new insertion sequences that are not present in the human reference genome and show that many of these are variable in copy number between individuals. Complete sequencing of 261 structural variants reveals considerable locus complexity and provides insights into the different mutational processes that have shaped the human genome. These data provide the first high-resolution sequence map of human structural variation—a standard for genotyping platforms and a prelude to future individual genome sequencing projects.

3D-1D threading methods for protein fold recognition.

The threading approach to protein fold recognition attempts to evaluate how well a query sequence fits into an already-solved fold. 3D-1D threaders rely on matching 1-dimensional strings of 3-dimensional information predicted from the query sequence with corresponding features of the target structure. In many cases this is combined with a sequence comparison. The combination of sequence and structure information has been shown to improve the accuracy of fold recognition, relative to the exclusive use of sequence or structure. In this paper, we review progress made since the introduction of threading methods a decade ago, highlighting recent advances. We focus on two emerging methods that are unconventional 3D-1D threaders: proximity correlation matrices and parallel cascade identification.

Automatic classification of protein sequences into structure/function groups via parallel cascade identification: a feasibility study.

AbstractA recent paper introduced the approach of using nonlinear system identification as a means for automatically classifying protein sequences into their structure/function families. The particular technique utilized, known as parallel cascade identification (PCI), could train classifiers on a very limited set of exemplars from the protein families to be distinguished and still achieve impressively good two-way classifications. For the nonlinear system classifiers to have numerical inputs, each amino acid in the protein was mapped into a corresponding hydrophobicity value, and the resulting hydrophobicity profile was used in place of the primary amino acid sequence. While the ensuing classification accuracy was gratifying, the use of (Rose scale) hydrophobicity values had some disadvantages. These included representing multiple amino acids by the same value, weighting some amino acids more heavily than others, and covering a narrow numerical range, resulting in a poor input for system identification. This paper introduces binary and multilevel sequence codes to represent amino acids, for use in protein classification. The new binary and multilevel sequences, which are still able to encode information such as hydrophobicity, polarity, and charge, avoid the above disadvantages and increase classification accuracy. Indeed, over a much larger test set than in the original study, parallel cascade models using numerical profiles constructed with the new codes achieved slightly higher two-way classification rates than did hidden Markov models (HMMs) using the primary amino acid sequences, and combining PCI and HMM approaches increased accuracy.

Recognition of adenosine triphosphate binding sites using parallel cascade system identification.

AbstractParallel cascade identification (PCI) is a method for approximating the behavior of a nonlinear system, from input/output training data, by constructing a parallel array of cascaded dynamic linear and static nonlinear elements. PCI has previously been shown to provide an effective means for classifying protein sequences into structure/function families. In the present study, PCI is used to distinguish proteins that are binding to adenosine triphosphate or guanine triphosphate molecules from those that are nonbinding. Classification accuracy of 87.1% using the hydrophobicity scale of Rose et al. (Hydrophobicity of amino acid residues in globular proteins. Science 229:834–838, 1985), and 88.8% using Korenbergs SARAH1 scale, are obtained, as measured by tenfold cross-validation testing. Nearest-neighbor and K-nearest-neighbor (KNN) classifiers are constructed, and the resulting accuracy is, respectively, 88.0% and 90.8% on the SARAH1–encoded test data set, as measured by the above testing protocol. Significantly improved classification accuracy is achieved by combining PCI and KNN classifiers using quadratic discriminant analysis: accuracy rises from 87.9% (PCI) and 87.4% (KNN) to 96.5% for the combination, as measured by twofold cross-validation testing on the SARAH1–encoded test data set.

Parallel cascade identification and its application to protein family prediction

Parallel cascade identification is a method for modeling dynamic systems with possibly high order nonlinearities and lengthy memory, given only input/output data for the system gathered in an experiment. While the method was originally proposed for nonlinear system identification, two recent papers have illustrated its utility for protein family prediction. One strength of this approach is the capability of training effective parallel cascade classifiers from very little training data. Indeed, when the amount of training exemplars is limited, and when distinctions between a small number of categories suffice, parallel cascade identification can outperform some state-of-the-art techniques. Moreover, the unusual approach taken by this method enables it to be effectively combined with other techniques to significantly improve accuracy. In this paper, parallel cascade identification is first reviewed, and its use in a variety of different fields is surveyed. Then protein family prediction via this method is considered in detail, and some particularly useful applications are pointed out.

Erratum to “Pathogen discovery from human tissue by sequence-based computational subtraction” ☆: [Genomics 81 (2003) 329–335]☆

a Department of Adult Oncology, Dana-Farber Cancer Institute, Boston, MA 02115, USA b Whitehead Institute/MIT Center for Genome Research, Cambridge, MA 02142, USA c Decision Systems Group, Brigham and Women’s Hospital, Boston, MA 02115, USA d Department of Pathology, Massachusetts General Hospital, Boston, MA 02114, USA e Department of Pathology, Harvard Medical School, Boston, MA 02115, USA f Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02138, USA

Progress toward photon force-based sensors: a system identification approach based on laser intensity modulation for measurement of the axial force constant of a single-beam gradient photon force t

New sensor technologies with the sensitivity and specificity capable of detecting biological and chemical agents at low concentration are of increasing importance for many environmental monitoring applications. We propose a potentially new class of microsensors that exploits the mechanical dynamics of a micrometer-sized particle held in a 3D optical force trap formed by a focused laser beam. Modulation of the laser trapping power axially perturbs the microparticle from its equilibrium position and permits measurement of the mechanical compliance transfer function (force input, displacement output) characterizing the particle micromechanical dynamics. In a mechanically homogeneous and isotropic environment, the particle motion is readily modeled as a forced harmonic oscillator; however, physico-chemical interactions between the particle and its surroundings impose external forces that modify the compliance transfer function. Our preliminary measurements indicate < 10 ppm changes in mass of a trapped microparticle can be detected with this method, suggesting possible applications as a chemical/biological sensor or for solubility measurements of microparticles.