Daniel Fasulo

Whole-genome shotgun assembly and comparison of human genome assemblies

We report a whole-genome shotgun assembly (called WGSA) of the human genome generated at Celera in 2001. The Celera-generated shotgun data set consisted of 27 million sequencing reads organized in pairs by virtue of end-sequencing 2-kbp, 10-kbp, and 50-kbp inserts from shotgun clone libraries. The quality-trimmed reads covered the genome 5.3 times, and the inserts from which pairs of reads were obtained covered the genome 39 times. With the nearly complete human DNA sequence [National Center for Biotechnology Information (NCBI) Build 34] now available, it is possible to directly assess the quality, accuracy, and completeness of WGSA and of the first reconstructions of the human genome reported in two landmark papers in February 2001 [Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., Smith, H. O., Yandell, M., Evans, C. A., Holt, R. A., et al. (2001) Science 291, 1304–1351; International Human Genome Sequencing Consortium (2001) Nature 409, 860–921]. The analysis of WGSA shows 97% order and orientation agreement with NCBI Build 34, where most of the 3% of sequence out of order is due to scaffold placement problems as opposed to assembly errors within the scaffolds themselves. In addition, WGSA fills some of the remaining gaps in NCBI Build 34. The early genome sequences all covered about the same amount of the genome, but they did so in different ways. The Celera results provide more order and orientation, and the consortium sequence provides better coverage of exact and nearly exact repeats.

Visualization challenges for a new cyber-pharmaceutical computing paradigm

Celera has encountered a number of visualization problems in the course of developing tools for bioinformatics research, applying them to our data generation efforts, and making that data available to our customers. This paper presents several examples from Celeras experience. In the area of genomics, challenging visualization problems have come up in assembling genomes, studying variations between individuals, and comparing different genomes to one another. The emerging area of proteomics has created new visualization challenges in interpreting protein expression data, studying protein regulatory networks, and examining protein structure. These examples illustrate how the field of bioinformatics is posing new challenges concerning the communication of data that are often very different from those that have heretofore dominated scientific computing. Addressing the level of detail, the degree of complexity, and the interdisciplinary barriers that characterize bioinformatic problems can be expected to be a sizable but rewarding task for the field of scientific visualization.

Molecular fingerprinting reflects different histotypes and brain region in low grade gliomas

BackgroundPaediatric low-grade gliomas (LGGs) encompass a heterogeneous set of tumours of different histologies, site of lesion, age and gender distribution, growth potential, morphological features, tendency to progression and clinical course. Among LGGs, Pilocytic astrocytomas (PAs) are the most common central nervous system (CNS) tumours in children. They are typically well-circumscribed, classified as grade I by the World Health Organization (WHO), but recurrence or progressive disease occurs in about 10-20% of cases. Despite radiological and neuropathological features deemed as classic are acknowledged, PA may present a bewildering variety of microscopic features. Indeed, tumours containing both neoplastic ganglion and astrocytic cells occur at a lower frequency.MethodsGene expression profiling on 40 primary LGGs including PAs and mixed glial-neuronal tumours comprising gangliogliomas (GG) and desmoplastic infantile gangliogliomas (DIG) using Affymetrix array platform was performed. A biologically validated machine learning workflow for the identification of microarray-based gene signatures was devised. The method is based on a sparsity inducing regularization algorithm l1l2 that selects relevant variables and takes into account their correlation. The most significant genetic signatures emerging from gene-chip analysis were confirmed and validated by qPCR.ResultsWe identified an expression signature composed by a biologically validated list of 15 genes, able to distinguish infratentorial from supratentorial LGGs. In addition, a specific molecular fingerprinting distinguishes the supratentorial PAs from those originating in the posterior fossa. Lastly, within supratentorial tumours, we also identified a gene expression pattern composed by neurogenesis, cell motility and cell growth genes which dichotomize mixed glial-neuronal tumours versus PAs. Our results reinforce previous observations about aberrant activation of the mitogen-activated protein kinase (MAPK) pathway in LGGs, but still point to an active involvement of TGF-beta signaling pathway in the PA development and pick out some hitherto unreported genes worthy of further investigation for the mixed glial-neuronal tumours.ConclusionsThe identification of a brain region-specific gene signature suggests that LGGs, with similar pathological features but located at different sites, may be distinguishable on the basis of cancer genetics. Molecular fingerprinting seems to be able to better sub-classify such morphologically heterogeneous tumours and it is remarkable that mixed glial-neuronal tumours are strikingly separated from PAs.

Alignment of mass spectrometry data by clique finding and optimization

Mass spectrometry (MS) is becoming a popular approach for quantifying the protein composition of complex samples. A great challenge for comparative proteomic profiling is to match corresponding peptide features from different experiments to ensure that the same protein intensities are correctly identified. Multi-dimensional data acquisition from liquid-chromatography mass spectrometry (LC-MS) makes the alignment problem harder. We propose a general paradigm for aligning peptide features using a bounded error model. Our method is tolerant of imperfect measurements, missing peaks, and extraneous peaks. It can handle an arbitrary number of dimensions of separation, and is very fast in practice even for large data sets. Finally, its parameters are intuitive and we describe a heuristic for estimating them automatically.We demonstrate results on single- and multi-dimensional data.